{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.insert(0, '/Users/joel/src/thundr/tracker')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy\n", "import requests\n", "import json\n", "import random\n", "import datetime\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "import pymysql\n", "from contextlib import contextmanager\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "conn_kwargs = {\n", " 'host': 'unicron.cluster-cbn1zk7uet6r.eu-west-1.rds.amazonaws.com', \n", " 'user': 'unicron', \n", " 'password': 'tydz5cy9xbvchqpa249rfekhcg9sey', \n", " 'db': 'unicron', \n", " 'charset': 'utf8mb4'\n", "}\n", "\n", "@contextmanager\n", "def get_cursor(commit_after=False):\n", " connection = pymysql.connect(**conn_kwargs)\n", " try:\n", " with connection.cursor() as cur:\n", " yield cur\n", " if commit_after:\n", " connection.commit()\n", " else:\n", " connection.rollback()\n", " finally:\n", " try:\n", " connection.close()\n", " except Exception as e:\n", " print(\"Warning, failed to close connection\")\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def _sql_to_dataframe(sql_statement, columns, sql_args=[], index_column=None):\n", " with get_cursor(False) as cur:\n", " cur.execute(sql_statement, sql_args)\n", " rows = cur.fetchall()\n", " df = pd.DataFrame(data=rows, columns=columns)\n", " if index_column:\n", " df = df.set_index(index_column)\n", " return df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def to_day(dt):\n", " return dt.date()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def to_minutes(dt):\n", " \"\"\"Return floating point number of hours through the day in `datetime` dt.\"\"\"\n", " return dt.hour*60 + dt.minute + dt.second / 60" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ADDED_STREAMS = 1399394.0" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# could store this in the db using spartus?\n", "global headers\n", "def refresh_request_headers():\n", " global headers\n", " cookies = {\n", " 'sp_dc': 'AQAbb-xbg9Nm8VmJkpOLMHDWTQsGQ6-6iRsDn54H5InejBBarQbZeelQFeTBpDaxTTcKJYKqdBgRw94ann0E_b9_T2rp6mBGwNUSIZtuvi0',\n", " 'sp_key': '0eee0e06-6b39-4a63-b40d-0571506e7001' \n", " }\n", "\n", " tokenRequest = requests.get(\n", " \"https://generic.wg.spotify.com/creator-auth-proxy/v1/web/token?client_id=6cf79a93be894c2086b8cbf737e0796b\", \n", " headers={\n", " \"accept\":\"application/json\",\n", " \"accept-language\":\"en-GB,en-US;q=0.9,en;q=0.8\",\n", " \"sec-fetch-dest\":\"empty\",\n", " \"sec-fetch-mode\":\"cors\",\n", " \"sec-fetch-site\":\"same-site\",\n", " \"origin\": \"https://artists.spotify.com\",\n", " \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36\",\n", " \"referrer\":\"https://artists.spotify.com/c/artist/6bmlMHgSheBauioMgKv2tn/song/7eJMfftS33KTjuF7lTsMCx/stats?segment-filter=streams&time-filter=7day\",\n", " }, cookies=cookies)\n", " tokenRequest\n", "\n", " accessToken = tokenRequest.json()['access_token']\n", "\n", " headers = {\n", " 'authority': 'generic.wg.spotify.com',\n", " 'authorization': f'Bearer {accessToken}',\n", " 'content-type': 'application/json',\n", " 'accept': 'application/json',\n", " 'sec-fetch-dest': 'empty',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'spotify-app-version': '1.0.0.3229887',\n", " 'app-platform': 'Browser',\n", " 'origin': 'https://artists.spotify.com',\n", " 'sec-fetch-site': 'same-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://artists.spotify.com/c/artist/6bmlMHgSheBauioMgKv2tn/song/7eJMfftS33KTjuF7lTsMCx/stats?segment-filter=streams&time-filter=7day',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',\n", " }\n", " return headers" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class UrlBuilder:\n", " def __init__(self, artist_id, recording_id):\n", " self.artist_id = artist_id\n", " self.recording_id = recording_id\n", " \n", " def build_url(self, path, timeFilter=None, aggregationLevel='recording'):\n", " query = {}\n", " if timeFilter is not None:\n", " query['time-filter'] = timeFilter\n", " if aggregationLevel is not None:\n", " query['aggregation-level'] = aggregationLevel\n", " query_str = \"&\".join(f'{k}={v}' for k,v in query.items())\n", " \n", " return (\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " f'/artist/{self.artist_id}/recording/{self.recording_id}'\n", " f'/{path}'\n", " f'?{query_str}'\n", " )\n", "url_builder = UrlBuilder('6bmlMHgSheBauioMgKv2tn', '7eJMfftS33KTjuF7lTsMCx')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "from collections import namedtuple\n", "Period = namedtuple('Period', \"queryParam tablename\")\n", "\n", "class PlaylistPeriods:\n", " day1 = Period('1day', 'deathbed_playlist_streams_1day')\n", " day7 = Period('7day', 'deathbed_playlist_streams_7day')\n", " day28 = Period('28day', 'deathbed_playlist_streams_28day')\n", " all_time = Period('since2015', 'deathbed_playlist_streams_all_time')\n", "\n", " \n", "def to_playlist_url(period: Period):\n", " return url_builder.build_url('top-playlists', timeFilter=period.queryParam)\n", "# return (\n", "# 'https://generic.wg.spotify.com/s4x-insights-api/v1'\n", "# '/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx'\n", "# '/top-playlists'\n", "# f'?time-filter={period.period}&aggregation-level=recording'\n", "# )\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "d1c = requests.get(url_builder.build_url('locations/streams', timeFilter=PlaylistPeriods.day28.queryParam), headers=headers)\n", "# to_playlist_url(PlaylistPeriods.day1)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "b'{\"geography\":[{\"name\":\"DE\",\"num\":\"3471170\"},{\"name\":\"TW\",\"num\":\"257716\"},{\"name\":\"PS\",\"num\":\"2279\"},{\"name\":\"HK\",\"num\":\"158932\"},{\"name\":\"PT\",\"num\":\"201857\"},{\"name\":\"HN\",\"num\":\"38071\"},{\"name\":\"DK\",\"num\":\"694866\"},{\"name\":\"LT\",\"num\":\"63991\"},{\"name\":\"LU\",\"num\":\"27432\"},{\"name\":\"PY\",\"num\":\"75919\"},{\"name\":\"LV\",\"num\":\"60717\"},{\"name\":\"DO\",\"num\":\"36421\"},{\"name\":\"HU\",\"num\":\"154375\"},{\"name\":\"QA\",\"num\":\"27611\"},{\"name\":\"MA\",\"num\":\"48113\"},{\"name\":\"MC\",\"num\":\"1074\"},{\"name\":\"DZ\",\"num\":\"26023\"},{\"name\":\"ID\",\"num\":\"4101553\"},{\"name\":\"IE\",\"num\":\"528045\"},{\"name\":\"EC\",\"num\":\"134533\"},{\"name\":\"US\",\"num\":\"17612824\"},{\"name\":\"EE\",\"num\":\"52938\"},{\"name\":\"EG\",\"num\":\"42024\"},{\"name\":\"AD\",\"num\":\"3179\"},{\"name\":\"IL\",\"num\":\"129105\"},{\"name\":\"UY\",\"num\":\"28463\"},{\"name\":\"AE\",\"num\":\"111528\"},{\"name\":\"IN\",\"num\":\"1054913\"},{\"name\":\"MT\",\"num\":\"32688\"},{\"name\":\"ZA\",\"num\":\"113303\"},{\"name\":\"IS\",\"num\":\"67785\"},{\"name\":\"MX\",\"num\":\"2059584\"},{\"name\":\"IT\",\"num\":\"1222330\"},{\"name\":\"MY\",\"num\":\"893900\"},{\"name\":\"ES\",\"num\":\"809177\"},{\"name\":\"AR\",\"num\":\"508123\"},{\"name\":\"AT\",\"num\":\"304124\"},{\"name\":\"AU\",\"num\":\"3997059\"},{\"name\":\"VN\",\"num\":\"285317\"},{\"name\":\"NI\",\"num\":\"14729\"},{\"name\":\"RO\",\"num\":\"122295\"},{\"name\":\"NL\",\"num\":\"1444049\"},{\"name\":\"NO\",\"num\":\"1173241\"},{\"name\":\"FI\",\"num\":\"768549\"},{\"name\":\"BE\",\"num\":\"434154\"},{\"name\":\"JO\",\"num\":\"19828\"},{\"name\":\"BG\",\"num\":\"32048\"},{\"name\":\"BH\",\"num\":\"12370\"},{\"name\":\"JP\",\"num\":\"396715\"},{\"name\":\"FR\",\"num\":\"1831400\"},{\"name\":\"NZ\",\"num\":\"789656\"},{\"name\":\"BO\",\"num\":\"68295\"},{\"name\":\"SA\",\"num\":\"129623\"},{\"name\":\"BR\",\"num\":\"1825435\"},{\"name\":\"SE\",\"num\":\"1803583\"},{\"name\":\"SG\",\"num\":\"871134\"},{\"name\":\"SK\",\"num\":\"74814\"},{\"name\":\"GB\",\"num\":\"4212057\"},{\"name\":\"CA\",\"num\":\"3155476\"},{\"name\":\"OM\",\"num\":\"9558\"},{\"name\":\"SV\",\"num\":\"53391\"},{\"name\":\"CH\",\"num\":\"421161\"},{\"name\":\"CL\",\"num\":\"456751\"},{\"name\":\"GR\",\"num\":\"92859\"},{\"name\":\"CO\",\"num\":\"188123\"},{\"name\":\"KW\",\"num\":\"30876\"},{\"name\":\"GT\",\"num\":\"103938\"},{\"name\":\"CR\",\"num\":\"104154\"},{\"name\":\"PA\",\"num\":\"44728\"},{\"name\":\"TH\",\"num\":\"256479\"},{\"name\":\"PE\",\"num\":\"321180\"},{\"name\":\"LB\",\"num\":\"10687\"},{\"name\":\"CY\",\"num\":\"19449\"},{\"name\":\"CZ\",\"num\":\"173165\"},{\"name\":\"PH\",\"num\":\"4231169\"},{\"name\":\"TN\",\"num\":\"22819\"},{\"name\":\"PL\",\"num\":\"457650\"},{\"name\":\"LI\",\"num\":\"1283\"},{\"name\":\"TR\",\"num\":\"432497\"}]}'" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1c.content" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx//locations/streams?time-filter=1day&aggregation-level=recording'" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url_builder.build_url('/locations/streams', timeFilter=PlaylistPeriods.day1.queryParam)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "day1country = _" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data': [{'uri': 'spotify:playlist:37i9dQZF1DXcBWIGoYBM5M',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '336326',\n", " 'dateAdded': '2020-03-16',\n", " 'title': \"Today's Top Hits\",\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002bfe8fd3804678a550345a75d',\n", " 'followers': '25994209'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbMDoHDwVN2tF',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '111696',\n", " 'title': 'Global Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_GLOBAL_DEFAULT.jpg',\n", " 'followers': '15083249'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXcZQSjptOQtk',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '60355',\n", " 'dateAdded': '2020-03-11',\n", " 'title': 'Top Hits Philippines',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002b7e8c48c97c7f639d61f20ed',\n", " 'followers': '3738194'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXa2EiKmMLhFD',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '53568',\n", " 'dateAdded': '2020-03-11',\n", " 'title': 'Top Hits Indonesia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002279161fcfa273204a11fe72c',\n", " 'followers': '1007754'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX4jP4eebSWR9',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '38417',\n", " 'dateAdded': '2020-03-10',\n", " 'title': 'Top Hits Deutschland',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000027e8ac79182d754e02a8c565b',\n", " 'followers': '2080345'},\n", " {'uri': 'spotify:user:spotify:playlist:radio',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '31835',\n", " 'title': 'Radio',\n", " 'thumbnailUrl': 'https://misc.scdn.co/s4x/radio.jpg',\n", " 'followers': '18446744073709551615',\n", " 'isPersonalized': True},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbObFQZ3JLcXt',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '29724',\n", " 'title': 'Indonesia Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_ID_DEFAULT.jpg',\n", " 'followers': '358405'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWXXs9GFYnvLB',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '26823',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'Hot Hits Australia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000250dafc7fd7444633cd009922',\n", " 'followers': '1209522'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWWhB4HOWKFQc',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '23675',\n", " 'dateAdded': '2020-03-12',\n", " 'title': 'Lagi Viral',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002e24ac05212b57635ae4bf698',\n", " 'followers': '99317'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWVuV87wUBNwc',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '18083',\n", " 'dateAdded': '2020-03-15',\n", " 'title': 'Hits du Moment',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000020cf8ff525367ccb7460d29eb',\n", " 'followers': '1605668'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWY4lFlS4Pnso',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '16991',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'Hot Hits UK',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000025897171670493e4deb430335',\n", " 'followers': '2636050'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLiRSasKsNU9',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '15732',\n", " 'title': 'Global Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_GLOBAL_DEFAULT.jpg',\n", " 'followers': '1610722'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWWvvyNmW9V9a',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '15050',\n", " 'dateAdded': '2020-02-18',\n", " 'title': 'Teen Beats',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000021702fcd0013021933ec5d009',\n", " 'followers': '129705'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLRQDuF5jeBp',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '13760',\n", " 'title': 'United States Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_US_DEFAULT.jpg',\n", " 'followers': '3249444'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLnolsZ8PSNw',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '13551',\n", " 'title': 'United Kingdom Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_GB_DEFAULT.jpg',\n", " 'followers': '2108866'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWXT8uSSn6PRy',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '13064',\n", " 'dateAdded': '2020-03-13',\n", " 'title': 'Hot Hits Canada',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000029053289f2a4e3a6a001074c7',\n", " 'followers': '643747'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbNBz9cRCSFkY',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '12977',\n", " 'title': 'Philippines Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_PH_DEFAULT.jpg',\n", " 'followers': '847919'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbJPcfkRz0wJ0',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '12883',\n", " 'title': 'Australia Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_AU_DEFAULT.jpg',\n", " 'followers': '739821'},\n", " {'uri': 'spotify:user:spotify:playlist:dailymix',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '12583',\n", " 'title': 'Your Daily Mix',\n", " 'thumbnailUrl': 'https://s4a.scdn.co/playlist_coverart/daily_mix.jpg',\n", " 'followers': '18446744073709551615',\n", " 'isPersonalized': True},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX6wfQutivYYr',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '12283',\n", " 'dateAdded': '2020-03-13',\n", " 'title': 'Hot Hits Italia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002a9ccf3329656e76983330280',\n", " 'followers': '974071'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbJvfa0Yxg7E7',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '11711',\n", " 'title': 'Norway Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_NO_DEFAULT.jpg',\n", " 'followers': '438975'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLoATJ81JYXz',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '10643',\n", " 'title': 'Sweden Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_SE_DEFAULT.jpg',\n", " 'followers': '566649'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWUa8ZRTfalHk',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '10619',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'Pop Rising',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000025e3951853b3cbba9ad22c10a',\n", " 'followers': '1901369'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbJiZcmkrIHGU',\n", " 'author': 'spotifycharts',\n", " 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'listeners': '0',\n", " 'streams': '1928',\n", " 'dateAdded': '2020-02-14',\n", " 'title': 'dead bed',\n", " 'thumbnailUrl': 'https://mosaic.scdn.co/300/ab67616d00001e020746507c8147aaffa04502deab67616d00001e025d084c9ce80631cbce8d84c2ab67616d00001e027e4135635fa0e393b31dc407ab67616d00001e02c157aa54ed14d68bae495d6a',\n", " 'followers': '633'},\n", " {'uri': 'spotify:playlist:1zqzEdfFSpNzKUv1i77ke2',\n", " 'author': 'nojukx',\n", " 'listeners': '0',\n", " 'streams': '1896',\n", " 'dateAdded': '2019-05-09',\n", " 'title': 'Death Bed – Powfu',\n", " 'thumbnailUrl': 'https://mosaic.scdn.co/300/ab67616d00001e020d71ca60d5554967fa2358feab67616d00001e026a07cce71681a170cdfe1838ab67616d00001e0279378616f1f52dcb4c27d862ab67616d00001e027e4135635fa0e393b31dc407',\n", " 'followers': '4948'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX0yN5997BIDH',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1882',\n", " 'dateAdded': '2020-03-05',\n", " 'title': 'Tarea Casual',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002c07a74a8f6a03e04af489ccf',\n", " 'isAlgotorial': True,\n", " 'followers': '213248'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXaPCIWxzZwR1',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1836',\n", " 'dateAdded': '2020-03-13',\n", " 'title': 'Pop Sauce',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002c4d608d0232ccd5eec2e2be4',\n", " 'isAlgotorial': True,\n", " 'followers': '525740'},\n", " {'uri': 'spotify:playlist:6T7asJNU7aJKbEOTvtPKp0',\n", " 'author': 'Filtr UK',\n", " 'listeners': '0',\n", " 'streams': '1731',\n", " 'dateAdded': '2020-03-13',\n", " 'title': 'UK Top 40 2020',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da84c428ff3a9d582170c112781f',\n", " 'followers': '112504'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXakrXW5YU9SI',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1717',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'Wiosenny chill',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002de12da43875ec261c2312643',\n", " 'isAlgotorial': True,\n", " 'followers': '117603'},\n", " {'uri': 'spotify:playlist:4e36iSHdNaSDaIfOKmCvXd',\n", " 'author': 'Indiemono',\n", " 'listeners': '0',\n", " 'streams': '1698',\n", " 'dateAdded': '2020-03-13',\n", " 'title': '2020 HITS VIRAL 🍍 ( Best Hits 2020 & Top Global 2020 Hits) 🍍 ',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da845b171710e42dde9b98a439a6',\n", " 'followers': '349791'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX2L0iB23Enbq',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1616',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'Big On The Internet',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002f7c3e627832536c9f9b5e9c0',\n", " 'followers': '10056'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbJNSeeHswcKB',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '1613',\n", " 'title': 'Belgium Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_BE_DEFAULT.jpg',\n", " 'followers': '249451'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX3PsJxMtn1AP',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1544',\n", " 'title': 'Boutique Tracks',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002ceb27dfc02aea59c78238bff',\n", " 'followers': '26360'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbKpV6RVDTWcZ',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '1540',\n", " 'title': 'Indonesia Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_ID_DEFAULT.jpg',\n", " 'followers': '76861'},\n", " {'uri': 'spotify:playlist:0WlMXA4QfTH3iQL8S42dCw',\n", " 'author': 'matthewhart169',\n", " 'listeners': '0',\n", " 'streams': '1439',\n", " 'dateAdded': '2020-02-26',\n", " 'title': 'Dead bed',\n", " 'thumbnailUrl': 'https://mosaic.scdn.co/300/ab67616d00001e027e4135635fa0e393b31dc407',\n", " 'followers': '243'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX0MD19TXm0aV',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1435',\n", " 'dateAdded': '2020-03-12',\n", " 'title': 'Top Hits Switzerland',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002fdf4ed993aba2f678ba5ac9a',\n", " 'followers': '71664'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX4WpYT2fah9c',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1406',\n", " 'dateAdded': '2020-03-12',\n", " 'title': 'Déclassé',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002f50fd3f684c005add3dc4e33',\n", " 'followers': '72603'},\n", " {'uri': 'spotify:playlist:4QuJ2DbcTe7R8lzqfNXz7v',\n", " 'author': 'Filtr Nederland',\n", " 'listeners': '0',\n", " 'streams': '1378',\n", " 'dateAdded': '2020-03-13',\n", " 'title': 'Love Songs & Chill',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da8424ec84ea4a06ba10b0c36ac7',\n", " 'followers': '536230'},\n", " {'uri': 'spotify:playlist:4gBN1HwWm6eQ1mRwBHymhJ',\n", " 'author': 'Filtr Sweden',\n", " 'listeners': '0',\n", " 'streams': '1314',\n", " 'dateAdded': '2020-03-16',\n", " 'title': 'TikTok Hits Sverige 🇸🇪',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da843ff18b0d1d2c046ed9904492',\n", " 'followers': '8494'},\n", " {'uri': 'spotify:playlist:2PX2bq5UYzOBggTlDkbseo',\n", " 'author': '7cloudsmusic',\n", " 'listeners': '0',\n", " 'streams': '1290',\n", " 'dateAdded': '2020-02-14',\n", " 'title': 'Pop All Day',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da8457178f7fad19a32d7b88b810',\n", " 'followers': '46634'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWY0DyDKedRYY',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '1263',\n", " 'dateAdded': '2020-03-08',\n", " 'title': 'On Our Radar: March',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002d42ed56f668ffeffe0b427d6',\n", " 'followers': '14440'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbMOkSwG072hV',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '1232',\n", " 'title': 'Brazil Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_BR_DEFAULT.jpg',\n", " 'followers': '317095'},\n", " {'uri': 'spotify:playlist:1Sl30EjqkvrlzfJBKy3m4C',\n", " 'author': 'George Robertson',\n", " 'listeners': '0',\n", " 'streams': '1192',\n", " 'dateAdded': '2020-02-08',\n", " 'title': \"don't stay awake for too long, don't go to bed.\",\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da845c15a753aec9e12d08c4d5c5',\n", " 'followers': '773'},\n", " {'uri': 'spotify:playlist:3xMQTDLOIGvj3lWH5e5x6F',\n", " 'author': 'SunsubiroRecords',\n", " 'listeners': '0',\n", " 'streams': '1184',\n", " 'dateAdded': '2020-03-14',\n", " 'title': 'Charts 2020 🔥 Top Hits 2020 🔥 Radio Hits 2020',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da84e83a683402ae2c69e628980b',\n", " 'followers': '147006'},\n", " {'uri': 'spotify:playlist:55UVG32oJPhoXZ6wJ2WqFn',\n", " 'author': 'Columbia Records',\n", " 'listeners': '0',\n", " 'streams': '1092',\n", " 'dateAdded': '2020-02-13',\n", " 'title': 'Fresh Cuts',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da8421a1dfefaf6ad201033c6b8f',\n", " 'followers': '35153'}],\n", " 'playlistOverall': '442150'}" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "day1country.json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Refresh Headers" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'Bearer BQClvIGapQb00n51J_HKBQAwV2iQE66f-_qW5WBkKhiQAb5MPJcs0X00iz73G4YaPpHkHgKPUVhnsiXUS5VJqHaM2LVPOzCESFa5aHhBpRSgrpmdZMh3wLUmJhi1gkZ6aLC6-1WnV5PAwiSnZOvJ2HNx9M8_ZVgXAiyZdpQ'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "refresh_request_headers();\n", "headers['authorization']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------\n", "\n", "\n", "## Daily Total Streams" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "url_total_daily = (\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " '/artist/6bmlMHgSheBauioMgKv2tn'\n", " '/recording/7eJMfftS33KTjuF7lTsMCx'\n", " '/timeline/streams'\n", " '?aggregation-level=recording'\n", " '&time-filter=28day'\n", ")" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "def check_data_updated():\n", " today = datetime.date.today()\n", " dailys = requests.get(url_total_daily, headers=headers).json()['timelinePoint']\n", " last_point = sorted([parser.parse(p['date']).date() for p in dailys])[-1]\n", " days_ago = (today - last_point).days\n", " if days_ago == 1:\n", " print(f\"Data appears to be updated for yesterday, days_ago={days_ago}\")\n", " return True\n", " elif days_ago == 0:\n", " print(\"Something strange, days_ago is 0\")\n", " return False\n", " else:\n", " print(f\"Data is not updated for yesterday, days_ago={days_ago}\")\n", " return False" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data appears to be updated for yesterday, days_ago=1\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "check_data_updated()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'scipy'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scipy'" ] } ], "source": [ "import scipy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def store_daily_streams(timelineJson):\n", " with get_cursor(True) as cur:\n", " prep_rows = []\n", " sql_args = []\n", " for row in timelineJson['timelinePoint']:\n", " sql_args.extend([row['date'], int(row['num'])])\n", " prep_rows.append(\"(%s, %s)\")\n", " prepared_str = \",\".join(prep_rows)\n", " sql = f'''insert ignore into deathbed_daily_streams (as_of, streams) values {prepared_str}'''\n", " \n", " print(sql[:30], sql_args[:3])\n", " cur.execute(sql, sql_args)\n", " \n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "totals = requests.get(url_total_daily, headers=headers)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insert ignore into deathbed_daily_streams (as_of, streams) values (%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s) ['2020-03-14', 2975805, '2020-03-13', 2947507, '2020-03-12', 2854021, '2020-03-11', 2824435, '2020-03-10', 2758199, '2020-03-09', 2700304, '2020-03-08', 2470073, '2020-03-07', 2541423, '2020-03-06', 2622000, '2020-03-05', 2537273, '2020-03-04', 2439409, '2020-03-03', 2403709, '2020-03-02', 2306110, '2020-03-01', 2166767, '2020-02-29', 2237775, '2020-02-28', 2240960, '2020-02-27', 2111440, '2020-02-26', 2052861, '2020-02-25', 1959335, '2020-02-24', 1844278, '2020-02-23', 1648041, '2020-02-22', 1670351, '2020-02-21', 1713451, '2020-02-20', 1625098, '2020-02-19', 1535673, '2020-02-18', 1451815, '2020-02-17', 1357659, '2020-02-16', 1216908]\n" ] } ], "source": [ "store_daily_streams(totals.json())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "daily_totals_df = _sql_to_dataframe(\n", " '''select as_of, streams from deathbed_daily_streams''', \n", " columns=['day', 'streams'], \n", " index_column='day'\n", ")\n", "daily_totals_df.plot(figsize=(10,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Country Timelines" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Countries\n", "def get_countries()\n", "top_countries_request = requests.get(\n", " ('https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " '/artist/6bmlMHgSheBauioMgKv2tn'\n", " '/recording/7eJMfftS33KTjuF7lTsMCx'\n", " '/locations/streams?time-filter=28day&aggregation-level=recording'),\n", " headers=headers\n", ")\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def store_top_countries(top_countries_json):\n", " with get_cursor(True) as cur:\n", " prep_rows = []\n", " sql_args = []\n", " for row in top_countries_json['geography']:\n", " sql_args.extend([row['name'], as_of.isoformat(), int(row['num'])])\n", " prep_rows.append(\"(%s, %s, %s)\")\n", " prepared_str = \",\".join(prep_rows)\n", " sql = f'''insert ignore into deathbed_country_streams (country, as_of, total) values {prepared_str}'''\n", " \n", " print(sql[:30], sql_args[:3])\n", " cur.execute(sql, sql_args)\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'top_countries_request' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtop_countries_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_countries_request\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mas_of\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutcnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'top_countries_request' is not defined" ] } ], "source": [ "top_countries_json = top_countries_request.json()\n", "as_of = datetime.datetime.utcnow().date() - datetime.timedelta(days=1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "79" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#top_30 = [c['name'] for c in sorted(top_countries_json, key=lambda x: int(x['num']), reverse=True)][:21]\n", "len(top_countries_json['geography'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'top_countries_json' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstore_top_countries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_countries_json\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'top_countries_json' is not defined" ] } ], "source": [ "store_top_countries(top_countries_json)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## YouTube From Database" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "yt1 = _sql_to_dataframe('''select ts, views1, comments1 from deathbed_youtubes''', columns=['ts', 'views', 'comments'])\n", "\n", "yt1['views_per_minute'] = (yt1.views.diff() / yt1.ts.astype(int).diff() * 1e9 * 60)\n", "yt1['comments_per_minute'] = (yt1.comments.diff() / yt1.ts.astype(int).diff() * 1e9 * 60)\n", "yt1 = yt1.set_index('ts')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yt1[['views_per_minute']].resample('1H').mean().iloc[-40*24:].rolling(24).mean().plot(figsize=(10,3))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "rolling24 = yt1[['views_per_minute']].resample('1H').mean().rolling(24).mean().iloc[-29*24:]" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n"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" 7.46832607e+00-6.69443587e+01j, -1.34203730e+02-9.94538044e+01j,\n", " 3.65045642e+01+1.24684930e+02j, 4.50465109e+01-8.37021209e+01j,\n", " -6.06900119e+01-1.25790291e+02j, 4.17391430e+00-9.76020397e+01j,\n", " -1.18171513e+02-5.00023339e+01j, -3.38382010e+01-1.30370293e+02j,\n", " -5.66721241e+01-1.55107863e+02j, -1.12437059e+01-1.89246032e+02j,\n", " -8.73968980e+01-2.05170279e+02j, -1.32045128e+02-1.98292000e+02j,\n", " -4.79535844e+01-2.87225392e+02j, -7.23243831e+00-2.59799003e+02j,\n", " 1.94554784e+00-4.03971937e+02j, -1.77494378e+02-4.58976269e+02j,\n", " 1.79379308e+01-3.72345035e+02j, 2.49684272e+01-4.16651028e+02j,\n", " -4.78113766e+01-1.50691756e+02j, -4.21910658e+01-4.27166993e+02j,\n", " -1.62427312e+02-1.88795021e+02j, -1.38649047e+02-3.77230352e+02j,\n", " -2.12297821e+02-6.52081215e+02j, 3.99940739e+02-7.22070171e+02j,\n", " 1.72423291e+02-4.99828717e+02j, -3.83282993e+01-8.00407704e+02j,\n", " 4.71968899e+01-5.74348211e+02j, -7.76653732e+02-7.23101603e+02j,\n", " 3.81977874e+02-1.64261630e+02j, 2.15174681e+02-1.46319879e+03j,\n", " 4.10235463e+02-6.47579253e+02j, 8.13163498e+02-3.79038913e+02j,\n", " -8.24322032e+02-1.30480808e+03j, -9.09639088e+02-5.29225309e+01j,\n", " -4.09268111e+01-1.44678860e+03j, -1.64568719e+02-3.36621651e+03j,\n", " -1.29272426e+03+5.80298661e+02j, 1.03766138e+03-2.12026244e+03j,\n", " 1.88192100e+03-6.52159655e+02j, 8.34320622e+02-4.20661585e+03j])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fftpack.fft(rolling24.views_per_minute.to_numpy())" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "object of type cannot be safely interpreted as an integer.