{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### This notebok describes the development of the stream predictor.\n", "\n", "The stream predictor will use the data we collect on tracks, to predict how many streams a song will reach. \n", "\n", "Streams are found for Sony tracks using the filtr dataset, which is a tool owned by Sony that accesses the private stream data used for billing between Spotify and Sony. We do not access that data ourselves currently, and may never do as we are not destined to be a pure Sony only tool. \n", "\n", "### What is it\n", "\n", "Given a track, the stream predictor will classify a track into one of a number of buckets, where those buckets indicate the success of the track.\n", "\n", "##### Playlists\n", "\n", "The first iteration of the stream predictor considered playlist appearance and position to predict where a track may land. For example: if a track appears on 'Playlist A' at number 5, and then 'Playlist B' at number 10 the next week, that track could expect to be a 'moderate hit' reaching 10M streams in 150 days.\n", "\n", "We realised that our current dataset does not hold enough data to make this determination possible. In fact, attempting to find any tracks in filtr that shared playlist position early, proved very difficult. So we have parked the idea of using playlist position at this time.\n", "\n", "We have added more playlists, to give more coverage so that in future we may have an improved dataset.\n", "\n", "Regardless, this is not big data. The number of tracks we have is not large.\n", "\n", "##### Popularity\n", "\n", "We have been tracking songs for over 100 days however, so it is now plausible to consider a simple model that looks at early popularity. And what that can indicate for stream success. This model will only be indicative, and is designed to being the conversation with the A&R teams, to understand if they might find it valuable." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tracker import db\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "db.setup_session()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Engine(postgres://bot:***@whitelist.cbn1zk7uet6r.eu-west-1.rds.amazonaws.com:5432/whitelist)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.Session.get_bind()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to create a table of track, date, popularity" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "google_api_key = 'AIzaSyBF9IkHkhlBQ4QINkPZYNerNQ43FD1JKGI'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def dfquery(sql):\n", " return pd.read_sql(sql, con=db.Session.get_bind())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "spotify_query = \"\"\"\n", "with\n", "\n", "all_popularity as (\n", " select\n", " track_spyid,\n", " json_agg(\n", " ('['||current_date::date - as_of::date||','||value||']')::json order by as_of) vals\n", " from spy_track_popularity\n", " group by 1\n", "),\n", "\n", "playlists as (\n", " select track_spyid, json_agg(distinct playlist_spyid) lists\n", " from spy_playlist_track\n", " group by 1\n", ")\n", "\n", "select\n", " spy_tracks.spyid,\n", " spy_tracks.data->'name' tune,\n", " spy_tracks.data->'artists'->0->'name' artist,\n", " spy_tracks.data->'external_urls'->>'spotify' link,\n", " all_popularity.vals,\n", " current_date as_of,\n", " spy_tracks.first_seen,\n", " playlists.lists,\n", " coalesce(spy_tracks.tags, '[]'::jsonb) as \"track_tags\",\n", " coalesce(sa.tags, '[]'::jsonb) as \"artist_tags\",\n", " coalesce(sa.data->'genres', '[]'::jsonb) as \"artist_genres\",\n", " coalesce((sa.data->'followers'->>'total')::int, 0) as \"artist_followers\",\n", " spy_tracks.album_data->'label' as label_name,\n", " spy_tracks.album_data->'copyrights' as copyrights\n", "\n", "from spy_tracks\n", "join all_popularity on all_popularity.track_spyid = spy_tracks.spyid\n", "join playlists on playlists.track_spyid = spy_tracks.spyid\n", "left join spy_artists sa on sa.spyid = spy_tracks.primary_artist_spyid\n", "order by 6 desc\n", "limit 10000\n", "\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df = pd.read_sql(\"\"\"\n", "select \n", "* \n", "from spy_track_popularity \n", "where as_of < '2016-11-15'\n", "\n", "\"\"\", con=db.Session.get_bind())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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as_of2016-10-16 18:00:002016-10-17 16:00:002016-10-18 18:00:002016-10-19 16:00:002016-10-19 17:00:002016-10-20 20:00:002016-10-22 10:00:002016-10-22 11:00:002016-10-25 07:00:002016-10-28 08:00:00...2016-11-05 08:00:002016-11-06 08:00:002016-11-07 08:00:002016-11-08 08:00:002016-11-09 08:00:002016-11-10 08:00:002016-11-11 08:00:002016-11-12 08:00:002016-11-13 08:00:002016-11-14 08:00:00
