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{
"cells": [
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
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"# Artist Investigations"
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]
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},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
"name": "stdout",
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"output_type": "stream",
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"text": [
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"538 scrobbles\n",
"1 days spent listening since Nov. 2017\n",
"1.52 minutes/day\n"
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]
},
{
"data": {
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"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>acousticness</th>\n",
" <th>danceability</th>\n",
" <th>duration_ms</th>\n",
" <th>energy</th>\n",
" <th>instrumentalness</th>\n",
" <th>key</th>\n",
" <th>liveness</th>\n",
" <th>loudness</th>\n",
" <th>mode</th>\n",
" <th>speechiness</th>\n",
" <th>tempo</th>\n",
" <th>time_signature</th>\n",
" <th>valence</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.175862</td>\n",
" <td>0.548939</td>\n",
" <td>201665.648752</td>\n",
" <td>0.672660</td>\n",
" <td>0.139468</td>\n",
" <td>5.715931</td>\n",
" <td>0.261474</td>\n",
" <td>-5.605276</td>\n",
" <td>0.573896</td>\n",
" <td>0.151126</td>\n",
" <td>110.033599</td>\n",
" <td>3.990403</td>\n",
" <td>0.301708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.185824</td>\n",
" <td>0.118423</td>\n",
" <td>68133.871857</td>\n",
" <td>0.136468</td>\n",
" <td>0.280774</td>\n",
" <td>3.886356</td>\n",
" <td>0.215839</td>\n",
" <td>2.190490</td>\n",
" <td>0.494984</td>\n",
" <td>0.141012</td>\n",
" <td>27.453952</td>\n",
" <td>0.394559</td>\n",
" <td>0.169343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.001230</td>\n",
" <td>0.174000</td>\n",
" <td>33568.000000</td>\n",
" <td>0.330000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.059200</td>\n",
" <td>-14.841000</td>\n",
" <td>0.000000</td>\n",
" <td>0.028200</td>\n",
" <td>51.466000</td>\n",
" <td>1.000000</td>\n",
" <td>0.055700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.035800</td>\n",
" <td>0.475000</td>\n",
" <td>147430.000000</td>\n",
" <td>0.551000</td>\n",
" <td>0.000114</td>\n",
" <td>2.000000</td>\n",
" <td>0.095200</td>\n",
" <td>-6.829000</td>\n",
" <td>0.000000</td>\n",
" <td>0.049600</td>\n",
" <td>86.711000</td>\n",
" <td>4.000000</td>\n",
" <td>0.188000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.093700</td>\n",
" <td>0.548000</td>\n",
" <td>203882.000000</td>\n",
" <td>0.683000</td>\n",
" <td>0.001660</td>\n",
" <td>6.000000</td>\n",
" <td>0.171000</td>\n",
" <td>-5.219000</td>\n",
" <td>1.000000</td>\n",
" <td>0.087700</td>\n",
" <td>109.862000</td>\n",
" <td>4.000000</td>\n",
" <td>0.263000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.278000</td>\n",
" <td>0.599000</td>\n",
" <td>250200.000000</td>\n",
" <td>0.761000</td>\n",
" <td>0.089300</td>\n",
" <td>9.000000</td>\n",
" <td>0.356000</td>\n",
" <td>-4.477000</td>\n",
" <td>1.000000</td>\n",
" <td>0.255000</td>\n",
" <td>133.687000</td>\n",
" <td>4.000000</td>\n",
" <td>0.431000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.884000</td>\n",
" <td>0.825000</td>\n",
" <td>626166.000000</td>\n",
" <td>0.910000</td>\n",
" <td>0.914000</td>\n",
" <td>11.000000</td>\n",
" <td>0.783000</td>\n",
" <td>-0.507000</td>\n",
" <td>1.000000</td>\n",
" <td>0.518000</td>\n",
" <td>168.266000</td>\n",
" <td>5.000000</td>\n",
" <td>0.718000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text/plain": [
" acousticness danceability duration_ms energy instrumentalness \\\n",
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"mean 0.175862 0.548939 201665.648752 0.672660 0.139468 \n",
"std 0.185824 0.118423 68133.871857 0.136468 0.280774 \n",
"min 0.001230 0.174000 33568.000000 0.330000 0.000000 \n",
"25% 0.035800 0.475000 147430.000000 0.551000 0.000114 \n",
"50% 0.093700 0.548000 203882.000000 0.683000 0.001660 \n",
"75% 0.278000 0.599000 250200.000000 0.761000 0.089300 \n",
"max 0.884000 0.825000 626166.000000 0.910000 0.914000 \n",
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"\n",
" key liveness loudness mode speechiness tempo \\\n",
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"mean 5.715931 0.261474 -5.605276 0.573896 0.151126 110.033599 \n",