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/numpy/core/function_base.py\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m80.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m2.0\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0myf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfftpack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mxf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(*args, **kwargs)\u001b[0m\n", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/numpy/core/function_base.py\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis)\u001b[0m\n\u001b[1;32m 119\u001b[0m raise TypeError(\n\u001b[1;32m 120\u001b[0m \u001b[0;34m\"object of type {} cannot be safely interpreted as an integer.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m .format(type(num)))\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: object of type cannot be safely interpreted as an integer." ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.fftpack\n", "\n", "# Number of samplepoints\n", "N = 600\n", "# sample spacing\n", "T = 1.0 / 800.0\n", "x = numpy.linspace(0.0, N*T, N)\n", "y = numpy.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)\n", "yf = scipy.fftpack.fft(y)\n", "xf = numpy.linspace(0.0, 1.0/(2.0*T), N/2)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(xf, 2.0/N * np.abs(yf[:N//2]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "tt1 = _sql_to_dataframe('''select ts, videos1 + videos2 from skechers_tiktoks''', columns=['ts', 'videos'])\n", "\n", "tt1['videos_per_minute'] = (tt1.videos.diff() / tt1.ts.astype(int).diff() * 1e9 * 60)\n", "tt1 = tt1.set_index('ts')" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tt1[['videos_per_minute']].resample('1H').mean().plot(figsize=(10,3), legend=False, title='Skechers TikToks / minute')" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['2020-03-23 20:34:21', '2020-03-23 20:54:21',\n", " '2020-03-23 21:14:21', '2020-03-23 21:34:21',\n", " '2020-03-23 21:54:21', '2020-03-23 22:14:20',\n", " '2020-03-23 22:34:21', '2020-03-23 22:54:21',\n", " '2020-03-23 23:14:21', '2020-03-23 23:34:21',\n", " ...\n", " '2020-03-25 12:34:21', '2020-03-25 12:54:21',\n", " '2020-03-25 13:14:21', '2020-03-25 13:34:22',\n", " '2020-03-25 13:54:22', '2020-03-25 14:14:23',\n", " '2020-03-25 14:34:21', '2020-03-25 14:54:21',\n", " '2020-03-25 15:14:21', '2020-03-25 15:34:21'],\n", " dtype='datetime64[ns]', name='ts', length=130, freq=None)" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tt1.index" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "merged = pd.merge(yt1.resample('1H').mean(), tt1.resample('1H').mean(), left_index=True, right_index=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m2 = merged.rolling(24).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from pandas import DataFrame\n", "\n", "fig, ax = plt.subplots(figsize=(15,7))\n", "# ax3 = ax.twinx()\n", "# rspine = ax3.spines['right']\n", "# rspine.set_position(('axes', 1.15))\n", "# ax3.set_frame_on(True)\n", "# ax3.patch.set_visible(False)\n", "# fig.subplots_adjust(right=0.7)\n", "\n", "m2.views_per_minute.plot(ax=ax, style='r-', title=\"YouTube And TikToks Rolling 24 Hour Average\")\n", "# same ax as above since it's automatically added on the right\n", "m2.videos_per_minute.plot(ax=ax, style='b-', secondary_y=True)\n", "# df.C.plot(ax=ax3, style='g-')\n", "\n", "# add legend --> take advantage of pandas providing us access\n", "# to the line associated with the right part of the axis\n", "ax.legend([ax.get_lines()[0], ax.right_ax.get_lines()[0]], ['YouTube Views / Minute', 'TikToks / Minute'])\n", "# ['A','B','C'], bbox_to_anchor=(1.5, 0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smoothing Listeners" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "full_listeners = _sql_to_dataframe('''select ts, listeners from deathbed_listeners''', columns=['ts', 'listeners'])" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "full_listeners.set_index('ts').resample('1H').median().rolling(24, min_periods=12).mean().plot(figsize=(12,6))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "widf = full_listeners.set_index('ts')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "widf.rolling?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Daily vs. Avg Listeners" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "listenersDf = _sql_to_dataframe(\n", " 'select ts, listeners from deathbed_listeners', columns=['ts', 'listeners'], index_column='ts')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "listeners1Min = listenersDf.resample('1Min').mean()\n", "listeners1Min['dday'] = listeners1Min.index.map(to_day)\n", "listeners1Min['tday'] = listeners1Min.index.map(to_minutes).astype(int)\n", "listeners1Min.pivot(index='tday', columns='dday', values=['listeners'])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_vs_avg_listeners = pd.merge(daily_totals_df, listeners1Min.groupby('dday').median(), left_index=True, right_index=True)[['streams', 'listeners']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_vs_avg_listeners.plot(kind='scatter', x='listeners', y='streams')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "listeners1Min.listeners * (listeners1Min.tday <= 1140)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for fourHour in range(4, 25, 4):\n", " print(fourHour)\n", " listeners1Min[f'list{fourHour}'] = listeners1Min.listeners * (listeners1Min.tday <= fourHour*60)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "listeners1Min" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "averageListenersAt4T = listeners1Min.pivot(index='tday', columns='dday', values=['list4', 'list8', 'list12', 'list16', 'list20', 'list24']).mean(axis=0).unstack().T\n", "\n", "for fourHour in range(4, 24, 4):\n", " averageListenersAt4T[f'list{fourHour}'] *= 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# removing the first row as it doens't have many readings\n", "streams_and_avg_listeners = pd.merge(daily_totals_df, averageListenersAt4T.iloc[1:], left_index=True, right_index=True, how='inner')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def add_linests(df):\n", " m_and_b = {}\n", " for hourInt in range(4, 25, 4):\n", " slope, yint = numpy.polyfit(x=streams_and_avg_listeners[f'list{hourInt}'], y=streams_and_avg_listeners.streams, deg=1)\n", " streams_and_avg_listeners[f'linest{hourInt}'] = streams_and_avg_listeners[f'list{hourInt}'].map(lambda x: slope*x + yint)\n", " m_and_b[hourInt] = (slope, yint)\n", " return m_and_b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_and_b = add_linests(streams_and_avg_listeners)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_and_avg_listeners[['streams', 'linest4', 'linest8', 'linest12', 'linest16', 'linest20', 'linest24']].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nrows = 2\n", "ncols = 3\n", "df = streams_and_avg_listeners\n", "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=False, figsize=(12, 6))\n", "fig.suptitle('Act. - Est. per 4Hour', fontsize=14)\n", "for i in range(6):\n", " ax = axes[int(i / ncols)][i % ncols]\n", " f = 4 + (i*4)\n", " # ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))\n", " ((df[f'linest{f}'] - df.streams)/df.streams).plot(legend=False, ax=ax, title=repr(m_and_b.get(f)))\n", " ax.xaxis.set_major_locator(\n", " mdates.MonthLocator()\n", " )\n", " ax.xaxis.set_minor_locator(\n", " mdates.DayLocator(interval=4)\n", " )\n", "plt.tight_layout()\n", "fig.subplots_adjust(top=0.91)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_and_avg_listeners" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numpy.polyfit(x=streams_vs_avg_listeners.listeners, y=streams_vs_avg_listeners.streams, deg=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_vs_avg_listeners['linest'] = streams_vs_avg_listeners.listeners.map(lambda x: 3.67321295e+02*x -6.67621208e+05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "streams_vs_avg_listeners[['streams', 'linest']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", ".\n", "\n", ".\n", "\n", ".\n", "\n", "## Sources\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 253, "metadata": {}, "outputs": [], "source": [ "refresh_request_headers();" ] }, { "cell_type": "code", "execution_count": 258, "metadata": {}, "outputs": [], "source": [ "url_source_breakdown = (\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " '/artist/6bmlMHgSheBauioMgKv2tn'\n", " '/recording/7eJMfftS33KTjuF7lTsMCx'\n", " '/source?time-filter=28day'\n", ")" ] }, { "cell_type": "code", "execution_count": 259, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 259, "metadata": {}, "output_type": "execute_result" } ], "source": [ "requests.get(url_source_breakdown, headers=headers)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.date(2020, 3, 15)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yesterday = datetime.date.today() - datetime.timedelta(days=1)\n", "yesterday" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def save_source_for_yesterday():\n", " sources = requests.get(url_source_breakdown, headers=headers).json()\n", " pct_rows = \",\\n\".join([\"(%s, %s, %s)\"] * len(sources))\n", " sql = f'''\n", "insert into deathbed_stream_sources \n", "(as_of, source, streams) \n", "values \n", "{pct_rows}\n", " '''\n", " args = []\n", " for source, streams in sources.items():\n", " args.extend([yesterday, source, streams])\n", " \n", " print(sql, args)\n", " with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "insert into deathbed_stream_sources \n", "(as_of, source, streams) \n", "values \n", "(%s, %s, %s),\n", "(%s, %s, %s),\n", "(%s, %s, %s),\n", "(%s, %s, %s),\n", "(%s, %s, %s),\n", "(%s, %s, %s)\n", " [datetime.date(2020, 3, 15), 'user', '25809147', datetime.date(2020, 3, 15), 'network', '2802620', datetime.date(2020, 3, 15), 'catalog', '4585780', datetime.date(2020, 3, 15), 'other', '4580819', datetime.date(2020, 3, 15), 'personalized', '1488005', datetime.date(2020, 3, 15), 'editorial', '21977352']\n" ] } ], "source": [ "save_source_for_yesterday()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "ALL_SOURCE_TOTALS = {\n", " \"2020-03-01\": {\n", " \"user\": \"14340463\",\n", " \"network\": \"1300651\",\n", " \"catalog\": \"3359368\",\n", " \"other\": \"3046999\",\n", " \"personalized\": \"776926\",\n", " \"editorial\": \"10708696\"\n", " },\n", " \"2020-03-07\": {\n", " \"user\": \"19432687\",\n", " \"network\": \"1845405\",\n", " \"catalog\": \"4107838\",\n", " \"other\": \"3834044\",\n", " \"personalized\": \"1080693\",\n", " \"editorial\": \"15318503\"\n", " },\n", " \"2020-03-08\": {\n", " \"user\": \"20296169\",\n", " \"network\": \"1958817\",\n", " \"catalog\": \"4165320\",\n", " \"other\": \"3977514\",\n", " \"personalized\": \"1137204\",\n", " \"editorial\": \"16146348\"\n", " },\n", " \"2020-03-09\": {\n", " \"user\": \"21178688\",\n", " \"network\": \"2083537\",\n", " \"catalog\": \"4205388\",\n", " \"other\": \"4064368\",\n", " \"personalized\": \"1196767\",\n", " \"editorial\": \"17067105\"\n", " },\n", " \"2020-03-10\": {'user': '22019753',\n", " 'network': '2205582',\n", " 'catalog': '4247094',\n", " 'other': '4147873',\n", " 'personalized': '1252320',\n", " 'editorial': '18033361'},\n", " '2020-03-11': {'user': '22845018',\n", " 'network': '2331941',\n", " 'catalog': '4296562',\n", " 'other': '4227663',\n", " 'personalized': '1305450',\n", " 'editorial': '18982586'}\n", "\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allSourcesDf = pd.DataFrame(ALL_SOURCE_TOTALS).astype(int)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allSrcT = allSourcesDf.T.copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allSrcT['total'] = allSrcT.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourcePercents = allSrcT.apply(lambda x: x / x['total'], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceChange7 = sourcePercents.drop(columns='total').iloc[[-2, -1]].pct_change().iloc[-1].to_frame('% Change').sort_values('% Change', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceChange7" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceChange7['total'] = allSourcesDf[allSourcesDf.columns[-1]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(sourceChange7).style.format({'% Change':\"{:.2%}\", 'total':\"{:,.0f}\"}).set_properties().bar(subset=['% Change'], align='zero', color=['#e6928f', '#92e68f']).set_precision(2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceChange7.total.sort_values().plot(kind='pie', autopct='%.1f %%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "sources complete\n", "\n", "\n", "----\n", "\n", "\n", "## backfill country data\n", "\n", "\n", "Normally we shouldn't need to do this, but if I miss a day." ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'authority': 'generic.wg.spotify.com',\n", " 'authorization': 'Bearer BQASohc5dJc5g7swYTmX-2kiogjw2VcPLT1BFuEzHFj8SYr0rjRra40QEgpQ6sIm4ZJG0N2CKXX8JaUtHTtd8xp4SAaQET3RN8MEFczf4EXdLokOmvh1Odq_JPf9HRuaOeJEFTy-iGhraza1ssJxMHjPYPz9zYtvDZAf0fo',\n", " 'content-type': 'application/json',\n", " 'accept': 'application/json',\n", " 'sec-fetch-dest': 'empty',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'spotify-app-version': '1.0.0.3229887',\n", " 'app-platform': 'Browser',\n", " 'origin': 'https://artists.spotify.com',\n", " 'sec-fetch-site': 'same-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://artists.spotify.com/c/artist/6bmlMHgSheBauioMgKv2tn/song/7eJMfftS33KTjuF7lTsMCx/stats?segment-filter=streams&time-filter=7day',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'}" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "refresh_request_headers()" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "def request_countries(codes: list, artist_id, track_id):\n", " return requests.get(\n", " ('https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " f'/artist/{artist_id}/recording/{track_id}/timeline/countries'\n", " f'?time-filter=28day&aggregation-level=recording&country-codes={\",\".join(codes)}'),\n", " headers=headers\n", " )\n", "\n", "def chunks(lst, n):\n", " \"\"\"Yield successive n-sized chunks from lst.\"\"\"\n", " for i in range(0, len(lst), n):\n", " yield lst[i:i + n]\n", "\n", "def get_uniq_country_codes(prefix):\n", " with get_cursor(False) as cur:\n", " cur.execute(f'''select country, as_of, total from {prefix}_country_streams_28day''')\n", " all_country_data = cur.fetchall()\n", " return list(set([r[0] for r in all_country_data]))\n", " \n", "def load_into_matrix(codes, matrix, all_country_timelines, artist_id, track_id):\n", " r2 = request_countries(codes, artist_id, track_id)\n", " print(codes, r2.reason)\n", " country_timelines = r2.json()['countryTimelines']\n", " for country in country_timelines:\n", " code = country['countryCode']\n", " all_country_timelines[code] = country['timelinePoint']\n", " for ev in country['timelinePoint']:\n", " matrix.append([ev['date'], code, int(ev['num'])])\n", " \n", "\n", "def load_all_country_timelines(artist_id, track_id, uniq_country_codes: list):\n", " all_country_timelines = {}\n", " matrix = []\n", " for chunk in chunks(uniq_country_codes, 3):\n", " load_into_matrix(chunk, matrix, all_country_timelines, artist_id, track_id)\n", " import time; time.sleep(3)\n", " return matrix, all_country_timelines" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "def insert_matrix_data(table_prefix, matrix):\n", " with get_cursor(True) as cur:\n", " for chunk in chunks(matrix, 500):\n", " pcts =\",\".join([\"(%s, %s, %s)\"] * len(chunk))\n", " sql = f\"\"\"insert into {table_prefix}_country_daily_streams (as_of, country, daily) values {pcts}\"\"\"\n", " args = [i for row in chunk for i in row]\n", " print(sql[:70], len(args), \":\", args[0:6], \"..\", args[-6:])\n", " cur.execute(sql, args)\n", " " ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "uniq_country_codes = get_uniq_country_codes('skechers')" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KW,DO,SA,SK,BR,UY,LV,MY,CH,BG,PL,DK,CL,MX,AT,LI,FR,GR,PS,GB,SE,ES,MA,TR,MT,ID,LU,US,TH,BH,LB,IL,IE,PA,EC,SG,LT,IS,HK,CO,CR,GT,RO,CZ,NO,JO,TW,QA,DZ,NI,EG,PE,CY,VN,FI,PY,DE,OM,AE,JP,NZ,MC,ZA,NL,SV,HN,IN,AD,EE,AR,BE,PH,CA,BO,TN,AU,PT,IT,HU \n", " 79\n" ] } ], "source": [ "print(\",\".join(uniq_country_codes), \"\\n\", len(uniq_country_codes))" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['KW', 'DO', 'SA'] OK\n", "['SK', 'BR', 'UY'] OK\n", "['LV', 'MY', 'CH'] OK\n", "['BG', 'PL', 'DK'] OK\n", "['CL', 'MX', 'AT'] OK\n", "['LI', 'FR', 'GR'] OK\n", "['PS', 'GB', 'SE'] OK\n", "['ES', 'MA', 'TR'] OK\n", "['MT', 'ID', 'LU'] OK\n", "['US', 'TH', 'BH'] OK\n", "['LB', 'IL', 'IE'] OK\n", "['PA', 'EC', 'SG'] OK\n", "['LT', 'IS', 'HK'] OK\n", "['CO', 'CR', 'GT'] OK\n", "['RO', 'CZ', 'NO'] OK\n", "['JO', 'TW', 'QA'] OK\n", "['DZ', 'NI', 'EG'] OK\n", "['PE', 'CY', 'VN'] OK\n", "['FI', 'PY', 'DE'] OK\n", "['OM', 'AE', 'JP'] OK\n", "['NZ', 'MC', 'ZA'] OK\n", "['NL', 'SV', 'HN'] OK\n", "['IN', 'AD', 'EE'] OK\n", "['AR', 'BE', 'PH'] OK\n", "['CA', 'BO', 'TN'] OK\n", "['AU', 'PT', 'IT'] OK\n", "['HU'] OK\n" ] } ], "source": [ "matrix, all_country_timelines = load_all_country_timelines('3O5HD95HTEPgoPFOjAb7yV', '4z2JaAVPemYJhFKm7e32RA', uniq_country_codes)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "#uniq_country_codes = 'LB,PL,NI,BH,VN,BE,LV,EC,NO,TW,IN,TR,PH,AT,ES,HK,PA,TH,IL,FR,CO,MX,MT,CR,HN,US,NZ,JO,GT,CZ,CH,AR,EE,RO,LI,IT,NL,PY,LT,IS,BO,KW,DO,SK,TN,MA,DZ,MY,IE,AD,PT,FI,CY,MC,OM,ID,SV,GR,ZA,EG,BG,DK,SG,AU,UY,BR,SE,HU,CA,LU,DE,PE,SA,PS,AE,QA,CL,GB,JP'.split(',')\n" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insert into skechers_country_daily_streams (as_of, country, daily) val 1500 : ['2020-02-25', 'DO', 29, '2020-02-26', 'DO', 22] .. ['2020-03-18', 'LI', 68, '2020-03-19', 'LI', 135]\n", "insert into skechers_country_daily_streams (as_of, country, daily) val 1500 : ['2020-03-20', 'LI', 53, '2020-03-21', 'LI', 25] .. ['2020-03-14', 'EC', 512, '2020-03-15', 'EC', 1142]\n", "insert into skechers_country_daily_streams (as_of, country, daily) val 1500 : ['2020-03-16', 'EC', 1258, '2020-03-17', 'EC', 1177] .. ['2020-03-10', 'VN', 82, '2020-03-11', 'VN', 102]\n", "insert into skechers_country_daily_streams (as_of, country, daily) val 1500 : ['2020-03-12', 'VN', 151, '2020-03-13', 'VN', 124] .. ['2020-03-06', 'PH', 428, '2020-03-07', 'PH', 472]\n", "insert into skechers_country_daily_streams (as_of, country, daily) val 636 : ['2020-03-08', 'PH', 529, '2020-03-09', 'PH', 556] .. ['2020-03-22', 'HU', 3591, '2020-03-23', 'HU', 4578]\n" ] } ], "source": [ "insert_matrix_data('skechers', matrix)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryADAEARATAUBEBGBHBOBR...SKSVTHTNTRTWUSUYVNZA
day
2020-02-2522201211832212002...1030364167100
2020-02-2633412593754522106...10190537329123
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\n", "

28 rows × 79 columns

\n", "
" ], "text/plain": [ "country AD AE AR AT AU BE BG BH BO BR ... SK SV TH \\\n", "day ... \n", "2020-02-25 2 22 0 121 183 221 2 0 0 2 ... 1 0 3 \n", "2020-02-26 3 34 1 259 375 452 2 1 0 6 ... 1 0 19 \n", "2020-02-27 7 43 1 413 574 667 3 1 0 9 ... 3 0 22 \n", "2020-02-28 10 68 3 559 809 985 8 1 0 11 ... 3 0 25 \n", "2020-02-29 12 89 4 700 998 1237 8 1 1 14 ... 4 0 36 \n", "2020-03-01 12 102 5 774 1220 1498 12 1 1 18 ... 6 0 46 \n", "2020-03-02 12 114 6 896 1440 1775 18 1 1 21 ... 9 0 57 \n", "2020-03-03 12 128 6 1022 1642 2011 22 1 1 26 ... 13 0 63 \n", "2020-03-04 12 145 7 1123 1846 2212 22 2 1 30 ... 15 0 74 \n", "2020-03-05 12 163 8 1190 2063 2400 30 2 1 33 ... 21 0 82 \n", "2020-03-06 12 177 12 1300 2314 2630 32 2 2 35 ... 35 0 84 \n", "2020-03-07 12 185 13 1383 2499 2808 37 3 2 37 ... 48 0 93 \n", "2020-03-08 12 194 13 1454 2699 2926 37 12 3 40 ... 51 0 97 \n", "2020-03-09 12 208 15 1525 2911 3127 38 14 4 42 ... 59 0 102 \n", "2020-03-10 12 213 16 1573 3125 3245 41 16 4 44 ... 62 0 108 \n", "2020-03-11 12 221 17 1631 3346 3405 44 19 4 52 ... 64 0 118 \n", "2020-03-12 13 232 18 1711 3539 3540 45 25 5 65 ... 69 0 122 \n", "2020-03-13 13 242 19 1781 3742 3673 46 26 5 76 ... 73 0 126 \n", "2020-03-14 13 252 20 1852 3930 3778 47 26 5 82 ... 74 0 129 \n", "2020-03-15 13 261 20 1897 4154 3874 47 27 5 86 ... 76 0 131 \n", "2020-03-16 13 268 23 1955 4443 4010 57 27 5 92 ... 80 0 133 \n", "2020-03-17 13 272 24 2009 4746 4136 58 28 5 103 ... 83 0 137 \n", "2020-03-18 13 282 24 2075 4997 4235 59 28 5 107 ... 89 1 144 \n", "2020-03-19 13 290 24 2139 5274 4363 60 28 5 111 ... 90 1 150 \n", "2020-03-20 13 295 26 2182 5555 4479 61 29 5 115 ... 94 1 153 \n", "2020-03-21 13 303 26 2205 5843 4586 63 31 5 119 ... 100 1 156 \n", "2020-03-22 13 308 29 2237 6033 4729 63 31 6 122 ... 104 1 161 \n", "2020-03-23 13 314 35 2273 6268 4853 69 33 7 125 ... 104 1 166 \n", "\n", "country TN TR TW US UY VN ZA \n", "day \n", "2020-02-25 0 36 4 167 1 0 0 \n", "2020-02-26 0 53 7 329 1 2 3 \n", "2020-02-27 0 70 11 478 1 2 10 \n", "2020-02-28 0 84 13 639 1 5 20 \n", "2020-02-29 1 93 14 753 3 5 27 \n", "2020-03-01 1 98 19 836 3 9 34 \n", "2020-03-02 1 108 21 1019 3 10 40 \n", "2020-03-03 2 120 24 1280 3 11 47 \n", "2020-03-04 2 132 26 1481 4 12 47 \n", "2020-03-05 2 137 28 1706 4 13 53 \n", "2020-03-06 2 141 28 1914 4 13 53 \n", "2020-03-07 2 145 28 2041 4 13 55 \n", "2020-03-08 2 151 29 2156 4 13 57 \n", "2020-03-09 3 160 33 2475 4 14 63 \n", "2020-03-10 3 173 37 2806 4 15 66 \n", "2020-03-11 3 184 38 3132 4 15 67 \n", "2020-03-12 3 199 39 3431 4 15 75 \n", "2020-03-13 4 210 42 3683 4 19 77 \n", "2020-03-14 4 218 48 3863 4 19 83 \n", "2020-03-15 5 224 50 4022 4 19 83 \n", "2020-03-16 5 230 59 4305 4 20 84 \n", "2020-03-17 5 237 61 4551 4 20 87 \n", "2020-03-18 5 247 63 4776 4 20 91 \n", "2020-03-19 5 266 67 5025 4 22 95 \n", "2020-03-20 6 277 68 5218 4 22 101 \n", "2020-03-21 6 285 72 5369 4 22 103 \n", "2020-03-22 6 315 73 5496 4 22 114 \n", "2020-03-23 8 340 78 5610 4 22 119 \n", "\n", "[28 rows x 79 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "daily_totals" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "daily_db = _sql_to_dataframe('select cast(as_of as char), country, total from deathbed_country_streams', columns=['day','country', 'total'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "joined = pd.merge(\n", " daily_totals.stack().to_frame('cumstreams'),\n", " daily_db.set_index(['day', 'country']),\n", " left_index=True,\n", " right_index=True,\n", " how='left'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "missing_data = joined[(joined.cumstreams > 0) & (joined['total'] - joined['total'] != 0)].cumstreams" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "row_tupes = [(k[0], k[1], v) for k,v in missing_data.to_dict().items()]\n", "insert_block = \",\\n\".join([ f\"\"\"('{r[0]}','{r[1]}',{r[2]})\"\"\" for r in row_tupes ])\n", "print(f\"{len(row_tupes)} missing rows.\\n{insert_block[:150]}...