track_spyid
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00BewNzph6p90JLNda4s7ENaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...48.048.048.048.048.048.048.048.048.048.0
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\n", "7zv8Fey2BaVJe0PomkkA16 NaN NaN \n", "\n", "as_of 2016-10-22 10:00:00 2016-10-22 11:00:00 \\\n", "track_spyid \n", "001BVhvaZTf2icV88rU3DA NaN NaN \n", "00BewNzph6p90JLNda4s7E NaN NaN \n", "01vXW7N434QDiktrEYr8OL NaN NaN \n", "02pxstqdIA3YAkGkv1Enxc NaN NaN \n", "03GwrTwkp5dC2DereUpPec NaN NaN \n", "03NSrtpqfPPnatooKm9II3 NaN NaN \n", "03qjALA7Esk2oUdQafLksU 51.0 NaN \n", "04AYgtKFrpldRXe5y0LNGQ NaN NaN \n", "06BpFi5mGe9vsqs4yM0dBe 0.0 NaN \n", "073A1FsNWqMxmdcRMeU57t NaN NaN \n", "073wmqmf5KfrQiNukyuqrq NaN NaN \n", "08JEs7dvDNb1ZsCTno1iHt NaN NaN \n", "09ReKDctK8PpRx9Kbh0Iry NaN NaN \n", "09UQu1bR1GnhkRH1gAO6gw 0.0 NaN \n", "09Z3CrRmReQLt4XH3OFBc6 NaN NaN \n", "09kSgjUwANILn4Hgy75cuE 36.0 NaN \n", "0AKejOKlGdiB53QpwAeenO 77.0 NaN \n", "0AvaN1Y5bv0C0xrDeDZjDh NaN NaN \n", "0B8B8cVRFIG1yznoQe7c9s NaN NaN \n", "0BJNiNpYtqjhEvLS8OEwLZ NaN NaN \n", "0BxAfKAWEtt4rw4dYhL6pA NaN NaN \n", "0C3U6XcIrIuYP3J0ZNLCiY NaN NaN \n", "0C5SPGlJkVNcAEwTQ6jYDE NaN NaN \n", "0CAfugmbafMWjFiBy8bdEf NaN NaN \n", "0CDiPsPxx1Xtg5t3d3yoUW NaN NaN \n", "0CgErl5Gg9GzmlEjEhukGU NaN NaN \n", "0D9fggHpYQ7HvgGKqtIKDf NaN NaN \n", "0DPXH5sPDVwjsqfgvmi1yt NaN NaN \n", "0F6qnU0F2IXTPjWAx1SKYQ NaN NaN \n", "0FwXYnBXPFaHyeG7EOjVt5 NaN NaN \n", "... ... ... \n", "7nHWAqCYnCFSZ151t1VuC8 40.0 NaN \n", "7o4WfzYXQA1zgguOVdsF6i NaN NaN \n", "7omcWmDwbwr6BahKoFyVTe NaN NaN \n", "7pZsJo9EVCNQgoIAZDuBNM NaN 48.0 \n", "7pe2OKY04MW1Cunyqrai83 NaN NaN \n", "7qDQhmvXb27hqKQxZ49GYr NaN NaN \n", "7qdgz117gc5StS0u2ViinE NaN NaN \n", "7qlshS5hOQMXd16RSU5TAA NaN NaN \n", "7qnzCgRJbl5DlQDqzsPkrG NaN NaN \n", "7qvMtWolAWBqEpAZMiS6JG NaN NaN \n", "7rdSgFebCeIdwpfqbUUOjy NaN NaN \n", "7re2V782TU8hOtfUt95728 36.0 NaN \n", "7sXg1VmdnM1Ho3aJPya5SC NaN NaN \n", "7szPJUFmkHGAFpg9WnahxV NaN NaN \n", "7t3q4ghl6rphMXn0oOJhUr 0.0 NaN \n", "7t6GSh0B5sI38sgwpTjrT8 NaN NaN \n", "7tMbZ0RGW0jFsBw7ipEPEV NaN NaN \n", "7tW9pGcZoNgnLKCyQX3Jva NaN NaN \n", "7uPZsTJJVdVrf9A9XTxNFP NaN NaN \n", "7uRBKpxTYDLRTJdYGjRgTr NaN NaN \n", "7vL1M0BHhpjbaKrwRimJ4L NaN NaN \n", "7wDVkMbtB7JFKiC7W9E69T NaN NaN \n", "7wmtlHacG4UFPiihCrXW7K 0.0 NaN \n", "7x2E5ZVHE87KYiPhXG9ga4 NaN 42.0 \n", "7xKqy0J23zlKjwp9CR5Kpc NaN NaN \n", "7xQLWdYlaZXQwegmt4bYNQ NaN NaN \n", "7xYpTcnzikKWSVRJI6GAlu NaN NaN \n", "7yopguXbVXyO8RtrVau2sy 37.0 NaN \n", "7zqhUgWpWzwYnT9eewPJEK NaN NaN \n", "7zv8Fey2BaVJe0PomkkA16 NaN NaN \n", "\n", "as_of 2016-10-25 07:00:00 2016-10-28 08:00:00 \\\n", "track_spyid \n", "001BVhvaZTf2icV88rU3DA NaN NaN \n", "00BewNzph6p90JLNda4s7E NaN NaN \n", "01vXW7N434QDiktrEYr8OL NaN NaN \n", "02pxstqdIA3YAkGkv1Enxc NaN NaN \n", "03GwrTwkp5dC2DereUpPec NaN NaN \n", "03NSrtpqfPPnatooKm9II3 NaN NaN \n", "03qjALA7Esk2oUdQafLksU 53.0 54.0 \n", "04AYgtKFrpldRXe5y0LNGQ NaN NaN \n", "06BpFi5mGe9vsqs4yM0dBe 2.0 3.0 \n", "073A1FsNWqMxmdcRMeU57t NaN NaN \n", "073wmqmf5KfrQiNukyuqrq NaN NaN \n", "08JEs7dvDNb1ZsCTno1iHt NaN NaN \n", "09ReKDctK8PpRx9Kbh0Iry NaN NaN \n", "09UQu1bR1GnhkRH1gAO6gw 47.0 53.0 \n", "09Z3CrRmReQLt4XH3OFBc6 NaN NaN \n", "09kSgjUwANILn4Hgy75cuE 38.0 39.0 \n", "0AKejOKlGdiB53QpwAeenO 79.0 81.0 \n", "0AvaN1Y5bv0C0xrDeDZjDh NaN NaN \n", "0B8B8cVRFIG1yznoQe7c9s NaN NaN \n", "0BJNiNpYtqjhEvLS8OEwLZ NaN NaN \n", "0BxAfKAWEtt4rw4dYhL6pA NaN NaN \n", "0C3U6XcIrIuYP3J0ZNLCiY NaN NaN \n", "0C5SPGlJkVNcAEwTQ6jYDE NaN NaN \n", "0CAfugmbafMWjFiBy8bdEf NaN NaN \n", "0CDiPsPxx1Xtg5t3d3yoUW NaN NaN \n", "0CgErl5Gg9GzmlEjEhukGU NaN NaN \n", "0D9fggHpYQ7HvgGKqtIKDf NaN NaN \n", "0DPXH5sPDVwjsqfgvmi1yt