257 lines
334 KiB
Plaintext
257 lines
334 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Track Investigations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"58 scrobbles\n4 hours spent listening since Nov. 2017\n"
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]
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}
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],
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"source": [
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"track_name = \"never\".lower()\n",
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"artist_name = \"jid\".lower()\n",
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"\n",
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"track_frame = scrobbles[scrobbles[\"track\"].str.lower() == track_name]\n",
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"track_frame = track_frame[track_frame[\"artist\"].str.lower() == artist_name]# FILTER SCROBBLES\n",
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"track_frame = track_frame.sort_index(ascending=False) # SORT\n",
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"# track_frame = track_frame.loc[:, descriptor_headers] # DESCRIPTORS\n",
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"\n",
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"total_time = track_frame[\"duration_ms\"].sum() / (1000 * 60 * 60) # hours\n",
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"\n",
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"print(f'{track_frame.count()[0]:,d} scrobbles')\n",
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"print(f'{total_time:.0f} hours spent listening since Nov. 2017')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 720x480 with 1 Axes>",
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"source": [
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"# resample by day and mean\n",
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"track_frame.resample(\"1M\").count()[\"track\"].plot()\n",
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"\n",
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"plt.title(f\"{track_name} - {artist_name} Scrobbles\")\n",
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"plt.grid()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Average Descriptor\n",
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"\n",
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"Below presents the average descriptor for the album. \"*All Listened Tracks*\" describes the descriptor based on the listening activity for this album. 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",
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"\n",
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"The difference between them could be described as which features are preferred for an album. If the blue bar is higher than the red, it would indicate that tracks from this album which have a higher value for this are listened to more. "
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]
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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": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 720x480 with 1 Axes>",
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},
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"metadata": {
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"needs_background": "light"
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|
}
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||
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|
}
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||
|
|
],
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||
|
|
"source": [
|
||
|
|
"plt.bar(np.arange(len(float_headers)), track_frame[float_headers].mean())\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(f\"{track_name} Average Descriptor\")\n",
|
||
|
|
"plt.xticks(np.arange(len(float_headers)), labels=[i[:6] for i in float_headers])\n",
|
||
|
|
"plt.ylim([0, 1])\n",
|
||
|
|
"plt.grid(axis='y')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Track Listening Time"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"LIMIT = 25\n",
|
||
|
|
"fig_size = (7,(10/20)*LIMIT)\n",
|
||
|
|
"### STATS ###\n",
|
||
|
|
"\n",
|
||
|
|
"stats_frame = scrobbles.reset_index()[[\"track\", \"album\", \"artist\", \"duration_ms\"]]\n",
|
||
|
|
"\n",
|
||
|
|
"track_count = stats_frame.groupby(['artist', 'track']).count()[\"album\"]\n",
|
||
|
|
"track_count.name='count'\n",
|
||
|
|
"\n",
|
||
|
|
"duration_sum = stats_frame.groupby(['artist', 'track']).sum()[\"duration_ms\"]\n",
|
||
|
|
"duration_sum.name=\"duration_sum\"\n",
|
||
|
|
"\n",
|
||
|
|
"duration_average = stats_frame.groupby(['artist', 'track']).mean()[\"duration_ms\"]\n",
|
||
|
|
"duration_average.name=\"duration_mean\"\n",
|
||
|
|
"\n",
|
||
|
|
"stats_frame = pd.concat([track_count, duration_sum, duration_average], axis=1).reset_index()\n",
|
||
|
|
"\n",
|
||
|
|
"stats_frame = stats_frame[stats_frame[\"track\"] != \"Mirror Reaper\"] # 1:30 hr long song, skews graphs\n",
|
||
|
|
"# stats_frame"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
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||
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|
"execution_count": 9,
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||
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|
"metadata": {},
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||
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|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
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||
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"data": {
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"text/plain": "<Figure size 840x1500 with 1 Axes>",
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"duration_frame = stats_frame.sort_values(by='duration_sum', ascending=False).head(LIMIT)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=fig_size)\n",
|
||
|
|
"plt.barh(np.arange(len(duration_frame))[::-1], duration_frame[\"duration_sum\"].to_numpy() / (1000 * 60 * 60) )\n",
|
||
|
|
"plt.yticks(np.arange(len(duration_frame))[::-1], labels=[f'{r[\"track\"]} - {r[\"artist\"]}' for i, r in duration_frame.iterrows()])\n",
|
||
|
|
"plt.xlabel(\"Time (Hours)\")\n",
|
||
|
|
"plt.grid(axis=\"x\")\n",
|
||
|
|
"plt.title(\"Time Listened (Since Nov 17)\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Imports & Setup"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"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\n",
|
||
|
|
"from analysis.query import *\n",
|
||
|
|
"from analysis import float_headers, days_since\n",
|
||
|
|
"\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)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Read Scrobble Frame"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles = get_query(cache=cache)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Write Scrobble Frame"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles.reset_index().to_csv(cache, sep='\\t')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"name": "pythonjvsc74a57bd0bce1a3677099e73bf385a0de8ef462673e03f7df0abce93e57e7ca76e8c504a2",
|
||
|
|
"display_name": "Python 3.8.9 ('.venv': poetry)"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.9"
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "bce1a3677099e73bf385a0de8ef462673e03f7df0abce93e57e7ca76e8c504a2"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|