\")" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [], "source": [ "def insert_daily_country(matrix_day_country_streams):\n", " pcts = \",\".join(['(%s, %s, %s)'] * len(matrix_day_country_streams))\n", " sql = f'''\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " values {pcts}\n", " '''\n", " args = [i for r in matrix_day_country_streams for i in r]\n", " with get_cursor(True) as cur:\n", " print(sql)\n", " print(args)\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " values (%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s)\n", " \n", "['2020-02-19', 'LB', 176, '2020-02-20', 'LB', 232, '2020-02-21', 'LB', 244, '2020-02-22', 'LB', 363, '2020-02-23', 'LB', 295, '2020-02-24', 'LB', 293, '2020-02-25', 'LB', 302, '2020-02-26', 'LB', 249, '2020-02-27', 'LB', 380, '2020-02-28', 'LB', 446, '2020-02-29', 'LB', 397, '2020-03-01', 'LB', 289, '2020-03-02', 'LB', 375, '2020-03-03', 'LB', 433, '2020-03-04', 'LB', 471, '2020-03-05', 'LB', 400, '2020-03-06', 'LB', 440, '2020-03-07', 'LB', 461, '2020-03-08', 'LB', 325, '2020-03-09', 'LB', 400, '2020-03-10', 'LB', 389, '2020-03-11', 'LB', 439, '2020-03-12', 'LB', 402, '2020-03-13', 'LB', 470, '2020-03-14', 'LB', 469, '2020-03-15', 'LB', 541, '2020-03-16', 'LB', 487, '2020-03-17', 'LB', 519, '2020-02-19', 'NI', 295, '2020-02-20', 'NI', 238, '2020-02-21', 'NI', 251, '2020-02-22', 'NI', 263, '2020-02-23', 'NI', 278, '2020-02-24', 'NI', 249, '2020-02-25', 'NI', 268, '2020-02-26', 'NI', 431, '2020-02-27', 'NI', 355, '2020-02-28', 'NI', 439, '2020-02-29', 'NI', 542, '2020-03-01', 'NI', 519, '2020-03-02', 'NI', 500, '2020-03-03', 'NI', 538, '2020-03-04', 'NI', 545, '2020-03-05', 'NI', 673, '2020-03-06', 'NI', 598, '2020-03-07', 'NI', 680, '2020-03-08', 'NI', 541, '2020-03-09', 'NI', 742, '2020-03-10', 'NI', 643, '2020-03-11', 'NI', 722, '2020-03-12', 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'HK', 5829, '2020-03-05', 'HK', 6294, '2020-03-06', 'HK', 6280, '2020-03-07', 'HK', 5614, '2020-03-08', 'HK', 4995, '2020-03-09', 'HK', 6107, '2020-03-10', 'HK', 6240, '2020-03-11', 'HK', 6365, '2020-03-12', 'HK', 6484, '2020-03-13', 'HK', 7315, '2020-03-14', 'HK', 6861, '2020-03-15', 'HK', 6567, '2020-03-16', 'HK', 7929, '2020-03-17', 'HK', 7797, '2020-02-19', 'TH', 6601, '2020-02-20', 'TH', 6822, '2020-02-21', 'TH', 6602, '2020-02-22', 'TH', 6357, '2020-02-23', 'TH', 6586, '2020-02-24', 'TH', 6731, '2020-02-25', 'TH', 8090, '2020-02-26', 'TH', 7827, '2020-02-27', 'TH', 7858, '2020-02-28', 'TH', 9013, '2020-02-29', 'TH', 8897, '2020-03-01', 'TH', 8785, '2020-03-02', 'TH', 9018, '2020-03-03', 'TH', 9301, '2020-03-04', 'TH', 9655, '2020-03-05', 'TH', 9819, '2020-03-06', 'TH', 10447, '2020-03-07', 'TH', 9602, '2020-03-08', 'TH', 9254, '2020-03-09', 'TH', 9899, '2020-03-10', 'TH', 11105, '2020-03-11', 'TH', 10906, '2020-03-12', 'TH', 11005, '2020-03-13', 'TH', 11971]\n" ] }, { "name": 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'2020-03-04', 'RO', 4298, '2020-03-05', 'RO', 4722, '2020-03-06', 'RO', 4591, '2020-03-07', 'RO', 4594, '2020-03-08', 'RO', 4093, '2020-03-09', 'RO', 4377, '2020-03-10', 'RO', 4805, '2020-03-11', 'RO', 5192, '2020-03-12', 'RO', 5217, '2020-03-13', 'RO', 5067, '2020-03-14', 'RO', 4963, '2020-03-15', 'RO', 4707, '2020-03-16', 'RO', 5462, '2020-03-17', 'RO', 5169, '2020-02-19', 'LI', 23, '2020-02-20', 'LI', 26, '2020-02-21', 'LI', 20, '2020-02-22', 'LI', 20, '2020-02-23', 'LI', 23, '2020-02-24', 'LI', 26, '2020-02-25', 'LI', 29, '2020-02-26', 'LI', 23, '2020-02-27', 'LI', 26, '2020-02-28', 'LI', 40, '2020-02-29', 'LI', 26, '2020-03-01', 'LI', 43, '2020-03-02', 'LI', 29, '2020-03-03', 'LI', 26, '2020-03-04', 'LI', 37, '2020-03-05', 'LI', 56, '2020-03-06', 'LI', 74, '2020-03-07', 'LI', 149, '2020-03-08', 'LI', 72, '2020-03-09', 'LI', 30]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " 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'2020-03-14', 'MC', 66, '2020-03-15', 'MC', 45, '2020-03-16', 'MC', 38, '2020-03-17', 'MC', 31, '2020-02-19', 'CY', 458, '2020-02-20', 'CY', 385, '2020-02-21', 'CY', 487, '2020-02-22', 'CY', 561, '2020-02-23', 'CY', 742, '2020-02-24', 'CY', 687, '2020-02-25', 'CY', 593, '2020-02-26', 'CY', 621, '2020-02-27', 'CY', 660, '2020-02-28', 'CY', 585, '2020-02-29', 'CY', 677, '2020-03-01', 'CY', 644, '2020-03-02', 'CY', 551, '2020-03-03', 'CY', 664, '2020-03-04', 'CY', 649, '2020-03-05', 'CY', 693]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " values (%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, 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'2020-03-03', 'DE', 124453, '2020-03-04', 'DE', 127563, '2020-03-05', 'DE', 133118, '2020-03-06', 'DE', 139327, '2020-03-07', 'DE', 134124, '2020-03-08', 'DE', 119207, '2020-03-09', 'DE', 131591, '2020-03-10', 'DE', 136654, '2020-03-11', 'DE', 151601, '2020-03-12', 'DE', 160370, '2020-03-13', 'DE', 178256, '2020-03-14', 'DE', 192607, '2020-03-15', 'DE', 176450, '2020-03-16', 'DE', 189853, '2020-03-17', 'DE', 196127, '2020-02-19', 'PE', 5420, '2020-02-20', 'PE', 5817, '2020-02-21', 'PE', 6131, '2020-02-22', 'PE', 5324, '2020-02-23', 'PE', 5191, '2020-02-24', 'PE', 6932, '2020-02-25', 'PE', 7396, '2020-02-26', 'PE', 8020, '2020-02-27', 'PE', 8584, '2020-02-28', 'PE', 9302, '2020-02-29', 'PE', 8459, '2020-03-01', 'PE', 9079, '2020-03-02', 'PE', 10083, '2020-03-03', 'PE', 11415, '2020-03-04', 'PE', 11315, '2020-03-05', 'PE', 12172, '2020-03-06', 'PE', 13737, '2020-03-07', 'PE', 13900, '2020-03-08', 'PE', 13332, '2020-03-09', 'PE', 15837, '2020-03-10', 'PE', 16005, '2020-03-11', 'PE', 15695, '2020-03-12', 'PE', 16441, '2020-03-13', 'PE', 16158, '2020-03-14', 'PE', 16312, '2020-03-15', 'PE', 15824, '2020-03-16', 'PE', 16185, '2020-03-17', 'PE', 21114, '2020-02-19', 'LU', 501, '2020-02-20', 'LU', 504, '2020-02-21', 'LU', 644, '2020-02-22', 'LU', 700, '2020-02-23', 'LU', 571, '2020-02-24', 'LU', 732, '2020-02-25', 'LU', 823, '2020-02-26', 'LU', 797, '2020-02-27', 'LU', 887, '2020-02-28', 'LU', 972, '2020-02-29', 'LU', 984, '2020-03-01', 'LU', 917]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " values (%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s)\n", " \n", "['2020-03-02', 'LU', 1053, '2020-03-03', 'LU', 1195, '2020-03-04', 'LU', 1086, '2020-03-05', 'LU', 1198, '2020-03-06', 'LU', 1171, '2020-03-07', 'LU', 1146, '2020-03-08', 'LU', 976, '2020-03-09', 'LU', 1192, '2020-03-10', 'LU', 1138, '2020-03-11', 'LU', 1109, '2020-03-12', 'LU', 1184, '2020-03-13', 'LU', 1291, '2020-03-14', 'LU', 1224, '2020-03-15', 'LU', 1118, '2020-03-16', 'LU', 1117, '2020-03-17', 'LU', 1202, '2020-02-19', 'PS', 33, '2020-02-20', 'PS', 45, '2020-02-21', 'PS', 51, '2020-02-22', 'PS', 106, '2020-02-23', 'PS', 72, '2020-02-24', 'PS', 41, '2020-02-25', 'PS', 42, '2020-02-26', 'PS', 35, '2020-02-27', 'PS', 70, '2020-02-28', 'PS', 63, '2020-02-29', 'PS', 59, '2020-03-01', 'PS', 50, '2020-03-02', 'PS', 51, '2020-03-03', 'PS', 90, '2020-03-04', 'PS', 194, '2020-03-05', 'PS', 75, '2020-03-06', 'PS', 72, '2020-03-07', 'PS', 60, '2020-03-08', 'PS', 94, '2020-03-09', 'PS', 48, '2020-03-10', 'PS', 62, '2020-03-11', 'PS', 79, '2020-03-12', 'PS', 69, '2020-03-13', 'PS', 182, '2020-03-14', 'PS', 109, '2020-03-15', 'PS', 150, '2020-03-16', 'PS', 114, '2020-03-17', 'PS', 163, '2020-02-19', 'AE', 2370, '2020-02-20', 'AE', 3332, '2020-02-21', 'AE', 2992, '2020-02-22', 'AE', 2823, '2020-02-23', 'AE', 2896, '2020-02-24', 'AE', 2661, '2020-02-25', 'AE', 3042, '2020-02-26', 'AE', 3183, '2020-02-27', 'AE', 3772, '2020-02-28', 'AE', 3701, '2020-02-29', 'AE', 3333, '2020-03-01', 'AE', 3336, '2020-03-02', 'AE', 3444, '2020-03-03', 'AE', 3786, '2020-03-04', 'AE', 3983, '2020-03-05', 'AE', 4738, '2020-03-06', 'AE', 4297, '2020-03-07', 'AE', 4517, '2020-03-08', 'AE', 4621, '2020-03-09', 'AE', 4383, '2020-03-10', 'AE', 4649, '2020-03-11', 'AE', 4825, '2020-03-12', 'AE', 5457, '2020-03-13', 'AE', 4558, '2020-03-14', 'AE', 4787, '2020-03-15', 'AE', 5343, '2020-03-16', 'AE', 5367, '2020-03-17', 'AE', 5332, '2020-02-19', 'SA', 2322, '2020-02-20', 'SA', 3573, '2020-02-21', 'SA', 4147, '2020-02-22', 'SA', 3367, '2020-02-23', 'SA', 2807, '2020-02-24', 'SA', 2902, '2020-02-25', 'SA', 3223, '2020-02-26', 'SA', 3882, '2020-02-27', 'SA', 4674, '2020-02-28', 'SA', 4194, '2020-02-29', 'SA', 3906, '2020-03-01', 'SA', 3185, '2020-03-02', 'SA', 3791, '2020-03-03', 'SA', 3445, '2020-03-04', 'SA', 3703, '2020-03-05', 'SA', 4981, '2020-03-06', 'SA', 5647, '2020-03-07', 'SA', 4837, '2020-03-08', 'SA', 4221, '2020-03-09', 'SA', 6235, '2020-03-10', 'SA', 6397, '2020-03-11', 'SA', 6448, '2020-03-12', 'SA', 6572, '2020-03-13', 'SA', 6226, '2020-03-14', 'SA', 6358, '2020-03-15', 'SA', 6512, '2020-03-16', 'SA', 5876, '2020-03-17', 'SA', 6192, '2020-02-19', 'QA', 677, '2020-02-20', 'QA', 972, '2020-02-21', 'QA', 795, '2020-02-22', 'QA', 770, '2020-02-23', 'QA', 624, '2020-02-24', 'QA', 643, '2020-02-25', 'QA', 679, '2020-02-26', 'QA', 735, '2020-02-27', 'QA', 847, '2020-02-28', 'QA', 791, '2020-02-29', 'QA', 863, '2020-03-01', 'QA', 1033, '2020-03-02', 'QA', 970, '2020-03-03', 'QA', 904, '2020-03-04', 'QA', 956, '2020-03-05', 'QA', 1144, '2020-03-06', 'QA', 1047, '2020-03-07', 'QA', 998, '2020-03-08', 'QA', 957, '2020-03-09', 'QA', 1126, '2020-03-10', 'QA', 1154, '2020-03-11', 'QA', 1223, '2020-03-12', 'QA', 1143, '2020-03-13', 'QA', 1049, '2020-03-14', 'QA', 1132, '2020-03-15', 'QA', 1231, '2020-03-16', 'QA', 1406, '2020-03-17', 'QA', 1742, '2020-02-19', 'CL', 8742, '2020-02-20', 'CL', 8761, '2020-02-21', 'CL', 9076, '2020-02-22', 'CL', 9660, '2020-02-23', 'CL', 8470, '2020-02-24', 'CL', 10938, '2020-02-25', 'CL', 11105, '2020-02-26', 'CL', 11564, '2020-02-27', 'CL', 11791, '2020-02-28', 'CL', 12323, '2020-02-29', 'CL', 11394, '2020-03-01', 'CL', 12253, '2020-03-02', 'CL', 13834, '2020-03-03', 'CL', 14694, '2020-03-04', 'CL', 15508, '2020-03-05', 'CL', 15947, '2020-03-06', 'CL', 16791, '2020-03-07', 'CL', 15516, '2020-03-08', 'CL', 14709, '2020-03-09', 'CL', 18800, '2020-03-10', 'CL', 19117, '2020-03-11', 'CL', 22041, '2020-03-12', 'CL', 27577, '2020-03-13', 'CL', 26902, '2020-03-14', 'CL', 25101, '2020-03-15', 'CL', 23229, '2020-03-16', 'CL', 28119, '2020-03-17', 'CL', 32789, '2020-02-19', 'GB', 115285, '2020-02-20', 'GB', 116400, '2020-02-21', 'GB', 118127, '2020-02-22', 'GB', 117306, '2020-02-23', 'GB', 114910, '2020-02-24', 'GB', 128783, '2020-02-25', 'GB', 136743, '2020-02-26', 'GB', 135998, '2020-02-27', 'GB', 137205, '2020-02-28', 'GB', 148885, '2020-02-29', 'GB', 152337, '2020-03-01', 'GB', 150374, '2020-03-02', 'GB', 152287, '2020-03-03', 'GB', 152488, '2020-03-04', 'GB', 150032, '2020-03-05', 'GB', 151203, '2020-03-06', 'GB', 157472, '2020-03-07', 'GB', 157449, '2020-03-08', 'GB', 148557, '2020-03-09', 'GB', 162478, '2020-03-10', 'GB', 166151, '2020-03-11', 'GB', 170532, '2020-03-12', 'GB', 170189, '2020-03-13', 'GB', 176890, '2020-03-14', 'GB', 184278, '2020-03-15', 'GB', 175944, '2020-03-16', 'GB', 180805, '2020-03-17', 'GB', 182949, '2020-02-19', 'JP', 9395, '2020-02-20', 'JP', 10441, '2020-02-21', 'JP', 10009, '2020-02-22', 'JP', 9936, '2020-02-23', 'JP', 9448, '2020-02-24', 'JP', 11082, '2020-02-25', 'JP', 14985, '2020-02-26', 'JP', 15283, '2020-02-27', 'JP', 15070, '2020-02-28', 'JP', 13754, '2020-02-29', 'JP', 15744, '2020-03-01', 'JP', 15061, '2020-03-02', 'JP', 12491, '2020-03-03', 'JP', 16466, '2020-03-04', 'JP', 15212, '2020-03-05', 'JP', 16211, '2020-03-06', 'JP', 15337, '2020-03-07', 'JP', 15724, '2020-03-08', 'JP', 14284, '2020-03-09', 'JP', 14398, '2020-03-10', 'JP', 15329, '2020-03-11', 'JP', 15809, '2020-03-12', 'JP', 16184, '2020-03-13', 'JP', 15823, '2020-03-14', 'JP', 15580, '2020-03-15', 'JP', 16087, '2020-03-16', 'JP', 15513, '2020-03-17', 'JP', 16059]\n" ] } ], "source": [ "for chunk in list(chunks(matrix, 500)):\n", " insert_daily_country(chunk)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " replace into deathbed_country_daily_streams \n", " (as_of, country, daily) \n", " values (%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, %s),(%s, %s, 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'2020-02-22', 'AE', 2823, '2020-02-23', 'AE', 2896, '2020-02-24', 'AE', 2661, '2020-02-25', 'AE', 3042, '2020-02-26', 'AE', 3183, '2020-02-27', 'AE', 3772, '2020-02-28', 'AE', 3701, '2020-02-29', 'AE', 3333, '2020-03-01', 'AE', 3336, '2020-03-02', 'AE', 3444, '2020-03-03', 'AE', 3786, '2020-03-04', 'AE', 3983, '2020-03-05', 'AE', 4738, '2020-03-06', 'AE', 4297, '2020-03-07', 'AE', 4517, '2020-03-08', 'AE', 4621, '2020-03-09', 'AE', 4383, '2020-03-10', 'AE', 4649, '2020-03-11', 'AE', 4825, '2020-03-12', 'AE', 5457, '2020-03-13', 'AE', 4558, '2020-03-14', 'AE', 4787, '2020-03-15', 'AE', 5343, '2020-03-16', 'AE', 5367, '2020-03-17', 'AE', 5332, '2020-02-19', 'SA', 2322, '2020-02-20', 'SA', 3573, '2020-02-21', 'SA', 4147, '2020-02-22', 'SA', 3367, '2020-02-23', 'SA', 2807, '2020-02-24', 'SA', 2902, '2020-02-25', 'SA', 3223, '2020-02-26', 'SA', 3882, '2020-02-27', 'SA', 4674, '2020-02-28', 'SA', 4194, '2020-02-29', 'SA', 3906, '2020-03-01', 'SA', 3185, '2020-03-02', 'SA', 3791, '2020-03-03', 'SA', 3445, '2020-03-04', 'SA', 3703, '2020-03-05', 'SA', 4981, '2020-03-06', 'SA', 5647, '2020-03-07', 'SA', 4837, '2020-03-08', 'SA', 4221, '2020-03-09', 'SA', 6235, '2020-03-10', 'SA', 6397, '2020-03-11', 'SA', 6448, '2020-03-12', 'SA', 6572, '2020-03-13', 'SA', 6226, '2020-03-14', 'SA', 6358, '2020-03-15', 'SA', 6512, '2020-03-16', 'SA', 5876, '2020-03-17', 'SA', 6192, '2020-02-19', 'QA', 677, '2020-02-20', 'QA', 972, '2020-02-21', 'QA', 795, '2020-02-22', 'QA', 770, '2020-02-23', 'QA', 624, '2020-02-24', 'QA', 643, '2020-02-25', 'QA', 679, '2020-02-26', 'QA', 735, '2020-02-27', 'QA', 847, '2020-02-28', 'QA', 791, '2020-02-29', 'QA', 863, '2020-03-01', 'QA', 1033, '2020-03-02', 'QA', 970, '2020-03-03', 'QA', 904, '2020-03-04', 'QA', 956, '2020-03-05', 'QA', 1144, '2020-03-06', 'QA', 1047, '2020-03-07', 'QA', 998, '2020-03-08', 'QA', 957, '2020-03-09', 'QA', 1126, '2020-03-10', 'QA', 1154, '2020-03-11', 'QA', 1223, '2020-03-12', 'QA', 1143, '2020-03-13', 'QA', 1049, '2020-03-14', 'QA', 1132, '2020-03-15', 'QA', 1231, '2020-03-16', 'QA', 1406, '2020-03-17', 'QA', 1742, '2020-02-19', 'CL', 8742, '2020-02-20', 'CL', 8761, '2020-02-21', 'CL', 9076, '2020-02-22', 'CL', 9660, '2020-02-23', 'CL', 8470, '2020-02-24', 'CL', 10938, '2020-02-25', 'CL', 11105, '2020-02-26', 'CL', 11564, '2020-02-27', 'CL', 11791, '2020-02-28', 'CL', 12323, '2020-02-29', 'CL', 11394, '2020-03-01', 'CL', 12253, '2020-03-02', 'CL', 13834, '2020-03-03', 'CL', 14694, '2020-03-04', 'CL', 15508, '2020-03-05', 'CL', 15947, '2020-03-06', 'CL', 16791, '2020-03-07', 'CL', 15516, '2020-03-08', 'CL', 14709, '2020-03-09', 'CL', 18800, '2020-03-10', 'CL', 19117, '2020-03-11', 'CL', 22041, '2020-03-12', 'CL', 27577, '2020-03-13', 'CL', 26902, '2020-03-14', 'CL', 25101, '2020-03-15', 'CL', 23229, '2020-03-16', 'CL', 28119, '2020-03-17', 'CL', 32789, '2020-02-19', 'GB', 115285, '2020-02-20', 'GB', 116400, '2020-02-21', 'GB', 118127, '2020-02-22', 'GB', 117306, '2020-02-23', 'GB', 114910, '2020-02-24', 'GB', 128783, '2020-02-25', 'GB', 136743, '2020-02-26', 'GB', 135998, '2020-02-27', 'GB', 137205, '2020-02-28', 'GB', 148885, '2020-02-29', 'GB', 152337, '2020-03-01', 'GB', 150374, '2020-03-02', 'GB', 152287, '2020-03-03', 'GB', 152488, '2020-03-04', 'GB', 150032, '2020-03-05', 'GB', 151203, '2020-03-06', 'GB', 157472, '2020-03-07', 'GB', 157449, '2020-03-08', 'GB', 148557, '2020-03-09', 'GB', 162478, '2020-03-10', 'GB', 166151, '2020-03-11', 'GB', 170532, '2020-03-12', 'GB', 170189, '2020-03-13', 'GB', 176890, '2020-03-14', 'GB', 184278, '2020-03-15', 'GB', 175944, '2020-03-16', 'GB', 180805, '2020-03-17', 'GB', 182949, '2020-02-19', 'JP', 9395, '2020-02-20', 'JP', 10441, '2020-02-21', 'JP', 10009, '2020-02-22', 'JP', 9936, '2020-02-23', 'JP', 9448, '2020-02-24', 'JP', 11082, '2020-02-25', 'JP', 14985, '2020-02-26', 'JP', 15283, '2020-02-27', 'JP', 15070, '2020-02-28', 'JP', 13754, '2020-02-29', 'JP', 15744, '2020-03-01', 'JP', 15061, '2020-03-02', 'JP', 12491, '2020-03-03', 'JP', 16466, '2020-03-04', 'JP', 15212, '2020-03-05', 'JP', 16211, '2020-03-06', 'JP', 15337, '2020-03-07', 'JP', 15724, '2020-03-08', 'JP', 14284, '2020-03-09', 'JP', 14398, '2020-03-10', 'JP', 15329, '2020-03-11', 'JP', 15809, '2020-03-12', 'JP', 16184, '2020-03-13', 'JP', 15823, '2020-03-14', 'JP', 15580, '2020-03-15', 'JP', 16087, '2020-03-16', 'JP', 15513, '2020-03-17', 'JP', 16059]\n" ] } ], "source": [ "insert_daily_country(matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing data should be updated.\n", "\n", "\n", "# Country Streams:" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "# df = _sql_to_dataframe('''\n", "# select country, as_of, total\n", "# from deathbed_country_streams\n", "# ''', columns=['country', 'as_of', 'total'])\n", "\n", "# df = _sql_to_dataframe('''\n", "# select country, as_of, daily\n", "# from canada_goose_country_daily_streams\n", "# ''', columns=['country', 'as_of', 'daily'])\n", "\n", "df = _sql_to_dataframe('''\n", " select country, as_of, daily\n", " from skechers_country_daily_streams\n", " ''', columns=['country', 'as_of', 'daily'])\n", "\n", "\n", "# df = _sql_to_dataframe('''\n", "# select country, as_of, daily\n", "# from deathbed_country_daily_streams\n", "# ''', columns=['country', 'as_of', 'daily'])\n", "\n", "dailyVals = df.pivot(index='as_of', columns='country', values='daily')\n", "dailyVals.index = pd.to_datetime(dailyVals.index)\n", "# zeroIndex = pv.index.min() - datetime.timedelta(days=1)\n", "# pv = pv.append(\n", "# pd.DataFrame(index=[zeroIndex], columns=pv.columns, data=[[0]*len(pv.columns)])\n", "# ).sort_index()\n", "# dailyVals = pv.diff()\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "total28 = _sql_to_dataframe(\n", " '''\n", " select country, as_of, total\n", " from deathbed_country_streams\n", " ''', \n", " columns=['country', 'as_of', 'total']\n", ").pivot(index='as_of', columns='country', values='total')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def create_actual_total28(s: pd.Series):\n", " shifts = len(s.index) // 28\n", " cur = s.shift(0, fill_value=0)\n", " for period in range(1, shifts + 1):\n", " cur = cur + s.shift(28*period, fill_value=0) \n", " return cur" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "totalsAllTime = total28.apply(create_actual_total28)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# using shift rather than diff() to fill zero on first row\n", "dailyVals = totalsAllTime - totalsAllTime.shift(1, fill_value=0)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryADAEARATAUBEBGBHBOBR...SKSVTHTNTRTWUSUYVNZA
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2020-02-270358811626491863814915250411448...1402625153434764435721640
2020-02-2863392194277322350179810458016837...1162482723574190670212452914
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2020-03-0221879209215824933152610857527839...151986359770226719373072895
2020-03-03022292602529270232202168351481212...2438103241762267798882075839
2020-03-04126612583131298793420234491451298...388521075469533201095611670750
2020-03-05246442943104303473312701549261311...45025103214194029411468013581222
2020-03-06743712993762324303572269261461399...4702697255121329312097223941951
2020-03-072138992563910299243803327354361178...47921104362143027813980231761601
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28 rows × 79 columns

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" ], "text/plain": [ "country AD AE AR AT AU BE BG BH BO BR ... \\\n", "as_of ... \n", "2020-02-25 2 1599 31 1453 15217 1107 24 176 3 173 ... \n", "2020-02-26 0 1981 43 1854 16322 1346 47 460 4 309 ... \n", "2020-02-27 0 3588 116 2649 18638 1491 52 504 11 448 ... \n", "2020-02-28 6 3392 194 2773 22350 1798 104 580 16 837 ... \n", "2020-02-29 2 2616 201 2710 22189 1654 78 417 8 692 ... \n", "2020-03-01 2 1846 168 2053 22263 1482 63 544 16 675 ... \n", "2020-03-02 2 1879 209 2158 24933 1526 108 575 27 839 ... \n", "2020-03-03 0 2229 260 2529 27023 2202 168 351 48 1212 ... \n", "2020-03-04 1 2661 258 3131 29879 3420 234 491 45 1298 ... \n", "2020-03-05 2 4644 294 3104 30347 3312 701 549 26 1311 ... \n", "2020-03-06 7 4371 299 3762 32430 3572 269 261 46 1399 ... \n", "2020-03-07 21 3899 256 3910 29924 3803 327 354 36 1178 ... \n", "2020-03-08 8 4032 309 3014 28360 3046 239 554 30 1162 ... \n", "2020-03-09 8 3910 345 3720 31739 3288 379 272 50 1399 ... \n", "2020-03-10 6 3819 409 4487 36594 4172 417 207 60 1738 ... \n", "2020-03-11 15 3855 680 5831 42498 6375 738 214 139 2072 ... \n", "2020-03-12 6 3971 846 6350 47375 6467 933 377 248 2177 ... \n", "2020-03-13 6 3739 966 6948 50653 7264 1105 423 251 2241 ... \n", "2020-03-14 8 4131 1462 7944 54926 8470 901 392 305 2242 ... \n", "2020-03-15 9 4077 1568 7220 52338 8489 945 439 740 2139 ... \n", "2020-03-16 4 4178 1910 7944 59157 8913 1418 371 574 2709 ... \n", "2020-03-17 7 4803 3223 7519 67029 10052 1262 440 601 3226 ... \n", "2020-03-18 31 4866 3131 8330 70600 10207 1155 414 860 3938 ... \n", "2020-03-19 25 4528 4154 8391 77331 11801 1416 381 825 4765 ... \n", "2020-03-20 51 3990 4959 9002 86698 13934 1805 480 1050 5672 ... \n", "2020-03-21 56 3780 5378 8872 87339 15417 1208 391 792 5787 ... \n", "2020-03-22 55 4109 5935 8650 87443 16830 1088 644 792 6475 ... \n", "2020-03-23 89 4010 5999 10207 91166 16908 1512 420 927 7453 ... \n", "\n", "country SK SV TH TN TR TW US UY VN ZA \n", "as_of \n", "2020-02-25 68 3 537 8 211 12 56363 6 10 511 \n", "2020-02-26 84 6 577 3 338 64 60379 5 19 638 \n", "2020-02-27 140 2 625 15 343 47 64435 7 21 640 \n", "2020-02-28 116 24 827 23 574 190 67021 24 52 914 \n", "2020-02-29 148 9 1048 35 546 216 75095 13 62 1141 \n", "2020-03-01 192 3 1053 40 661 261 78535 15 37 851 \n", "2020-03-02 151 9 863 59 770 226 71937 30 72 895 \n", "2020-03-03 243 8 1032 41 762 267 79888 20 75 839 \n", "2020-03-04 388 52 1075 46 953 320 109561 16 70 750 \n", "2020-03-05 450 25 1032 141 940 294 114680 13 58 1222 \n", "2020-03-06 470 26 972 55 1213 293 120972 23 94 1951 \n", "2020-03-07 479 21 1043 62 1430 278 139802 31 76 1601 \n", "2020-03-08 580 23 1030 64 1222 280 132893 31 69 1026 \n", "2020-03-09 589 31 1059 63 1107 324 134823 37 96 1099 \n", "2020-03-10 715 40 961 69 1361 296 166430 37 82 1292 \n", "2020-03-11 875 100 1230 92 1817 322 175390 59 102 2035 \n", "2020-03-12 846 204 1439 128 2169 345 181901 107 151 2240 \n", "2020-03-13 1023 253 1955 127 2089 366 202232 83 124 3162 \n", "2020-03-14 850 250 2214 133 2203 337 232995 115 167 4321 \n", "2020-03-15 1140 296 2488 233 2304 317 240012 101 151 3934 \n", "2020-03-16 1337 337 2810 394 2729 337 276820 211 199 4401 \n", "2020-03-17 1438 301 3272 329 3585 338 300216 183 238 5065 \n", "2020-03-18 1626 481 3820 312 4147 331 312129 323 250 6523 \n", "2020-03-19 2406 453 3969 467 4982 373 320068 311 229 6603 \n", "2020-03-20 2581 1173 3690 476 6033 497 336403 327 231 5973 \n", "2020-03-21 2313 917 5335 725 6128 373 338114 364 248 5596 \n", "2020-03-22 2287 908 5798 459 6988 399 338993 480 245 4745 \n", "2020-03-23 2487 1047 5702 539 8723 389 354319 440 237 5472 \n", "\n", "[28 rows x 79 columns]" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dailyVals" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Seems to match rolling 28 --\n" ] } ], "source": [ "# check 28 day\n", "if ((dailyVals.rolling(28, min_periods=1).sum() - total28).sum(axis=1) == 0).sum() != len(dailyVals.index):\n", " print(\"Fail\")\n", "else:\n", " print(\"-- Seems to match rolling 28 --\")" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [], "source": [ "total28 = _sql_to_dataframe('''select country, as_of, total from deathbed_country_streams''', \n", " columns=['country', 'as_of', 'total'])\n", "\n", "total28 = total28.pivot(index='as_of', columns='country', values='total')\n", "total28.index = pd.to_datetime(total28.index)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryADAEARATAUBEBGBHBOBR...SKSVTHTNTRTWUSUYVNZA
as_of
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0 rows × 79 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [AD, AE, AR, AT, AU, BE, BG, BH, BO, BR, CA, CH, CL, CO, CR, CY, CZ, DE, DK, DO, DZ, EC, EE, EG, ES, FI, FR, GB, GR, GT, HK, HN, HU, ID, IE, IL, IN, IS, IT, JO, JP, KW, LB, LI, LT, LU, LV, MA, MC, MT, MX, MY, NI, NL, NO, NZ, OM, PA, PE, PH, PL, PS, PT, PY, QA, RO, SA, SE, SG, SK, SV, TH, TN, TR, TW, US, UY, VN, ZA]\n", "Index: []\n", "\n", "[0 rows x 79 columns]" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "badRows = total28[((total28 - dailyVals.rolling(28, min_periods=1).sum()) != 0).apply(sum, axis=1) > 0]\n", "badRows" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryADAEARATAUBEBGBHBOBR...SKSVTHTNTRTWUSUYVNZA
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2 rows × 79 columns

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" ], "text/plain": [ "country AD AE AR AT AU BE BG BH \\\n", "as_of \n", "2020-03-06 2381 71214 286769 210434 2789690 281911 22788 7522 \n", "2020-03-16 3154 108354 483231 295586 3907019 418366 31428 11908 \n", "\n", "country BO BR ... SK SV TH TN TR TW \\\n", "as_of ... \n", "2020-03-06 37493 805180 ... 52000 27106 167026 12918 229771 182413 \n", "2020-03-16 65214 1707662 ... 73168 50183 251592 21895 412188 255957 \n", "\n", "country US UY VN ZA \n", "as_of \n", "2020-03-06 13730174 16549 189057 83253 \n", "2020-03-16 17429041 27047 278126 111235 \n", "\n", "[2 rows x 79 columns]" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "replacementRows = dailyVals.rolling(28, min_periods=1).sum().astype(int).loc[badRows.index]\n", "replacementRows" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "fixed_data = [(str(ix[1]), ix[0], v) for ix, v in replacementRows.unstack().to_dict().items()]" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [], "source": [ "pcts = \",\".join(['(%s, %s, %s)'] * len(fixed_data))\n", "args = [i for r in fixed_data for i in r]\n", "sql = f'''replace into deathbed_country_streams (as_of, country, total) values {pcts}'''" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "replacementRows.to_dict?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countryADAEARATAUBEBGBHBOBR...SKSVTHTNTRTWUSUYVNZA
as_of
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\n", " \\begin{cases}\n", " d{n} & \\quad \\text{if } n > 0 \\\\\n", " 0 & \\quad \\text{if } n <= 0\n", " \\end{cases}\n", "\\end{equation*}\n", "\n", "Let's make $Y_{n}$ be the 28 day total on day $n$, and $Y_{0} ... Y_{-\\infty} = 0$\n", "\n", "\\begin{equation*}\n", "d_n = Y_{n} - Y_{n-1} - Y_{n-28} + Y_{n-29}\n", "\\end{equation*}\n", "\n", "#### That didn't work!, \n", "\n", "Instead let's try expressing the day's streams, as a delta of the Total streams. \n", "\n", "Just work with 28 day totals $Y_n$\n", "\n", "\\begin{equation*}\n", "d_n = T_n - T_{n-1} \\\\\n", "T_n = Y_n + Y_{n-28} + Y_{n-56} + ... \\\\\n", "d_n = (Y_n + Y_{n-28} + Y_{n-56} + ...) - (Y_{n-1} + Y_{n-29} + Y_{n-57} + ...)\n", "\\end{equation*}\n", "\n", "\n", "To do that with Pandas, calculate T from Y:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"dailyValsFixed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use $S$ as 7 day:\n", "\n", "\\begin{equation}\n", "d_n = (S_n + S_{n-7} + S_{n-14} + ...) - (S_{n-1} + S_{n-8} + S_{n-15} + ...)\n", "\\end{equation}\n", "\n", "To infer:\n", "\n", "\\begin{equation}\n", "Y_{n-28} = d_n - Y_n - Y_{n-56} - ... + Y_{n-1} + Y_{n-29} + Y_{n-57} + ... \\\\\n", "Y_1 = d_{29} - Y_{29} + Y_{28} = S_{29} + S_{22} + S_{15} + S_{8} + S_{1} - S_{28} - S_{21} - S_{14} - S_{7} - Y_{29} + Y_{28}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SK SV TH TN TR \\\n", "as_of ... \n", "2020-02-07 NaN NaN ... NaN NaN NaN NaN NaN \n", "2020-02-08 117.0 1199.0 ... 127.0 33.0 193.0 91.0 340.0 \n", "2020-02-09 248.0 2521.0 ... 223.0 42.0 718.0 130.0 743.0 \n", "2020-02-10 450.0 3693.0 ... 406.0 198.0 865.0 76.0 1072.0 \n", "2020-02-11 364.0 4060.0 ... 414.0 182.0 803.0 120.0 965.0 \n", "2020-02-12 430.0 5726.0 ... 699.0 136.0 950.0 129.0 1225.0 \n", "2020-02-13 556.0 7425.0 ... 868.0 310.0 1098.0 146.0 1773.0 \n", "2020-02-14 498.0 13771.0 ... 835.0 377.0 2317.0 269.0 2714.0 \n", "2020-02-15 1143.0 18824.0 ... 1466.0 466.0 4370.0 308.0 4369.0 \n", "2020-02-16 1059.0 17348.0 ... 1563.0 502.0 5109.0 552.0 4830.0 \n", "2020-02-17 879.0 22049.0 ... 1692.0 677.0 5785.0 367.0 4981.0 \n", "2020-02-18 932.0 23817.0 ... 1858.0 796.0 6409.0 364.0 6584.0 \n", "2020-02-19 1001.0 25577.0 ... 1909.0 782.0 6601.0 376.0 8881.0 \n", "2020-02-20 978.0 27646.0 ... 2128.0 751.0 6822.0 356.0 7696.0 \n", "2020-02-21 1151.0 28708.0 ... 2186.0 744.0 6602.0 573.0 7715.0 \n", "2020-02-22 1291.0 24566.0 ... 2428.0 773.0 6357.0 426.0 7798.0 \n", "2020-02-23 1015.0 20871.0 ... 2150.0 1098.0 6586.0 480.0 7799.0 \n", "2020-02-24 1009.0 25748.0 ... 2512.0 938.0 6731.0 523.0 9253.0 \n", "2020-02-25 1587.0 30072.0 ... 2381.0 1109.0 8090.0 585.0 10444.0 \n", "2020-02-26 1845.0 36559.0 ... 2373.0 1271.0 7827.0 588.0 10743.0 \n", "2020-02-27 1847.0 42668.0 ... 2484.0 1154.0 7858.0 574.0 10477.0 \n", "2020-02-28 2133.0 47453.0 ... 2592.0 1289.0 9013.0 583.0 11105.0 \n", "2020-02-29 2334.0 46201.0 ... 2398.0 1594.0 8897.0 629.0 11009.0 \n", "2020-03-01 2057.0 40817.0 ... 2513.0 1758.0 8785.0 663.0 13670.0 \n", "2020-03-02 2270.0 53415.0 ... 2638.0 1607.0 9018.0 641.0 14491.0 \n", "2020-03-03 2350.0 55736.0 ... 2730.0 1897.0 9301.0 699.0 16960.0 \n", "2020-03-04 2326.0 55297.0 ... 2672.0 1911.0 9655.0 801.0 17113.0 \n", "2020-03-05 2743.0 60943.0 ... 2821.0 2039.0 9819.0 916.0 17465.0 \n", "2020-03-06 2763.0 61271.0 ... 2807.0 2639.0 10254.0 862.0 17216.0 \n", "2020-03-07 3022.0 63936.0 ... 2615.0 2717.0 9795.0 1106.0 17123.0 \n", "2020-03-08 3076.0 63416.0 ... 2663.0 2238.0 9254.0 1034.0 15279.0 \n", "2020-03-09 3475.0 90549.0 ... 2928.0 2812.0 9899.0 1073.0 15716.0 \n", "2020-03-10 3328.0 91245.0 ... 2882.0 2596.0 11105.0 1208.0 16451.0 \n", "2020-03-11 3372.0 99778.0 ... 2937.0 2333.0 10906.0 1018.0 18208.0 \n", "2020-03-12 3048.0 103537.0 ... 3010.0 2683.0 11005.0 1310.0 20872.0 \n", "2020-03-13 3738.0 114444.0 ... 3076.0 2482.0 11971.0 1070.0 24027.0 \n", "2020-03-14 3466.0 117056.0 ... 2910.0 2657.0 10878.0 1138.0 26916.0 \n", "2020-03-15 3088.0 115167.0 ... 