NaN NaN \n", "0F6qnU0F2IXTPjWAx1SKYQ NaN NaN \n", "0FwXYnBXPFaHyeG7EOjVt5 NaN NaN \n", "... ... ... \n", "7nHWAqCYnCFSZ151t1VuC8 40.0 41.0 \n", "7o4WfzYXQA1zgguOVdsF6i NaN 0.0 \n", "7omcWmDwbwr6BahKoFyVTe NaN NaN \n", "7pZsJo9EVCNQgoIAZDuBNM 48.0 49.0 \n", "7pe2OKY04MW1Cunyqrai83 NaN NaN \n", "7qDQhmvXb27hqKQxZ49GYr NaN NaN \n", "7qdgz117gc5StS0u2ViinE NaN NaN \n", "7qlshS5hOQMXd16RSU5TAA NaN NaN \n", "7qnzCgRJbl5DlQDqzsPkrG NaN NaN \n", "7qvMtWolAWBqEpAZMiS6JG NaN NaN \n", "7rdSgFebCeIdwpfqbUUOjy NaN NaN \n", "7re2V782TU8hOtfUt95728 38.0 39.0 \n", "7sXg1VmdnM1Ho3aJPya5SC NaN NaN \n", "7szPJUFmkHGAFpg9WnahxV 42.0 47.0 \n", "7t3q4ghl6rphMXn0oOJhUr 50.0 59.0 \n", "7t6GSh0B5sI38sgwpTjrT8 NaN NaN \n", "7tMbZ0RGW0jFsBw7ipEPEV NaN NaN \n", "7tW9pGcZoNgnLKCyQX3Jva NaN NaN \n", "7uPZsTJJVdVrf9A9XTxNFP NaN NaN \n", "7uRBKpxTYDLRTJdYGjRgTr NaN NaN \n", "7vL1M0BHhpjbaKrwRimJ4L 0.0 58.0 \n", "7wDVkMbtB7JFKiC7W9E69T NaN NaN \n", "7wmtlHacG4UFPiihCrXW7K 45.0 49.0 \n", "7x2E5ZVHE87KYiPhXG9ga4 42.0 41.0 \n", "7xKqy0J23zlKjwp9CR5Kpc NaN NaN \n", "7xQLWdYlaZXQwegmt4bYNQ NaN NaN \n", "7xYpTcnzikKWSVRJI6GAlu NaN 45.0 \n", "7yopguXbVXyO8RtrVau2sy 39.0 40.0 \n", "7zqhUgWpWzwYnT9eewPJEK NaN NaN \n", "7zv8Fey2BaVJe0PomkkA16 52.0 59.0 \n", "\n", "as_of ... 2016-11-05 08:00:00 \\\n", "track_spyid ... \n", "001BVhvaZTf2icV88rU3DA ... NaN \n", "00BewNzph6p90JLNda4s7E ... 48.0 \n", "01vXW7N434QDiktrEYr8OL ... 26.0 \n", "02pxstqdIA3YAkGkv1Enxc ... NaN \n", "03GwrTwkp5dC2DereUpPec ... 48.0 \n", "03NSrtpqfPPnatooKm9II3 ... NaN \n", "03qjALA7Esk2oUdQafLksU ... 57.0 \n", "04AYgtKFrpldRXe5y0LNGQ ... NaN \n", "06BpFi5mGe9vsqs4yM0dBe ... 5.0 \n", "073A1FsNWqMxmdcRMeU57t ... 59.0 \n", "073wmqmf5KfrQiNukyuqrq ... NaN \n", "08JEs7dvDNb1ZsCTno1iHt ... NaN \n", "09ReKDctK8PpRx9Kbh0Iry ... 47.0 \n", "09UQu1bR1GnhkRH1gAO6gw ... 57.0 \n", "09Z3CrRmReQLt4XH3OFBc6 ... 12.0 \n", "09kSgjUwANILn4Hgy75cuE ... 37.0 \n", "0AKejOKlGdiB53QpwAeenO ... 84.0 \n", "0AvaN1Y5bv0C0xrDeDZjDh ... 48.0 \n", "0B8B8cVRFIG1yznoQe7c9s ... 0.0 \n", "0BJNiNpYtqjhEvLS8OEwLZ ... 0.0 \n", "0BxAfKAWEtt4rw4dYhL6pA ... 39.0 \n", "0C3U6XcIrIuYP3J0ZNLCiY ... 48.0 \n", "0C5SPGlJkVNcAEwTQ6jYDE ... NaN \n", "0CAfugmbafMWjFiBy8bdEf ... 0.0 \n", "0CDiPsPxx1Xtg5t3d3yoUW ... 28.0 \n", "0CgErl5Gg9GzmlEjEhukGU ... NaN \n", "0D9fggHpYQ7HvgGKqtIKDf ... NaN \n", "0DPXH5sPDVwjsqfgvmi1yt ... NaN \n", "0F6qnU0F2IXTPjWAx1SKYQ ... 43.0 \n", "0FwXYnBXPFaHyeG7EOjVt5 ... 40.0 \n", "... ... ... \n", "7nHWAqCYnCFSZ151t1VuC8 ... 39.0 \n", "7o4WfzYXQA1zgguOVdsF6i ... 48.0 \n", "7omcWmDwbwr6BahKoFyVTe ... NaN \n", "7pZsJo9EVCNQgoIAZDuBNM ... 50.0 \n", "7pe2OKY04MW1Cunyqrai83 ... 59.0 \n", "7qDQhmvXb27hqKQxZ49GYr ... 53.0 \n", "7qdgz117gc5StS0u2ViinE ... 53.0 \n", "7qlshS5hOQMXd16RSU5TAA ... NaN \n", "7qnzCgRJbl5DlQDqzsPkrG ... 33.0 \n", "7qvMtWolAWBqEpAZMiS6JG ... 37.0 \n", "7rdSgFebCeIdwpfqbUUOjy ... 0.0 \n", "7re2V782TU8hOtfUt95728 ... 38.0 \n", "7sXg1VmdnM1Ho3aJPya5SC ... NaN \n", "7szPJUFmkHGAFpg9WnahxV ... 47.0 \n", "7t3q4ghl6rphMXn0oOJhUr ... 62.0 \n", "7t6GSh0B5sI38sgwpTjrT8 ... 57.0 \n", "7tMbZ0RGW0jFsBw7ipEPEV ... 0.0 \n", "7tW9pGcZoNgnLKCyQX3Jva ... 58.0 \n", "7uPZsTJJVdVrf9A9XTxNFP ... 50.0 \n", "7uRBKpxTYDLRTJdYGjRgTr ... 39.0 \n", "7vL1M0BHhpjbaKrwRimJ4L ... 73.0 \n", "7wDVkMbtB7JFKiC7W9E69T ... 7.0 \n", "7wmtlHacG4UFPiihCrXW7K ... 56.0 \n", "7x2E5ZVHE87KYiPhXG9ga4 ... 39.0 \n", "7xKqy0J23zlKjwp9CR5Kpc ... 49.0 \n", "7xQLWdYlaZXQwegmt4bYNQ ... 13.0 \n", "7xYpTcnzikKWSVRJI6GAlu ... 56.0 \n", "7yopguXbVXyO8RtrVau2sy ... 38.0 \n", "7zqhUgWpWzwYnT9eewPJEK ... 56.0 \n", "7zv8Fey2BaVJe0PomkkA16 ... 64.0 \n", "\n", "as_of 2016-11-06 08:00:00 2016-11-07 08:00:00 \\\n", "track_spyid \n", "001BVhvaZTf2icV88rU3DA NaN NaN \n", "00BewNzph6p90JLNda4s7E 48.0 48.0 \n", "01vXW7N434QDiktrEYr8OL 26.0 26.0 \n", "02pxstqdIA3YAkGkv1Enxc NaN NaN \n", "03GwrTwkp5dC2DereUpPec 49.0 49.0 \n", "03NSrtpqfPPnatooKm9II3 NaN NaN \n", "03qjALA7Esk2oUdQafLksU 57.0 57.0 \n", "04AYgtKFrpldRXe5y0LNGQ NaN 57.0 \n", "06BpFi5mGe9vsqs4yM0dBe 4.0 4.0 \n", "073A1FsNWqMxmdcRMeU57t 60.0 60.0 \n", "073wmqmf5KfrQiNukyuqrq NaN NaN \n", "08JEs7dvDNb1ZsCTno1iHt NaN 62.0 \n", "09ReKDctK8PpRx9Kbh0Iry 47.0 47.0 \n", "09UQu1bR1GnhkRH1gAO6gw 57.0 57.0 \n", "09Z3CrRmReQLt4XH3OFBc6 