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"# Playlists\n",
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"\n",
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"Below are investigations into listening from a playlist context. The start includes data for individual playlists before comparing a group of playlists.\n",
"\n",
"## Individual Playlist Investigations\n",
"\n",
"Below prepares a frame for processing and gives some high-level stats."
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"10,999 scrobbles\n28 days spent listening since Nov. 2017\n35.41 minutes/day\n"
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]
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" duration_ms mode loudness key tempo \\\n",
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"mean 223128.125193 0.631694 -6.733293 5.352487 118.354445 \n",
"std 63610.972469 0.482367 2.482966 3.733147 31.002195 \n",
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"min 60867.000000 0.000000 -17.819000 0.000000 61.113000 \n",
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"25% 182741.000000 0.000000 -8.320000 1.000000 91.923000 \n",
"50% 211200.000000 1.000000 -6.518000 6.000000 116.696000 \n",
"75% 258827.000000 1.000000 -5.062000 8.000000 140.032000 \n",
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"max 774920.000000 1.000000 -0.140000 11.000000 207.982000 \n",
"\n",
" time_signature acousticness danceability energy instrumentalness \\\n",
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"mean 4.007455 0.179857 0.66556 0.709464 0.008645 \n",
"std 0.255755 0.186482 0.15002 0.145923 0.056757 \n",
"min 1.000000 0.000017 0.26100 0.252000 0.000000 \n",
"25% 4.000000 0.035000 0.55200 0.598000 0.000000 \n",
"50% 4.000000 0.120000 0.67400 0.720000 0.000000 \n",
"75% 4.000000 0.254000 0.77500 0.821000 0.000051 \n",
"max 5.000000 0.864000 0.97500 0.993000 0.847000 \n",
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"\n",
" liveness speechiness valence \n",
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"mean 0.244309 0.274996 0.480991 \n",
"std 0.172633 0.129368 0.223523 \n",
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"min 0.033300 0.032300 0.027200 \n",
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"25% 0.113000 0.181000 0.314000 \n",
"50% 0.181000 0.282000 0.483000 \n",
"75% 0.333000 0.361000 0.642000 \n",
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"max 0.979000 0.827000 0.962000 "
],
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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>duration_ms</th>\n <th>mode</th>\n <th>loudness</th>\n <th>key</th>\n <th>tempo</th>\n <th>time_signature</th>\n <th>acousticness</th>\n <th>danceability</th>\n <th>energy</th>\n <th>instrumentalness</th>\n <th>liveness</th>\n <th>speechiness</th>\n <th>valence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>mean</th>\n <td>223128.125193</td>\n <td>0.631694</td>\n <td>-6.733293</td>\n <td>5.352487</td>\n <td>118.354445</td>\n <td>4.007455</td>\n <td>0.179857</td>\n <td>0.66556</td>\n <td>0.709464</td>\n <td>0.008645</td>\n <td>0.244309</td>\n <td>0.274996</td>\n <td>0.480991</td>\n </tr>\n <tr>\n <th>std</th>\n <td>63610.972469</td>\n <td>0.482367</td>\n <td>2.482966</td>\n <td>3.733147</td>\n <td>31.002195</td>\n <td>0.255755</td>\n <td>0.186482</td>\n <td>0.15002</td>\n <td>0.145923</td>\n <td>0.056757</td>\n <td>0.172633</td>\n <td>0.129368</td>\n <td>0.223523</td>\n </tr>\n <tr>\n <th>min</th>\n <td>60867.000000</td>\n <td>0.000000</td>\n <td>-17.819000</td>\n <td>0.000000</td>\n <td>61.113000</td>\n <td>1.000000</td>\n <td>0.000017</td>\n <td>0.26100</td>\n <td>0.252000</td>\n <td>0.000000</td>\n <td>0.033300</td>\n <td>0.032300</td>\n <td>0.027200</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>182741.000000</td>\n <td>0.000000</td>\n <td>-8.320000</td>\n <td>1.000000</td>\n <td>91.923000</td>\n <td>4.000000</td>\n <td>0.035000</td>\n <td>0.55200</td>\n <td>0.598000</td>\n <td>0.000000</td>\n <td>0.113000</td>\n <td>0.181000</td>\n <td>0.314000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>211200.000000</td>\n <td>1.000000</td>\n <td>-6.518000</td>\n <td>6.000000</td>\n <td>116.696000</td>\n <td>4.000000</td>\n <td>0.120000</td>\n <td>0.67400</td>\n <td>0.720000</td>\n <td>0.000000</td>\n <td>0.181000</td>\n <td>0.282000</td>\n <td>0.483000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>258827.000000</td>\n <td>1.000000</td>\n <td>-5.062000</td>\n <td>8.000000</td>\n <td>140.032000</td>\n <td>4.000000</td>\n <td>0.254000</td>\n <td>0.77500</td>\n <td>0.821000</td>\n <td>0.000051</td>\n <td>0.333000</td>\n <td>0.361000</td>\n <td>0.642000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>774920.000000</td>\n <td>1.000000</td>\n <td>-0.140000</td>\n <td>11.000000</td>\n <td>207.982000</td>\n <td>5.000000</td>\n <td>0.864000</td>\n <td>0.97500</td>\n <td>0.993000</td>\n <td>0.847000</td>\n <td>0.979000</td>\n <td>0.827000</td>\n <td>0.962000</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"playlist_name = \"ALL RAP\"\n",
"playlist = get_playlist(playlist_name, spotnet)\n",
"playlist_frame = pd.merge(track_frame(playlist.tracks), scrobbles, on=['track', 'artist']) # FILTER SCROBBLES\n",
"playlist_frame = playlist_frame.sort_index(ascending=False) # SORT\n",
"playlist_frame = playlist_frame.loc[:, spotify_descriptor_headers] # DESCRIPTORS\n",
"\n",
"total_time = playlist_frame[\"duration_ms\"].sum() / (1000 * 60) # minutes\n",
"total_days = total_time / (60 * 24) # days\n",
"print(f'{playlist_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",
"playlist_frame.describe()[1:]"
]
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"source": [
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"## Listnening Frequency Over Time"
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# filter playlists by join with playlist track/artist names\n",
"filtered_playlist = pd.merge(track_frame(playlist.tracks), scrobbles.reset_index(), on=['track', 'artist'])\n",
"# to time series, select only descriptor float columns\n",
"filtered_playlist = filtered_playlist.set_index('time').loc[:, [\"energy\"]]\n",
"# resample by day and mean\n",
"filtered_playlist = filtered_playlist.resample(\"3W\").count()\n",
"\n",
"filtered_playlist[\"energy\"].plot()\n",
"\n",
"plt.title(f\"{playlist_name} Scrobbles\")\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"source": [
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"## Listening Characteristics Over Time"
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],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": "<Figure size 720x480 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# filter playlists by join with playlist track/artist names\n",
"filtered_playlist = pd.merge(track_frame(playlist.tracks), scrobbles.reset_index(), on=['track', 'artist'])\n",
"# to time series\n",
"filtered_playlist = filtered_playlist.set_index('time')\n",
"# select only descriptor float columns\n",
"filtered_playlist = filtered_playlist.loc[:, float_headers]\n",
"# resample by day and mean\n",
"filtered_playlist = filtered_playlist.resample(\"2W\").mean()\n",
"\n",
"# filtered_playlist[\"energy\"].plot()\n",
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"filtered_playlist.plot(lw=3)\n",
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"\n",
"plt.title(f\"{playlist_name} Characteristics Over Time\")\n",
"plt.legend(loc = \"upper left\", fontsize = \"x-small\")\n",
"plt.ylim([0, 1])\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"source": [
"# Playlist Comparisons\n",
"\n",
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"Comparing different playlists over parameters like listening count, average plays per track and sound characteristics."