2883.0 2378.0 10494.0 1131.0 25800.0 \n", "\n", "country TW US UY VN ZA \n", "as_of \n", "2020-02-07 NaN NaN NaN NaN NaN \n", "2020-02-08 36.0 44211.0 15.0 292.0 492.0 \n", "2020-02-09 111.0 125106.0 50.0 1082.0 1014.0 \n", "2020-02-10 261.0 207891.0 86.0 1214.0 851.0 \n", "2020-02-11 236.0 234936.0 132.0 1423.0 1036.0 \n", "2020-02-12 423.0 271049.0 155.0 2200.0 1064.0 \n", "2020-02-13 1077.0 318557.0 133.0 2155.0 1701.0 \n", "2020-02-14 3378.0 440639.0 252.0 2595.0 2168.0 \n", "2020-02-15 5277.0 453602.0 303.0 3101.0 2474.0 \n", "2020-02-16 4167.0 438513.0 344.0 3727.0 2714.0 \n", "2020-02-17 5585.0 496798.0 416.0 5355.0 2438.0 \n", "2020-02-18 8708.0 521256.0 424.0 5748.0 2675.0 \n", "2020-02-19 11328.0 535891.0 586.0 5835.0 3002.0 \n", "2020-02-20 10743.0 559156.0 562.0 6257.0 2938.0 \n", "2020-02-21 10327.0 563826.0 661.0 7765.0 3686.0 \n", "2020-02-22 9155.0 531866.0 657.0 7571.0 3574.0 \n", "2020-02-23 8799.0 516522.0 731.0 8031.0 3217.0 \n", "2020-02-24 10439.0 579383.0 686.0 9004.0 3187.0 \n", "2020-02-25 9374.0 591027.0 789.0 10561.0 3811.0 \n", "2020-02-26 8181.0 613949.0 938.0 10590.0 3888.0 \n", "2020-02-27 7843.0 605302.0 919.0 10809.0 3677.0 \n", "2020-02-28 7657.0 631934.0 949.0 10589.0 4278.0 \n", "2020-02-29 7926.0 611835.0 954.0 10080.0 4764.0 \n", "2020-03-01 7723.0 580702.0 760.0 10202.0 4164.0 \n", "2020-03-02 7559.0 627014.0 914.0 10498.0 3992.0 \n", "2020-03-03 8426.0 649009.0 1029.0 10887.0 3871.0 \n", "2020-03-04 8972.0 644926.0 956.0 10458.0 4028.0 \n", "2020-03-05 9394.0 663842.0 1083.0 10690.0 3982.0 \n", "2020-03-06 9272.0 627221.0 1050.0 10046.0 4075.0 \n", "2020-03-07 9015.0 683132.0 1105.0 10004.0 5380.0 \n", "2020-03-08 8760.0 616818.0 925.0 10117.0 4373.0 \n", "2020-03-09 9705.0 676896.0 1511.0 11027.0 4059.0 \n", "2020-03-10 9633.0 706477.0 1156.0 11456.0 4144.0 \n", "2020-03-11 9917.0 697119.0 1165.0 11582.0 4159.0 \n", "2020-03-12 9464.0 674857.0 1244.0 11509.0 4226.0 \n", "2020-03-13 9583.0 680850.0 1271.0 11468.0 4488.0 \n", "2020-03-14 8645.0 673189.0 1062.0 11487.0 4890.0 \n", "2020-03-15 9283.0 654618.0 1302.0 11677.0 4186.0 \n", "\n", "[38 rows x 79 columns]" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dailyValsFixed" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "debugVals = (countryAsOf[['US', 'CA', 'NL', 'GB']]).copy()" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "mergedVals = pd.merge(debugVals, debugVals.apply(lambda s: s.shift(1, fill_value=0)), \n", " right_index=True, left_index=True, suffixes=('', '_1'))\n", "mergedVals = pd.merge(mergedVals, debugVals.apply(lambda s: s.shift(27, fill_value=0)), \n", " right_index=True, left_index=True, suffixes=('', '_27'))\n", "mergedVals = pd.merge(mergedVals, debugVals.apply(lambda s: s.shift(28, fill_value=0)), \n", " right_index=True, left_index=True, suffixes=('', '_28'))\n", "mergedVals = pd.merge(mergedVals, debugVals.apply(lambda s: s.shift(29, fill_value=0)), \n", " right_index=True, left_index=True, suffixes=('', '_29'))\n", "mergedVals = pd.merge(mergedVals, debugVals.apply(lambda s: s.shift(30, fill_value=0)), \n", " right_index=True, left_index=True, suffixes=('', '_30'))" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "actual = pd.DataFrame(matrix, columns=['as_of', 'country', 'daily'])\n", "actual['as_of'] = actual.as_of.astype(numpy.datetime64)\n", "actual['daily'] = actual.daily.astype(int)\n", "actualPv = actual.pivot(index='as_of', columns='country', values='daily')" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "withActuals = pd.merge(mergedVals, actualPv, \n", " right_index=True, left_index=True, how='left', suffixes=('', '_actual'))" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "us1 = withActuals[[c for c in withActuals.columns if c[0:2] == 'US']]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "from matplotlib.dates import MO, TU, WE, TH, FR, SA, SU\n", "import datetime \n", "\n", "def redgreens(df):\n", " nrows = 8\n", " countryTotals = df.sum(axis=0)\n", " sortedColumns = sorted(df.columns, key=lambda col: countryTotals.loc[col], reverse=True)\n", " fig, axes = plt.subplots(nrows=nrows, ncols=1, sharex=True, sharey=True, figsize=(10,13))\n", " pct50 = df[sortedColumns[:30]].pct_change()\n", " for i in range(1, nrows+1):\n", " ax = axes[i-1]\n", " pct50.iloc[-i].plot(\n", " kind='bar', \n", " legend=False, \n", " ax=ax, \n", " title=str(pct50.iloc[-i].name),\n", " color=pct50.iloc[-i].map(lambda x: tuple([min(1, -1*min(x, 0)*5), min(1, max(x, 0)*5), 0.2])),\n", " width=0.95\n", " )\n", " # ax.xaxis.set_major_locator(\n", " # mdates.MonthLocator()\n", " # )\n", " # ax.xaxis.set_minor_locator(\n", " # mdates.DayLocator(interval=4)\n", " # )\n", " plt.tight_layout()\n", "\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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SAACwqqq6b7XtLrEAAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAnWMG5Kq6oaoerqp7um2nVdVnqure4eup47YJAACbYz1nkG9McvHUtquT3NFaOzPJHcNzAABYeMcMyK21zyZ5dGrz65LcNDy+Kcnr59wXAABsiY1eg7y7tfb14fE3kuxe68Cq2ldVy1W1fPjw4Q0uBwAAm2PmD+m11lqSdpT9+1tre1tre3ft2jXrcgAAMKqNBuRvVtWLkmT4+vD8WgIAgK2z0YB8W5Irh8dXJrl1Pu0AAMDWWs9t3j6W5PNJXlpVD1bVW5Jcl+S1VXVvkguH5wAAsPB2HOuA1tqb1th1wZx7AQCALec36QEAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAnR2zTK6qP0vyRJIfJPl+a23vPJoCAICtMlNAHvxca+2ROdQBAIAt5xILAADozBqQW5LfqaoDVbVvHg0BAMBWmvUSi1e31h6qqr+a5DNVdai19tn+gCE470uSPXv2zLgcAACMa6YzyK21h4avDye5Jcn5qxyzv7W2t7W2d9euXbMsBwAAo9twQK6qE6vq5JXHSS5Kcs+8GgMAgK0wyyUWu5PcUlUrdW5urX16Ll0BAMAW2XBAbq19Lcm5c+wFAAC2nNu8AQBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgM8tv0gMAngUqe+des2V57jVhXpxBBgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICO36QHwOjG+E1sid/GBoxDQAYAOE75NeEbM1NArqqLk3woyXOS/Fpr7bq5dAUAwLZ2PIfvDQfkqnpOkg8neW2SB5PcVVW3tdb+eF7NAcf3P0DbhfcYWC//Xjw7zPIhvfOT/Glr7Wutte8l+XiS182nLQAA2BqzXGJxepIHuucPJvmZ6YOqal+SfUmyZ8+epxW5psb54Mb72nJeM0Lt/9CWc8sIdX++Leh/PY7x59eW869GqPsP2vI4/SZJW87yCLX3tuXxziyM9Gc3Zu1/MkLd9474Hn99pL9vLxrr7/KI/w6NeoZsrPdiwd7jJKP1vHD/Do34b/1Y78XBEXo+a8w/u4z4fb0NvvdG/5Bea21/kv1Jsnfv3ja9/30j/mPxH0aqvbBhdoH8gwV8j/cuYM+L5r0L9h6/aMH6BbbOWf692FZmCcgPJfnJ7vlPDNt4Nlm0b+hF63dMY74X3ufxeY+f4r14yqK9F4vWL5tjG/y9mOUa5LuSnFlVP1VVz0vyC0lum09bAACwNTZ8Brm19v2quirJ7Znc5u2G1trBuXUGAFtpG5zFYoP82TGjma5Bbq19Ksmn5tQLAABsuVkusQAAgOOOgAwAAB0BGQAAOtXa025NPN5iVYeT3DdDiZ1JHplTO5tRd8za6o5fe9Hqjllb3fFrL1rdMWsvWt0xa6s7fu1Fqztm7Wdj3TNaa7umN25qQJ5VVS231ub+61XGqjtmbXXHr71odcesre74tRet7pi1F63umLXVHb/2otUds7a6T3GJBQAAdARkAADoLFpA3r9gdcesre74tRet7pi11R2/9qLVHbP2otUds7a649detLpj1lZ3sFDXIAMAwNgW7QwyAACMSkAGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADCykqnp+VV1fVfdV1RNV9aWqumTqmAuq6lBVfaeqfq+qzuj2/WpV3TvMPVRVb56ae15VHRjmHqiq847Rz9HW+kBVPVBVjw/9XnOMWsfqbX9V/UlV/Zeq+sVj1Kqqen9VfWsY76+q2sjr3K61AOZNQAYW1Y4kDyR5TZIfSfKeJJ+oqqUkqaqdST6Z5L1JTkuynOS3u/nfTnLpMPfKJB+qqlcNc5+X5NYkv5nk1CQ3Jbl12P4061jr+iQva62dkuRVSa6oqjcc5bWt2dvgy0n+lyRfOEqNFfuSvD7JuUnOGeq+dSOvcxvXApiv1pphGMZxMZLcneSy4fG+JJ/r9p2Y5MlMgupqc29L8s7h8UVJHkpS3f77k1y8xtx1r5Xk9CR/lORdz+B1/WVvU9t/P8kvHmPu55Ls656/JckfbvB1bstahmEY8x7OIAPHharaneQlSQ4Om87K5ExrkqS19u0kXx22T899YZJXTs29u7XWusPuXm3ueteqqqur6kiSBzMJ0Dev83VN9/ZM/VBvw+Ozun1rvs6quryq7t5utQDGJiADC6+qnpvkt5Lc1Fo7NGw+KcljU4c+luTkVUp8NJOAdvsG5q7r+NbadcPzVyT5jVWOX8t0b8/UdG+PJTlpuN73qH231m5urZ2z3WoBjE1ABhZaVZ2QSeD8XpKrul1HkpwydfgpSZ6Ymv/BJGcneWN3xnLNuVW1p6qOrIxnslab+GIml19cO6z/0a7eD314b43enqnp3k5JcmSot66+F6AWwFwJyMDCGs42Xp9kdybXHv9Ft/tgJh8AWzn2xCQvTnepQlVdm+SSJBe11h6fmntOf1eFTD5IdrC1dn9r7aSVsd61puwY9qe19rau3vvW0dsz9UO9DY8PdvtWfZ0LVgtgrgRkYJF9JMlPJ7m0tfbk1L5bkpxdVZdV1QuS/Eom17UeSpKqeneSy5Nc2Fr71tTcO5P8IMkv1+R2citnpn93jT7WXKuqTqiqt1bVqcOtzc5P8vYkd6z1oo7RW6rqecM6leS5VfWC4Uz6an49yTuq6vSq+vEk70xy4wZf53atBTBfW/0pQcMwjI2MJGckaUm+m8n/kl8ZV3THXJjkUCaXNNyZZKnb15L8+dTca7r9L09yYJj7hSQvP0Y/q66VyYmITyd5dFjjK0muSXeHhlVqHau3O4dj+vG316hVST4wrP/o8LjW8zqTXJHJWfNtVcswDGPsUa1t9LI2AAA4/rjEAgAAOgIyAAB0BGQAAOgIyAAA0NmxmYvt3LmzLS0tbeaSAACwqgMHDjzSWts1vX1TA/LS0lKWl5c3c0kAAFhVVd232naXWAAAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAIDOMQNyVd1QVQ9X1T3dttOq6jNVde/w9dRx2wQAgM2xnjPINya5eGrb1UnuaK2dmeSO4TkAACy8Ywbk1tpnkzw6tfl1SW4aHt+U5PVz7gsAALbERq9B3t1a+/rw+BtJds+pHwAA2FIzf0ivtdaStLX2V9W+qlququXDhw/PuhwAAIxqowH5m1X1oiQZvj681oGttf2ttb2ttb27du3a4HIAALA5NhqQb0ty5fD4yiS3zqcdAADYWuu5zdvHknw+yUur6sGqekuS65K8tqruTXLh8BwAABbejmMd0Fp70xq7LphzLwAAsOX8Jj0AAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAZ8csk6vqz5I8keQHSb7fWts7j6YAAGCrzBSQBz/XWntkDnUAAGDLucQCAAA6swbkluR3qupAVe1b7YCq2ldVy1W1fPjw4RmXAwCAcc0akF/dWntFkkuSvL2qfnb6gNba/tba3tba3l27ds24HAAAjGumgNxae2j4+nCSW5KcP4+mAABgq2w4IFfViVV18srjJBcluWdejQEAwFaY5S4Wu5PcUlUrdW5urX16Ll0BAMAW2XBAbq19Lcm5c+wFAAC2nNu8AQBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQ2bHVDQAAzFNl7yh1W5ZHqcv24wwyAAB0BGQAAOgIyAAA0HENMgCwJca4Vth1wszDTAG5qi5O8qEkz0nya6216+bSFQAcp8YMhQLn4vJnt71sOCBX1XOSfDjJa5M8mOSuqrqttfbH82oOGI9/jAFgdbOcQT4/yZ+21r6WJFX18SSvSyIgw7NcvWGE8P3JxQvf9XdHutXUv1289wJgkcwSkE9P8kD3/MEkPzN9UFXtS7IvSfbs2TPDcjyb/Meaf7D4W23kUDFCzxmx5zHP9i5cmB3pz24Rg+wtI7wXP9+Wx3mPk6Qt5z+NUPv8Rf3eG6v2WN8jI/U76v/NWrD3YlRj/dzbBj9PR/+QXmttf5L9SbJ379429nocH0YPs2NYxJ5hys8v4N/jMcMsA+8xzzKzBOSHkvxk9/wnhm0Ai8UPf4DtYxv8mzxLQL4ryZlV9VOZBONfSHL5XLoCANiOtkF42zaO4/diwwG5tfb9qroqye2Z3Obthtbawbl1BsDx4zj+QQocf2a6Brm19qkkn5pTLwAAsOX8qmkAAOgIyAAA0BGQAQCgU61t3q2Jq+pwkvtmKLEzySNzamcz6o5ZW93xay9a3TFrqzt+7UWrO2btRas7Zm11x6+9aHXHrP1srHtGa23X9MZNDcizqqrl1trcf73KWHXHrK3u+LUXre6YtdUdv/ai1R2z9qLVHbO2uuPXXrS6Y9ZW9ykusQAAgI6ADAAAnUULyPsXrO6YtdUdv/ai1R2ztrrj1160umPWXrS6Y9ZWd/zai1Z3zNrqDhbqGmQAABjbop1BBgCAUQnIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkIGFVFXPr6rrq+q+qnqiqr5UVZdMHXNBVR2qqu9U1e9V1Rndvl+tqnuHuYeq6s1Tc8+rqgPD3ANVdd4x+jnaWh+oqgeq6vGh32uOUWtuvQ3v0w3D2t+oqnest+9FqQUwbwIysKh2JHkgyWuS/EiS9yT5RFUtJUlV7UzyySTvTXJakuUkv93N/3aSS4e5Vyb5UFW9apj7vCS3JvnNJKcmuSnJrcP2p1nHWtcneVlr7ZQkr0pyRVW94SivbW69Jfnfk5yZ5IwkP5fkXVV18Tr7XpRaAPPVWjMMwzguRpK7k1w2PN6X5HPdvhOTPJlJUF1t7m1J3jk8vijJQ0mq239/kovXmLvutZKcnuSPkrzrGbyuWXr7f5Nc1D3/J0k+vsH3aFvWMgzDmPdwBhk4LlTV7iQvSXJw2HRWki+v7G+tfTvJV4ft03NfmOSVU3Pvbq217rC7V5u73rWq6uqqOpLkwUwC383rfF0b7q2qTk3yor634fHKsUfte+j53263WgBj27HVDQDMqqqem+S3ktzUWjs0bD4pyeGpQx9LcvIqJT6aSSC7vZv72Drnrmut1tp1VfX+JOclef0q9dcyS28ndftXO/aofbfWrtuOtQDG5gwysNCq6oQkv5Hke0mu6nYdSXLK1OGnJHliav4Hk5yd5I3dWdk151bVnqo6sjKeyVpt4ouZXC5w7bD+R7t6P/ThvWfaW57uSLd/tWOPh1oAcycgAwurqiqTD8DtzuTa47/odh9Mcm537IlJXpynLlVIVV2b5JJMroV9fGruOUP9FeckOdhau7+1dtLKWO9aU3YM+9Nae1tX732z9Da9SGvt/0vy9b634fHKsevue7vWAhiDgAwsso8k+ekkl7bWnpzad0uSs6vqsqp6QZJfyeTa3UNJUlXvTnJ5kgtba9+amntnkh8k+eXhdmQrZ6Z/d40+1lyrqk6oqrdW1ak1cX6Stye5Y60XNefefj3Je4b1X5bkf0py47H6XrBaAPO11Z8SNAzD2MjI5PZgLcl3M/lf8ivjiu6YC5McyuSShjuTLHX7WpI/n5p7Tbf/5UkODHO/kOTlx+hn1bUyORHx6SSPDmt8Jck16e5CsUqtufWW5PlJbkjyeJJvJnnHevoe9l2T5N9vt1qGYRhjj2qt/yA0AAA8u7nEAgAAOgIyAAB0BGQAAOgIyAAA0NnU36S3c+fOtrS0tJlLAgDAqlSry9UAACAASURBVA4cOPBIa23X9PZNDchLS0tZXl7ezCUBAGBVVXXfattdYgEAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6xwzIVXVDVT1cVfd0206rqs9U1b3D11PHbRMAADbHes4g35jk4qltVye5o7V2ZpI7hucAALDwjhmQW2ufTfLo1ObXJblpeHxTktfPuS8AANgSG70GeXdr7evD428k2b3WgVW1r6qWq2r58OHDG1wOAAA2x8wf0muttSTtKPv3t9b2ttb27tq1a9blAABgVBsNyN+sqhclyfD14fm1BAAAW2ejAfm2JFcOj69Mcut82gEAgK21ntu8fSzJ55O8tKoerKq3JLkuyWur6t4kFw7PAQBg4e041gGttTetseuCOfcCAABbzm/SAwCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHR2zDK5qv4syRNJfpDk+621vfNoCgAAtspMAXnwc621R+ZQBwAAtpxLLAAAoDNrQG5JfqeqDlTVvtUOqKp9VbVcVcuHDx+ecTkAABjXrAH51a21VyS5JMnbq+pnpw9ore1vre1tre3dtWvXjMsBAMC4ZgrIrbWHhq8PJ7klyfnzaAoAALbKhgNyVZ1YVSevPE5yUZJ75tUYAABshVnuYrE7yS1VtVLn5tbap+fSFQAAbJENB+TW2teSnDvHXgAAYMu5zRsAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOgIyAAB0dmx1AwAALJ7K3rnXbFmee82NcAYZAAA6MwXkqrq4qv6kqv60qq6eV1MAALBVNnyJRVU9J8mHk7w2yYNJ7qqq21prfzyv5gAAtpPj+bICnjLLNcjnJ/nT1trXkqSqPp7kdUkEZIAk9dX5/yBNkvbiZT+kAUY0S0A+PckD3fMHk/zM9EFVtS/JviTZs2fPDMs9c9+o+f8A+a/aYv4A+TsjvBefGvG9eOUI/d419p/dCD2nLefdI9T9Z205nxyh7htW3uOR3otFq9tePN7fudHC7Fjv8ZhG6vlvjlD3D9py7h+h7p4FfY8Xqu5Qe+G+90b8nl649+IZGP0uFq21/Un2J8nevXvb2Ov1FjXMjmHMMDuG0cPsAvlnI70Xb/Aew1H9wUjfI6OHWWBmswTkh5L8ZPf8J4Zt8Ozkh95TxnovFq0uwHr5d2hbmSUg35XkzKr6qUyC8S8kuXwuXQGwNRbxh/Qi9rxoFu0/Tv2deMoivhfboOcNB+TW2ver6qoktyd5TpIbWmsH59YZAABsgZmuQW6tfSrJp+bUCwAAbDm/SQ8AADoCMgAAdARkAADoVGubd2viqjqc5L4ZSuxM8sic2tmMumPWVnf82otWd8za6o5fe9Hqjll70eqOWVvd8WsvWt0xaz8b657RWts1vXFTA/Ksqmq5tTb3X68yVt0xa6s7fu1FqztmbXXHr71odcesvWh1x6yt7vi1F63umLXVfYpLLAAAoCMgAwBAZ9EC8v4FqztmbXXHr71odcesre74tRet7pi1F63umLXVHb/2otUds7a6g4W6BhkAAMa2aGeQAQBgVAIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZGAhVdXzq+r6qrqvqp6oqi9V1SVTx1xQVYeq6jtV9XtVdUa371er6t5h7qGqevPU3POq6sAw90BVnXeMfo621geq6oGqenzo95pj1HpjVX1uqHXnKvsvrap7qurIcNxfP0qtqqr3V9W3hvH+qqqNvM7tWgtg3gRkYFHtSPJAktck+ZEk70nyiapaSpKq2pnkk0nem+S0JMtJfrub/+0klw5zr0zyoap61TD3eUluTfKbSU5NclOSW4ftT7OOta5P8rLW2ilJXpXkiqp6w1Fe26NJ/kWS61ZZ68wkv5XkbUl+NMm/SXJbVe1Yo9a+JK9Pcm6Sc4bX/NaNvM5tXAtgvlprhmEYx8VIcneSy4bH+5J8rtt3YpInMwmqq829Lck7h8cXJXkoSXX7709y8Rpz171WktOT/FGSd63j9fxSkjuntl2V5N91z08Y1rpgjRqfS7Kve/6WJH+4wde5LWsZhmHMeziDDBwXqmp3kpckOThsOivJl1f2t9a+neSrw/bpuS9M8sqpuXe31lp32N2rzV3vWlV1dVUdSfJgJgH65mfw8p7W8tTjSnL2enobHp/V7VvzdVbV5VV193arBTA2ARlYeFX13EwuO7iptXZo2HxSksemDn0sycmrlPhoJgHt9g3MXdfxrbXrhuevSPIbqxy/Xv93ktdU1d8eLjm4JsnzkvyVdfb2WJKThut9j9p3a+3m1to5260WwNgEZGChVdUJmQTO72Vy+cGKI0lOmTr8lCRPTM3/YCZnX9/YnbFcc25V7Rk+HHdkOCO87rXaxBczuSTi2mH9j3b1jvrhvaHGoUyumf4/knw9yc4kf5zJmenVTPd2SpIjw2tdV98LUAtgrgRkYGENZxuvT7I7k2uP/6LbfTCTD4CtHHtikhfnqcsoUlXXJrkkyUWttcen5p7T31Uhkw+SHWyt3d9aO2llrHetKTuG/Wmtva2r9771vO7W2r9urZ3dWvuxJP84yVKSu9Y4/Id6Gx4f7Pat+joXrBbAXAnIwCL7SJKfTnJpa+3JqX23JDm7qi6rqhck+ZVMrms9lCRV9e4klye5sLX2ram5dyb5QZJfrsnt5FbOTP/uGn2suVZVnVBVb62qU4dbm52f5O1J7ljrRVXVc4Y6O5KcUFUvGC4jWdn/N4ZjdiXZn+S27tKSab+e5B1VdXpV/XiSdya5cYOvc7vWApivrf6UoGEYxkZGkjOStCTfzeR/ya+MK7pjLkxyKJNLGu5MstTta0n+fGruNd3+lyc5MMz9QpKXH6OfVdfK5ETEpzO5dduRJF/J5LrhOkqtXxz668eN3f7fz+Ryg0eT/MskJx6lViX5wHDso8PjWs/rTHJFJmfNt1UtwzCMsUe11n9IGAAAnt1cYgEAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0NmxmYvt3LmzLS0tbeaSAACwqgMHDjzSWts1vX1TA/LS0lKWl5c3c0kAAFhVVd232naXWAAAQGdTzyADAMDRVPbOvWbLM7uCwRlkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAB0BGQAAOscMyFV1Q1U9XFX3dNtOq6rPVNW9w9dTx20TAAA2x3rOIN+Y5OKpbVcnuaO1dmaSO4bnAACw8I4ZkFtrn03y6NTm1yW5aXh8U5LXz7kvAADYEhu9Bnl3a+3rw+NvJNk9p34AAGBLzfwhvdZaS9LW2l9V+6pquaqWDx8+POtyAAAwqo0G5G9W1YuSZPj68FoHttb2t9b2ttb27tq1a4PLAQDA5thoQL4tyZXD4yuT3DqfdgAAYGut5zZvH0vy+SQvraoHq+otSa5L8tqqujfJhcNzAABYeDuOdUBr7U1r7Lpgzr0AAMCW85v0AACgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANARkAEAoCMgAwBAR0AGAICOgAwAAJ0ds0yuqj9L8kSSHyT5fmtt7zyaAgCArTJTQB78XGvtkTnUAQCALecSCwAA6MwakFuS36mqA1W1b7UDqmpfVS1X1fLhw4dnXA4AAMY1a0B+dWvtFUkuSfL2qvrZ6QNaa/tba3tba3t37do143IAADCumQJya+2h4evDSW5Jcv48mgIAgK2y4YBcVSdW1ckrj5NclOSeeTUGAABbYZa7WOxOcktVrdS5ubX26bl0BQAAW2TDAbm19rUk586xFwAA2HJu8wYAAB0BGQAAOgIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADo7troBAGB7q+yde82W5bnXhHlxBhkAADoznUGuqouTfCjJc5L8Wmvturl0BcBxZYwzkMnkLKSzm7A1jufvvQ0H5Kp6TpIPJ3ltkgeT3FVVt7XW/nhezQHH9z9AAEz4t357meUM8vlJ/rS19rUkqaqPJ3ldkuM+INenRvhL/HcW8y9xfWyE9+JNy6n3jlD3nyyn/ulIZ7H+0XLq/SP0/A8X9O/FoRHei5ct5nsBwOKp1trGJlb9vSQXt9Z+aXj+95P8TGvtqqnj9iXZlyR79uz5G/fdd99sHT8DH675/5B+e/NDGrbK74/wPf3qBf2e/sgI78X/3JaTEepmQd/jhbRof35j9TtG3aH2N0eovXvE9/jfj9DvJcfR93RVHWitPe1NGj0g9/bu3duWl4+fNxUAtpVFC8iwxdYKyLPcxeKhJD/ZPf+JYRsAACysWa5BvivJmVX1U5kE419IcvlcugIAnjlne2EuNhyQW2vfr6qrktyeyW3ebmitHZxbZwAAsAVmug9ya+1TST41p14AAGDL+U16AADQEZABAKAjIAMAQGfD90He0GJVh5PM8ptCdiZ5ZE7tbEbdMWurO37tRas7Zm11x6+9aHXHrL1odcesre74tRet7pi1n411z2it7ZreuKkBeVZVtbzazZy3a90xa6s7fu1FqztmbXXHr71odcesvWh1x6yt7vi1F63umLXVfYpLLAAAoCMgAwBAZ9EC8v4FqztmbXXHr71odcesre74tRet7pi1F63umLXVHb/2otUds7a6g4W6BhkAAMa2aGeQAQBgVAIyAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZGAhVdXzq+r6qrqvqp6oqi9V1SVTx1xQVYeq6jtV9XtVdUa371er6t5h7qGqevPU3POq6sAw90BVnXeMfo621geq6oGqenzo95pj1HpjVX1uqHXnKvv/26r6wlDva1W17xj1/teq+sZw/A1V9fxu39LQ73eG/i9cxFoA8yQgA4tqR5IHkrwmyY8keU+ST1TVUpJU1c4kn0zy3iSnJVlO8tvd/G8nuXSYe2WSD1XVq4a5z0tya5LfTHJqkpuS3Dpsf5p1rHV9kpe11k5J8qokV1TVG47y2h5N8i+SXLfKWs9NckuSfzn0/j8k+edVde4avf13Sa5OckGSM5L8tSTXdod8LMkXk/xYkn+U5F9X1a5FqgUwd601wzCM42IkuTvJZcPjfUk+1+07McmTmQTV1ebeluSdw+OLkjyUpLr99ye5eI25614ryelJ/ijJu9bxen4pyZ1T23YnaUn+SrftriRvWqPGzUne1z2/IMk3hscvSfLnSU7u9v/HJG9bpFqGYRjzHs4gA8eFqtqdSbA6OGw6K8mXV/a31r6d5KvD9um5L0zyyqm5d7fWWnfY3avNXe9aVXV1VR1J8mAmAfrmZ/Dy/lJr7ZuZnF39B1X1nKr6bzI5A/v76+lteLy7qn5s2Pe11toTU/vPGnp+dVX95+1WC2BsAjKw8IbLDn4ryU2ttUPD5pOSPDZ16GNJTl6lxEczCWC3b2Duuo5vrV03PH9Fkt9Y5fhn4mNJfiWTs6z/Mck/aq09sM7eVh6ffKy+W2u/31r70e1WC2BsAjKw0KrqhEwC5/eSXNXtOpLklKnDT0nSn5VMVX0wydlJ3tidMV5zblXtqaojK+OZrNUmvpjJ5RfXDut/tKt31A/vDce/LMnHk7w5yfMyOav6rqr679eYMt3byuMn1tv3AtQCmCsBGVhYVVWZfABudybXHv9Ft/tgknO7Y09M8uI8dRlFquraJJckuai19vjU3HOG+ivOSXKwtXZ/a+2klbHetabsGPantfa2rt771vGyz07yldba7a21/9Ja+5Mk/254Hav5od6Gx99srX1r2PfXqurkqf1r9b1dawHMlYAMLLKPJPnpJJe21p6c2ndLkrOr6rKqekEmlyTcvXIJRlW9O8nlSS4cQlnvziQ/SPLLw+3kVs5M/+4afay5VlWdUFVvrapTa+L8JG9PcsdaL2q4tvgFmQTpE6rqBcNlJMnkzg5nDrd6q6p6cZK/m8k10qv59SRvqaq/XlU/msndPm5MktbaV5J8Kck/Htb4+Uz+Q+D/WrBaAPO11Z8SNAzD2MjI5INpLcl3M/lf8ivjiu6YC5McyuSShjuTLHX7WibX8PZzr+n2vzzJgWHuF5K8/Bj9rLpWJiciPp3JrduOJPlKkmvS3SFjlVq/OPTXjxu7/W9Mck8mlxw8mOT9SU44Sr13JPlmkseT/Kskz+/2LQ39PpnkTzL5D4aVfX8ryZHtVsswDGPsUa31H9IGAIBnN5dYAABAR0AGAICOgAwAAB0BGQAAOjs2c7GdO3e2paWlzVwSAABWdeDAgUdaa7umt29qQF5aWsry8vJmLgkAAKuqqvtW2+4SCwAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAANA5ZkCuqhuq6uGquqfbdlpVfaaq7h2+njpumwAAsDnWcwb5xiQXT227OskdrbUzk9wxPAcAgIV3zIDcWvtskkenNr8uyU3D45uSvH7OfQEAwJbY6DXIu1trXx8efyPJ7rUOrKp9VbVcVcuHDx/e4HIAALA5Zv6QXmutJWlH2b+/tba3tbZ3165dsy4HAACj2mhA/mZVvShJhq8Pz68lAADYOhsNyLcluXJ4fGWSW+fTDgAAbK313ObtY0k+n+SlVfVgVb0lyXVJXltV9ya5cHgOAAALb8exDmitvWmNXRfMuRcAANhyfpMeAAB0BGQAAOgIyAAA0BGQAQCgIyADAEBHQAYAgI6ADAAAHQEZAAA6AjIAAHQEZAAA6AjIAADQEZABAKAjIAMAQEdABgCAjoAMAAAdARkAADoCMgAAdARkAADoCMgAAPz/7d17uCVleef97w/aI4cEQofxhI0RoyOCaIeMh0k8gK/k1cAoMSBRnGjaXIFoRjOJomMkyfh6IqMmjr4kKGDAQxIRdBg0QUlMPHarIBiMSgTBA41kFFRIwHv+qNrysNh7d/eqVXvv1Xw/11XXqlWHu55Va62qu556qkoNE2RJkiSpsW7IzEm+CtwA3ArcUlUbZ1EoSZIkabUMSpB7j6+q62YQR5IkSVp1NrGQJEmSGkMT5AI+lGRLkk2LTZBkU5LNSTZv3bp14OIkSZKkcQ1NkB9bVY8AjgBOSPJzkxNU1alVtbGqNq5fv37g4iRJkqRxDUqQq+qa/vVa4Bzg0FkUSpIkSVotUyfISXZLssdCP/Ak4NJZFUySJElaDUPuYrEvcE6ShThnV9UFMymVJEmStEqmTpCr6grg4BmWRZIkSVp13uZNkiRJapggS5IkSQ0TZEmSJKkxi0dNS5KkNSBsnHnMYvPMY0prnTXIkiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIaJsiSJElSwwRZkiRJavioaUmStCp8NLbWKmuQJUmSpIYJsiRJktQwQZYkSZIatkGWJEnaSdnOezqDapCTPDnJF5N8OclLZlUoSZIkabVMXYOcZFfgzcDhwNXAp5OcV1VfmFXhJEkry9omLcbfhRazM/8uhjSxOBT4clVdAZDkXcCRgAmyBtuZ/3RrRS4eYR0fPJ/r2N+bJKk1JEG+D/C15v3VwM9OTpRkE7AJYL/99huwON2ZjJVcfDmzT4QAHlibecEIsd9Um2GMMtfmuUxmLx5hXRxcm0f7vb14pN/bKTXedzfWujhppHXxqhHXxVg+NcK6OLRfD2N9f/MWd5TtJsCI2+TRtvUjHqzP1fe3g9uK0S/Sq6pTgVMBNm7cWGMvT1rOA0fcmb5pDnfU8+bgOVvHYyay82YeE9mxHOq6kNa8IQnyNcD9mvf37YdJmiV3ppK0Y8bcbrpNvlMYkiB/Gjggyf50ifExwDNnUipJkqQ7ExPvNWXqBLmqbklyIvBBYFfgbVV12cxKJkmSJK2CQW2Qq+p84PwZlUWSJEl3dmugNt1HTUuSJEkNE2RJkiSpYYIsSZIkNVK1crcmTrIVuHJAiH2A62ZUnJWIO2Zs444fe97ijhnbuOPHnre4Y8aet7hjxjbu+LHnLe6Yse+Mce9fVesnB65ogjxUks1VNfPHq4wVd8zYxh0/9rzFHTO2ccePPW9xx4w9b3HHjG3c8WPPW9wxYxv3NjaxkCRJkhomyJIkSVJj3hLkU+cs7pixjTt+7HmLO2Zs444fe97ijhl73uKOGdu448eet7hjxjZub67aIEuSJEljm7caZEmSJGlUJsiSJElSwwRZkiRJapggS5IkSQ0TZEmSJKlhgixJkiQ1TJAlSZKkhgmyJEmS1DBBliRJkhomyJIkSVLDBFnSXEpytySnJbkyyQ1JPpfkiIlpnpjk8iTfT/KRJPdvxr0+yZf6eS9P8uyJeR+eZEs/75YkD99GeZZb1muTfC3Jd/vynrSNWM9I8rE+1kUT4/5jkhsnukry9CViJclrkny7716TJNN8zrUaS5JmzQRZ0rxaB3wN+Hngx4CXA+9JsgEgyT7Ae4H/BuwNbAbe3cz/PeCp/bzHA29M8uh+3rsC5wJ/DuwFnAGc2w+/g+1Y1mnAg6tqT+DRwHFJnrbMZ7seeAPw6skRVfXRqtp9oQOeAtwIXLBErE3AUcDBwEH9Z37+NJ9zDceSpJkyQZY0l6rqe1X1yqr6alX9sKo+APwz8Mh+kqcBl1XVX1TVTcArgYOTPLif//eq6vJ+3k8CHwUe1c/7OLoE/A1VdXNVvQkI8IQlirOtZX2xqr7XTP9D4IHLfLa/qar3AF/fjlVxPPCXE/Enx59SVVdX1TXAKcBz+nGPY8c+51qNJUkzZYIsaaeQZF/gQcBl/aCHAhcvjO8TyK/0wyfnvQfwMxPzXlJV1Ux2yWLzbu+ykrwkyY3A1cBuwNk78PEWlWQ34Gi6Gtal3K5sff9Dm3FLfs4kz0xyyVqLJUljM0GWNPeS3AU4Czijqi7vB+8OfGdi0u8AeywS4q10CdoHp5h3u6avqlf37x8BvGOR6afxNOA64G+XmWaybN8Bdu/b+y5b7qo6u6oOWmuxJGlsJsiS5lqSXegSzn8FTmxG3QjsOTH5nsANE/O/DjgQeEZTY7nkvEn2ay+Q25FlVeezwA+Ak/vlv7WJt+zFe4s4HjhzoqZ10mTZ9gRu7OfZrnLPQSxJmikTZElzq69tPA3YF3h6Vf1bM/oyugvAFqbdDfgpbmtGQZKTgSOAJ1XVdyfmPai9qwLdhWSXVdVVExfJbdeyJqzrx1NVv97Ee9UOfPb70bXVPXMbk96ubH3/Zc24RT/nnMWSpJkyQZY0z94CPAR4alX9YGLcOcCBSZ6e5O7AK+jatV4OkOSlwDOBw6rq2xPzXgTcCrwg3e3kFmqmP7xEOZZcVpJdkjw/yV79rc0OBU4ALlzqQyXZtY+zDtglyd37ZiStZwEfq6qvLBWndybwoiT3SXJv4MXA6VN+zrUaS5Jmq6rs7Ozs5q4D7g8UcBPdKfmF7rhmmsOAy+maNFwEbGjGFXDzxLwnNeMPAbb0834GOGQb5Vl0WXQVERfQ3brtRuCfgJOALBPrOX352u70iWkuB567HespwGv75V/f92d7PidwHF2t+ZqKZWdnZzd2l6rlmq5JkiRJdy42sZAkSZIaJsiSJElSwwRZkiRJapggS5IkSQ0TZEmSJKmxbiUXts8++9SGDRtWcpGSJEnSorZs2XJdVa2fHL6iCfKGDRvYvHnzSi5SkiRJWlSSKxcbbhMLSZIkqWGCLEmSJDVMkCVJkqSGCbIkSZLUMEGWJEmSGibIkiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpsc0EOcnbklyb5NJm2N5J/jrJl/rXvcYtpiRJkrQytqcG+XTgyRPDXgJcWFUHABf27yVJkqS5t80Euar+Drh+YvCRwBl9/xnAUTMulyRJkrQqpm2DvG9VfaPv/yaw74zKI0mSJK2qwRfpVVUBtdT4JJuSbE6yeevWrUMXJ0mSJI1q2gT5W0nuBdC/XrvUhFV1alVtrKqN69evn3JxkiRJ0sqYNkE+Dzi+7z8eOHc2xZEkSZJW1/bc5u2dwMeBn05ydZLnAq8GDk/yJeCw/r0kSZI099Zta4KqOnaJUU+ccVkkSZKkVeeT9CRJkqSGCbIkSZLUMEGWJEmSGibIkiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIaJsiSJElSwwRZkiRJapggS5IkSQ0TZEmSJKlhgixJkiQ1TJAlSZKkhgmyJEmS1DBBliRJkhomyJIkSVJj3ZCZk3wVuAG4FbilqjbOolCSJEnSahmUIPceX1XXzSCOJEmStOpsYiFJkiQ1hibIBXwoyZYkmxabIMmmJJuTbN66devAxUmSJEnjGpogP7aqHgEcAZyQ5OcmJ6iqU6tqY1VtXL9+/cDFSZIkSeMalCBX1TX967XAOcChsyiUJEmStFqmTpCT7JZkj4V+4EnApbMqmCRJkrQahtzFYl/gnCQLcc6uqgtmUipJkiRplUydIFfVFcDBMyyLJEmStOq8zZskSZLUMEGWJEmSGibIkiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIaJsiSJElSnO+NgwAAHR9JREFUwwRZkiRJapggS5IkSQ0TZEmSJKmxbrULIEmSNEth4yhxi82jxNXaYw2yJEmS1DBBliRJkho2sZAkSdpJjdHc5M7Q1MQEWZL0I+5MJckmFpIkSdLtDEqQkzw5yReTfDnJS2ZVKEmSJGm1TN3EIsmuwJuBw4GrgU8nOa+qvjCrwunOy9O8Woy/i/nlbbdu4+9YWvuGtEE+FPhyVV0BkORdwJGACbIkae7lqhES2f3mM5HNF0ZYF/9+TtfFSAc4OWGEuG+ez3W8FqSqppsxORp4clU9r3//LOBnq+rEiek2AZsA9ttvv0deeeWVw0qsqbwps//jvaD847XeNcI6PmbMdTxCeVko75ixBcCnRljHh87pOt4ywrp45Jyui7n7781beXV7Y31/K/i7SLKlqu6wwNHvYlFVpwKnAmzcuHG6bFyDmcyOb9Rkdgxjlnfe1sUcmtdkdgxzm8xK824n/u8NSZCvAe7XvL9vP0ySJGnbduIESwOsgd/FkLtYfBo4IMn+Se4KHAOcN5tiSZIkSatj6hrkqrolyYnAB4FdgbdV1WUzK5kkSdoxa6DmTdoZDGqDXFXnA+fPqCySJEnSqvNJepIkSVLDBFmSJElqmCBLkiRJjakfFDLVwpKtwJAnhewDXDej4qxE3DFjG3f82PMWd8zYxh0/9rzFHTP2vMUdM7Zxx489b3HHjH1njHv/qlo/OXBFE+Shkmxe7GknazXumLGNO37seYs7Zmzjjh973uKOGXve4o4Z27jjx563uGPGNu5tbGIhSZIkNUyQJUmSpMa8JcinzlncMWMbd/zY8xZ3zNjGHT/2vMUdM/a8xR0ztnHHjz1vcceMbdzeXLVBliRJksY2bzXIkiRJ0qhMkCVJkqSGCbIkSZLUMEGWJEmSGibIkiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsqS5lORuSU5LcmWSG5J8LskRE9M8McnlSb6f5CNJ7t+Me32SL/XzXp7k2RPzPjzJln7eLUkevo3yLLes1yb5WpLv9uU9aRuxnpHkY32sixYZv2uSP0zy9b78n03y40vESpLXJPl2370mSab5nGs1liTNmgmypHm1Dvga8PPAjwEvB96TZANAkn2A9wL/Ddgb2Ay8u5n/e8BT+3mPB96Y5NH9vHcFzgX+HNgLOAM4tx9+B9uxrNOAB1fVnsCjgeOSPG2Zz3Y98Abg1UuMP7mP8yhgT+BZwE1LTLsJOAo4GDio/8zPn+ZzruFYkjRbVWVnZ2e3U3TAJcDT+/5NwMeacbsBP6BLVBeb9zzgxX3/k4BrgDTjrwKevMS8270s4D7A54Hf2Y7P8zzgoolhewE3Aj+1nevkY8Cm5v1zgU9M+TnXZCw7Ozu7WXfWIEvaKSTZF3gQcFk/6KHAxQvjq+p7wFf64ZPz3gP4mYl5L6mqaia7ZLF5t3dZSV6S5EbgaroE+uwd+HithwG3AEcn+WaSf0pywjLT365sff9Dm3FLfs4kz0xyyVqLJUljM0GWNPeS3AU4Czijqi7vB+8OfGdi0u8AeywS4q10CdoHp5h3u6avqlf37x8BvGOR6bfXfemahTwI2B84GnhlksO3s2zfAXbv2/suW+6qOruqDlprsSRpbCbIkuZakl3oEs5/BU5sRt1I1z63tSdww8T8rwMOBJ7R1FguOW+S/ZLcuNDtyLKq81m65hcn98t/axNv2Yv3ej/oX3+/qn5QVZcA7wJ+YYnpJ8u2J3Bj/1m3q9xzEEuSZsoEWdLc6msbTwP2pWt7/G/N6MvoLgBbmHY34Ke4rRkFSU4GjgCeVFXfnZj3oPauCnQXkl1WVVdV1e4L3fYua8K6fjxV9etNvFdtx8deaKbQNj+oxSZcrGx9/2XNuEU/55zFkqSZMkGWNM/eAjwEeGpV/WBi3DnAgUmenuTuwCvo2rVeDpDkpcAzgcOq6tsT814E3Aq8IN3t5BZqpj+8RDmWXFaSXZI8P8le/a3NDgVOAC5c6kP1t3G7O10ivUuSu/fNSKiqrwAfBV7Wl+0hwDHAB5YIdybwoiT3SXJv4MXA6VN+zrUaS5Jma7WvErSzs7ObpgPuT1dzehPdKfmF7rhmmsOAy+maJVwEbGjGFXDzxLwnNeMPAbb0834GOGQb5Vl0WXQVERfQ3brtRuCfgJNo7tCwSKzn9OVru9Ob8ffpY94IXAE8f5lYAV7bL//6vj/b8zmB4+hqzddULDs7O7uxu1Qtd2ZOkiRJunOxiYUkSZLUMEGWJEmSGibIkiRJUsMEWZIkSWqsW8mF7bPPPrVhw4aVXKQkSZK0qC1btlxXVesnh69ogrxhwwY2b968kouUJEmSFpXkysWG28RCkiRJapggS5IkSQ0TZEmSJKlhgixJkiQ1TJAlSZKkhgmyJEmS1DBBliRJkhomyJIkSVLDBFmSJElqmCBLkiRJjW0myEneluTaJJc2w/ZO8tdJvtS/7jVuMSVJkqSVsT01yKcDT54Y9hLgwqo6ALiwfy9JkiTNvW0myFX1d8D1E4OPBM7o+88AjppxuSRJkqRVMW0b5H2r6ht9/zeBfZeaMMmmJJuTbN66deuUi5MkSZJWxuCL9KqqgFpm/KlVtbGqNq5fv37o4iRJkqRRTZsgfyvJvQD612tnVyRJkiRp9UybIJ8HHN/3Hw+cO5viSJIkSatre27z9k7g48BPJ7k6yXOBVwOHJ/kScFj/XpIkSZp767Y1QVUdu8SoJ864LJIkSdKq80l6kiRJUsMEWZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIaJsiSJElSwwRZkiRJapggS5IkSQ0TZEmSJKlhgixJkiQ1TJAlSZKkhgmyJEmS1DBBliRJkhomyJIkSVLDBFmSJElqmCBLkiRJDRNkSZIkqbFuyMxJvgrcANwK3FJVG2dRKEmSJGm1DEqQe4+vqutmEEeSJEladTaxkCRJkhpDE+QCPpRkS5JNi02QZFOSzUk2b926deDiJEmSpHENTZAfW1WPAI4ATkjyc5MTVNWpVbWxqjauX79+4OIkSZKkcQ1KkKvqmv71WuAc4NBZFEqSJElaLVMnyEl2S7LHQj/wJODSWRVMkiRJWg1D7mKxL3BOkoU4Z1fVBTMplSRJkrRKpk6Qq+oK4OAZlkWSJEladd7mTZIkSWqYIEuSJEkNE2RJkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIaJsiSJElSwwRZkiRJapggS5IkSQ0TZEmSJKlhgixJkiQ1TJAlSZKkhgmyJEmS1DBBliRJkhomyJIkSVJj3WoXQJKktShsnHnMYvPMY0qaPRNkSZpDJm+SNB4TZGlGTFgkSRpuLexPB7VBTvLkJF9M8uUkLxkSS5IkSVoLpq5BTrIr8GbgcOBq4NNJzquqL8yqcFr71sJR3s7OdSztXPxPS2vfkCYWhwJfrqorAJK8CzgS2OkT5HxthI3b/cbduLlB7oyxHsB1sWAe1wO4LuZdbhzh+9vd72+ejbmtH2t74XZobUlVTTdjcjTw5Kp6Xv/+WcDPVtWJE9NtAjYB7Lfffo+88sorh5V4J3ZgZv/nuLTm889x/gjr4hfmdF1ofh0wwu8Y4Etz+Fv+7kjrYs85XBejGmM9j7mO5628ur2d4PtLsqWq7vBBRr9Ir6pOBU4F2Lhx43TZ+J3EvCazYzCZ1c5gHhNZSdKwBPka4H7N+/v2wyRJuh1rerUofxfzbSf+/oYkyJ8GDkiyP11ifAzwzJmUSpIk7bidOGGRVtLUCXJV3ZLkROCDwK7A26rqspmVTJIkSVoFg9ogV9X5wPkzKoskSZK06gY9KESSJEna2ZggS5IkSQ0TZEmSJKkx9YNCplpYshUY8qSQfYDrZlSclYg7Zmzjjh973uKOGdu448eet7hjxp63uGPGNu74sect7pix74xx719V6ycHrmiCPFSSzYs97WStxh0ztnHHjz1vcceMbdzxY89b3DFjz1vcMWMbd/zY8xZ3zNjGvY1NLCRJkqSGCbIkSZLUmLcE+dQ5iztmbOOOH3ve4o4Z27jjx563uGPGnre4Y8Y27vix5y3umLGN25urNsiSJEnS2OatBlmSJEkalQmyJEmS1DBBliRJkhomyJorSe6yzLj9V7IskqTbS7JLkuNWuxw7kyTrVrsMd0Z3uov0kjyhqj7c9+9fVf/cjHtaVb139Uq3uCS/UlV/3vc/pqr+oRl3YlX9yQjLvKqq9hsw/08CJwAP7QddBvzPqvrWwHKdDxxVVf86Mfxg4Nyq2jBFzKdNDCq6J/N8rqpumLasyyxvL+D/1MA/32r8LsaSZL+qumrKeV+03Piq+qMp436oqp40zbzTSnI/4Jiqet1KLneoJL9VVW+Yct6PAy9b2C5PjLuwqp44ZdwV//6GSPLTVfXFJcbd7v89Rez7021zvtO/fzxwFN2Tbf9kcnu6nTH3pNvG3wc4D/hr4ETgxcDFVXXkgPLO3bYtybOXG19VZw6I/ZmqesS08y8R84/p9nWLqqoXzGg5u/fxbpxFvGWWs66qbpllzDVZg5xknyS/l+QFSXZP8pYklyY5N8kDB4Z/fdP/VxPjXj5t0CTvafpfMzHuQ9PG7bUJwB9PjPvVgbGXkqlnTB4DfLp/e2bfAXyyHzfEZ4D/neSezfIeB3wA+LUpYz51ovtF4LeBS5I8YUhhk7wiyYP7/rsl+QjwFeBbSQ4bEpuRfhdJ3p/kvKW6aeP2sR+V5Oj+AIokByU5G5h65w/s0XS/PfF+jwFx7/Do0TEkWZ/kN5J8FLgI2HdArF9LckDfnyRvT/LdJJckmekOdsKyBynbsB/wJ0lev8gZor0HxB3t+0tyQ79eF7rvJPlKkj9L8hNThv3HJGcsJBQTJv/fO+o9wG4ASR4O/AVwFXAw8D+njPkO4KeBzwPPAz4CHE1XgTF1ctwbbZ830ncH8DNLdH8AvG1ImRmwP17GZmBL3/1i07/QDdJv066iOwi7KsmVSX5jYMz39wd7k8MPAz43JPZi1mq1/dl0X94BwKeAtwNvBP4j8GfA4wbEzhL9i73fEQc0/YcDv9u8H7qhHqvMyxlSu3kK3Ubys82w85KcA/z/wM9OXaiqlyd5OfDBJEcATwLeAPynqto8Zcz/vNjw/o/4niHlBX6ZbgMJcHz/uh54EHAG8DcDYo/1u3j9tifZcUleBzyFbkP2u0k+SLdj/f8YsNOrqpObZRzVvh/oxxY5u9Aud+qzTUn2AJ4GPJPut/BeYP+quu+0MXsvBE7v+48FDgL2Bw7htm3oGIb83r4FPBZ4E91B9LFNTeqQ7dBo319V3eHAqz8z9BzgrcAvTRH2MuBq4DNJnl1Vn2jDT1POxj2q6ut9/68Ab6uqU5LswvSJxQOq6mEASf4M+AawX1XdNLCsMOI+b6Tvjqr6zSZegOPo8oBPAP99mpiN9cudKZvmLFlVnbHQ358BOmO56XdEv49+NPC4qrqiH/YA4I1J9q6qP5wy9LuAjyQ5DXgt3b70DcD9uW3/OjNrNUHet6pO6n9kVzanGy9PcsLA2LVE/2Lvp427I+N2NPbMyrzMHy7AYrUY22vPieQYgKr6XJ8YDFJVf5jk+3RHuQGeUFVfHhp3keVcuUiN1o7616Ypxf8DvKuqbqWrLRr6/xvrt/zP0zZ32Ib/Fzikqm7qd0hfAw6sqq/OcBmzbDP2Y3QJ/WI75KJLaqd1Ld3B/8uBv6+qSvKfBsRbcEtV/Vvf/xTgzKr6NvA3SV47g/hLGbTeq+r7wPP6hPavk7yqqt7KsGRozO/vjgGr/gX4H0meNWWIf6uql/UHjmclOQP4w6r6IcN/1+06eALwUoCq+mG3m53Kwu+Mqro1ydUzSo5hvG3b4gsb/t0BP2or/By6M1mfAI5eqtnMDtqVbp+8FivEFvMs4OD291BVVyR5BnAxMFWCXFVnJfkAXXL8j8Bd6A4+/nRok8XFrNUE+VaAfqdx3cS4Hw6M/YD+NHGafvr3Qy7yumeSQ+iardyjOZ0Z4B4D4gI8OMklfayf6vsXYj9gQNzlktU3DoibJHv1G5124N4MbNaT5P10f+bQHT1+GfijhY18Vf3ikPgTy3owcPPAMDcnOZCuluzxdBvOBfdcfJbtNtbv4n3AIwCS/FVVPX1YMX/kpoUNZlX9S5IvzTg5nrUrq2qsJkwvBY6hO739ziTvnlHcHya5F/AvwBO5fc3VoO1Qkhu47b8Ht+1UZ7GN6wJWvTfJJ4HTk/wCww7Ux/z+FtUfUA/ar1bV3yV5JPAW4KOZzQVvH07XDPAbwF7AwnU49wJ2uP1x7+FJvtv3h26/992+v6pqzwHlHWvbtqSh311fefdC4ELgyTPetn2jqn5/hvHGVosdLFXVD5IMzeH+PXAoXQXDRromaetoDthmZa0myGMlsQBt26jJU8lDTi1/g65pQYBvTsT65oC4AA8ZOP+iZngqetL/AD6U5Lfp2gwDPBJ4TT9uiNcv0T+1Julu7Q3ci+505BAvBP6SLpn/o4WLQvud/x1q2XfQKL8Lbl9LMcudUftfBti/fT/twU2Sz3Pb9/fAZme6EPegaeKyRG1NkscCx1bV1Gez+gva3tCfdjyG7qDk3kl+B3hfVf3TlKFfQdc8bVfgvKq6rC/zzwNXTFvevsyDz/4s4dqJ5VwDHJ7kv9I1oZrWWLVti13YC13i+ct0//epwi70VNX/AY5Ncjzw9ww/APmtvmz3Ah7bnGX4d8DLpox5cVUdMrBcS1ls2xbgfvS139Ma6buDrq30tXTNhR7T1MwvHDBMux1aiDFTSb5HXxlJV8HXHuwMPcC5JskTq+rCiWU+gS5XmkrftOIQ4Deq6uNJdgNOBi7um4kMvd7r9ssboVZ6sH5jPulHtRVV9bczWs56gKraOoNYhwJfq6pv9O+PB54OfBV4ZVVdP3QZs5bkFcuMrqr6g2XGbyv2U4Df4fZ3sXhdVb1/2piLLGMm398iv7cCrqdLkn95SCLUN2Npa9wW7pDx99XcQWVWkuwDfHvI6aY0V0xnhldPL/G//pFp/9fpLkrbl67JRut+wDenbX6T5MCqurTvP4SuvfAvAf8MvLeqpr5wKt3FxvtOXJ3/MLozNz9fVbsOiL0O2KM9g9PvSDLkSvIkdwd+HXggcAldO9aZXjU+S0keunCAMELst08MKuDbwEVV9b+mjPkCYEtN3K2i/9+8vKoOn6qwI5nltmEby5n87/1VDbiLxRjfXR/3DheP3W4hVVcOiH1v4Bl0/73PA6cN/e8l+exYBzhJHgqcS3dwt3DB30bgMcCR0/4vk/wX4E19M8V2+MPo7pI102ss1mqCfCRw36p6c//+U3Q1cAX8blX9xYDYoatl+U260/0BbgH+eMgpjCSfAQ6rquuT/BxdY/LfBB4OPKSqjh4Qe+HU5h1GMeBIL8mLFxm8G/Bc4CeqasjpzdEk+T1m/P01sWe9Mf69RQbvTdce+ZVV9a4Bsf8D8Gq6ZP4P6K4q34duvTy7qi6YMu6twPe47dT59xdGMez3NvWt3LYR9wPAS6vq8xPDHwa8qqqeOmXcB9Fd6HYs3UHNu4Hfrqpld4TbGXupMh9EV+anTBn3d6rqtX3/L7Xbyr5d70kDyvxuutOYHwWOoGvC8MJp4zVxRzlQH2u7uR3LneqWd2P9jvsYM18XSa4Glrw4rKa8vWIfe7T/3jwa47839gFOf0D9TG6rJPsCcNYM26mPbq0myP9Ady/Qr/XvP0fXnm434O015X0x+1gvovuBbWpOdz+Arr3XBVU1VROAJBdX1cF9/5uBrVX1yoXyV9XDpy3zSkh38dwL6ZLj9wCnVNW1y8+1ZKwxa6Zn/v2txsY4XXvsvxmygUqyGTiJ7mKkU4EjquoT6dpOv3PE059TmaiZnlnb5iSfrqqfWWLc56u/0n6KuD+k2yE9d6EWOskVVTW42cmIZV6y9n/oDrEtV19L/alZ7GCXOFC/J90dTtbsgfpSMuU95Mf6TYwlyTfotruLnv6vAU34Rv7vvWm58TXl/X/HPCAb47835gHOMsvcha552llTzr/sbUZrhtcgwdptg3zXheS49/fVXYn97f5U4RDPAg6vqh9d/Ffd1ZW/AnyI6dvI7prbblT9RGBTM26trueFRO1FdLekOQN4RE1cXDeF7y0y7Ec109x227NpjPH9XU63MX5KszH+LwPKuE39mYah7crWVd/mKsnvV39bqKq6fHjoUYzVtvnHlxk3pO3m0+jaB38kyQV0Z4VmtWLHKvOYt4Rs71pwy6x+Y1V1ykJ/c6D+q3Tr+5Sl5lvDpl0xY/0mxjLmhWNj/vfae/yeDCx2lm+H1Xht9GGc/95od8bI7R8icy7d7UxPoLtI/WJgqgQZeBRdU7p3Ap9khLK31mritlf7pqpObN4OvafwXdrkqlnG1gy7pdc7gb9Nd9eNH9AlXAttDb8zIO5o0t2X9ml0tY8PG9I+sbXEDu8/M5sd3hjf35gb40Wle5LV0AOR9mrgH0yMW3unhpa/ddMQm5P8WlX9aTswyfMYcMP7qnof8L7+oPxIugudfjLJW4BzatgFIaOUmXFvj3VwRrprwUgH6qtl2vU81m9iLKNtI8f879WI9/8d0Rj/vTEPcN5Bt3/7ON0DvF5GV9ajqmrIAz3+Hd1zJo6la77xv+jOlo5zrcEabWJxFl2D+ckNxfPpbjx97IDYS55mnMEpyP9Ad5Xwh6rqe/2wBwG7V9Vnlp15FfSnsW6ma8Pb/hDG2OG9cRY7vJG/v4WN8bF09wo9k4Eb49z+DgsL9ga+TtdO+PIBsZdrK3z3qhp6D+eZGrFt877AOXS3q2ovCLkr3QNkht5Fpl3WXnTt0395YFOvUco8b78JuMOB+ptndaA+pm2cTr9HVe1w5dNK/o5nId0DH1bs4vNZ/fcmYq7IhYZrUca9SK9tErIrs32IzMIy7ka3r34dcHKN8PjxtZog/yTdbY9u5va3Cbsb3RHItwbEXtiB3GEUa3QHMm/G3OGt1Pc3w0Rosh1z0d1lYrHPoAH6WvkD+7eXVdWHV7M822MeyzxrYx6ozyN/EyvnTp4gj3aAM+vrHyZi343uoVPHAhuA8+juqHPNLOLfbllrMUFekO6eeT+6TZgbivngDk+StBZN1P7fkxmdydJtJiqy2rNZQ88Wnkl38Hg+3VNpL51BcZde3lpOkCVJkqS+8m0h8R698s0EWZIkSWrsstoFkCRJktYSE2RJkiSpYYIsSTuJJL+V5J6rXQ5Jmne2QZaknUSSrwIbF3uYTpJdq+rWlS+VJM0fa5AlaQUleXaSS5JcnOQdSTYk+XA/7MIk+/XTnZ7k6Ga+G/vXxyW5KMlfJrk8yVnpvAC4N90TIT+yME+SU5JcDLwsyfuaeIcnOWdFP7wkzYm1+qhpSdrpJHko8HLg0VV1Xf/EyTOAM6rqjCS/CrwJOGoboQ6hu0f814F/AB5TVW9K8iLg8U0N8m7AJ6vqxUkC/GOS9VW1le7x72+b+YeUpJ2ANciStHKeAPzFQgLbP8nqUcDZ/fh3AI/djjifqqqrq+qHwOfonii1mFuBv+qXVX38X0ny4/1y//eUn0OSdmrWIEvS2nQLfSVGkl2Auzbjbm76b2XpbflNE+2O3w68H7iJLlG/ZXbFlaSdhzXIkrRyPgz8UpKfAOibWHwMOKYffxzw0b7/q8Aj+/5fBO6yHfFvAPZYamRVfZ2uWcbL6ZJlSdIirEGWpBVSVZcl+e/A3ya5Ffgs8JvA25P8V2ChbTDAnwLn9hfYXcBtj1hdzqnABUm+XlWPX2Kas4D1VfWPQz6LJO3MvM2bJN2JJPkT4LNVddpql0WS1ioTZEm6k0iyha4m+vCqunlb00vSnZUJsiRJktTwIj1JkiSpYYIsSZIkNUyQJUmSpIYJsiRJktQwQZYkSZIa/xfO0xxHyeaxzQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "redgreens(dailyVals)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pct1 = dailyVals[sortedColumns[:20]].pct_change()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_meta(df):\n", " countryTotals = df.sum(axis=0)\n", " sortedColumns = sorted(df.columns, key=lambda col: countryTotals.loc[col], reverse=True)\n", " topN = df[sortedColumns[:20]]\n", " \n", " dailyMeta = topN.idxmax().to_frame('Best Day')\n", " dailyMeta['Best Streams'] = topN.max()\n", " dailyMeta['Yesterday'] = topN.iloc[-1]\n", " dailyMeta['D2'] = topN.iloc[-2]\n", " dailyMeta['D8'] = topN.iloc[-8]\n", " dailyMeta['% Yesterday'] = topN.pct_change().iloc[-1]\n", " dailyMeta['% 7 Day'] = topN.pct_change(7).iloc[-1]\n", " dailyMeta['Country Name'] = topN.index.map(countrymapping.get)\n", " return dailyMeta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = dailyVals\n", "countryTotals = df.sum(axis=0)\n", "sortedColumns = sorted(df.columns, key=lambda col: countryTotals.loc[col], reverse=True)\n", "topN = df[sortedColumns[:20]].copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topN['Other'] = (tdf.sort_index().streams - topN.sum(axis=1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topN" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dailyMeta = topN.idxmax().to_frame('Best Day')\n", "dailyMeta['Best Streams'] = topN.max()\n", "dailyMeta['Yesterday'] = topN.iloc[-1]\n", "dailyMeta['D2'] = topN.iloc[-2]\n", "dailyMeta['D8'] = topN.iloc[-8]\n", "dailyMeta['% Yesterday'] = topN.pct_change().iloc[-1]\n", "dailyMeta['% 7 Day'] = topN.pct_change(7).iloc[-1]\n", "dailyMeta['Country Name'] = [countrymapping.get(c) or 'Other' for c in topN.columns]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dailyMeta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topN.sum(axis=1).to_frame('topN')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "meta = create_meta(dailyVals)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "countryTotals = dailyVals.sum(axis=0)\n", "sortedColumns = sorted(dailyVals.columns, key=lambda col: countryTotals.loc[col], reverse=True)\n", "topN = dailyVals[sortedColumns[:20]]\n", "topN.