12.0 12.0 \n", "09kSgjUwANILn4Hgy75cuE 37.0 37.0 \n", "0AKejOKlGdiB53QpwAeenO 85.0 85.0 \n", "0AvaN1Y5bv0C0xrDeDZjDh 48.0 48.0 \n", "0B8B8cVRFIG1yznoQe7c9s 30.0 30.0 \n", "0BJNiNpYtqjhEvLS8OEwLZ 13.0 13.0 \n", "0BxAfKAWEtt4rw4dYhL6pA 41.0 41.0 \n", "0C3U6XcIrIuYP3J0ZNLCiY 48.0 48.0 \n", "0C5SPGlJkVNcAEwTQ6jYDE NaN NaN \n", "0CAfugmbafMWjFiBy8bdEf 0.0 0.0 \n", "0CDiPsPxx1Xtg5t3d3yoUW 28.0 28.0 \n", "0CgErl5Gg9GzmlEjEhukGU NaN NaN \n", "0D9fggHpYQ7HvgGKqtIKDf NaN NaN \n", "0DPXH5sPDVwjsqfgvmi1yt NaN NaN \n", "0F6qnU0F2IXTPjWAx1SKYQ 44.0 44.0 \n", "0FwXYnBXPFaHyeG7EOjVt5 40.0 40.0 \n", "... ... ... \n", "7nHWAqCYnCFSZ151t1VuC8 39.0 39.0 \n", "7o4WfzYXQA1zgguOVdsF6i 48.0 48.0 \n", "7omcWmDwbwr6BahKoFyVTe 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"7vL1M0BHhpjbaKrwRimJ4L 75.0 75.0 \n", "7wDVkMbtB7JFKiC7W9E69T 37.0 40.0 \n", "7wmtlHacG4UFPiihCrXW7K 58.0 58.0 \n", "7x2E5ZVHE87KYiPhXG9ga4 37.0 37.0 \n", "7xKqy0J23zlKjwp9CR5Kpc 51.0 52.0 \n", "7xQLWdYlaZXQwegmt4bYNQ 49.0 51.0 \n", "7xYpTcnzikKWSVRJI6GAlu 57.0 57.0 \n", "7yopguXbVXyO8RtrVau2sy 37.0 37.0 \n", "7zqhUgWpWzwYnT9eewPJEK 55.0 55.0 \n", "7zv8Fey2BaVJe0PomkkA16 66.0 67.0 \n", "\n", "as_of 2016-11-14 08:00:00 \n", "track_spyid \n", "001BVhvaZTf2icV88rU3DA 42.0 \n", "00BewNzph6p90JLNda4s7E 48.0 \n", "01vXW7N434QDiktrEYr8OL 29.0 \n", "02pxstqdIA3YAkGkv1Enxc 0.0 \n", "03GwrTwkp5dC2DereUpPec 52.0 \n", "03NSrtpqfPPnatooKm9II3 0.0 \n", "03qjALA7Esk2oUdQafLksU 59.0 \n", "04AYgtKFrpldRXe5y0LNGQ 70.0 \n", "06BpFi5mGe9vsqs4yM0dBe 4.0 \n", "073A1FsNWqMxmdcRMeU57t 62.0 \n", "073wmqmf5KfrQiNukyuqrq 0.0 \n", "08JEs7dvDNb1ZsCTno1iHt 66.0 \n", "09ReKDctK8PpRx9Kbh0Iry 46.0 \n", "09UQu1bR1GnhkRH1gAO6gw 59.0 \n", "09Z3CrRmReQLt4XH3OFBc6 13.0 \n", "09kSgjUwANILn4Hgy75cuE 36.0 \n", 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"7t6GSh0B5sI38sgwpTjrT8 57.0 \n", "7tMbZ0RGW0jFsBw7ipEPEV 48.0 \n", "7tW9pGcZoNgnLKCyQX3Jva 58.0 \n", "7uPZsTJJVdVrf9A9XTxNFP 49.0 \n", "7uRBKpxTYDLRTJdYGjRgTr 40.0 \n", "7vL1M0BHhpjbaKrwRimJ4L 75.0 \n", "7wDVkMbtB7JFKiC7W9E69T 40.0 \n", "7wmtlHacG4UFPiihCrXW7K 58.0 \n", "7x2E5ZVHE87KYiPhXG9ga4 37.0 \n", "7xKqy0J23zlKjwp9CR5Kpc 52.0 \n", "7xQLWdYlaZXQwegmt4bYNQ 51.0 \n", "7xYpTcnzikKWSVRJI6GAlu 57.0 \n", "7yopguXbVXyO8RtrVau2sy 37.0 \n", "7zqhUgWpWzwYnT9eewPJEK 55.0 \n", "7zv8Fey2BaVJe0PomkkA16 67.0 \n", "\n", "[979 rows x 28 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.pivot(index='track_spyid', columns='as_of', values='value')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('track_spyid')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "san_holo = pd.read_sql(\"\"\"select * from spy_track_popularity where track_spyid = '74Ru27B7Jx8mBt5MGvGLLv'\"\"\", con=db.Session.get_bind())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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track_spyidas_ofvalue
074Ru27B7Jx8mBt5MGvGLLv2016-11-25 08:00:0035
174Ru27B7Jx8mBt5MGvGLLv2016-11-26 08:00:0044
274Ru27B7Jx8mBt5MGvGLLv2016-11-27 08:00:0048
374Ru27B7Jx8mBt5MGvGLLv2016-11-28 08:00:0054
474Ru27B7Jx8mBt5MGvGLLv2016-11-29 08:00:0056
574Ru27B7Jx8mBt5MGvGLLv2016-11-30 08:00:0056
674Ru27B7Jx8mBt5MGvGLLv2016-12-01 08:00:0059
774Ru27B7Jx8mBt5MGvGLLv2016-12-02 08:00:0060
874Ru27B7Jx8mBt5MGvGLLv2016-12-03 08:00:0061
974Ru27B7Jx8mBt5MGvGLLv2016-12-04 08:00:0062
1074Ru27B7Jx8mBt5MGvGLLv2016-12-05 08:00:0065
1174Ru27B7Jx8mBt5MGvGLLv2016-12-06 08:00:0066
1274Ru27B7Jx8mBt5MGvGLLv2016-12-07 08:00:0067
1374Ru27B7Jx8mBt5MGvGLLv2016-12-08 08:00:0068
1474Ru27B7Jx8mBt5MGvGLLv2016-12-09 08:00:0068
1574Ru27B7Jx8mBt5MGvGLLv2016-12-10 08:00:0068
1674Ru27B7Jx8mBt5MGvGLLv2016-12-12 08:00:0071
1774Ru27B7Jx8mBt5MGvGLLv2016-12-12 09:00:0071
1874Ru27B7Jx8mBt5MGvGLLv2016-12-13 08:00:0072
1974Ru27B7Jx8mBt5MGvGLLv2016-12-14 08:00:0073
2074Ru27B7Jx8mBt5MGvGLLv2016-12-16 08:00:0074
2174Ru27B7Jx8mBt5MGvGLLv2016-12-17 08:00:0075
2274Ru27B7Jx8mBt5MGvGLLv2016-12-18 08:00:0076
2374Ru27B7Jx8mBt5MGvGLLv2016-12-20 03:00:0077
2474Ru27B7Jx8mBt5MGvGLLv2016-12-21 03:00:0078
2574Ru27B7Jx8mBt5MGvGLLv2016-12-22 03:00:0078
2674Ru27B7Jx8mBt5MGvGLLv2016-12-23 03:00:0078
2774Ru27B7Jx8mBt5MGvGLLv2016-12-24 03:00:0079
2874Ru27B7Jx8mBt5MGvGLLv2016-12-25 03:00:0080
2974Ru27B7Jx8mBt5MGvGLLv2016-12-26 03:00:0080
............