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],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [],
"source": [
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"playlist_names = [\"RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\", \"POP\"] # super-genres\n",
"# playlist_names = [\"DNB\", \"HOUSE\", \"TECHNO\", \"GARAGE\", \"DUBSTEP\", \"BASS\"] # EDM playlists\n",
"# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\"] # rap decades\n",
"# playlist_names = [\"UK RAP\", \"US RAP\"] # UK/US split\n",
"# playlist_names = [\"uk rap\", \"grime\", \"drill\", \"afro bash\"] # british rap playlists\n",
"# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\", \"trap\", \"gangsta rap\", \"industrial rap\", \"weird rap\", \"jazz rap\", \"boom bap\", \"trap metal\"] # american rap playlists\n",
"# playlist_names = [\"rock\", \"indie\", \"punk\", \"pop rock\", \"bluesy rock\", \"hard rock\", \"chilled rock\", \"emo\", \"pop punk\", \"stoner rock/metal\", \"post-hardcore\", \"melodic hardcore\", \"art rock\", \"post-rock\", \"classic pop punk\", \"90s rock & grunge\", \"90s indie & britpop\", \"psych\"] # rock playlists\n",
"# playlist_names = [\"metal\", \"metalcore\", \"mathcore\", \"hardcore\", \"black metal\", \"death metal\", \"doom metal\", \"sludge metal\", \"classic metal\", \"industrial\", \"nu metal\", \"calm metal\", \"thrash metal\"] # metal playlists"
]
},
{
"source": [
"Pull and process playlist information.\n",
"\n",
"1. Get live playlist track information from spotify\n",
"2. Filter listening history for these tracks\n",
"3. Calculate various parameters and stats"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"playlists = [get_playlist(i, spotnet) for i in playlist_names] # 1)\n",
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"\n",
"# filter playlists by join with playlist track/artist names\n",
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"filtered_playlists = [pd.merge(track_frame(i.tracks), scrobbles, on=['track', 'artist']) for i in playlists] # 2)\n",
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"\n",
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"### STATS, 3) ###\n",
"# number of scrobbles per playlist\n",
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"playlist_counts = [i.count()[0] for i in filtered_playlists]\n",
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"# total time listenened per playlist\n",
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"playlist_time = [i[\"duration_ms\"].sum() for i in filtered_playlists]\n",
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"# average track time per playlist\n",
"playlist_average_track_time = [i.drop_duplicates(['uri'])[\"duration_ms\"].mean() for i in filtered_playlists]\n",
"# number of tracks per playlist\n",
"playlist_track_count = np.array([len(i.tracks) for i in playlists])\n",
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"\n",
"# distinct on uri\n",
"filtered_playlists = [i.drop_duplicates(['uri']) for i in filtered_playlists]\n",
"# select only descriptor float columns\n",
"filtered_playlists = [i.loc[:, float_headers] for i in filtered_playlists]\n",
"\n",
"playlist_mean = [i.mean() for i in filtered_playlists]\n",
"playlist_std = [i.std() for i in filtered_playlists]"
]
},
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{
"source": [
"## Listening Characteristics Over Different Playlists"
],
"cell_type": "markdown",
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": "<Figure size 720x480 with 1 Axes>",
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},
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}
}
],
"source": [
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"x_pos = np.arange(len(float_headers))\n",
"for idx, mean in enumerate(playlist_mean):\n",
" plt.plot(x_pos, mean, lw=2)\n",
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" \n",
"plt.legend(playlist_names)\n",
"plt.xticks(x_pos, [i[:6] for i in float_headers])\n",
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"plt.ylim([0, 1])\n",
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"plt.title('Average Playlist Descriptor')\n",
"plt.grid()\n",
"plt.show()"
]
},
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{
"source": [
"## Time Spent Listening To Each"
],
"cell_type": "markdown",
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": "<Figure size 720x480 with 1 Axes>",
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2021-02-01 21:43:27 +00:00
},
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}
}
],
"source": [
"plt.barh(np.arange(len(playlist_names)), np.array(playlist_time) / (1000 * 60 * 60 * 24) )\n",
"plt.yticks(np.arange(len(playlist_names)), labels=playlist_names)\n",
"plt.xlabel(\"Time (Days)\")\n",
"plt.grid(axis=\"x\")\n",
"plt.title(\"Time Spent Listening to Playlists\")\n",
"plt.show()"
]
},
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{
"source": [
"## Normalised\n",
"\n",
"Below normalises the above chart by dividing through by the average track length multiplied by the number of tracks per playlist. The units are essentially average plays per song. The existing data could be described as:\n",
"\n",
"*number of plays \\* track time \\* number of songs in playlist*\n",
"\n",
"By normalising by the last two terms, we get the average number of plays per song."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 28,
2021-02-03 16:08:06 +00:00
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 720x480 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plt.barh(np.arange(len(playlist_names)), np.array(playlist_time) / (playlist_track_count * playlist_average_track_time) )\n",
"plt.yticks(np.arange(len(playlist_names)), labels=playlist_names)\n",
"plt.xlabel(\"Average plays per song\")\n",
"plt.grid(axis=\"x\")\n",
"plt.title(\"Normalised listening time\")\n",
"plt.show()"
]
},
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{
"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",
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"mpl.rcParams['figure.dpi'] = 120\n",
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"\n",
"from analysis.net import get_spotnet, get_playlist, track_frame\n",
"from analysis.query import *\n",
"from analysis import spotify_descriptor_headers, float_headers, days_since\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"client = bigquery.Client()\n",
"spotnet = get_spotnet()\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": {}
},
{
"source": [
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"scrobbles.reset_index().to_csv(cache, sep='\\t')"
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],
"cell_type": "code",
"metadata": {},
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"execution_count": 6,
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"outputs": []
}
]
}