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "totalsAndTop = pd.merge(tdf.sort_index().streams.to_frame('total'), topN.sum(axis=1).to_frame('top20'), left_index=True, right_index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "totalsAndTop['remaining'] = totalsAndTop.total - totalsAndTop.top20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "withRemaining = dailyVals.copy()\n", "withRemaining['remaining'] = totalsAndTop.total - totalsAndTop.top20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "meta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dailyMeta['Yesterday'].plot(kind='pie', figsize=(6.5,6.5), autopct='%.1f', title=\"Streams Yesterday\")" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "r100 = requests.get(\n", " ('https://gist.githubusercontent.com/ssskip/5a94bfcd2835bf1dea52/raw/'\n", " 'aeed5b0cb3a7eda19e614915c3d88ce113e4a914/ISO3166-1.alpha2.json')\n", ")" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "countrymapping = r100.json()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "def small_multiples(data_frame, nrows, ncols, title=\"\"):\n", " nrows = 5\n", " ncols = 4\n", " countryTotals = data_frame.sum(axis=0)\n", " sortedColumns = sorted(data_frame.columns, key=lambda col: countryTotals.loc[col], reverse=True)\n", " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, sharey=False, figsize=(12, 9.2))\n", " fig.suptitle(title, fontsize=14)\n", " for i, c in list(enumerate(sortedColumns))[:20]:\n", " ax = axes[int(i / ncols)][i % ncols]\n", "\n", " # ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))\n", " data_frame[[c]].plot(legend=False, ax=ax, title=countrymapping[c])\n", " ax.xaxis.set_major_locator(\n", " mdates.MonthLocator()\n", " )\n", " ax.xaxis.set_minor_locator(\n", " mdates.DayLocator(interval=4)\n", " )\n", " plt.tight_layout()\n", " fig.subplots_adjust(top=0.91)\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "small_multiples(dailyVals, 5, 4, title=\"Skechers, Streams / Day (Top 20 Markets)\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rolling7day = df.pivot(index='as_of', columns='country', values='total').diff(7) / 7" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "small_multiples(rolling7day, 5, 4, title=\"Streams / Day Rolling 7 Avg. (Top 20 Markets)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Playlists" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "top_playlists = 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=28day&aggregation-level=recording'\n", "top_playlists7 = 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=7day&aggregation-level=recording'\n", "recent_playlists = 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/recent-playlists?time-filter=7day&aggregation-level=recording'\n", "top_playlists_all = 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists&aggregation-level=recording'\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def get_top_playlists():\n", " return requests.get(top_playlists, headers=headers).json()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def get_top_playlists7():\n", " return requests.get(top_playlists7, headers=headers).json()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def store_7_and_28_top_playlists(marchDay):\n", " j7 = get_top_playlists7()\n", " j28 = get_top_playlists()\n", " fname7 = f'death_bed_top_playlists_7day_2020_03_{marchDay}.json'\n", " fname28 = f'death_bed_top_playlists_28day_2020_03_{marchDay}.json'\n", " with open(fname7, 'w') as fout:\n", " json.dump(j7, fout)\n", " print(f'wrote {fname7}')\n", " with open(fname28, 'w') as fout:\n", " json.dump(j28, fout)\n", " print(f'wrote {fname28}')\n", " \n" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=1day&aggregation-level=recording',\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=7day&aggregation-level=recording',\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=28day&aggregation-level=recording',\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=since2015&aggregation-level=recording']" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "def insert_playlist_yesterday(period: PlaylistPeriod, yesterday=None):\n", " yesterday = yesterday or datetime.date.today() - datetime.timedelta(days=1)\n", " url = to_playlist_url(period)\n", " print(yesterday, url, period)\n", " \n", " j7 = requests.get(url, headers=headers).json()\n", " record_count = len(j7['data'])\n", " print(f\"Found {record_count}\")\n", " rowpcts = \",\\n\".join([\"(%s, %s, %s, %s)\"] * record_count)\n", " sql = f'''\n", "replace into {period.tablename}\n", "(as_of, spyid, streams, followers)\n", "values\n", "{rowpcts}\n", " '''\n", " args = []\n", " for row in j7['data']:\n", " spyid = row['uri'].split('playlist:')[-1]\n", " args.extend([yesterday, spyid, int(row['streams']), int(row['followers']) if len(row['followers']) < 10 else 0])\n", " print(sql, args)\n", " with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-03-15 https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=since2015&aggregation-level=recording PlaylistPeriod(period='since2015', tablename='deathbed_playlist_streams_all_time')\n", "Found 99\n", "\n", "replace into deathbed_playlist_streams_all_time\n", "(as_of, spyid, streams, followers)\n", "values\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, 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'radio', 818386, 0, datetime.date(2020, 3, 15), '37i9dQZF1DXa2EiKmMLhFD', 814786, 1006295, datetime.date(2020, 3, 15), '37i9dQZF1DWWhB4HOWKFQc', 508983, 98338, datetime.date(2020, 3, 15), '37i9dQZF1DWXXs9GFYnvLB', 506172, 1209233, datetime.date(2020, 3, 15), '37i9dQZF1DWVuV87wUBNwc', 492488, 1604981, datetime.date(2020, 3, 15), '37i9dQZF1DX4jP4eebSWR9', 467266, 2079127, datetime.date(2020, 3, 15), '37i9dQZEVXbLiRSasKsNU9', 428535, 1609978, datetime.date(2020, 3, 15), '37i9dQZF1DWXT8uSSn6PRy', 352460, 643562, datetime.date(2020, 3, 15), '37i9dQZF1DWWvvyNmW9V9a', 334705, 127477, datetime.date(2020, 3, 15), '37i9dQZF1DWY4lFlS4Pnso', 327640, 2635205, datetime.date(2020, 3, 15), '37i9dQZEVXbLRQDuF5jeBp', 296580, 3248528, datetime.date(2020, 3, 15), '37i9dQZEVXbNBz9cRCSFkY', 275145, 846856, datetime.date(2020, 3, 15), '37i9dQZEVXbJPcfkRz0wJ0', 268426, 739623, datetime.date(2020, 3, 15), '37i9dQZEVXbObFQZ3JLcXt', 259239, 357588, datetime.date(2020, 3, 15), 'dailymix', 252636, 0, 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'37i9dQZEVXbL3J0k32lWnN', 40650, 155482, datetime.date(2020, 3, 15), 'discoverweekly', 38593, 0, datetime.date(2020, 3, 15), '1zqzEdfFSpNzKUv1i77ke2', 38444, 4753, datetime.date(2020, 3, 15), '3o6P4ZAJLETYN8b0eL8MYx', 37931, 51377, datetime.date(2020, 3, 15), '37i9dQZEVXbKpV6RVDTWcZ', 36147, 76826, datetime.date(2020, 3, 15), '37i9dQZEVXbNv6cjoMVCyg', 35336, 145705, datetime.date(2020, 3, 15), '37i9dQZF1DX2L0iB23Enbq', 33973, 9566, datetime.date(2020, 3, 15), '37i9dQZF1DX0F4i7Q9pshJ', 33827, 50605, datetime.date(2020, 3, 15), '7aRkIB2UvssMbJyzOH8nST', 33264, 292570, datetime.date(2020, 3, 15), '4Sfccot1TksnIXvpANJ9yl', 31447, 33770, datetime.date(2020, 3, 15), '37i9dQZEVXbJlfUljuZExa', 31135, 170220, datetime.date(2020, 3, 15), '37i9dQZEVXbJiZcmkrIHGU', 30389, 1996179, datetime.date(2020, 3, 15), '37i9dQZEVXbMfVLvbaC3bj', 29162, 156711, datetime.date(2020, 3, 15), '37i9dQZF1DX5drguwUcl5X', 29059, 269493, datetime.date(2020, 3, 15), '37i9dQZF1DXcBNDRHS1adf', 28977, 137581, datetime.date(2020, 3, 15), '1y033SLUSiJ0H2uwb3xAo0', 28807, 94591, datetime.date(2020, 3, 15), '55UVG32oJPhoXZ6wJ2WqFn', 28009, 35039, datetime.date(2020, 3, 15), '3qDfz5dvSy3Okgc8snlL47', 27979, 581, datetime.date(2020, 3, 15), '37i9dQZF1DX6wfQutivYYr', 27120, 972975, datetime.date(2020, 3, 15), '37i9dQZEVXbMOkSwG072hV', 27041, 317035, datetime.date(2020, 3, 15), '37i9dQZEVXbIPOivNiyjjS', 26591, 51789, datetime.date(2020, 3, 15), '37i9dQZF1DX6xZZEgC9Ubl', 26591, 583132, datetime.date(2020, 3, 15), '37i9dQZF1DX7oMO417tEZs', 26380, 64837, datetime.date(2020, 3, 15), '37i9dQZF1DXbHhDw60mrNa', 26033, 14248, datetime.date(2020, 3, 15), '4FtdilLkWkfiLwGpbj4nLE', 25941, 486145, datetime.date(2020, 3, 15), '37i9dQZEVXbL3DLHfQeDmV', 25360, 131980, datetime.date(2020, 3, 15), '37i9dQZF1DX59NCqCqJtoH', 25245, 13825, datetime.date(2020, 3, 15), '0PzTB0SzhsYdmq3lvb0zgv', 24865, 205688, datetime.date(2020, 3, 15), '37i9dQZF1DX8vwRmUsEIMT', 23986, 470248, datetime.date(2020, 3, 15), '37i9dQZF1DX2iUp2zRnhtp', 23832, 189537, datetime.date(2020, 3, 15), '37i9dQZF1DXd0PFYKeP9rl', 23191, 7729, datetime.date(2020, 3, 15), '5KBNmVZuGX8zikPDIhsUi0', 23060, 3085, datetime.date(2020, 3, 15), '37i9dQZF1DX9vYRBO9gjDe', 21846, 146829, datetime.date(2020, 3, 15), '37i9dQZF1DX2Z1pVUBGGZs', 21741, 19064, datetime.date(2020, 3, 15), '37i9dQZF1DX4W3aJJYCDfV', 20905, 777790, datetime.date(2020, 3, 15), '37i9dQZEVXbLuUZrygauiA', 20787, 203459, datetime.date(2020, 3, 15), '37i9dQZEVXbJv2Mvelmc3I', 20014, 177471]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2020-03-15 https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=28day&aggregation-level=recording PlaylistPeriod(period='28day', tablename='deathbed_playlist_streams_28day')\n", "Found 99\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_of, spyid, streams, followers)\n", "values\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, 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'1Sl30EjqkvrlzfJBKy3m4C', 18682, 697, datetime.date(2020, 3, 15), '37i9dQZF1DX2Z1pVUBGGZs', 18422, 19064]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2020-03-15 https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=7day&aggregation-level=recording PlaylistPeriod(period='7day', tablename='deathbed_playlist_streams_7day')\n", "Found 99\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_of, spyid, streams, followers)\n", "values\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, 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datetime.date(2020, 3, 15), '37i9dQZF1DX59NCqCqJtoH', 9589, 13825, datetime.date(2020, 3, 15), '6IapJiXJAoNhBbXTfIuE1L', 9570, 57708, datetime.date(2020, 3, 15), '37i9dQZEVXbKpV6RVDTWcZ', 9338, 76826, datetime.date(2020, 3, 15), '7aRkIB2UvssMbJyzOH8nST', 9131, 292570, datetime.date(2020, 3, 15), '37i9dQZEVXbMOkSwG072hV', 8806, 317035, datetime.date(2020, 3, 15), '1Sl30EjqkvrlzfJBKy3m4C', 8664, 697, datetime.date(2020, 3, 15), '37i9dQZF1DX60OAKjsWlA2', 8625, 256055, datetime.date(2020, 3, 15), '5u8gxrgx4ekLpVtovOSSYa', 8495, 537, datetime.date(2020, 3, 15), '37i9dQZF1DX2L0iB23Enbq', 8446, 9566, datetime.date(2020, 3, 15), '37i9dQZF1DX0MD19TXm0aV', 7711, 71565, datetime.date(2020, 3, 15), '37i9dQZEVXbNv6cjoMVCyg', 7604, 145705, datetime.date(2020, 3, 15), '4QuJ2DbcTe7R8lzqfNXz7v', 7579, 536222, datetime.date(2020, 3, 15), '37i9dQZF1DXd0PFYKeP9rl', 7510, 7729, datetime.date(2020, 3, 15), '37i9dQZF1DX7oMO417tEZs', 6981, 64837, datetime.date(2020, 3, 15), '4Sfccot1TksnIXvpANJ9yl', 6956, 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deathbed_playlist_streams_1day\n", "(as_of, spyid, streams, followers)\n", "values\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, %s),\n", "(%s, %s, %s, 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15), '5q2ZlpjN1cl7OPT1kxqk9E', 851, 14213]\n" ] } ], "source": [ "# refresh_request_headers()\n", "insert_playlist_yesterday(Periods.all_time)\n", "insert_playlist_yesterday(Periods.day28)\n", "insert_playlist_yesterday(Periods.day7)\n", "insert_playlist_yesterday(Periods.day1)" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-03-15 https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=28day&aggregation-level=recording PlaylistPeriod(period='28day', tablename='deathbed_playlist_streams_28day')\n", "2020-03-15 https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=7day&aggregation-level=recording PlaylistPeriod(period='7day', tablename='deathbed_playlist_streams_7day')\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topMerged = pd.merge(topD0.set_index('uri'), topD1.set_index('uri'), left_index=True, right_index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topMerged['streamsDelta'] = topMerged.streams_x.astype(int) - topMerged.streams_y.astype(int) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topJson" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "requests.get(\n", " ('https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " '/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/timeline/playlists'\n", " f'?time-filter=28day&aggregation-level=recording'),\n", " headers=headers\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "6944034 / 34515434" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Apollo" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "playlistsToday = pd.read_csv('/Users/joel/Downloads/Current Playlists - Powfu, beabadoobee - death bed (feat. beabadoobee).csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "playlistsToday['']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "export1 = '''\n", "'https://apollo.stream/export'\n", "-H 'authority: apollo.stream'\n", "-H 'cache-control: max-age=0' \n", "-H 'origin: https://apollo.stream' \n", "-H 'upgrade-insecure-requests: 1' \n", "-H 'content-type: application/x-www-form-urlencoded' \n", "-H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36' -H 'sec-fetch-dest: document' -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9' \n", "-H 'sec-fetch-site: same-origin' \n", "-H 'sec-fetch-mode: navigate' \n", "-H 'sec-fetch-user: ?1' \n", "-H 'referer: https://apollo.stream/spotify/playlist/37i9dQZEVXbMDoHDwVN2tF' \n", "-H 'accept-language: en-GB,en-US;q=0.9,en;q=0.8' \n", "-H 'cookie: auth0.ssodata=%22{%5C%22lastUsedConnection%5C%22:%5C%22MergedUsersDB%5C%22%2C%5C%22lastUsedSub%5C%22:%5C%22auth0|5e177a486af9130e9e057412%5C%22}%22; amplitude_id_33073124b188bb31105a0c9e4faa4dfeapollo.stream=eyJkZXZpY2VJZCI6ImMyMDcxYjI0LTE5N2ItNDRjYy1iZjVjLTg5OTcxMGEyMThmMVIiLCJ1c2VySWQiOiJhdXRoMHw1ZTE3N2E0ODZhZjkxMzBlOWUwNTc0MTIiLCJvcHRPdXQiOmZhbHNlLCJzZXNzaW9uSWQiOjE1ODMyNzEzNjEzNTgsImxhc3RFdmVudFRpbWUiOjE1ODMyNzEzNjEzNTgsImV2ZW50SWQiOjY4LCJpZGVudGlmeUlkIjozLCJzZXF1ZW5jZU51bWJlciI6NzF9; intercom-session-hmfnwger=a3VTalpUM2g2VWI2aElVS1NPQXBiRTNnVTFYbVlTSGxjM1pwajBuVitFdzFQdE9WeHdHY1JyUjFoK3NyZGFmNy0tait6L3VZdXlaL1BKSlJZNEVxOUpRQT09--0372644cebba800a2f7e590b705dae87a77a95e9' \n", "--data 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22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3Anull%2C%22Tracks+in+Playlist%22%3Anull%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%2C%7B%22Date%22%3A%222020-02-22%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3Anull%2C%22Tracks+in+Playlist%22%3Anull%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%2C%7B%22Date%22%3A%222020-02-23%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3Anull%2C%22Tracks+in+Playlist%22%3Anull%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%2C%7B%22Date%22%3A%222020-02-24%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3Anull%2C%22Tracks+in+Playlist%22%3Anull%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%2C%7B%22Date%22%3A%222020-02-25%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A47%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A27%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-02-26%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A47%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A28%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-02-27%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A41%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A26%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-02-28%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A35%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A26%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-02-29%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A34%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A26%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-03-01%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A36%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3A26%2C%22Market+Streams%22%3A0%7D%2C%7B%22Date%22%3A%222020-03-02%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3Anull%2C%22Tracks+in+Playlist%22%3Anull%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%2C%7B%22Date%22%3A%222020-03-03%22%2C%22Artists%22%3A%22Powfu%2C+beabadoobee%22%2C%22Track+Name%22%3A%22death+bed+%28feat.+beabadoobee%29%22%2C%22Track+URI%22%3A%22spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx%22%2C%22Playlist+Name%22%3A%22Global+Top+50%22%2C%22Playlist+URI%22%3A%22spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF%22%2C%22Position%22%3A36%2C%22Tracks+in+Playlist%22%3A50%2C%22Worldwide+Streams%22%3Anull%2C%22Market+Streams%22%3Anull%7D%5D%2C%22filename%22%3A%22Playlist+Position+-+death+bed+%28feat.+beabadoobee%29+-+Global+Top+50.csv%22%7D' \n", "--compressed'''" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "playlistPositions1 = '''\n", "'https://privateapi-auth0.filtr.com/playlisttrackgraph/position?playlistURI=spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF&trackURI=spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx&startDate=2020-02-25&endDate=2020-03-03'\n", "-H 'authority: privateapi-auth0.filtr.com' \n", "-H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36' \n", "-H 'sec-fetch-dest: empty' \n", "-H 'authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik5rSTBNRVpGT0RkR05VSTBORGs1T0RFME9EUXdNRUZFUmtKRk9VVXlNREJEUmpZM05rWkVRdyJ9.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.MU4YXTAZK7KPq58cZ2oBrWRRvFsuiqIVmfiqiOGWE7SZRDp5tJrP9UnqyouWQzXaH401RCuvLqaHDkJIplMqH56unO_YGXbK-D82TJTkdhKfaJ9mryCA7LiLyWu8AmJ1v29AHNRo1kjMOGihxe7eMpgl47ow0KhR6RYXMfTrOUCiTg7JfeWnBpC69ZhHNmwn7Ap6YjG5fHnxa88bama_oJF5Wji6Y7DOofDsx2mquE0AnaWsHNcWSaV7ofPL_ypNUzg2IjtPSVLaxg24jPgSLsA4umJhlWIyRVFiSQlc0qYM2XSEYLVsqYJjQUY1o0TYlgTOHBiZAYRGPEGk7HYpxA' \n", "-H 'accept: */*' \n", "-H 'origin: https://apollo.stream' \n", "-H 'sec-fetch-site: cross-site' \n", "-H 'sec-fetch-mode: cors' \n", "-H 'referer: https://apollo.stream/spotify/playlist/37i9dQZEVXbMDoHDwVN2tF' \n", "-H 'accept-language: en-GB,en-US;q=0.9,en;q=0.8' \n", "--compressed\n", "'''" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "header = {\n", " 'authority': 'privateapi-auth0.filtr.com',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'sec-fetch-dest': 'empty',\n", " 'authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik5rSTBNRVpGT0RkR05VSTBORGs1T0RFME9EUXdNRUZFUmtKRk9VVXlNREJEUmpZM05rWkVRdyJ9.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.MU4YXTAZK7KPq58cZ2oBrWRRvFsuiqIVmfiqiOGWE7SZRDp5tJrP9UnqyouWQzXaH401RCuvLqaHDkJIplMqH56unO_YGXbK-D82TJTkdhKfaJ9mryCA7LiLyWu8AmJ1v29AHNRo1kjMOGihxe7eMpgl47ow0KhR6RYXMfTrOUCiTg7JfeWnBpC69ZhHNmwn7Ap6YjG5fHnxa88bama_oJF5Wji6Y7DOofDsx2mquE0AnaWsHNcWSaV7ofPL_ypNUzg2IjtPSVLaxg24jPgSLsA4umJhlWIyRVFiSQlc0qYM2XSEYLVsqYJjQUY1o0TYlgTOHBiZAYRGPEGk7HYpxA',\n", " 'accept': '*/*',\n", " 'origin': 'https://apollo.stream',\n", " 'sec-fetch-site': 'cross-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://apollo.stream/spotify/playlist/37i9dQZEVXbMDoHDwVN2tF',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r = requests.get('https://privateapi-auth0.filtr.com/playlisttrackgraph/position?playlistURI=spotify%3Aplaylist%3A37i9dQZEVXbMDoHDwVN2tF&trackURI=spotify%3Atrack%3A7eJMfftS33KTjuF7lTsMCx&startDate=2020-02-03&endDate=2020-03-03', headers=header)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r.json()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from urllib.parse import unquote" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "json.loads(unquote(postData.split('=')[1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r2 = requests.get(\n", " \"https://privateapi-auth0.filtr.com/spotifyplaylistswithtrack?trackID=7eJMfftS33KTjuF7lTsMCx&limit=50\", \n", " headers=header\n", ");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "pd.DataFrame(r2.json()['items'])[['spotifyLink', 'position', 'trackCount']].to_dict('split')['data']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "positionsAndTotals = {pl['spotifyLink'].split(':')[-1]: (pl['position'], pl['trackCount']) for pl in r2.json()['items']}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "apolloAccessToken = 'eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik5rSTBNRVpGT0RkR05VSTBORGs1T0RFME9EUXdNRUZFUmtKRk9VVXlNREJEUmpZM05rWkVRdyJ9.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.MU4YXTAZK7KPq58cZ2oBrWRRvFsuiqIVmfiqiOGWE7SZRDp5tJrP9UnqyouWQzXaH401RCuvLqaHDkJIplMqH56unO_YGXbK-D82TJTkdhKfaJ9mryCA7LiLyWu8AmJ1v29AHNRo1kjMOGihxe7eMpgl47ow0KhR6RYXMfTrOUCiTg7JfeWnBpC69ZhHNmwn7Ap6YjG5fHnxa88bama_oJF5Wji6Y7DOofDsx2mquE0AnaWsHNcWSaV7ofPL_ypNUzg2IjtPSVLaxg24jPgSLsA4umJhlWIyRVFiSQlc0qYM2XSEYLVsqYJjQUY1o0TYlgTOHBiZAYRGPEGk7HYpxA'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "apolloAccessToken = 'eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6Ik5rSTBNRVpGT0RkR05VSTBORGs1T0RFME9EUXdNRUZFUmtKRk9VVXlNREJEUmpZM05rWkVRdyJ9.