4974Ru27B7Jx8mBt5MGvGLLv2017-01-15 03:00:0084
5074Ru27B7Jx8mBt5MGvGLLv2017-01-16 03:00:0085
5174Ru27B7Jx8mBt5MGvGLLv2017-01-17 03:00:0085
5274Ru27B7Jx8mBt5MGvGLLv2017-01-18 03:00:0085
5374Ru27B7Jx8mBt5MGvGLLv2017-01-19 03:00:0085
5474Ru27B7Jx8mBt5MGvGLLv2017-01-20 03:00:0085
5574Ru27B7Jx8mBt5MGvGLLv2017-01-21 03:00:0085
5674Ru27B7Jx8mBt5MGvGLLv2017-01-22 03:00:0085
5774Ru27B7Jx8mBt5MGvGLLv2017-01-23 03:00:0084
5874Ru27B7Jx8mBt5MGvGLLv2017-01-24 03:00:0084
5974Ru27B7Jx8mBt5MGvGLLv2017-01-25 03:00:0084
6074Ru27B7Jx8mBt5MGvGLLv2017-01-26 03:00:0084
6174Ru27B7Jx8mBt5MGvGLLv2017-01-27 03:00:0083
6274Ru27B7Jx8mBt5MGvGLLv2017-01-28 03:00:0083
6374Ru27B7Jx8mBt5MGvGLLv2017-01-29 03:00:0083
6474Ru27B7Jx8mBt5MGvGLLv2017-01-30 03:00:0083
6574Ru27B7Jx8mBt5MGvGLLv2017-01-31 03:00:0083
6674Ru27B7Jx8mBt5MGvGLLv2017-02-01 03:00:0083
6774Ru27B7Jx8mBt5MGvGLLv2017-02-02 03:00:0083
6874Ru27B7Jx8mBt5MGvGLLv2017-02-03 03:00:0083
6974Ru27B7Jx8mBt5MGvGLLv2017-02-04 03:00:0083
7074Ru27B7Jx8mBt5MGvGLLv2017-02-05 03:00:0082
7174Ru27B7Jx8mBt5MGvGLLv2017-02-06 03:00:0082
7274Ru27B7Jx8mBt5MGvGLLv2017-02-07 03:00:0082
7374Ru27B7Jx8mBt5MGvGLLv2017-02-08 03:00:0082
7474Ru27B7Jx8mBt5MGvGLLv2017-02-08 22:00:0082
7574Ru27B7Jx8mBt5MGvGLLv2017-02-09 03:00:0082
7674Ru27B7Jx8mBt5MGvGLLv2017-02-10 03:00:0082
7774Ru27B7Jx8mBt5MGvGLLv2017-02-11 03:00:0082
7874Ru27B7Jx8mBt5MGvGLLv2017-02-12 03:00:0082
\n", "

79 rows × 3 columns

\n", "
" ], "text/plain": [ " track_spyid as_of value\n", "0 74Ru27B7Jx8mBt5MGvGLLv 2016-11-25 08:00:00 35\n", "1 74Ru27B7Jx8mBt5MGvGLLv 2016-11-26 08:00:00 44\n", "2 74Ru27B7Jx8mBt5MGvGLLv 2016-11-27 08:00:00 48\n", "3 74Ru27B7Jx8mBt5MGvGLLv 2016-11-28 08:00:00 54\n", "4 74Ru27B7Jx8mBt5MGvGLLv 2016-11-29 08:00:00 56\n", "5 74Ru27B7Jx8mBt5MGvGLLv 2016-11-30 08:00:00 56\n", "6 74Ru27B7Jx8mBt5MGvGLLv 2016-12-01 08:00:00 59\n", "7 74Ru27B7Jx8mBt5MGvGLLv 2016-12-02 08:00:00 60\n", "8 74Ru27B7Jx8mBt5MGvGLLv 2016-12-03 08:00:00 61\n", "9 74Ru27B7Jx8mBt5MGvGLLv 2016-12-04 08:00:00 62\n", "10 74Ru27B7Jx8mBt5MGvGLLv 2016-12-05 08:00:00 65\n", "11 74Ru27B7Jx8mBt5MGvGLLv 2016-12-06 08:00:00 66\n", "12 74Ru27B7Jx8mBt5MGvGLLv 2016-12-07 08:00:00 67\n", "13 74Ru27B7Jx8mBt5MGvGLLv 2016-12-08 08:00:00 68\n", "14 74Ru27B7Jx8mBt5MGvGLLv 2016-12-09 08:00:00 68\n", "15 74Ru27B7Jx8mBt5MGvGLLv 2016-12-10 08:00:00 68\n", "16 74Ru27B7Jx8mBt5MGvGLLv 2016-12-12 08:00:00 71\n", "17 74Ru27B7Jx8mBt5MGvGLLv 2016-12-12 09:00:00 71\n", "18 74Ru27B7Jx8mBt5MGvGLLv 2016-12-13 08:00:00 72\n", "19 74Ru27B7Jx8mBt5MGvGLLv 2016-12-14 08:00:00 73\n", "20 74Ru27B7Jx8mBt5MGvGLLv 2016-12-16 08:00:00 74\n", "21 74Ru27B7Jx8mBt5MGvGLLv 2016-12-17 08:00:00 75\n", "22 74Ru27B7Jx8mBt5MGvGLLv 2016-12-18 08:00:00 76\n", "23 74Ru27B7Jx8mBt5MGvGLLv 2016-12-20 03:00:00 77\n", "24 74Ru27B7Jx8mBt5MGvGLLv 2016-12-21 03:00:00 78\n", "25 74Ru27B7Jx8mBt5MGvGLLv 2016-12-22 03:00:00 78\n", "26 74Ru27B7Jx8mBt5MGvGLLv 2016-12-23 03:00:00 78\n", "27 74Ru27B7Jx8mBt5MGvGLLv 2016-12-24 03:00:00 79\n", "28 74Ru27B7Jx8mBt5MGvGLLv 2016-12-25 03:00:00 80\n", "29 74Ru27B7Jx8mBt5MGvGLLv 2016-12-26 03:00:00 80\n", ".. ... ... ...\n", "49 74Ru27B7Jx8mBt5MGvGLLv 2017-01-15 03:00:00 84\n", "50 74Ru27B7Jx8mBt5MGvGLLv 2017-01-16 03:00:00 85\n", "51 74Ru27B7Jx8mBt5MGvGLLv 2017-01-17 03:00:00 85\n", "52 74Ru27B7Jx8mBt5MGvGLLv 2017-01-18 03:00:00 85\n", "53 74Ru27B7Jx8mBt5MGvGLLv 2017-01-19 03:00:00 85\n", "54 74Ru27B7Jx8mBt5MGvGLLv 2017-01-20 03:00:00 85\n", "55 74Ru27B7Jx8mBt5MGvGLLv 2017-01-21 03:00:00 85\n", "56 74Ru27B7Jx8mBt5MGvGLLv 2017-01-22 03:00:00 85\n", "57 74Ru27B7Jx8mBt5MGvGLLv 2017-01-23 03:00:00 84\n", "58 74Ru27B7Jx8mBt5MGvGLLv 2017-01-24 03:00:00 84\n", "59 74Ru27B7Jx8mBt5MGvGLLv 2017-01-25 03:00:00 84\n", "60 74Ru27B7Jx8mBt5MGvGLLv 2017-01-26 03:00:00 84\n", "61 74Ru27B7Jx8mBt5MGvGLLv 2017-01-27 03:00:00 83\n", "62 74Ru27B7Jx8mBt5MGvGLLv 2017-01-28 03:00:00 83\n", "63 74Ru27B7Jx8mBt5MGvGLLv 2017-01-29 03:00:00 83\n", "64 74Ru27B7Jx8mBt5MGvGLLv 2017-01-30 03:00:00 83\n", "65 74Ru27B7Jx8mBt5MGvGLLv 2017-01-31 03:00:00 83\n", "66 74Ru27B7Jx8mBt5MGvGLLv 2017-02-01 03:00:00 83\n", "67 74Ru27B7Jx8mBt5MGvGLLv 2017-02-02 03:00:00 83\n", "68 74Ru27B7Jx8mBt5MGvGLLv 2017-02-03 03:00:00 83\n", "69 74Ru27B7Jx8mBt5MGvGLLv 2017-02-04 03:00:00 83\n", "70 74Ru27B7Jx8mBt5MGvGLLv 2017-02-05 03:00:00 82\n", "71 74Ru27B7Jx8mBt5MGvGLLv 2017-02-06 03:00:00 82\n", "72 74Ru27B7Jx8mBt5MGvGLLv 2017-02-07 03:00:00 82\n", "73 74Ru27B7Jx8mBt5MGvGLLv 2017-02-08 03:00:00 82\n", "74 74Ru27B7Jx8mBt5MGvGLLv 2017-02-08 22:00:00 82\n", "75 74Ru27B7Jx8mBt5MGvGLLv 2017-02-09 03:00:00 82\n", "76 74Ru27B7Jx8mBt5MGvGLLv 2017-02-10 03:00:00 82\n", "77 74Ru27B7Jx8mBt5MGvGLLv 2017-02-11 03:00:00 82\n", "78 74Ru27B7Jx8mBt5MGvGLLv 2017-02-12 03:00:00 82\n", "\n", "[79 rows x 3 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "san_holo" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "san_holo.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin our study using 'San Holo - Light' a track that we signed, and is doing well" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sh_id = '74Ru27B7Jx8mBt5MGvGLLv'" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0
spyid74Ru27B7Jx8mBt5MGvGLLv
nameLight
first_seen2016-11-25 08:19:26.845269
added_sourceNone
last_updated2017-02-12 03:00:16.185152
data{'duration_ms': 240500, 'external_ids': {'isrc...
tags[signed]
primary_artist_spyid0jNDKefhfSbLR9sFvcPLHo
album_data{'genres': [], 'external_ids': {'upc': '871885...
