{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.insert(0, '/Users/joel/src/thundr/tracker/')\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "os.environ.update({\n", " 'UC_MYSQL_HOST': 'unicron.cluster-cbn1zk7uet6r.eu-west-1.rds.amazonaws.com',\n", " 'UC_MYSQL_PASSWORD': 'tydz5cy9xbvchqpa249rfekhcg9sey',\n", "})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy\n", "import requests" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import random\n", "import datetime\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from tracker.unicorn import mysql\n", "from tracker.unicorn.mysql import get_cursor" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def _sql_to_dataframe(sql_statement, columns=None, sql_args=[], index_column=None):\n", " with get_cursor(False) as cur:\n", " cur.execute(sql_statement, sql_args)\n", " rows = cur.fetchall()\n", " columns = [c[0] for c in cur.description] if columns is None else columns\n", " df = pd.DataFrame(data=rows, columns=columns)\n", " if index_column:\n", " df = df.set_index(index_column)\n", " return df\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dfraw = _sql_to_dataframe(\n", " '''select ts, listeners from before_i_go_listeners ''', \n", " columns=['ts', 'listeners'],\n", " index_column='ts'\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfraw.resample('1H').mean().rolling(24).mean().plot(title=\"B4IGO Rolling 24 Hour\", figsize=(10,3))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def smooth(y, box_pts):\n", " box = numpy.ones(box_pts)/box_pts\n", " y_smooth = numpy.convolve(y, box, mode='same')\n", " return y_smooth\n", "\n", "def resample_and_remove_bad_points(dframe):\n", " df1 = dframe.resample('1Min').mean().interpolate('time')\n", " w = 50\n", " df1[f'sm'] = smooth(df1.listeners, w)\n", " df1[f'delt'] = df1.listeners - df1[f'sm']\n", " smoothness = numpy.sqrt((df1.delt ** 2).rolling(10).max() / df1.sm)\n", " return df1\n", "# return df1[(smoothness <= 20) | (df1.index > (df1.index.max() - numpy.timedelta64(30, 'm')))].resample('5Min').mean()\n", "# return df1[\n", "# ((df1.delt ** 2).rolling(10).max() < 1e6) | (df1.index > (df1.index.max() - numpy.timedelta64(30, 'm')))\n", "# ].resample('5Min').mean()\n", "\n", "# excluded_points = df1.copy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dfraw.listeners.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "resampled = resample_and_remove_bad_points(dfraw)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mean = numpy.abs(resampled.delt).mean()\n", "std = numpy.abs(resampled.delt).std()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "smoothed = resampled[numpy.abs(resampled.delt) < (mean + std)].listeners.resample('5Min').mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oops = dfraw.resample('5Min').mean().iloc[-100:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oops.listeners.idxmax(), oops.listeners.idxmin()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "oops.loc[numpy.datetime64(\"2020-04-08 14:00\")]" ] }, { "cell_type": "code", "execution_count": null, "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\n", "\n", "def to_day(dt):\n", " return dt.date()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def pivot_by_days(dframe):\n", " excluded_points = resample_and_remove_bad_points(dframe)\n", " excluded_points['dday'] = excluded_points.index.map(to_day)\n", " excluded_points['tday'] = excluded_points.index.map(to_minutes).astype(int)\n", " return excluded_points.pivot(index='tday', columns='dday', values=['listeners'])\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fmpv2[fmpv2.columns[-1:]].plot(figsize=(10,9))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = numpy.linspace(0,2*numpy.pi,100)\n", "y = numpy.sin(x) + numpy.random.random(100) * 0.8\n", "\n", "\n", "plt.plot(x, y,'o')\n", "plt.plot(x, smooth(y,3), 'r-', lw=2)\n", "plt.plot(x, smooth(y,19), 'g-', lw=2)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1.listeners.plot(figsize=(10,6))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "errors = (df1.del10 ** 2).rolling(5).mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "errors[errors < 1e7].plot(), errors[errors < 1e6].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with get_cursor() as cur:\n", " cur.execute('select ts, listeners from deathbed_listeners limit 10')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[c[0] for c in cur.description]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(df1.index)\n", "df1.loc > numpy.datetime64" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df1.index.max() - numpy.timedelta64(20, 'm')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# YouTube Views" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "ds = _sql_to_dataframe('select as_of, streams from before_i_go_daily_streams')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds.plot()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "yt1 = _sql_to_dataframe('''select ts, views1 + views2, comments1 from before_i_go_youtubes''', columns=['ts', 'views', 'comments'])\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yt1.set_index('ts').resample('1H').max().diff().rolling(24).mean().views.