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.hOF6Xi7NfKs694Ik4sV8ma8vZeaf3aZmM-MRUzRtOSwcZfQhLt_LA9WP7HYQGU_EQDbVyYoRthOdYk6CaNkGOemErjqvs_G1b4P8csD2xoOZXt1l2MpiCTPxe6BEuVkaa0O0nR0fpuEWAaicdBjvVRJERU7c2VtrTHE-cAj5s9gRnutD4iJEn-T4mA-xJjOgiD65aWuRrXYqaeIkEHZmXn-JJ39FSMwCVL3ZDDsDM2Yo7hjplI4n-PJkVB9MyCP1iHeJdZ0w4wq6r5Ll7BTZhn7amJfcD4tEh5WqHMjhyhr5FzpmPr4AddKGyzKsQYDzJqIh4hTRELeeR5YQQV-4LQ'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_todays_playlist_positions_apollo():\n", " header = {\n", " 'authority': 'privateapi-auth0.filtr.com',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'sec-fetch-dest': 'empty',\n", " 'authorization': f'Bearer {apolloAccessToken}',\n", " 'accept': '*/*',\n", " 'origin': 'https://apollo.stream',\n", " 'sec-fetch-site': 'cross-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://apollo.stream/spotify/playlist/37i9dQZEVXbMDoHDwVN2tF',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'\n", " }\n", " r1 = requests.get(\n", " \"https://privateapi-auth0.filtr.com/spotifyplaylistswithtrack?trackID=7eJMfftS33KTjuF7lTsMCx&limit=200\", \n", " headers=header\n", " );\n", " import time; time.sleep(1)\n", " r2 = requests.get(\n", " \"https://privateapi-auth0.filtr.com/spotifyplaylistswithtrack?trackID=7eJMfftS33KTjuF7lTsMCx&limit=200&offset=200\", \n", " headers=header\n", " );\n", " d1 = {pl['spotifyLink'].split(':')[-1]: (pl['position'], pl['trackCount'], pl['countryCode']) for pl in r1.json()['items']}\n", " d1.update({pl['spotifyLink'].split(':')[-1]: (pl['position'], pl['trackCount'], pl['countryCode'], pl['earliestAdded'], pl['tracksLastAdded']) for pl in r2.json()['items']}) \n", " return d1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "allApollo = get_todays_playlist_positions_apollo()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "header = {\n", " 'authority': 'privateapi-auth0.filtr.com',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'sec-fetch-dest': 'empty',\n", " 'authorization': f'Bearer {apolloAccessToken}',\n", " 'accept': '*/*',\n", " 'origin': 'https://apollo.stream',\n", " 'sec-fetch-site': 'cross-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://apollo.stream/spotify/playlist/37i9dQZEVXbMDoHDwVN2tF',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'\n", "}\n", "r3 = requests.get(\n", " \"https://privateapi-auth0.filtr.com/spotifyplaylistswithtrack?trackID=7eJMfftS33KTjuF7lTsMCx&limit=200&offset=200\", \n", " headers=header\n", ");" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r3.json()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "{pl['spotifyLink'].split(':')[-1]: (pl['position'], pl['trackCount'], pl['countryCode']) for pl in r3.json()['items']}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_todays_playlist_stream_counts():\n", " top_playlists_url = 'https://generic.wg.spotify.com/s4x-insights-api/v1/artist/6bmlMHgSheBauioMgKv2tn/recording/7eJMfftS33KTjuF7lTsMCx/top-playlists?time-filter=28day&aggregation-level=recording'\n", " headers = refresh_request_headers()\n", " res = requests.get(top_playlists_url, headers=headers)\n", " # {'uri': 'spotify:playlist:37i9dQZF1DXcBWIGoYBM5M',\n", " # 'author': 'Spotify',\n", " # 'listeners': '0',\n", " # 'streams': '4636212',\n", " # 'dateAdded': '2020-03-02',\n", " # 'title': \"Today's Top Hits\",\n", " # 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002763a8ee15e2326bee786ae0b',\n", " # 'followers': '25880039'}\n", " return {pl['uri'].split(':')[-1]: }" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "r2.json()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "topPlaylistsDict = {\n", " pl['uri'].split(':')[-1]: ({k:v for k,v in pl.items() if k in ['title', 'streams', 'followers']})\n", " for pl in topJson['data']\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "playlistPositionsDict = {pl['spotifyLink'].split(':')[-1]: (pl['position'], pl['trackCount'], pl['countryCode']) for pl in r3.json()['items']}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "playlistPositionsDict.update()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### With Days\n", "\n", "---------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other stuff\n", "\n", "-----------" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "merged = [[k] + list(v.values()) + list(allApollo.get(k) or []) for k,v in topPlaylistsDict.items()]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergedDf = pd.DataFrame(data=merged, columns=['spyid'] + ['streams', 'title', 'followers'] + ['position', 'trackCount', 'countryCode'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists = mergedDf[(mergedDf.followers.str.len() < 11)].copy()\n", "valid_playlists['streams'] = valid_playlists.streams.astype(numpy.int64)\n", "valid_playlists['followers'] = valid_playlists.followers.astype(numpy.int64)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists['streams_per_follower'] = (valid_playlists.streams / valid_playlists.followers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists['score1'] = valid_playlists.streams_per_follower / (100 - valid_playlists.position + 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists.sort_index(inplace=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "((valid_playlists[(valid_playlists.streams_per_follower < 30) & (valid_playlists.position > -1)]\n", " ).sort_values('score1')).streams_per_follower.plot(kind='bar', figsize=(13, 3.3), title=\"Streams / Follower\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(\n", " 1/(\n", " (valid_playlists[\n", " (valid_playlists.streams_per_follower < 30) & (valid_playlists.position > -1)\n", " ]).sort_values('score1').position + 1)\n", ").plot(kind='bar', figsize=(13, 3.3))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists[\n", " (valid_playlists.streams_per_follower < 30) & (valid_playlists.position > -1)\n", " ].sort_values('score1').head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists.loc[60]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergedDf['streams'] = mergedDf.streams.astype(int)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergedDf['followers'] = mergedDf.followers.map(lambda x: int(x) if len(x) < 11 else numpy.NaN)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "followerTotalsDict = mergedDf.groupby('countryCode').followers.sum().to_dict()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergedDf['followerWeights'] = mergedDf.followers / mergedDf.countryCode.map(followerTotalsDict.get)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergedDf['posPower'] = mergedDf.followerWeights * mergedDf.posPercent" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groupedCountry = mergedDf[['countryCode', 'streams', 'posPower', 'posPercent', 'trackCount']].groupby('countryCode').agg({'streams':'sum', 'posPower':'mean', 'posPercent':'mean', 'trackCount': 'count'})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groupedCountry.streams.sort_values(ascending=False).plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "((groupedCountry.posPower - 1)*-100).sort_values(ascending=False).plot(kind='bar', title=\"Playlist Position Power (higher is better)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Playlist Position Power is the average position in the playlist weighted by the size of the playlist in terms of followers, it's a value from 1 to 100" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "groupedCountry.posPercent.sort_values()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "valid_playlists[valid_playlists.position.isna()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deducing the Last 7 days of playlist daily streams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Death Bed hasn't been out for 28 days. And we have three days of 28 day (and therefore total days) of total streams. We also have three days of 7 day streams.\n", "\n", "\\begin{align}\n", "d_n = \\text{streams achieved on day n (one-based indexing)} \\\\\n", "T_{w,n} = \\text{total streams over period w as of on day n} \\\\\n", "\\end{align}\n", "\n", "\n", "\\begin{equation*}\n", "d'_{n} = \n", " \\begin{cases}\n", " d{n} & \\quad \\text{if } n > 0 \\\\\n", " 0 & \\quad \\text{if } n <= 0\n", " \\end{cases}\n", "\\end{equation*}\n", "\n", "\\begin{equation*}\n", "d_n = \n", " \\begin{cases}\n", " T_{w,n} - T_{w,n-1} + d_{n-w} & \\quad \\text{if } n > w \\\\\n", " T_{w,n} - T_{w,n-1} & \\quad \\text{if } n <= w\n", " \\end{cases} \n", "\\end{equation*}\n", "\n", "\\begin{equation*}\n", "d_n = T_{w,n} - T_{w,n-1} + d'{n}\n", "\\end{equation*}\n", "\n", "\\begin{align}\n", "\\dot{x} & = \\sum(y-x) \\\\\n", "\\dot{y} & = \\rho x - y - xz \\\\\n", "\\dot{z} & = -\\beta z + xy\n", "\\end{align}\n", "\n", "\\begin{equation*}\n", "\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq \\left( \\sum_{k=1}^n a_k^2 \\right) \\left( \\sum_{k=1}^n b_k^2 \\right)\n", "\\end{equation*}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{align}\n", "\\dot{x} & = \\sigma(y-x) \\\\\n", "\\dot{y} & = \\rho x - y - xz \\\\\n", "\\dot{z} & = -\\beta z + xy\n", "\\end{align}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "days_stored = {\n", " '2020_03_02': datetime.date(2020,3,2), \n", " '2020_03_03': datetime.date(2020,3,3), \n", " '2020_03_04': datetime.date(2020,3,4),\n", " '2020_03_05': datetime.date(2020,3,5),\n", " '2020_03_06': datetime.date(2020,3,6),\n", " '2020_03_07': datetime.date(2020,3,7),\n", " '2020_03_08': datetime.date(2020,3,8),\n", " '2020_03_09': datetime.date(2020,3,9),\n", " '2020_03_10': datetime.date(2020,3,10),\n", " '2020_03_11': datetime.date(2020,3,11),\n", "}\n", "windows = {\n", " '28day': 28,\n", " '7day': 7\n", "}\n", "\n", "files = [\n", " (f'death_bed_top_playlists_{swindow}_{sday}.json', window, day)\n", " for swindow, window in windows.items()\n", " for sday, day in days_stored.items()\n", "]\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def read_pl_df(fname, window, day):\n", " with open(fname) as fin:\n", " df = pd.DataFrame(json.load(fin)['data'])\n", " df['window'] = window\n", " df['day'] = day\n", " return df" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "test1 = read_pl_df(*files[0])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "with open(files[0][0]) as fin:\n", " raw1 = json.load(fin)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'uri': 'spotify:playlist:37i9dQZF1DXcBWIGoYBM5M',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '4636212',\n", " 'dateAdded': '2020-03-02',\n", " 'title': \"Today's Top Hits\",\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002763a8ee15e2326bee786ae0b',\n", " 'followers': '25880039'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWUa8ZRTfalHk',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '734774',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Pop Rising',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000029abefad20f333b9931f6187e',\n", " 'followers': '1893268'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXcZQSjptOQtk',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '627292',\n", " 'dateAdded': '2020-02-19',\n", " 'title': 'Top Hits Philippines',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000024b747e5c58885e269836d5b8',\n", " 'followers': '3720614'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbMDoHDwVN2tF',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '414478',\n", " 'title': 'Global Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_GLOBAL_DEFAULT.jpg',\n", " 'followers': '15017361'},\n", " {'uri': 'spotify:user:spotify:playlist:radio',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '393860',\n", " 'title': 'Radio',\n", " 'thumbnailUrl': 'https://misc.scdn.co/s4x/radio.jpg',\n", " 'followers': '18446744073709551615',\n", " 'isPersonalized': True},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWWhB4HOWKFQc',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '261173',\n", " 'dateAdded': '2020-02-27',\n", " 'title': 'Lagi Viral',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002f15296b5c632ca55a3060e11',\n", " 'followers': '90352'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLiRSasKsNU9',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '233987',\n", " 'title': 'Global Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_GLOBAL_DEFAULT.jpg',\n", " 'followers': '1602789'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX4JAvHpjipBk',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '198705',\n", " 'title': 'New Music Friday',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002954e10e34a45a44693bbb939',\n", " 'followers': '3674695'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXa2EiKmMLhFD',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '179217',\n", " 'dateAdded': '2020-02-25',\n", " 'title': 'Top Hits Indonesia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000286be22678ae649acb8d1907d',\n", " 'followers': '995633'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWY4lFlS4Pnso',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '178064',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Hot Hits UK',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000023781cc0d66ae42bcf98bc6f8',\n", " 'followers': '2626983'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWXXs9GFYnvLB',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '171021',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Hot Hits Australia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000020bbce932e70e88c5e4333cb7',\n", " 'followers': '1206600'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLRQDuF5jeBp',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '158575',\n", " 'title': 'United States Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_US_DEFAULT.jpg',\n", " 'followers': '3242060'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWXT8uSSn6PRy',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '152212',\n", " 'dateAdded': '2020-02-28',\n", " 'title': 'Hot Hits Canada',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002c12afc68a6c3566f95958416',\n", " 'followers': '640736'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWT1y71ZcMPe5',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '137594',\n", " 'dateAdded': '2020-03-02',\n", " 'title': \"It's a Hit!\",\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000206dda30722de48b3477206e8',\n", " 'followers': '2110732'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWVuV87wUBNwc',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '133075',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Hits du Moment',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002ae1cd4ab804bd4c35539dcfb',\n", " 'followers': '1598984'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXdwmD5Q7Gxah',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '129083',\n", " 'dateAdded': '2020-02-11',\n", " 'title': 'Lorem',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000020277c9d4e9fd62e46e9a61a2',\n", " 'followers': '543976'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWWvvyNmW9V9a',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '127402',\n", " 'dateAdded': '2020-02-18',\n", " 'title': 'Teen Beats',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000021702fcd0013021933ec5d009',\n", " 'followers': '108438'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbNBz9cRCSFkY',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '107711',\n", " 'title': 'Philippines Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_PH_DEFAULT.jpg',\n", " 'followers': '837969'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbJPcfkRz0wJ0',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '107060',\n", " 'title': 'Australia Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_AU_DEFAULT.jpg',\n", " 'followers': '737477'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX4jP4eebSWR9',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '104005',\n", " 'dateAdded': '2020-02-25',\n", " 'title': 'Top Hits Deutschland',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002e1af1d3e26cb15f910b8a19a',\n", " 'followers': '2068804'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXe3Jzj3uus9u',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '94497',\n", " 'dateAdded': '2020-02-27',\n", " 'title': 'Trending Tracks',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000273d9ef28d9958d141c4c3baa',\n", " 'followers': '285535'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX5WTH49Vcnqp',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '94190',\n", " 'dateAdded': '2020-02-27',\n", " 'title': 'Front Left',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002b1113e3a43b767df49786281',\n", " 'followers': '535309'},\n", " {'uri': 'spotify:playlist:4FLeoROn5GT7n2tZq5XB4V',\n", " 'author': 'TikTok',\n", " 'listeners': '0',\n", " 'streams': '91522',\n", " 'dateAdded': '2020-02-14',\n", " 'title': 'TikTok Trending',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da8494f1a16d30e23ad795f290e8',\n", " 'followers': '149645'},\n", " {'uri': 'spotify:user:spotify:playlist:dailymix',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '91051',\n", " 'title': 'Your Daily Mix',\n", " 'thumbnailUrl': 'https://s4a.scdn.co/playlist_coverart/daily_mix.jpg',\n", " 'followers': '18446744073709551615',\n", " 'isPersonalized': True},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbLnolsZ8PSNw',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '75374',\n", " 'title': 'United Kingdom Top 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/REGIONAL_GB_DEFAULT.jpg',\n", " 'followers': '2104941'},\n", " {'uri': 'spotify:playlist:25aExpL7xcWdekX3uJ0PdQ',\n", " 'author': 'Patrick',\n", " 'listeners': '0',\n", " 'streams': '74986',\n", " 'dateAdded': '2020-02-15',\n", " 'title': ' TikTok Dances 2020',\n", " 'thumbnailUrl': 'https://mosaic.scdn.co/300/ab67616d00001e022999d7d2bae1eb1cdcc43677ab67616d00001e02600adbc750285ea1a8da249fab67616d00001e026376adb8412509a697896090ab67616d00001e0282b243023b937fd579a35533',\n", " 'followers': '92718'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWWuGaVZsglfu',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '71497',\n", " 'dateAdded': '2020-02-26',\n", " 'title': 'Top Hits Malaysia',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002e6eda2cc16a77e13329684e1',\n", " 'followers': '280895'},\n", " {'uri': 'spotify:playlist:7wFEdIAin9SERVgudjiNo9',\n", " 'author': 'sgareyes',\n", " 'listeners': '0',\n", " 'streams': '71157',\n", " 'dateAdded': '2020-02-12',\n", " 'title': 'Death Bed - Powfu',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da84a71250c2abf810a8477d5e89',\n", " 'followers': '40216'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX2YSAZIuAiB1',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '70501',\n", " 'dateAdded': '2020-02-26',\n", " 'title': 'Swag!',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002b639f5fd2611ebd2ab90c697',\n", " 'followers': '572865'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX0X277P8nU0l',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '65747',\n", " 'dateAdded': '2020-03-01',\n", " 'title': 'Café / Croissant',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f000000023724e4a55c5bb7f326ca4213',\n", " 'followers': '158700'},\n", " {'uri': 'spotify:playlist:4Ao85ZB0F474SYUelDH1Xi',\n", " 'author': 'Promoting Sounds',\n", " 'listeners': '0',\n", " 'streams': '59205',\n", " 'dateAdded': '2020-02-14',\n", " 'title': 'Sad Rap',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da848b65787b69f19c4ebe1962b5',\n", " 'followers': '237938'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXarRysLJmuju',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '53665',\n", " 'dateAdded': '2020-02-28',\n", " 'title': 'Pop All Day',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002108309bd568e5c583582d5a0',\n", " 'followers': '440272'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DZ06evO3Ec90s',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '53098',\n", " 'title': 'This Is Powfu',\n", " 'thumbnailUrl': 'https://thisis-images.scdn.co/37i9dQZF1DZ06evO3Ec90s-default.jpg',\n", " 'followers': '7097'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbKuaTI1Z1Afx',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '51127',\n", " 'title': 'United States Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_US_DEFAULT.jpg',\n", " 'followers': '337533'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWTMYgB8TqtmR',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '51051',\n", " 'dateAdded': '2020-02-28',\n", " 'title': 'Hits idag',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000292fb58f80d71697114c6695c',\n", " 'followers': '591362'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX4WpYT2fah9c',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '50890',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Déclassé',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f0000000244d1fc6f394ecf90c786e70a',\n", " 'followers': '70639'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DX5D8j6p4CXZT',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '50277',\n", " 'dateAdded': '2020-02-27',\n", " 'title': 'Chill af',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/2f32c65607d75f1fdc929278d56ff3c9f2594eb9',\n", " 'followers': '118991'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXdEF3AqJpXE3',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '45867',\n", " 'dateAdded': '2020-02-26',\n", " 'title': 'Top Hits Singapore',\n", " 'thumbnailUrl': 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'spotify:playlist:37i9dQZF1DX924zU1IARaD',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '10230',\n", " 'dateAdded': '2020-02-27',\n", " 'title': 'Indie Station',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002a6b68abd5c93e1eeb5199029',\n", " 'followers': '52411'},\n", " {'uri': 'spotify:playlist:1NA6Nq3NTiTy0EOtaxhelK',\n", " 'author': 'Flows on Flows',\n", " 'listeners': '0',\n", " 'streams': '9993',\n", " 'dateAdded': '2020-02-10',\n", " 'title': 'chill rap',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da8472b5b823f69d7eff34e446f3',\n", " 'followers': '20864'},\n", " {'uri': 'spotify:playlist:4yeekUmUtpmhWnfQpVLEAP',\n", " 'author': 'Slimb',\n", " 'listeners': '0',\n", " 'streams': '9763',\n", " 'dateAdded': '2020-02-12',\n", " 'title': 'sade ',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706c0000da84d03c0e3651e5726c65ed877b',\n", " 'followers': '17959'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DXd0PFYKeP9rl',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '9628',\n", " 'dateAdded': '2020-03-02',\n", " 'title': 'Online Juice',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002648bf218a0d0826051ca7a67',\n", " 'followers': '4602'},\n", " {'uri': 'spotify:playlist:37i9dQZF1DWT2SPAYawYcO',\n", " 'author': 'Spotify',\n", " 'listeners': '0',\n", " 'streams': '9608',\n", " 'title': 'New Music Friday AU & NZ',\n", " 'thumbnailUrl': 'https://i.scdn.co/image/ab67706f00000002ed6f929198b29896257c608a',\n", " 'followers': '315054'},\n", " {'uri': 'spotify:playlist:37i9dQZEVXbO5MSE9RdfN2',\n", " 'author': 'spotifycharts',\n", " 'listeners': '0',\n", " 'streams': '9580',\n", " 'title': 'Australia Viral 50',\n", " 'thumbnailUrl': 'https://charts-images.scdn.co/VIRAL_AU_DEFAULT.jpg',\n", " 'followers': '67409'}]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw1['data']" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uri varchar(255)\n", "author varchar(255)\n", "listeners varchar(255)\n", "streams varchar(255)\n", "dateAdded varchar(255)\n", "title varchar(255)\n", "thumbnailUrl varchar(255)\n", "followers varchar(255)\n", "isPersonalized varchar(255)\n", "isAlgotorial varchar(255)\n", "window varchar(255)\n", "day varchar(255)\n" ] } ], "source": [ "print(\"\\n\".join([c + ' varchar(255)' for c in test1.columns]))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def insert_playlist_information(playlist_json, last_update_datetime=None):\n", " last_update_datetime = last_update_datetime or datetime.datetime.utcnow()\n", " rows = playlist_json['data']\n", " pcts = \",\".join([\"(%s, %s, %s, %s, %s, %s, %s, %s, %s)\"] * len(rows))\n", " sql = f'''replace into spotify_playlists (spyid, uri, author, title, thumbnail_url, personalized, algotorial, last_updated, last_follower_count) values {pcts}'''\n", " args = []\n", " for row in rows:\n", " args.extend([\n", " row['uri'].split(':')[-1],\n", " row['uri'],\n", " row['author'],\n", " row['title'],\n", " row['thumbnailUrl'],\n", " row.get('isPersonalized') or False,\n", " row.get('isAlgotorial') or False,\n", " last_update_datetime,\n", " int(row['followers']) if len(row['followers']) < 11 else 0,\n", " ])\n", " with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 43\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 46\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 48\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 64\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 44\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 49\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 47\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 51\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 67\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x99\\\\x80' for column 'title' at row 28\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 48\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 54\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 69\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 45\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 55\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 71\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 50\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 57\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 58\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 61\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 76\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 46\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 66\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 80\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 51\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 68\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 84\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 39\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\xA9\\\\xF0\\\\x9F...' for column 'title' at row 90\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 91\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 40\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 68\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\xA9\\\\xF0\\\\x9F...' for column 'title' at row 97\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 41\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 49\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 73\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 90\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 96\")\n", " result = self._query(query)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x99\\\\x80' for column 'title' at row 23\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 53\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 78\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 95\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x99\\\\x80' for column 'title' at row 29\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 56\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 82\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 99\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 57\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 85\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 97\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE6\\\\x80\\\\xA5\\\\xE4\\\\xB8\\\\x8A...' for column 'title' at row 92\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 95\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 58\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 88\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 62\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 83\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x89' for column 'title' at row 63\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 84\")\n", " result = self._query(query)\n" ] } ], "source": [ "for row in files:\n", " fname, window, day = row\n", " with open(fname) as fin:\n", " raw2 = json.load(fin)\n", " insert_playlist_information(raw2, day)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime.date(2020, 3, 3)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "day" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 44\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE5\\\\x85\\\\xA8\\\\xE7\\\\x90\\\\x83...' for column 'title' at row 49\")\n", " result = self._query(query)\n" ] } ], "source": [ "insert_playlist_information(raw2, day)" ] }, { "cell_type": "code", "execution_count": 242, "metadata": {}, "outputs": [], "source": [ "def make_insert_statement_and_args(fname, window, day):\n", " with open(fname) as fin:\n", " df = pd.DataFrame(json.load(fin)['data'])\n", " df['spyid'] = df.uri.str.split(':').str[-1]\n", " df['streams'] = df.streams.map(safe_int)\n", " df['followers'] = df.followers.map(safe_int)\n", " df['day'] = day.isoformat()\n", " tablename = f'deathbed_playlist_streams_{window}day'\n", " data_matrix = df[['day', 'spyid', 'streams','followers']].to_dict('split')['data']\n", " pcts = \",\".join(['(%s,%s,%s,%s)'] * len(data_matrix))\n", " sql = f'''\n", "replace into {tablename}\n", "(as_of, spyid, streams, followers)\n", "values\n", "{pcts}\n", " '''\n", " args = [arg for row in data_matrix for arg in row]\n", " return sql, args\n", " " ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('death_bed_top_playlists_28day_2020_03_02.json',\n", " 28,\n", " datetime.date(2020, 3, 2)),\n", " ('death_bed_top_playlists_28day_2020_03_03.json',\n", " 28,\n", " datetime.date(2020, 3, 3)),\n", " ('death_bed_top_playlists_28day_2020_03_04.json',\n", " 28,\n", " datetime.date(2020, 3, 4)),\n", " ('death_bed_top_playlists_28day_2020_03_05.json',\n", " 28,\n", " datetime.date(2020, 3, 5)),\n", " ('death_bed_top_playlists_28day_2020_03_06.json',\n", " 28,\n", " datetime.date(2020, 3, 6)),\n", " ('death_bed_top_playlists_28day_2020_03_07.json',\n", " 28,\n", " datetime.date(2020, 3, 7)),\n", " ('death_bed_top_playlists_28day_2020_03_08.json',\n", " 28,\n", " datetime.date(2020, 3, 8)),\n", " ('death_bed_top_playlists_28day_2020_03_09.json',\n", " 28,\n", " datetime.date(2020, 3, 9)),\n", " ('death_bed_top_playlists_28day_2020_03_10.json',\n", " 28,\n", " datetime.date(2020, 3, 10)),\n", " ('death_bed_top_playlists_28day_2020_03_11.json',\n", " 28,\n", " datetime.date(2020, 3, 11)),\n", " ('death_bed_top_playlists_7day_2020_03_02.json',\n", " 7,\n", " datetime.date(2020, 3, 2)),\n", " ('death_bed_top_playlists_7day_2020_03_03.json',\n", " 7,\n", " datetime.date(2020, 3, 3)),\n", " ('death_bed_top_playlists_7day_2020_03_04.json',\n", " 7,\n", " datetime.date(2020, 3, 4)),\n", " ('death_bed_top_playlists_7day_2020_03_05.json',\n", " 7,\n", " datetime.date(2020, 3, 5)),\n", " ('death_bed_top_playlists_7day_2020_03_06.json',\n", " 7,\n", " datetime.date(2020, 3, 6)),\n", " ('death_bed_top_playlists_7day_2020_03_07.json',\n", " 7,\n", " datetime.date(2020, 3, 7)),\n", " ('death_bed_top_playlists_7day_2020_03_08.json',\n", " 7,\n", " datetime.date(2020, 3, 8)),\n", " ('death_bed_top_playlists_7day_2020_03_09.json',\n", " 7,\n", " datetime.date(2020, 3, 