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" ], "text/plain": [ " 0\n", "spyid 74Ru27B7Jx8mBt5MGvGLLv\n", "name Light\n", "first_seen 2016-11-25 08:19:26.845269\n", "added_source None\n", "last_updated 2017-02-12 03:00:16.185152\n", "data {'duration_ms': 240500, 'external_ids': {'isrc...\n", "tags [signed]\n", "primary_artist_spyid 0jNDKefhfSbLR9sFvcPLHo\n", "album_data {'genres': [], 'external_ids': {'upc': '871885..." ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfquery(\"\"\"select * from spy_tracks where spyid = '74Ru27B7Jx8mBt5MGvGLLv'\"\"\").T" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameplaylist_spyidtrack_spyidadded_atfirst_seenlast_seenfirst_positionlast_position
0New Music Friday UK6LY8RIt0Wg6IkpJBtxP2xu74Ru27B7Jx8mBt5MGvGLLv2016-11-25 00:05:102016-11-25 08:21:11.9938102016-12-01 08:21:22.1184584747
1Weekly Buzz6Hw1sUOCpE59V7AlIIHhCz74Ru27B7Jx8mBt5MGvGLLv2016-11-28 03:31:392016-11-28 08:21:18.3390232016-12-04 08:21:18.1749343737
2The Needle - Current7xom7CO8dOHxx9fnGwwrnO74Ru27B7Jx8mBt5MGvGLLv2016-12-05 20:11:402016-12-06 08:25:51.8040722016-12-16 08:21:18.1691107758
3Wild Cards03n3Y7ugrETGIdODxD1iFe74Ru27B7Jx8mBt5MGvGLLv2016-12-28 23:12:572016-12-29 03:00:10.9755972017-02-12 03:00:16.19186711
4Cool Tracks6M9bQZk3grXylwzKiTWhe974Ru27B7Jx8mBt5MGvGLLv2016-12-29 02:36:122016-12-29 03:00:18.6013462017-02-12 03:00:09.4345289552
5Tomorrow's Hits6LHwwT5kakVUc7UqUuMLYT74Ru27B7Jx8mBt5MGvGLLv2017-01-11 15:21:292017-01-12 03:00:15.3159812017-01-27 03:00:10.9717371624
6Good Vibes3xgbBiNc7mh3erYsCl8Fwg74Ru27B7Jx8mBt5MGvGLLv2017-02-01 18:07:202017-02-08 22:20:47.9659072017-02-12 03:00:06.8122784112
\n", "
" ], "text/plain": [ " name playlist_spyid track_spyid \\\n", "0 New Music Friday UK 6LY8RIt0Wg6IkpJBtxP2xu 74Ru27B7Jx8mBt5MGvGLLv \n", "1 Weekly Buzz 6Hw1sUOCpE59V7AlIIHhCz 74Ru27B7Jx8mBt5MGvGLLv \n", "2 The Needle - Current 7xom7CO8dOHxx9fnGwwrnO 74Ru27B7Jx8mBt5MGvGLLv \n", "3 Wild Cards 03n3Y7ugrETGIdODxD1iFe 74Ru27B7Jx8mBt5MGvGLLv \n", "4 Cool Tracks 6M9bQZk3grXylwzKiTWhe9 74Ru27B7Jx8mBt5MGvGLLv \n", "5 Tomorrow's Hits 6LHwwT5kakVUc7UqUuMLYT 74Ru27B7Jx8mBt5MGvGLLv \n", "6 Good Vibes 3xgbBiNc7mh3erYsCl8Fwg 74Ru27B7Jx8mBt5MGvGLLv \n", "\n", " added_at first_seen last_seen \\\n", "0 2016-11-25 00:05:10 2016-11-25 08:21:11.993810 2016-12-01 08:21:22.118458 \n", "1 2016-11-28 03:31:39 2016-11-28 08:21:18.339023 2016-12-04 08:21:18.174934 \n", "2 2016-12-05 20:11:40 2016-12-06 08:25:51.804072 2016-12-16 08:21:18.169110 \n", "3 2016-12-28 23:12:57 2016-12-29 03:00:10.975597 2017-02-12 03:00:16.191867 \n", "4 2016-12-29 02:36:12 2016-12-29 03:00:18.601346 2017-02-12 03:00:09.434528 \n", "5 2017-01-11 15:21:29 2017-01-12 03:00:15.315981 2017-01-27 03:00:10.971737 \n", "6 2017-02-01 18:07:20 2017-02-08 22:20:47.965907 2017-02-12 03:00:06.812278 \n", "\n", " first_position last_position \n", "0 47 47 \n", "1 37 37 \n", "2 77 58 \n", "3 1 1 \n", "4 95 52 \n", "5 16 24 \n", "6 41 12 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfquery(\"\"\"\n", "select p.name, pt.* from spy_playlist_track pt join spy_playlist p on pt.playlist_spyid = p.spyid where track_spyid = '74Ru27B7Jx8mBt5MGvGLLv' order by first_seen;\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that San Holo first came in on NMF on 25 Nov. We look for other tracks that were identified then." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "scrolled": false }, "outputs": [], "source": [ "same_tracks = dfquery(\"\"\"\n", "select spyid, name, album_data->'copyrights'->0->'text' as rights from spy_tracks where first_seen::date = '2016-11-25'\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 (C) 2016 Island Records, a division of Univers...\n", "1 2016 Ninja Tune\n", "2 2016 Weird Smiling licensed to Moshi Moshi Rec...\n", "3 (C) 2016 Fiction Records, a division of Univer...\n", "4 (P) 2016 John Legend Music, Inc. and Columbia ...\n", "5 (C) 2016 The Weeknd XO, Inc., Manufactured and...\n", "6 2016 The Van T's, distributed by Spinnup\n", "7 (P) 2016 RCA Records, a division of Sony Music...\n", "8 2016 RecordShop\n", "9 (C) 2016 The Weeknd XO, Inc., Manufactured and...\n", "10 2016 GS Believe\n", "11 2016 Firewheel LLP under exclusive licence to ...\n", "12 2016 Armada Music B.V.\n", "13 While She Sleeps\n", "14 2016 True Say Recordings\n", "15 2016 BMG Rights Management (UK) Ltd.\n", "16 2016 Wiley\n", "17 2016 SHED\n", "18 2016 SKELETON KEY RECORDS\n", "19 (C) 2016 EGA Records\n", "20 (C) 2016 Melodramatic Records Limited, Under E...\n", "21 (C) 2016 Circa Waves, under exclusive license ...\n", "22 2016 Decisive\n", "23 Future Classic\n", "24 2016 LVNA\n", "25 (P) 2016 Parkwood Entertainment LLC, under exc...\n", "26 (C) 2016 DDM Recordings\n", "27 2016 Götterfunk Productions\n", "28 (C) 2016 UMC, a division of Universal Music Op...\n", "29 2016 antigen records\n", "30 2016 Angel Haze\n", "31 2016 Warner Bros Records UK, A Warner Music Gr...\n", "32 (C) 2016 Promotone B.V., under exclusive licen...\n", "33 2016 Artium Recordings\n", "34 2016 Juno Music\n", "35 2016 Lemon Records\n", "36 2016 Rando\n", "37 2016 Velociraptor\n", "38 2016 Disconap AB under exclusive licence to Wa...\n", "39 2016 Honeycomb Records\n", "40 2016 TONSPIEL under exclusive license to Warne...\n", "41 (C) 2016 Capitol Records\n", "42 (C) 2016 The Weeknd XO, Inc., Manufactured and...\n", "43 2016 bitbird\n", "44 (C) 2016 Island Records, a division of Univers...\n", "45 (C) 2016 Universal Studios and Republic Record...\n", "46 Kitsuné Musique\n", "47 2016 Bruno Major\n", "48 2016 MAXXIMIZE / under exclusive license to Sp...\n", "Name: rights, dtype: object" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "same_tracks['rights']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 43 tracks to consider. Let's look at those tracks popularity in filtr" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [], "source": [ " links = [ \"\"\"{0}\"\"\".format(i) for i in same_tracks['spyid'] ]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.core.display import display, HTML" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [], "source": [ " links = [ \"\"\"{0}\"\"\".format(i) for i in same_tracks['spyid'] ]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "5vOfjFB3mf21TCnRVjLOkv