"std 3.886356 0.215839 2.190490 0.494984 0.141012 27.453952 \n",
"min 0.000000 0.059200 -14.841000 0.000000 0.028200 51.466000 \n",
"25% 2.000000 0.095200 -6.829000 0.000000 0.049600 86.711000 \n",
"50% 6.000000 0.171000 -5.219000 1.000000 0.087700 109.862000 \n",
"75% 9.000000 0.356000 -4.477000 1.000000 0.255000 133.687000 \n",
"max 11.000000 0.783000 -0.507000 1.000000 0.518000 168.266000 \n",
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"\n",
" time_signature valence \n",
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"mean 3.990403 0.301708 \n",
"std 0.394559 0.169343 \n",
"min 1.000000 0.055700 \n",
"25% 4.000000 0.188000 \n",
"50% 4.000000 0.263000 \n",
"75% 4.000000 0.431000 \n",
"max 5.000000 0.718000 "
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
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"artist_name = \"Flume\".lower()\n",
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"artist_frame = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name] # FILTER SCROBBLES\n",
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"artist_frame = artist_frame.sort_index(ascending=False) # SORT\n",
"# artist_frame = artist_frame.loc[:, descriptor_headers] # DESCRIPTORS\n",
"\n",
"total_time = artist_frame[\"duration_ms\"].sum() / (1000 * 60) # minutes\n",
"total_days = total_time / (60 * 24) # days\n",
"print(f'{artist_frame.count()[0]:,d} scrobbles')\n",
"print(f'{total_days:.0f} days spent listening since Nov. 2017')\n",
"print(f'{total_time / days_since(first_day).days:.2f} minutes/day')\n",
"\n",
"artist_frame.describe()[1:]"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"source": [
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"filtered_artist = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name]\n",
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"# resample by day and mean\n",
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"filtered_artist = filtered_artist.resample(\"2W\").count()\n",
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"\n",
"filtered_artist[\"energy\"].plot()\n",
"\n",
"plt.title(f\"{artist_name} Scrobbles\")\n",
"plt.grid()\n",
"plt.show()"
]
},
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{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"## Average Descriptor\n",
"\n",
"Below presents the average descriptor for the artist. \"*All Listened Tracks*\" describes the descriptor based on the listening activity for this artist. This takes into account how many times a track is listened to. \"*Distinct Tracks*\" takes only the distinct set of tracks that have been listened to and takes that average.\n",
"\n",
"The difference between them could be described as which features are preferred for an artist. If the blue bar is higher than the red, it would indicate that tracks from this artist which have a higher value for this are listened to more. "
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]
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},
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{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"source": [
"filtered_artist = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name]\n",
"filtered_artist_no_dup = filtered_artist.drop_duplicates(['uri'])\n",
"\n",
"filtered_artist_no_dup = filtered_artist_no_dup.loc[:, float_headers].mean()\n",
"filtered_artist_dup = filtered_artist.loc[:, float_headers].mean()\n",
"\n",
"x = np.arange(len(float_headers))\n",
"width = 0.35\n",
"plt.bar(x - width/2, filtered_artist_no_dup, width, label='All Listened Tracks')\n",
"plt.bar(x + width/2, filtered_artist_dup, width, label='Distinct Tracks', color=(1, 0, 0))\n",
"\n",
"plt.title(f\"{artist_name} Average Descriptor\")\n",
"plt.legend(['All Listened Tracks', 'Distinct Tracks'])\n",
"plt.xticks(x, labels=[i[:6] for i in float_headers])\n",
"plt.ylim([0, 1])\n",
"plt.grid(axis='y')\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"source": [
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"filtered_artist = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name]\n",
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"# select only descriptor float columns\n",
"filtered_artist = filtered_artist.loc[:, float_headers]\n",
"# resample by day and mean\n",
"filtered_artist = filtered_artist.resample(\"1M\").mean()\n",
"\n",
"# filtered_playlist[\"energy\"].plot()\n",