9)),\n", " ('death_bed_top_playlists_7day_2020_03_10.json',\n", " 7,\n", " datetime.date(2020, 3, 10)),\n", " ('death_bed_top_playlists_7day_2020_03_11.json',\n", " 7,\n", " datetime.date(2020, 3, 11))]" ] }, "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ "files" ] }, { "cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-02', '37i9dQZF1DXcBWIGoYBM5M', 4636212, 25880039, '2020-03-02']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-03', '37i9dQZF1DXcBWIGoYBM5M', 4998478, 25888935, '2020-03-03']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-04', '37i9dQZF1DXcBWIGoYBM5M', 5356922, 25894440, '2020-03-04']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-05', '37i9dQZF1DXcBWIGoYBM5M', 5728332, 25901807, '2020-03-05']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-06', '37i9dQZF1DXcBWIGoYBM5M', 6099183, 25909917, '2020-03-06']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-07', '37i9dQZF1DXcBWIGoYBM5M', 6440972, 25924532, '2020-03-07']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-08', '37i9dQZF1DXcBWIGoYBM5M', 6750822, 25929079, '2020-03-08']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-09', '37i9dQZF1DXcBWIGoYBM5M', 7088672, 25939345, '2020-03-09']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-10', '37i9dQZF1DXcBWIGoYBM5M', 7432412, 25946045, '2020-03-10']\n", "\n", "replace into deathbed_playlist_streams_28day\n", "(as_\n", "['2020-03-11', '37i9dQZF1DXcBWIGoYBM5M', 7777354, 25957420, '2020-03-11']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-02', '37i9dQZF1DXcBWIGoYBM5M', 2194485, 25880103, '2020-03-02']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-03', '37i9dQZF1DXcBWIGoYBM5M', 2278013, 25888884, '2020-03-03']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-04', '37i9dQZF1DXcBWIGoYBM5M', 2359579, 25894462, '2020-03-04']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-05', '37i9dQZF1DXcBWIGoYBM5M', 2457524, 25901807, '2020-03-05']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-06', '37i9dQZF1DXcBWIGoYBM5M', 2474802, 25909917, '2020-03-06']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-07', '37i9dQZF1DXcBWIGoYBM5M', 2464400, 25924532, '2020-03-07']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-08', '37i9dQZF1DXcBWIGoYBM5M', 2467445, 25929079, '2020-03-08']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-09', '37i9dQZF1DXcBWIGoYBM5M', 2452462, 25939345, '2020-03-09']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-10', '37i9dQZF1DXcBWIGoYBM5M', 2433936, 25946045, '2020-03-10']\n", "\n", "replace into deathbed_playlist_streams_7day\n", "(as_o\n", "['2020-03-11', '37i9dQZF1DXcBWIGoYBM5M', 2420434, 25957420, '2020-03-11']\n" ] } ], "source": [ "for f in files:\n", " sql, args = make_insert_statement_and_args(*f)\n", " print(sql[:50])\n", " print(args[0:5])\n", " with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "day28df = _sql_to_dataframe('''select as_of, spyid, streams, followers from deathbed_playlist_streams_28day''', columns=['as_of', 'spyid', 'streams', 'followers'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [], "source": [ "day28pv = day28df.pivot(index='as_of', columns='spyid', values='followers')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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292576.0 \n", "\n", "spyid 7vOtwzdPcI23sAFbMv68Ja 7wFEdIAin9SERVgudjiNo9 dailymix \\\n", "as_of \n", "2020-03-02 32933.0 40216.0 0.0 \n", "2020-03-03 33028.0 40294.0 0.0 \n", "2020-03-04 33063.0 40328.0 0.0 \n", "2020-03-05 33124.0 40350.0 0.0 \n", "2020-03-06 33185.0 40384.0 0.0 \n", "2020-03-07 33301.0 40502.0 0.0 \n", "2020-03-08 33317.0 40585.0 0.0 \n", "2020-03-09 33405.0 40638.0 0.0 \n", "2020-03-10 NaN 40749.0 0.0 \n", "2020-03-11 NaN 40885.0 0.0 \n", "2020-03-12 NaN 92019.0 0.0 \n", "2020-03-13 NaN 85777.0 0.0 \n", "2020-03-14 NaN 84384.0 0.0 \n", "2020-03-15 NaN 41501.0 0.0 \n", "2020-03-16 NaN 41688.0 0.0 \n", "\n", "spyid discoverweekly onrepeat radio \n", "as_of \n", "2020-03-02 0.0 0.0 0.0 \n", "2020-03-03 0.0 0.0 0.0 \n", "2020-03-04 0.0 0.0 0.0 \n", "2020-03-05 0.0 0.0 0.0 \n", "2020-03-06 0.0 0.0 0.0 \n", "2020-03-07 0.0 0.0 0.0 \n", "2020-03-08 0.0 0.0 0.0 \n", "2020-03-09 0.0 0.0 0.0 \n", "2020-03-10 0.0 0.0 0.0 \n", "2020-03-11 0.0 0.0 0.0 \n", "2020-03-12 0.0 0.0 0.0 \n", "2020-03-13 0.0 0.0 0.0 \n", "2020-03-14 0.0 0.0 0.0 \n", "2020-03-15 0.0 0.0 0.0 \n", "2020-03-16 0.0 0.0 0.0 \n", "\n", "[15 rows x 113 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "day28pv" ] }, { "cell_type": "code", "execution_count": 287, "metadata": {}, "outputs": [], "source": [ "pvpv = day28pv.iloc[[-5, -1]].T.copy()" ] }, { "cell_type": "code", "execution_count": 299, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 299, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pvpv[pvpv.isna().agg(sum, axis=1) == 0].T.pct_change().iloc[-1].sort_values().plot()" ] }, { "cell_type": "code", "execution_count": 303, "metadata": {}, "outputs": [], "source": [ "pct28 = pvpv[pvpv.isna().agg(sum, axis=1) == 0].T.pct_change().iloc[-1].to_frame('pct')" ] }, { "cell_type": "code", "execution_count": 307, "metadata": {}, "outputs": [], "source": [ "pct28['prev'] = pvpv[pvpv.isna().agg(sum, axis=1) == 0].T.iloc[0]" ] }, { "cell_type": "code", "execution_count": 311, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pct prev\n", "spyid \n", "37i9dQZF1DX59NCqCqJtoH 0.255221 11014.0\n", "5FvSWecLOC4rBsvylNlWDy 0.229885 435.0\n", "3qDfz5dvSy3Okgc8snlL47 0.188139 489.0\n", "3o6P4ZAJLETYN8b0eL8MYx 0.164615 44115.0\n", "37i9dQZF1DXd0PFYKeP9rl 0.103670 7003.0" ] }, "execution_count": 311, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pct28.sort_values('pct', ascending=False).head()" ] }, { "cell_type": "code", "execution_count": 316, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 316, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "day28pv['3o6P4ZAJLETYN8b0eL8MYx'].plot(figsize=(10,4))" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [], "source": [ "all_pl_dfs = [read_pl_df(fname, window, day) for fname, window, day in files]" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [], "source": [ "df0 = all_pl_dfs[0]" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2020-03-02\n", "1 2020-03-02\n", "2 2020-03-02\n", "3 2020-03-02\n", "4 2020-03-02\n", " ... \n", "94 2020-03-02\n", "95 2020-03-02\n", "96 2020-03-02\n", "97 2020-03-02\n", "98 2020-03-02\n", "Name: day, Length: 99, dtype: object" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0.day" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": [ "def safe_int(s: str):\n", " return int(s) if len(s) < 11 else 0" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "df0['spyid'] = df0.uri.str.split(':').str[-1]\n", "df0['streams'] = df0.streams.map(safe_int)\n", "df0['followers'] = df0.followers.map(safe_int)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[datetime.date(2020, 3, 2), '37i9dQZF1DXcBWIGoYBM5M', 4636212, 25880039],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DWUa8ZRTfalHk', 734774, 1893268],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DXcZQSjptOQtk', 627292, 3720614],\n", " [datetime.date(2020, 3, 2), '37i9dQZEVXbMDoHDwVN2tF', 414478, 15017361],\n", " [datetime.date(2020, 3, 2), 'radio', 393860, 0],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DWWhB4HOWKFQc', 261173, 90352],\n", " [datetime.date(2020, 3, 2), '37i9dQZEVXbLiRSasKsNU9', 233987, 1602789],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DX4JAvHpjipBk', 198705, 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3, 2), '37i9dQZEVXbJv2Mvelmc3I', 12463, 177191],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DXaLvcwjNfHBR', 11064, 4965],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DX59NCqCqJtoH', 11041, 5712],\n", " [datetime.date(2020, 3, 2), '37i9dQZEVXbLuUZrygauiA', 10952, 203113],\n", " [datetime.date(2020, 3, 2), '32hJXySZtt9YvnwcYINGZ0', 10877, 129031],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DX8OCw6EqwHPA', 10773, 6396],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DX924zU1IARaD', 10230, 52411],\n", " [datetime.date(2020, 3, 2), '1NA6Nq3NTiTy0EOtaxhelK', 9993, 20864],\n", " [datetime.date(2020, 3, 2), '4yeekUmUtpmhWnfQpVLEAP', 9763, 17959],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DXd0PFYKeP9rl', 9628, 4602],\n", " [datetime.date(2020, 3, 2), '37i9dQZF1DWT2SPAYawYcO', 9608, 315054],\n", " [datetime.date(2020, 3, 2), '37i9dQZEVXbO5MSE9RdfN2', 9580, 67409]]" ] }, "execution_count": 216, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df0[['day', 'spyid', 'streams','followers']].to_dict('split')['data']" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "all_pl_df = pd.concat(all_pl_dfs, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[ uri author listeners \\\n", " 0 spotify:playlist:37i9dQZF1DXcBWIGoYBM5M Spotify 0 \n", " 1 spotify:playlist:37i9dQZF1DWUa8ZRTfalHk Spotify 0 \n", " 2 spotify:playlist:37i9dQZF1DXcZQSjptOQtk Spotify 0 \n", " 3 spotify:playlist:37i9dQZEVXbMDoHDwVN2tF spotifycharts 0 \n", " 4 spotify:user:spotify:playlist:radio Spotify 0 \n", " .. ... ... ... \n", " 94 spotify:playlist:1NA6Nq3NTiTy0EOtaxhelK Flows on Flows 0 \n", " 95 spotify:playlist:4yeekUmUtpmhWnfQpVLEAP Slimb 0 \n", " 96 spotify:playlist:37i9dQZF1DXd0PFYKeP9rl Spotify 0 \n", " 97 spotify:playlist:37i9dQZF1DWT2SPAYawYcO Spotify 0 \n", " 98 spotify:playlist:37i9dQZEVXbO5MSE9RdfN2 spotifycharts 0 \n", " \n", " streams dateAdded 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2020 🔥 Radio Hits 2020 \n", " 96 4532 2020-03-10 softly \n", " 97 4493 2020-03-10 Our Generation \n", " 98 4368 2020-03-06 Hip Hop 2020 : Top 100 Rap HITS by Lit Hits (2... \n", " \n", " thumbnailUrl followers \\\n", " 0 https://i.scdn.co/image/ab67706f00000002b27dd9... 25946045 \n", " 1 https://i.scdn.co/image/ab67706f00000002b46aa9... 3731466 \n", " 2 https://charts-images.scdn.co/REGIONAL_GLOBAL_... 15053028 \n", " 3 https://i.scdn.co/image/ab67706f0000000286be22... 1002690 \n", " 4 https://i.scdn.co/image/ab67706f00000002aa71ef... 1208271 \n", " .. ... ... \n", " 94 https://charts-images.scdn.co/VIRAL_MX_DEFAULT... 203352 \n", " 95 https://i.scdn.co/image/ab67706c0000da84e83a68... 144988 \n", " 96 https://i.scdn.co/image/ab67706f000000020ab841... 6982 \n", " 97 https://i.scdn.co/image/ab67706f00000002563239... 18553 \n", " 98 https://i.scdn.co/image/ab67706c0000da844b31de... 121335 \n", " \n", " isPersonalized isAlgotorial window day \n", " 0 NaN NaN 7 2020-03-10 \n", " 1 NaN NaN 7 2020-03-10 \n", " 2 NaN NaN 7 2020-03-10 \n", " 3 NaN NaN 7 2020-03-10 \n", " 4 NaN NaN 7 2020-03-10 \n", " .. ... ... ... ... \n", " 94 NaN NaN 7 2020-03-10 \n", " 95 NaN NaN 7 2020-03-10 \n", " 96 NaN NaN 7 2020-03-10 \n", " 97 NaN NaN 7 2020-03-10 \n", " 98 NaN NaN 7 2020-03-10 \n", " \n", " [99 rows x 12 columns],\n", " uri author listeners \\\n", " 0 spotify:playlist:37i9dQZF1DXcBWIGoYBM5M Spotify 0 \n", " 1 spotify:playlist:37i9dQZF1DXcZQSjptOQtk Spotify 0 \n", " 2 spotify:playlist:37i9dQZEVXbMDoHDwVN2tF spotifycharts 0 \n", " 3 spotify:playlist:37i9dQZF1DXa2EiKmMLhFD Spotify 0 \n", " 4 spotify:playlist:37i9dQZF1DWXXs9GFYnvLB Spotify 0 \n", " .. ... ... ... \n", " 94 spotify:playlist:37i9dQZEVXbL3DLHfQeDmV spotifycharts 0 \n", " 95 spotify:playlist:0njnvMkgsTb0wL8BFDwAGS Filtr Italy 0 \n", " 96 spotify:playlist:3xMQTDLOIGvj3lWH5e5x6F SunsubiroRecords 0 \n", " 97 spotify:playlist:6IapJiXJAoNhBbXTfIuE1L Patrick 0 \n", " 98 spotify:playlist:37i9dQZF1DX8OCw6EqwHPA Spotify 0 \n", " \n", " streams dateAdded title \\\n", " 0 2420434 2020-03-11 Today's Top Hits \n", " 1 531984 2020-03-11 Top Hits Philippines \n", " 2 446863 NaN Global Top 50 \n", " 3 383083 2020-03-11 Top Hits Indonesia \n", " 4 186466 2020-03-09 Hot Hits Australia \n", " .. ... ... ... \n", " 94 4635 NaN United Kingdom Viral 50 \n", " 95 4618 2020-03-06 Top Hits Italy 2020 \n", " 96 4555 2020-03-11 Charts 2020 🔥 Top Hits 2020 🔥 Radio Hits 2020 \n", " 97 4547 2020-03-02 Tik Tok Dances 2020 \n", " 98 4528 2020-03-10 softly \n", " \n", " thumbnailUrl followers \\\n", " 0 https://i.scdn.co/image/ab67706f00000002b27dd9... 25957420 \n", " 1 https://i.scdn.co/image/ab67706f00000002b46aa9... 3732746 \n", " 2 https://charts-images.scdn.co/REGIONAL_GLOBAL_... 15058697 \n", " 3 https://i.scdn.co/image/ab67706f00000002279161... 1003625 \n", " 4 https://i.scdn.co/image/ab67706f0000000250dafc... 1208546 \n", " .. ... ... \n", " 94 https://charts-images.scdn.co/VIRAL_GB_DEFAULT... 131886 \n", " 95 https://i.scdn.co/image/ab67706c0000da843db2c4... 135364 \n", " 96 https://i.scdn.co/image/ab67706c0000da84e83a68... 145318 \n", " 97 https://mosaic.scdn.co/300/ab67616d00001e02600... 54136 \n", " 98 https://i.scdn.co/image/ab67706f000000020ab841... 7054 \n", " \n", " isPersonalized isAlgotorial window day \n", " 0 NaN NaN 7 2020-03-11 \n", " 1 NaN NaN 7 2020-03-11 \n", " 2 NaN NaN 7 2020-03-11 \n", " 3 NaN NaN 7 2020-03-11 \n", " 4 NaN NaN 7 2020-03-11 \n", " .. ... ... ... ... \n", " 94 NaN NaN 7 2020-03-11 \n", " 95 NaN NaN 7 2020-03-11 \n", " 96 NaN NaN 7 2020-03-11 \n", " 97 NaN NaN 7 2020-03-11 \n", " 98 NaN NaN 7 2020-03-11 \n", " \n", " [99 rows x 12 columns]]" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "deltaFrames = []\n", "for w in [7, 28]:\n", " for d in [11, 10, 9, 8, 7, 6, 5, 4, 3]:\n", " n = all_pl_df[(all_pl_df.day == datetime.date(2020,3,d)) & (all_pl_df.window == w)].set_index('uri').streams.astype(int)\n", " n_1 = all_pl_df[(all_pl_df.day == datetime.date(2020,3,d-1)) & (all_pl_df.window == w)].set_index('uri').streams.astype(int)\n", " deltaFrames.append(n.to_frame(f'streams_{w}_{d}'))\n", " deltaFrames.append(n_1.to_frame(f'streams_{w}_{d}_1'))\n", " deltaFrames.append((n - n_1).to_frame(f'delta_streams_{w}_{d}'))\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[(pldf.window.max(), pldf.day.max().day, pldf.uri.count()) for pldf in all_pl_dfs]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[(dlpf.columns, len(dlpf.index)) for dlpf in deltaFrames]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergeLeft = deltaFrames[0].copy()\n", "for frame in deltaFrames[1:]:\n", " mergeLeft = pd.merge(mergeLeft, frame, right_index=True, left_index=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mergeLeft.iloc[-10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# YouTube Views" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime, date, timedelta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ytdf = pd.read_csv('/Users/joel/Downloads/YouTube Hourly Tracking - powfu death bed (1).tsv', sep=\"\\t\", thousands=',')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def excel_to_dt(d):\n", " return datetime(1899,12,30,0,0,0) + timedelta(days=int(d), seconds=(d - int(d))*24*60*60)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = 43879.05449" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "excel_to_dt(d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ytdf['ts'] = ytdf.Time.map(excel_to_dt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ytdelta = ytdf[['ts', 'Views']].set_index('ts').diff()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ytdelta.insert(0, 'ts', ytdelta.index.astype(int) / 1e6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ytdelta[ytdelta.Views < 1e5].Views.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('/Users/joel/src/thundr/unicapp/src/test/youtube_sample.json','w') as fout:\n", " simplejson.dump( ytdelta[ytdelta.Views < 1e5].to_numpy().tolist(),fout,ignore_nan=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tiktok videos" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import simplejson\n", "import requests\n", "#__NEXT_DATA__.props.pageProps.musicData.posts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tiktoks = pd.read_csv('/Users/joel/Downloads/YouTube Hourly Tracking - death bed tiktoks (4).tsv', sep=\"\\t\", thousands=',')\n", "# tiktoks = pd.read_csv('https://docs.google.com/spreadsheets/d/e/2PACX-1vREPbuSODE7aSdK_FdYhf7FattN8B0MBghQgL9vA2kUTIdHYSjBuROL39W2-922Wq02O05-s-njbmEF/pub?gid=1687580280&single=true&output=tsv', sep=\"\\t\", thousands=',')\n", "tiktoks.index = tiktoks.time.astype(numpy.datetime64)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ttdelta = tiktoks[['tiktok videos']].diff()\n", "ttdelta['ts'] = (tiktoks.index.astype(int) / 1e6).astype(int)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('/Users/joel/src/thundr/unicapp/src/test/tiktok_sample.json','w') as fout:\n", " simplejson.dump(slopes[['ts', 'videos']].to_numpy().tolist(),fout,ignore_nan=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "slopes" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "slopes = (tiktoks['tiktok videos'].diff() / tiktoks.index.to_frame().diff().time.dt.seconds)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "slopes = slopes.to_frame('videos')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "slopes['ts'] = (slopes.index.astype(int) / 1e6).astype(int)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "slopes['videos'] = slopes['videos'] * 3600" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pymysql" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tiktokdump = pd.read_csv('/Users/joel/Downloads/YouTube Hourly Tracking - death bed tiktoks (4).tsv', sep=\"\\t\", thousands=',')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tiktokdump = tiktokdump[['time', 'tiktok videos']].rename(columns={'tiktok videos': 'tiktoks'})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tiktokdump" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import io" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "buff = io.StringIO()" ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 211, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scripts import run_spartus_daily\n", "from importlib import reload\n", "reload(run_spartus_daily)" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [], "source": [ "CG = run_spartus_daily.get_for_canada_goose()" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "CG.refresh_request_headers();" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insert into canada_goose_daily_listeners (as_of, listeners) values (%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s) ['2020-02-26', '15399', '2020-02-27', '15837', '2020-02-28', '17104', '2020-02-29', '17482', '2020-03-01', '16740', '2020-03-02', '17721', '2020-03-03', '18077', '2020-03-04', '18599', '2020-03-05', '18981', '2020-03-06', '20160', '2020-03-07', '19675', '2020-03-08', '18704', '2020-03-09', '20029', '2020-03-10', '21048', '2020-03-11', '21395', '2020-03-12', '21409', '2020-03-13', '22693', '2020-03-14', '22531', '2020-03-15', '21342', '2020-03-16', '22133', '2020-03-17', '22039', '2020-03-18', '22589', '2020-03-19', '22948', '2020-03-20', '23248', '2020-03-21', '22590', '2020-03-22', '20711', '2020-03-23', '21370', '2020-03-24', '20655']\n" ] } ], "source": [ "CG.insert_daily_listeners()" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [], "source": [ "CGco = run_spartus_daily.CountryTimelinesGetter(CG)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [], "source": [ "uniq_codes = CGco.get_uniq_country_codes()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['KW', 'DO', 'SA'] OK\n", "['SK', 'BR', 'UY'] OK\n", "['LV', 'MY', 'CH'] OK\n", "['BG', 'PL', 'DK'] OK\n", "['CL', 'MX', 'AT'] OK\n", "['LI', 'FR', 'GR'] OK\n", "['PS', 'GB', 'SE'] OK\n", "['ES', 'MA', 'TR'] OK\n", "['MT', 'ID', 'LU'] OK\n", "['US', 'TH', 'BH'] OK\n", "['LB', 'IL', 'IE'] OK\n", "['PA', 'EC', 'SG'] OK\n", "['LT', 'IS', 'HK'] OK\n", "['CO', 'CR', 'GT'] OK\n", "['RO', 'CZ', 'NO'] OK\n", "['JO', 'TW', 'QA'] OK\n", "['DZ', 'NI', 'EG'] OK\n", "['PE', 'CY', 'VN'] OK\n", "['FI', 'PY', 'DE'] OK\n", "['OM', 'AE', 'JP'] OK\n", "['NZ', 'MC', 'ZA'] OK\n", "['NL', 'SV', 'HN'] OK\n", "['IN', 'AD', 'EE'] OK\n", "['AR', 'BE', 'PH'] OK\n", "['CA', 'BO', 'TN'] OK\n", "['AU', 'PT', 'IT'] OK\n", "['HU'] OK\n" ] } ], "source": [ "matrix, all_timelines = CGco.load_all_country_timelines(uniq_codes)" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "replace into canada_goose_country_daily_streams (as_of, country, daily 1500 : ['2020-02-26', 'DO', 0, '2020-02-27', 'DO', 0] .. ['2020-03-19', 'LI', 0, '2020-03-20', 'LI', 0]\n", "replace into canada_goose_country_daily_streams (as_of, country, daily 1500 : ['2020-03-21', 'LI', 0, '2020-03-22', 'LI', 0] .. ['2020-03-15', 'EC', 0, '2020-03-16', 'EC', 0]\n", "replace into canada_goose_country_daily_streams (as_of, country, daily 1500 : ['2020-03-17', 'EC', 1, '2020-03-18', 'EC', 0] .. ['2020-03-11', 'VN', 0, '2020-03-12', 'VN', 0]\n", "replace into canada_goose_country_daily_streams (as_of, country, daily 1500 : ['2020-03-13', 'VN', 4, '2020-03-14', 'VN', 0] .. ['2020-03-07', 'PH', 0, '2020-03-08', 'PH', 1]\n", "replace into canada_goose_country_daily_streams (as_of, country, daily 636 : ['2020-03-09', 'PH', 0, '2020-03-10', 'PH', 0] .. ['2020-03-23', 'HU', 5, '2020-03-24', 'HU', 4]\n" ] } ], "source": [ "CGco.insert_matrix_data(matrix)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting daily update\n", "Bearer BQBBhLRdSyxvStv0SDs8JuIS_9D_kHByfMgRUNtt8PBpa2RBnNRBwWO98YjPXl4biiE8e8abqIL3o68sySPLAlabL1s9XqLSdd9fkxyqRQi00NLnkrMZNgBVIBkUUkgz6Gw_PxlH-Sou7c6HI7FZDMdUMcoDLD3iviMQ9_c\n", "\n", "Countries:\n", "79 countries, (237 args), inserting: insert ignore into canada_goo...\n", "\n", "Total Daily:\n", "28 points, (56 args), inserting: insert ignore into canada_goos...\n", "\n", "Sources:\n", "6 sources, (18 args), inserting: \n", " insert into canada_go...\n", "\n", "Playlists:\n", "Found 100 playlists for PlaylistPeriod(period='since2015', tablename='canada_goose_playlist_streams_all_time')\n", "\n", " replace into canada_goose ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x8A' for column 'title' at row 11\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\xA4' for column 'title' at row 16\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xB1\\\\xE2\\\\x80...' for column 'title' at row 19\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB5\\\\xF0\\\\x9F...' for column 'title' at row 20\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB5' for column 'title' at row 22\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xC4\\\\x93' for column 'author' at row 26\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA7\\\\x9F\\\\xE2\\\\x80...' for column 'title' at row 27\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x97\\\\xBC\\\\xEF\\\\xB8\\\\x8F' for column 'author' at row 33\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x98\\\\xA0\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 34\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x9A\\\\xA7\\\\xF0\\\\x9F...' for column 'title' at row 35\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x81\\\\xE2\\\\x9D...' for column 'title' at row 37\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x90\\\\x90' for column 'title' at row 39\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5' for column 'title' at row 43\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x89\\\\xF0\\\\x9F...' for column 'title' at row 47\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xA7\\\\xF0\\\\x9F...' for column 'title' at row 49\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x9F\\\\xF0\\\\x9F...' for column 'title' at row 50\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x8A\\\\xF0\\\\x9F...' for column 'title' at row 55\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x8C\\\\xE2\\\\x9D\\\\x8C...' for column 'title' at row 56\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xAB' for column 'title' at row 59\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 63\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xB6\\\\xF0\\\\x9F...' for column 'title' at row 64\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9A\\\\xBD\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 65\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE3\\\\x80\\\\x9C(\\\\xEA\\\\x92...' for column 'title' at row 66\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x95\\\\xBA\\\\xF0\\\\x9F...' for column 'title' at row 67\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x84\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 68\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x99' for column 'title' at row 69\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x9D' for column 'title' at row 71\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x86\\\\x98\\\\xF0\\\\x9F...' for column 'title' at row 73\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x9F\\\\xF0\\\\x9F...' for column 'author' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x8C\\\\xE2\\\\x99\\\\xA5...' for column 'title' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB6\\\\xF0\\\\x9F...' for column 'title' at row 76\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xC4\\\\xB1' for column 'author' at row 77\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA7\\\\xA2' for column 'title' at row 80\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x84\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 81\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5' for column 'title' at row 83\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\x8A' for column 'title' at row 88\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xF0\\\\x9F...' for column 'title' at row 91\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9D\\\\x92\\\\xA9\\\\xF0\\\\x9D...' for column 'title' at row 92\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB5\\\\xF0\\\\x9F...' for column 'title' at row 95\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA7\\\\x99\\\\xE2\\\\x80...' for column 'title' at row 98\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE3\\\\x81\\\\xB2\\\\xF0\\\\x9F\\\\xA5...' for column 'title' at row 100\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='28day', tablename='canada_goose_playlist_streams_28day')\n", "\n", " replace into canada_goose ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x95\\\\xBA' for column 'title' at row 10\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\xA6\\\\xF0\\\\x9F...' for column 'title' at row 16\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xC4\\\\x93' for column 'author' at row 20\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 21\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\xAE\\\\xF0\\\\x9F...' for column 'title' at row 27\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\xA4\\\\xEF\\\\xB8\\\\x8F...' for column 'title' at row 29\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB0' for column 'title' at row 35\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\xB4' for column 'title' at row 39\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB6\\\\xE2\\\\x9D...' for column 'title' at row 42\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\x8C' for column 'title' at row 52\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 53\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x98\\\\xF0\\\\x9F...' for column 'title' at row 54\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x81\\\\xE2\\\\x9D...' for column 'title' at row 55\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\x9E' for column 'title' at row 57\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 59\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x8C\\\\xF0\\\\x9F...' for column 'title' at row 62\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB6\\\\xF0\\\\x9F...' for column 'title' at row 63\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB0' for column 'title' at row 69\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA7\\\\x99\\\\xE2\\\\x80...' for column 'title' at row 70\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x86\\\\x98' for column 'title' at row 76\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x98\\\\xF0\\\\x9F...' for column 'title' at row 85\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\xA9\\\\xF0\\\\x9F...' for column 'title' at row 93\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x86\\\\x98\\\\xF0\\\\x9F...' for column 'title' at row 96\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='7day', tablename='canada_goose_playlist_streams_7day')\n", "\n", " replace into canada_goose ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xE2\\\\xAD...' for column 