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\".join(links)))" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": false }, "outputs": [], "source": [ " links = [ i for i in same_tracks['rights'] ]" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [], "source": [ " links = [ \"\"\"{0}\"\"\".format(i) for i in same_tracks['spyid'] ]" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(C) 2016 Island Records, a division of Universal Music Operations Limited\n", "2016 Ninja Tune\n", "2016 Weird Smiling licensed to Moshi Moshi Records\n", "(C) 2016 Fiction Records, a division of Universal Music Operations Limited.\n", "(P) 2016 John Legend Music, Inc. and Columbia Records, a division of Sony Music Entertainment\n", "(C) 2016 The Weeknd XO, Inc., Manufactured and Marketed by Republic Records, a Division of UMG Recordings, Inc.\n", "2016 The Van T's, distributed by Spinnup\n", "(P) 2016 RCA Records, a division of Sony Music Entertainment\n", "2016 RecordShop\n", "(C) 2016 The Weeknd XO, Inc., Manufactured and Marketed by Republic Records, a Division of UMG Recordings, Inc.\n", "2016 GS Believe\n", "2016 Firewheel LLP under exclusive licence to Parlophone Records Limited, a Warner Music Group company\n", "2016 Armada Music B.V.\n", "While She Sleeps\n", "2016 True Say Recordings\n", "2016 BMG Rights Management (UK) Ltd.\n", "2016 Wiley\n", "2016 SHED\n", "2016 SKELETON KEY RECORDS\n", "(C) 2016 EGA Records\n", "(C) 2016 Melodramatic Records Limited, Under Exclusive Licence to Mercury Records Limited\n", "(C) 2016 Circa Waves, under exclusive license to Virgin EMI Records, a division of Universal Music Operations Ltd.\n", "2016 Decisive\n", "Future Classic\n", "2016 LVNA\n", "(P) 2016 Parkwood Entertainment LLC, under exclusive license to Columbia Records, a Division of Sony Music Entertainment\n", "(C) 2016 DDM Recordings\n", "2016 Götterfunk Productions\n", "(C) 2016 UMC, a division of Universal Music Operations Ltd.\n", "2016 antigen records\n", "2016 Angel Haze\n", "2016 Warner Bros Records UK, A Warner Music Group company.\n", "(C) 2016 Promotone B.V., under exclusive licence to Universal International Music B.V.\n", "2016 Artium Recordings\n", "2016 Juno Music\n", "2016 Lemon Records\n", "2016 Rando\n", "2016 Velociraptor\n", "2016 Disconap AB under exclusive licence to Warner Music Sweden AB\n", "2016 Honeycomb Records\n", "2016 TONSPIEL under exclusive license to Warner Music Group Germany Holding GmbH / A Warner Music Group Company\n", "(C) 2016 Capitol Records\n", "(C) 2016 The Weeknd XO, Inc., Manufactured and Marketed by Republic Records, a Division of UMG Recordings, Inc.\n", "2016 bitbird\n", "(C) 2016 Island Records, a division of Universal Music Operations Limited\n", "(C) 2016 Universal Studios and Republic Records, a division of UMG Recordings, Inc.\n", "Kitsuné Musique\n", "2016 Bruno Major\n", "2016 MAXXIMIZE / under exclusive license to Spinnin' Records BV\n" ] } ], "source": [ "print(\"\\n\".join(links))" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "r = db.Session.execute(\"\"\"\n", "select spyid, min(spy_artist_metrics.followers), min(as_of) as rights from spy_tracks \n", "join spy_artist_metrics on primary_artist_spyid = artist_spyid\n", "where first_seen::date = '2016-11-25'\n", "group by 1\n", "order by 1 asc\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "scrolled": false }, "outputs": [], "source": [ "data = r.fetchall()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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artist_spyidas_ofpopularityfollowers
03r17AfJCCUqC9Lf0OAc73G2016-11-26 12:00:0072422489
15Z3cYfm62l4JYDIihkXpQc2016-11-26 12:00:0035251
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" ], "text/plain": [ " artist_spyid as_of popularity followers\n", "0 3r17AfJCCUqC9Lf0OAc73G 2016-11-26 12:00:00 72 422489\n", "1 5Z3cYfm62l4JYDIihkXpQc 2016-11-26 12:00:00 35 251\n", "2 2LRoIwlKmHjgvigdNGBHNo 2016-11-26 12:00:00 63 5078\n", "3 4UqXejsLj7uwZmzQ9vkmg8 2016-11-26 12:00:00 47 3941\n", "4 6LuN9FCkKOj5PcnpouEgny 2016-11-26 12:00:00 65 21935\n", "5 5WUlDfRSoLAfcVSX1WnrxN 2016-11-26 12:00:00 92 2699245\n", "6 4nXezpatStChCEWVvYLjd5 2016-11-26 12:00:00 60 50201\n", "7 7mKwhB3UiepqzM946jBOyi 2016-11-26 12:00:00 35 1533\n", "8 7mg37PCD4PY9qHp6OzaTys 2016-11-26 12:00:00 24 132\n", "9 5CYSZBI4BEAeMeiYRiT1Xd 2016-11-26 12:00:00 32 783" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfquery(\"\"\"\n", "select * from spy_artist_metrics limit 10\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3983799\n", "156927\n", "0\n", "4097\n", "228020\n", "11\n", "168389\n", "57951\n", "55134\n", "4998\n", "7539\n", "541583\n", "119490\n", "87817\n", "145\n", "341\n", "346556\n", "75\n", "3766\n", "258991\n", "1416221\n", "61056\n", "1048\n", "370\n", "1767\n", "59972\n", "0\n", "11701\n", "1820342\n", "1110\n", "22\n", "3983799\n", "0\n", "24767\n", "7\n", "3651\n", "0\n", "236\n", "4958\n", "905\n", "76937\n", "26230\n", "1376898\n", "0\n", "881486\n", "8120\n", "0\n", "157850\n", "3983799\n" ] } ], "source": [ "print(\"\\n\".join([ str(r[1]) for r in data ]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('3r17AfJCCUqC9Lf0OAc73G', datetime.datetime(2016, 11, 26, 12, 0), 72, 422489),\n", " ('5Z3cYfm62l4JYDIihkXpQc', datetime.datetime(2016, 11, 26, 12, 0), 35, 251),\n", " ('2LRoIwlKmHjgvigdNGBHNo', datetime.datetime(2016, 11, 26, 12, 0), 63, 5078),\n", " ('4UqXejsLj7uwZmzQ9vkmg8', datetime.datetime(2016, 11, 26, 12, 0), 47, 3941),\n", " ('6LuN9FCkKOj5PcnpouEgny', datetime.datetime(2016, 11, 26, 12, 0), 65, 21935),\n", " ('5WUlDfRSoLAfcVSX1WnrxN', datetime.datetime(2016, 11, 26, 12, 0), 92, 2699245),\n", " ('4nXezpatStChCEWVvYLjd5', datetime.datetime(2016, 11, 26, 12, 0), 60, 50201),\n", " ('7mKwhB3UiepqzM946jBOyi', datetime.datetime(2016, 11, 26, 12, 0), 35, 1533),\n", " ('7mg37PCD4PY9qHp6OzaTys', datetime.datetime(2016, 11, 26, 12, 0), 24, 132),\n", " ('5CYSZBI4BEAeMeiYRiT1Xd', datetime.datetime(2016, 11, 26, 12, 0), 32, 783)]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r.fetchall()" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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as_offollowers