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"filtered_artist.plot(lw=3)\n",
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"\n",
"plt.title(f\"{artist_name} Characteristics Over Time\")\n",
"plt.legend(loc = \"upper left\", fontsize = \"xx-small\")\n",
"plt.ylim([0, 1])\n",
"plt.grid()\n",
"plt.show()"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"# Artist Listening Time"
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]
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},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [],
"source": [
"limit = 35\n",
"artists = fmnet.top_artists(period=fmnet.Range.OVERALL, limit=limit)\n",
"\n",
"filtered_artists = [scrobbles[scrobbles[\"artist\"] == i.name] for i in artists]\n",
"\n",
"### STATS ###\n",
"# total time listening to artists \n",
"artists_time = [i[\"duration_ms\"].sum() for i in filtered_artists]\n",
"# total number of tracks\n",
"artists_track_count = np.array([i.drop_duplicates([\"uri\"]).count()[0] for i in filtered_artists])\n",
"# average length of artist's tracks (ms)\n",
"artists_average_track_length = np.array([i.drop_duplicates([\"uri\"])[\"duration_ms\"].mean() for i in filtered_artists])\n",
"\n",
"fig_size = (7,(5/20)*limit)"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"For these charts the ordering is left as retrieved from the API, i.e by most played."
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]
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},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 840x1050 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"source": [
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"plt.figure(figsize=fig_size)\n",
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"plt.barh(np.arange(len(artists))[::-1], np.array(artists_time) / (1000 * 60 * 60) )\n",
"plt.yticks(np.arange(len(artists))[::-1], labels=[i.name for i in artists])\n",
"plt.xlabel(\"Time (Hours)\")\n",
"plt.grid(axis=\"x\")\n",
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"plt.title(\"Time Listened (Since Nov 17)\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 840x1050 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"source": [
"plt.figure(figsize=fig_size)\n",
"plt.barh(np.arange(len(artists))[::-1], np.array(artists_time) / (artists_track_count * artists_average_track_length) )\n",
"plt.yticks(np.arange(len(artists))[::-1], labels=[i.name for i in artists])\n",
"plt.xlabel(\"Average plays per song\")\n",
"plt.grid(axis=\"x\")\n",
"plt.title(\"Normalised listening time\")\n",
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"plt.show()"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"# Imports & Setup"
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]
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},
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"from google.cloud import bigquery\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"mpl.rcParams['figure.dpi'] = 120\n",
"\n",
"from analysis.net import get_spotnet, get_fmnet, get_playlist, track_frame\n",
"from analysis.query import *\n",
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"from analysis import float_headers, days_since\n",
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"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"client = bigquery.Client()\n",
"spotnet = get_spotnet()\n",
"fmnet = get_fmnet()\n",
"cache = 'query.csv'\n",
"first_day = datetime(year=2017, month=11, day=3)"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"## Read Scrobble Frame"
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]
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},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
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"scrobbles = get_query(cache=cache)"
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]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"## Write Scrobble Frame"
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]
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},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
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"scrobbles.reset_index().to_csv(cache, sep='\\t')"
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]
}
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],
"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": "3.8.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