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yt1 = _sql_to_dataframe('''select ts, views1 + views2 + views3, comments1 from skechers_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')\n", "\n", "yt1[['views_per_minute']].resample('1H').mean().iloc[-21*24:].rolling(24).mean().plot(figsize=(10,3), title=\"YouTube Views / Minute\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Error in callback .post_execute at 0x10e46f7b8> (for post_execute):\n" ] }, { "ename": "ValueError", "evalue": "view limit minimum -36877.231166666665 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units", "output_type": "error", "traceback": [ "\u001b[0;31m-------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\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 108\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\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--> 109\u001b[0;31m 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This often happens if you pass a non-datetime value to an axis that has datetime units" ] }, { "ename": "ValueError", "evalue": "view limit minimum -36877.231166666665 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units", "output_type": "error", "traceback": [ "\u001b[0;31m-------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 37\u001b[0m display(\n\u001b[1;32m 38\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m 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lightness\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_ticklabels\u001b[0;34m(self, minor, which)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mminor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_minorticklabels\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-> 1296\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_majorticklabels\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 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m 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"\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1426\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\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 1427\u001b[0m \u001b[0;34m'Return the locations of the ticks'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1428\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\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 1429\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_locator\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 1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/src/thundr/tracker/venv/lib/python3.7/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36mrefresh\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrefresh\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 1447\u001b[0m \u001b[0;34m'Refresh internal information based on current limits.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m \u001b[0mdmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewlim_to_dt\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 1449\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_locator\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: view limit minimum -36877.231166666665 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units" ] } ], "source": [ "yt1.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tiktok videos" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tt1 = _sql_to_dataframe('''select ts, videos1, videos2, videos3 from skechers_tiktoks''', index_column='ts')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tt1['total'] = tt1.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hourly = tt1.resample('1H').max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "after = numpy.datetime64('2020-04-01 17:34:22')\n", "before = numpy.datetime64('2020-04-06 19:10:28')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "removed_bad = tt1[~(tt1.index >= after) | ~(tt1.index <= before)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(removed_bad.resample('1H').max().interpolate('time').diff().total / 60).plot(figsize=(13,5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hourly.interpolate('time').total.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hourly.total.interpolate()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tt1[['videos_per_minute']].resample('1H').mean().rolling(24).mean().plot(figsize=(10,3), legend=False, title='Deathbed TikToks / minute')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tt1[['videos_per_minute']].resample('24H').median().plot(figsize=(10,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SoundCloud\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sc1 = _sql_to_dataframe('''select ts, plays_1 + plays_2 + plays_3 from skechers_sc_plays''', columns=['ts', 'plays'])\n", "\n", "sc1['plays_per_minute'] = (sc1.plays.diff() / sc1.ts.astype(int).diff() * 1e9 * 60)\n", "sc1 = sc1.set_index('ts')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sc1[['plays_per_minute']].resample('1H').mean().rolling(24).mean().plot(figsize=(10,3), legend=False, title='Skechers SoundCloud Plays / minute')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sc1.plays.resample('1H').max().diff().resample('24H').max().plot(figsize=(10,5), title=\"Canada Goose, Peak Hourly Plays, last 10 days\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# could store this in the db using spartus?\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" ] } ], "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 }