'author' at row 12\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\xAD\\\\x95\\\\xF0\\\\x9F\\\\x98...' for column 'title' at row 12\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\xA9\\\\xE2\\\\x80...' for column 'title' at row 20\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9B\\\\x93' for column 'title' at row 22\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x99\\\\xF0\\\\x9F...' for column 'title' at row 25\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 26\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x83\\\\x8F\\\\xE2\\\\x9A...' for column 'title' at row 27\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\xA8' for column 'title' at row 28\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 30\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x8B' for column 'title' at row 31\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x91' for column 'title' at row 32\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xA5' for column 'title' at row 39\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB6' for column 'title' at row 42\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB6' for column 'title' at row 45\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x99\\\\xA5\\\\xEF\\\\xB8\\\\x8F...' for column 'title' at row 49\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 50\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xAF' for column 'title' at row 51\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x84\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 55\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8E\\\\xA7\\\\xF0\\\\x9F...' for column 'title' at row 57\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\xAD\\\\x95\\\\xEF\\\\xB8\\\\x8F...' for column 'title' at row 59\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8E\\\\x88\\\\xF0\\\\x9F...' for column 'title' at row 69\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x97\\\\xF0\\\\x9F...' for column 'title' at row 72\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xA4' for column 'title' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xAF' for column 'title' at row 78\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x9A\\\\xAB' for column 'author' at row 79\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 80\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\xA4\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 84\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x90\\\\xB8' for column 'title' at row 85\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 88\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\x8C' for column 'title' at row 91\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x98' for column 'title' at row 92\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\xA4\\\\xEF\\\\xB8\\\\x8F...' for column 'title' at row 93\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\xBB\\\\xF0\\\\x9F...' for column 'title' at row 97\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x9E' for column 'title' at row 98\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\x82\\\\xF0\\\\x9F...' for column 'title' at row 99\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='1day', tablename='canada_goose_playlist_streams_1day')\n", "\n", " replace into canada_goose ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9B\\\\x93' for column 'title' at row 5\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xE2\\\\xAD...' for column 'author' at row 8\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\xAD\\\\x95\\\\xF0\\\\x9F\\\\x98...' for column 'title' at row 8\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88' for column 'title' at row 14\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x87' for column 'title' at row 18\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9A\\\\xBD' for column 'author' at row 24\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8E\\\\xBC\\\\xF0\\\\x9F...' for column 'title' at row 33\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xAA' for column 'title' at row 37\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x95\\\\xF0\\\\x9F...' for column 'title' at row 40\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x8F' for column 'title' at row 45\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\xA8' for column 'title' at row 52\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA7\\\\x9F\\\\xE2\\\\x80...' for column 'title' at row 53\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\x95\\\\xF0\\\\x9F...' for column 'title' at row 54\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x97\\\\xA1' for column 'title' at row 56\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9C\\\\x88\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 58\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8E\\\\xB5\\\\xF0\\\\x9F...' for column 'title' at row 59\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x9A\\\\x97\\\\xF0\\\\x9F...' for column 'title' at row 62\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x8D' for column 'title' at row 64\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x8D' for column 'title' at row 66\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x85\\\\xB0\\\\xF0\\\\x9F...' for column 'title' at row 68\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x8D' for column 'title' at row 71\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\xA4\\\\xEF\\\\xB8\\\\x8F' for column 'title' at row 73\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B' for column 'title' at row 75\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xAF' for column 'title' at row 80\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xAF\\\\xF0\\\\x9F...' for column 'title' at row 82\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x92\\\\xA4' for column 'title' at row 83\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B\\\\xF0\\\\x9F...' for column 'title' at row 84\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9D\\\\x8CMAR...' for column 'title' at row 88\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBA\\\\xF0\\\\x9F...' for column 'title' at row 89\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8E\\\\xBC' for column 'title' at row 96\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B' for column 'title' at row 97\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA5\\\\xB5' for column 'title' at row 98\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA4\\\\x9F\\\\xF0\\\\x9F...' for column 'title' at row 100\")\n", " result = self._query(query)\n" ] } ], "source": [ "CG.main()" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "DB = run_spartus_daily.get_for_deathbed()" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "DB.refresh_request_headers();" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data appears to be updated for yesterday, days_ago=1\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DB.check_data_updated()" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting daily update\n", "Bearer BQBX1VBKShJ9u42U8mYBjU82brhRFffi1P78cZPDL3KqdKO5qGUnZsiXNAUeZ0m_ewCDnAvxDw7fRhDs6uTMAQLvyqt6c2iyszx1O-kp9Zr8ouF30dFUiJPqOwT2UkAXVgVpcRfpx8NZ8CAn5hugtGSDjMnK06bdGqwNo8I\n", "\n", "Countries:\n", "79 countries, (237 args), inserting: insert ignore into deathbed_c...\n", "\n", "Total Daily:\n", "28 points, (56 args), inserting: insert ignore into deathbed_da...\n", "\n", "Sources:\n", "6 sources, (18 args), inserting: \n", " insert into deathbed_...\n", "\n", "Playlists:\n", "Found 99 playlists for PlaylistPeriod(period='since2015', tablename='deathbed_playlist_streams_all_time')\n", "\n", " replace into deathbed_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x89' for column 'title' at row 41\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 48\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 56\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 94\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 96\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 99 playlists for PlaylistPeriod(period='28day', tablename='deathbed_playlist_streams_28day')\n", "\n", " replace into deathbed_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x89' for column 'title' at row 36\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 46\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 63\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 88\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 91\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 99 playlists for PlaylistPeriod(period='7day', tablename='deathbed_playlist_streams_7day')\n", "\n", " replace into deathbed_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x89' for column 'title' at row 28\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 58\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 70\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 82\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x8D (...' for column 'title' at row 87\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 99 playlists for PlaylistPeriod(period='1day', tablename='deathbed_playlist_streams_1day')\n", "\n", " replace into deathbed_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8D\\\\x89' for column 'title' at row 30\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x8C\\\\xBC (...' for column 'title' at row 69\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE1\\\\xBA\\\\xA1 Ng...' for column 'title' at row 71\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5 T...' for column 'title' at row 80\")\n", " result = self._query(query)\n" ] } ], "source": [ "DB.main()" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "_first_non_zero_num() takes 1 positional argument but 2 were given", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert_daily_listeners\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/src/thundr/tracker/scripts/run_spartus_daily.py\u001b[0m in \u001b[0;36minsert_daily_listeners\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minsert_daily_listeners\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0mlisteners_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_daily_listeners\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_non_zero_num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlisteners_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timelinePoint'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 290\u001b[0m \u001b[0mtablename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'{self.TABLE_PREFIX}_daily_listeners'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"(as_of, listeners)\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: _first_non_zero_num() takes 1 positional argument but 2 were given" ] } ], "source": [ "CG.insert_daily_listeners()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79 countries, (237 args), inserting: insert ignore into canada_goo...\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": [ "drip = run_spartus_daily.get_for_skechers()" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'authority': 'generic.wg.spotify.com',\n", " 'authorization': 'Bearer BQD1IeDZWF655r2hXKiSwsqIAHhyq7vl3ZCsu6rmxHPNX5UMm0jH2puM55riEEcx-MHIXhJp6NwT5Bh6FCPob4jhs0gigw8KWrx-OlMHXk7zBM-ajFGQU_hoEjcD0ghZ5Qzqvyzh_JgPgkwbzEMKjdY8ANMe9NxSrfRNPGk',\n", " 'content-type': 'application/json',\n", " 'accept': 'application/json',\n", " 'sec-fetch-dest': 'empty',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'spotify-app-version': '1.0.0.3229887',\n", " 'app-platform': 'Browser',\n", " 'origin': 'https://artists.spotify.com',\n", " 'sec-fetch-site': 'same-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://artists.spotify.com/c/artist/3O5HD95HTEPgoPFOjAb7yV/song/4z2JaAVPemYJhFKm7e32RA/stats?segment-filter=streams&time-filter=7day',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'}" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drip.refresh_request_headers()" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['KW', 'DO', 'SA'] OK\n", "['SK', 'BR', 'UY'] OK\n", "['LV', 'MY', 'CH'] OK\n", "['BG', 'PL', 'DK'] OK\n", "['CL', 'MX', 'AT'] OK\n", "['LI', 'FR', 'GR'] OK\n", "['PS', 'GB', 'SE'] OK\n", "['ES', 'MA', 'TR'] OK\n", "['MT', 'ID', 'LU'] OK\n", "['US', 'TH', 'BH'] OK\n", "['LB', 'IL', 'IE'] OK\n", "['PA', 'EC', 'SG'] OK\n", "['LT', 'IS', 'HK'] OK\n", "['CO', 'CR', 'GT'] OK\n", "['RO', 'CZ', 'NO'] OK\n", "['JO', 'TW', 'QA'] OK\n", "['DZ', 'NI', 'EG'] OK\n", "['PE', 'CY', 'VN'] OK\n", "['FI', 'PY', 'DE'] OK\n", "['OM', 'AE', 'JP'] OK\n", "['NZ', 'MC', 'ZA'] OK\n", "['NL', 'SV', 'HN'] OK\n", "['IN', 'AD', 'EE'] OK\n", "['AR', 'BE', 'PH'] OK\n", "['CA', 'BO', 'TN'] OK\n", "['AU', 'PT', 'IT'] OK\n", "['HU'] OK\n", "replace into skechers_country_daily_streams (as_of, country, daily) va 1500 : ['2020-02-26', 'DO', 22, '2020-02-27', 'DO', 32] .. ['2020-03-19', 'LI', 135, '2020-03-20', 'LI', 53]\n", "replace into skechers_country_daily_streams (as_of, country, daily) va 1500 : ['2020-03-21', 'LI', 25, '2020-03-22', 'LI', 36] .. ['2020-03-15', 'EC', 1142, '2020-03-16', 'EC', 1258]\n", "replace into skechers_country_daily_streams (as_of, country, daily) va 1500 : ['2020-03-17', 'EC', 1177, '2020-03-18', 'EC', 1716] .. ['2020-03-11', 'VN', 102, '2020-03-12', 'VN', 151]\n", "replace into skechers_country_daily_streams (as_of, country, daily) va 1500 : ['2020-03-13', 'VN', 124, '2020-03-14', 'VN', 167] .. ['2020-03-07', 'PH', 472, '2020-03-08', 'PH', 529]\n", "replace into skechers_country_daily_streams (as_of, country, daily) va 636 : ['2020-03-09', 'PH', 556, '2020-03-10', 'PH', 700] .. ['2020-03-23', 'HU', 4578, '2020-03-24', 'HU', 4551]\n" ] } ], "source": [ "drip.update_daily_country_timelines()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "url_total_daily = (\n", " \"https://generic.wg.spotify.com/s4x-insights-api/v1\"\n", " f\"/artist/{drip.ARTIST_ID}\"\n", " f\"/recording/{drip.TRACK_ID}\"\n", " \"/timeline/streams\"\n", " \"?aggregation-level=recording\"\n", " \"&time-filter=28day\"\n", ")\n", "totals = requests.get(url_total_daily, headers=drip.headers)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'authority': 'generic.wg.spotify.com',\n", " 'authorization': 'Bearer BQCNVbTZSjW6O1Tx5nzrhAA_EIbs1lW95qIBS81_hQPIHF-icZIIDqy6h7yjJrV88XrjEoRL9sYsTOwwdNwWvrOIETtcaH_XTJ9zu1aa5viBl5400Pw1xLCuhu8XWfRUnL7nz3GHca4oE9jpSpEqOu2UEZm1__bSKCvF020',\n", " 'content-type': 'application/json',\n", " 'accept': 'application/json',\n", " 'sec-fetch-dest': 'empty',\n", " 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.116 Safari/537.36',\n", " 'spotify-app-version': '1.0.0.3229887',\n", " 'app-platform': 'Browser',\n", " 'origin': 'https://artists.spotify.com',\n", " 'sec-fetch-site': 'same-site',\n", " 'sec-fetch-mode': 'cors',\n", " 'referer': 'https://artists.spotify.com/c/artist/3O5HD95HTEPgoPFOjAb7yV/song/4z2JaAVPemYJhFKm7e32RA/stats?segment-filter=streams&time-filter=7day',\n", " 'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8'}" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drip.refresh_request_headers()" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting daily update\n", "Bearer BQAYPtGszT44bAi76AtmbH1kqCFI8A04bnoe2ykFwQLlNAtx0XPs90jzbz8ZJ8p_ka2lZfl6RrjFv5QymxlawWTZaXeTZh3l8zur25gBQP2refQjGKgrGyIyLe4hAQ1uYYJf2OlrO_fC6_hpWEZu6aqfrZEmo3ofgqXNwKQ\n", "\n", "Countries:\n", "79 countries, (237 args), inserting: insert ignore into skechers_c...\n", "\n", "Total Daily:\n", "28 points, (56 args), inserting: insert ignore into skechers_da...\n", "\n", "Sources:\n", "6 sources, (18 args), inserting: \n", " insert into skechers_...\n", "\n", "Playlists:\n", "Found 100 playlists for PlaylistPeriod(period='since2015', tablename='skechers_playlist_streams_all_time')\n", "\n", " replace into skechers_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xF0\\\\x9F...' for column 'author' at row 8\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 23\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9C\\\\x85 Be...' for column 'title' at row 50\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x9F' for column 'title' at row 51\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x84\\\\x95\\\\xF0\\\\x9D\\\\x95...' for column 'author' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B\\\\xF0\\\\x9F...' for column 'title' at row 74\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 84\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='28day', tablename='skechers_playlist_streams_28day')\n", "\n", " replace into skechers_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xF0\\\\x9F...' for column 'author' at row 7\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 21\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x9F' for column 'title' at row 49\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9C\\\\x85 Be...' for column 'title' at row 55\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x84\\\\x95\\\\xF0\\\\x9D\\\\x95...' for column 'author' at row 75\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B\\\\xF0\\\\x9F...' for column 'title' at row 75\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 91\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='7day', tablename='skechers_playlist_streams_7day')\n", "\n", " replace into skechers_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\x88\\\\xF0\\\\x9F...' for column 'author' at row 5\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x9F' for column 'title' at row 48\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x84\\\\x95\\\\xF0\\\\x9D\\\\x95...' for column 'author' at row 64\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B\\\\xF0\\\\x9F...' for column 'title' at row 64\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x9C\\\\x85 Be...' for column 'title' at row 87\")\n", " result = self._query(query)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 100 playlists for PlaylistPeriod(period='1day', tablename='skechers_playlist_streams_1day')\n", "\n", " replace into skechers_pla ...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x94\\\\xA5\\\\xF0\\\\x9F...' for column 'title' at row 25\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x9F' for column 'title' at row 80\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x91' for column 'title' at row 86\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xE2\\\\x84\\\\x95\\\\xF0\\\\x9D\\\\x95...' for column 'author' at row 91\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\xA6\\\\x8B\\\\xF0\\\\x9F...' for column 'title' at row 91\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x98\\\\xB4\\\\xF0\\\\x9F...' for column 'title' at row 98\")\n", " result = self._query(query)\n", "/Users/joel/src/thundr/tracker/venv/lib/python3.7/site-packages/pymysql/cursors.py:170: Warning: (1366, \"Incorrect string value: '\\\\xF0\\\\x9F\\\\x91\\\\x9F' for column 'title' at row 99\")\n", " result = self._query(query)\n" ] } ], "source": [ "drip.main()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n", "Shifts 0\n" ] } ], "source": [ "tablename = 'canada_goose'\n", "df = _sql_to_dataframe(f'''\n", " select country, as_of, total\n", " from {tablename}_country_streams_28day\n", "''', columns=['country', 'as_of', 'total'])\n", "\n", "\n", "pv = df.pivot(index='as_of', columns='country', values='total')\n", "pv.index = pd.to_datetime(pv.index)\n", "\n", "def create_actual_total28(s: pd.Series):\n", " shifts = len(s.index) // 28\n", " print(\"Shifts\", shifts)\n", " cur = s.shift(0, fill_value=0)\n", " for period in range(1, shifts+1):\n", " print(\"shift\", period)\n", " cur = cur + s.shift(28 * period, fill_value=0)\n", " return cur\n", "\n", "pv = pv.apply(create_actual_total28)\n", "zeroIndex = pv.index.min() - datetime.timedelta(days=1)\n", "dailyVals = pv.append(\n", " pd.DataFrame(index=[zeroIndex], columns=pv.columns, data=[[0] * len(pv.columns)])\n", ").sort_index().diff()\n", "\n", "countryTotals = df.groupby('country').max()" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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as_oftotal
country
AD2020-03-2313
AE2020-03-23314
AR2020-03-2335
AT2020-03-232273
AU2020-03-236268
.........
TW2020-03-2378
US2020-03-235610
UY2020-03-234
VN2020-03-2322
ZA2020-03-23119
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79 rows × 2 columns

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" ], "text/plain": [ " as_of total\n", "country \n", "AD 2020-03-23 13\n", "AE 2020-03-23 314\n", "AR 2020-03-23 35\n", "AT 2020-03-23 2273\n", "AU 2020-03-23 6268\n", "... ... ...\n", "TW 2020-03-23 78\n", "US 2020-03-23 5610\n", "UY 2020-03-23 4\n", "VN 2020-03-23 22\n", "ZA 2020-03-23 119\n", "\n", "[79 rows x 2 columns]" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "countryTotals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Daily listeners for backfilling etc." ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [], "source": [ "def _get_daily_listeners():\n", " url = (\n", " 'https://generic.wg.spotify.com/s4x-insights-api/v1'\n", " '/artist/3O5HD95HTEPgoPFOjAb7yV/audience/timeline/listeners/3O5HD95HTEPgoPFOjAb7yV'\n", " '?time-filter=since2015&aggregation-level=recording'\n", " )\n", " listreq = requests.get(url, headers=headers)\n", " return listreq.json()\n", "\n", "def _first_non_zero_num(timeline):\n", " timeline_asc = sorted(timeline, key=lambda t: t['date'])\n", " ix = next((i for i,p in enumerate(timeline_asc) if int(p['num']) > 0), 0)\n", " return timeline_asc[ix:]\n", "\n", "def insert_daily_listeners():\n", " listeners_json = _get_daily_listeners()\n", " rows = _first_non_zero_num(listeners_json['timelinePoint'])\n", " tablename = 'skechers_daily_listeners'\n", " cols = \"(as_of, listeners)\"\n", " pcts = \",\".join([\"(%s, %s)\"] * len(rows))\n", " sql = f'''insert into {tablename} {cols} values {pcts}'''\n", " args = [i for row in rows for i in [row['date'], row['num']]]\n", " print(sql, args)\n", " with get_cursor(True) as cur:\n", " cur.execute(sql, args)" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [], "source": [ "df1 = pd.DataFrame(data=listreq.json()['timelinePoint'])\n", "\n", "df1.set_index(df1.date.astype(numpy.datetime64), inplace=True)\n", "df1['listeners'] = df1.num.astype(int)\n", "df1 = df1[['listeners']].sort_index()" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [], "source": [ "first_non_zero = df1[df1.listeners > 0].idxmin()" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2019-10-29 00:00:00')" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "first_non_zero.listeners" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df1.loc[first_non_zero.listeners:].plot()" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insert into skechers_daily_listeners (as_of, listeners) values (%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s),(%s, %s) ['2019-10-29', '589', '2019-10-30', '769', '2019-10-31', '650', '2019-11-01', '961', '2019-11-02', '1182', '2019-11-03', '1254', '2019-11-04', '1599', '2019-11-05', '1830', '2019-11-06', '1745', '2019-11-07', '1889', '2019-11-08', '2001', '2019-11-09', '2128', '2019-11-10', '2028', '2019-11-11', '2001', '2019-11-12', '2189', '2019-11-13', '2850', '2019-11-14', '3172', '2019-11-15', '3457', '2019-11-16', '3781', '2019-11-17', '3470', '2019-11-18', '3122', '2019-11-19', '3179', '2019-11-20', '3191', '2019-11-21', '3305', '2019-11-22', '3627', '2019-11-23', '3887', '2019-11-24', '3551', '2019-11-25', '3393', '2019-11-26', '3566', '2019-11-27', '3747', '2019-11-28', '3645', '2019-11-29', '3804', '2019-11-30', '3733', '2019-12-01', '3521', '2019-12-02', '3079', '2019-12-03', '3139', '2019-12-04', '3259', '2019-12-05', '3233', '2019-12-06', '3541', '2019-12-07', '3848', '2019-12-08', '3641', '2019-12-09', '3142', '2019-12-10', '3370', '2019-12-11', '3598', '2019-12-12', '3763', '2019-12-13', '4124', '2019-12-14', '4258', '2019-12-15', '3978', '2019-12-16', '3532', '2019-12-17', '3429', '2019-12-18', '3520', '2019-12-19', '3576', '2019-12-20', '3869', '2019-12-21', '3986', '2019-12-22', '3694', '2019-12-23', '3756', '2019-12-24', '3482', '2019-12-25', '3369', '2019-12-26', '3342', '2019-12-27', '3497', '2019-12-28', '3320', '2019-12-29', '3186', '2019-12-30', '3320', '2019-12-31', '3477', '2020-01-01', '3269', '2020-01-02', '3047', '2020-01-03', '3274', '2020-01-04', '3316', '2020-01-05', '3092', '2020-01-06', '2973', '2020-01-07', '3067', '2020-01-08', '3196', '2020-01-09', '3464', '2020-01-10', '3789', '2020-01-11', '3993', '2020-01-12', '3954', '2020-01-13', '3512', '2020-01-14', '3726', '2020-01-15', '3753', '2020-01-16', '3796', '2020-01-17', '4056', '2020-01-18', '4524', '2020-01-19', '4476', '2020-01-20', '4532', '2020-01-21', '4146', '2020-01-22', '4208', '2020-01-23', '4613', '2020-01-24', '5344', '2020-01-25', '5517', '2020-01-26', '4946', '2020-01-27', '4332', '2020-01-28', '4551', '2020-01-29', '4952', '2020-01-30', '5397', '2020-01-31', '6036', '2020-02-01', '8585', '2020-02-02', '10631', '2020-02-03', '12886', '2020-02-04', '16976', '2020-02-05', '21208', '2020-02-06', '25447', '2020-02-07', '30448', '2020-02-08', '30559', '2020-02-09', '30591', '2020-02-10', '33801', '2020-02-11', '36210', '2020-02-12', '37808', '2020-02-13', '39243', '2020-02-14', '42790', '2020-02-15', '45459', '2020-02-16', '49163', '2020-02-17', '54623', '2020-02-18', '56253', '2020-02-19', '61662', '2020-02-20', '64020', '2020-02-21', '69684', '2020-02-22', '71265', '2020-02-23', '70035', '2020-02-24', '69985', '2020-02-25', '72365', '2020-02-26', '79653', '2020-02-27', '88142', '2020-02-28', '104292', '2020-02-29', '107043', '2020-03-01', '107287', '2020-03-02', '113462', '2020-03-03', '127775', '2020-03-04', '154158', '2020-03-05', '162860', '2020-03-06', '175934', '2020-03-07', '177967', '2020-03-08', '174722', '2020-03-09', '187890', '2020-03-10', '217766', '2020-03-11', '245783', '2020-03-12', '263904', '2020-03-13', '295237', '2020-03-14', '318256', '2020-03-15', '320960', '2020-03-16', '359506', '2020-03-17', '395146', '2020-03-18', '425306', '2020-03-19', '462028', '2020-03-20', '501572', '2020-03-21', '517865', '2020-03-22', '518452', '2020-03-23', '566027', '2020-03-24', '589196']\n" ] } ], "source": [ "insert_daily_listeners()" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": [ "tablename = 'skechers'\n", "track_id = 2\n", "cols = \"videos1 + videos2\" if track_id == 2 else \"videos\"\n", "tiktoks = _sql_to_dataframe(f'''select ts, {cols} from {tablename}_tiktoks''', columns=['time', 'value'], index_column='time')\n", "\n", "cols = \"views1 + views2 + views3\" if track_id > 1 else \"views1\"\n", "youtubes = _sql_to_dataframe(f'''select ts, {cols} from {tablename}_youtubes''', columns=['time', 'value'], index_column='time')\n", "\n", "cols = \"listeners\"\n", "listeners = _sql_to_dataframe(f'''select as_of, {cols} from {tablename}_daily_listeners''', columns=['time', 'value'], index_column='time')" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 224, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.merge(listeners, tiktoks.resample('24H').max().diff()" ] }, { "cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [], "source": [ "dfi = _sql_to_dataframe('''select ts, listeners from deathbed_listeners where ts > '2020-03-25 04:50:00' and ts < '2020-03-26 01:30:00' ''', columns=['ts', 'listeners'])" ] }, { "cell_type": "code", "execution_count": 248, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 248, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfi.set_index('ts').plot()" ] }, { "cell_type": "code", "execution_count": 251, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "listeners 12864.660739\n", "dtype: float64" ] }, "execution_count": 251, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfi.set_index('ts').mean()" ] }, { "cell_type": "code", "execution_count": 260, "metadata": {}, "outputs": [], "source": [ "import sys" ] }, { "cell_type": "code", "execution_count": 266, "metadata": {}, "outputs": [], "source": [ "sys.path.insert(0, '/Users/joel/src/thundr/unicapi')" ] }, { "cell_type": "code", "execution_count": 268, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'api.tracks'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtracks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'api.tracks'" ] } ], "source": [ "import api.tracks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 4 }