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{
"source": [
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"# Artist Investigations"
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],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"538 scrobbles\n1 days spent listening since Nov. 2017\n1.52 minutes/day\n"
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]
},
{
"output_type": "execute_result",
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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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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</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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},
"metadata": {},
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"execution_count": 51
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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": 52,
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"metadata": {},
"outputs": [
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},
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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()"
]
},
2021-02-04 13:34:25 +00:00
{
"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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},
2021-02-01 21:43:27 +00:00
{
"cell_type": "code",
2021-02-04 13:34:25 +00:00
"execution_count": 53,
2021-02-01 21:43:27 +00:00
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2021-02-04 13:34:25 +00:00
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},
"metadata": {
"needs_background": "light"
}
}
],
"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",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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},
"metadata": {
"needs_background": "light"
}
}
],
"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()"
]
},
{
"source": [
"# Artist Listening Time"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 48,
"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)"
]
},
{
"source": [
"For these charts the ordering is left as retrieved from the API, i.e by most played."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 49,
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"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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2021-02-01 21:43:27 +00:00
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
2021-02-03 16:08:06 +00:00
"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",
2021-02-03 16:08:06 +00:00
"plt.title(\"Time Listened (Since Nov 17)\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"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()"
]
},
{
"source": [
"# Imports & Setup"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 2,
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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)"
]
},
{
"source": [
"## Read Scrobble Frame"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
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"scrobbles = get_query(cache=cache)"
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]
},
{
"source": [
"## Write Scrobble Frame"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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"scrobbles.reset_index().to_csv(cache, sep='\\t')"
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]
}
]
}