02016-11-26 12:00:005295931
12016-12-04 12:00:000
22016-12-06 00:00:005341350
32016-12-10 00:00:005357894
42016-12-19 12:00:005439030
52016-12-20 00:00:005443582
62016-12-30 00:00:005546940
72017-01-12 00:00:005661029
82017-01-13 00:00:005669910
92017-01-14 00:00:005678854
102017-01-15 00:00:005688718
112017-01-22 00:00:005749603
122017-01-26 00:00:005781526
132017-01-27 00:00:005789283
142017-01-28 00:00:005797720
152017-01-29 00:00:005806964
162017-01-30 00:00:005815989
172017-01-31 00:00:005823495
182017-02-01 00:00:005829634
192017-02-02 00:00:005836374
202017-02-03 00:00:005843836
212017-02-04 00:00:005851304
222017-02-05 00:00:005859638
232017-02-06 00:00:005867593
242017-02-07 00:00:005874396
252017-02-08 00:00:005880787
262017-02-09 00:00:005887471
272017-02-10 00:00:005895744
282017-02-11 00:00:005904606
292017-02-12 00:00:005914787
\n", "
" ], "text/plain": [ " as_of followers\n", "0 2016-11-26 12:00:00 5295931\n", "1 2016-12-04 12:00:00 0\n", "2 2016-12-06 00:00:00 5341350\n", "3 2016-12-10 00:00:00 5357894\n", "4 2016-12-19 12:00:00 5439030\n", "5 2016-12-20 00:00:00 5443582\n", "6 2016-12-30 00:00:00 5546940\n", "7 2017-01-12 00:00:00 5661029\n", "8 2017-01-13 00:00:00 5669910\n", "9 2017-01-14 00:00:00 5678854\n", "10 2017-01-15 00:00:00 5688718\n", "11 2017-01-22 00:00:00 5749603\n", "12 2017-01-26 00:00:00 5781526\n", "13 2017-01-27 00:00:00 5789283\n", "14 2017-01-28 00:00:00 5797720\n", "15 2017-01-29 00:00:00 5806964\n", "16 2017-01-30 00:00:00 5815989\n", "17 2017-01-31 00:00:00 5823495\n", "18 2017-02-01 00:00:00 5829634\n", "19 2017-02-02 00:00:00 5836374\n", "20 2017-02-03 00:00:00 5843836\n", "21 2017-02-04 00:00:00 5851304\n", "22 2017-02-05 00:00:00 5859638\n", "23 2017-02-06 00:00:00 5867593\n", "24 2017-02-07 00:00:00 5874396\n", "25 2017-02-08 00:00:00 5880787\n", "26 2017-02-09 00:00:00 5887471\n", "27 2017-02-10 00:00:00 5895744\n", "28 2017-02-11 00:00:00 5904606\n", "29 2017-02-12 00:00:00 5914787" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfquery(\"\"\"\n", "select as_of, followers \n", "from spy_tracks \n", "join spy_artist_metrics on primary_artist_spyid = artist_spyid\n", "where spyid = '7tTElNyvXsfFcxDXIiH5cm' \n", "order by 1 asc\"\"\")" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "spot_data=dfquery(\"\"\"\n", "with\n", "\n", "\n", "all_popularity as (\n", " select\n", " track_spyid,\n", " json_agg(\n", " value order by as_of) vals\n", " from spy_track_popularity\n", " where as_of < '2016-12-01'\n", " group by 1\n", "),\n", "\n", "playlists as (\n", " select track_spyid, json_agg(distinct playlist_spyid) lists\n", " from spy_playlist_track\n", " group by 1\n", ")\n", "\n", "select\n", " spy_tracks.spyid,\n", " spy_tracks.data->'name' tune,\n", " spy_tracks.data->'artists'->0->'name' artist,\n", " -- spy_tracks.data->'external_urls'->>'spotify' link,\n", " all_popularity.vals\n", " -- current_date as_of,\n", " -- spy_tracks.first_seen,\n", " -- playlists.lists,\n", " -- coalesce(spy_tracks.tags, '[]'::jsonb) as \"track_tags\",\n", " -- coalesce(sa.tags, '[]'::jsonb) as \"artist_tags\",\n", " -- coalesce(sa.data->'genres', '[]'::jsonb) as \"artist_genres\",\n", " -- coalesce((sa.data->'followers'->>'total')::int, 0) as \"artist_followers\",\n", " -- spy_tracks.album_data->'label' as label_name,\n", " -- spy_tracks.album_data->'copyrights' as copyrights\n", "\n", "from spy_tracks\n", "join all_popularity on all_popularity.track_spyid = spy_tracks.spyid\n", "join playlists on playlists.track_spyid = spy_tracks.spyid\n", "left join spy_artists sa on sa.spyid = spy_tracks.primary_artist_spyid\n", "where spy_tracks.first_seen::date = '2016-11-25'\n", "order by spy_tracks.spyid\n", "limit 10000\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "scrolled": false }, "outputs": [], "source": [ "r = spot_data['vals']" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "def stringit(arr):\n", " return \",\".join(str(i) for i in arr)" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0,0,0,64,68,68\n", "36,40,42,47,49,49\n", "0,17,23,39,41,41\n", "0,0,1,40,42,42\n", "0,0,6,47,50,50\n", "32,33,33,33,33,33\n", "0,0,7,46,49,49\n", "0,0,1,38,41,41\n", "0,0,0,5,8,8\n", "0,0,0,35,37,37\n", "0,0,1,40,43,43\n", "0,0,34,64,67,67\n", "0,0,22,48,50,50\n", "0,0,6,45,47,47\n", "12,15,17,34,35,35\n", "0,0,0,36,37,37\n", "27,36,39,50,51,51\n", "0,0,22,40,42,42\n", "0,0,23,37,38,38\n", "0,0,6,36,39,39\n", "0,0,7,54,59,59\n", "36,38,40,43,44,44\n", "0,0,21,45,48,48\n", "0,0,0,33,34,34\n", "0,0,0,35,37,37\n", "0,0,28,55,58,58\n", "0,0,0,41,43,43\n", "19,27,30,41,42,42\n", "0,0,28,51,53,53\n", "0,1,2,36,37,37\n", "2,2,3,33,34,34\n", "0,0,0,68,73,73\n", "0,0,36,48,50,50\n", "0,0,21,44,46,46\n", "0,0,0,34,35,35\n", "0,0,1,40,41,41\n", "0,0,1,41,43,43\n", "25,31,34,40,41,41\n", "19,23,25,39,40,40\n", "0,0,0,34,35,35\n", "35,44,48,54,56,56\n", "0,0,4,49,51,51\n", "0,0,30,58,60,60\n", "0,0,0,39,40,40\n", "0,0,32,44,47,47\n", "0,0,1,3,4,4\n", "46,49,52,61,64,64\n", "0,0,26,56,59,59\n", "0,0,0,66,71,71\n" ] } ], "source": [ "print('\\n'.join(stringit(row) for row in r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }