309 lines
1.0 MiB
Plaintext
309 lines
1.0 MiB
Plaintext
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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3",
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"language": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"source": [
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"# Album Investigations"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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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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"342 scrobbles\n1 days spent listening since Nov. 2017\n1.37 minutes/day\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" acousticness danceability duration_ms energy instrumentalness \\\n",
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"mean 0.241967 0.614392 278004.622807 0.700585 0.000010 \n",
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"std 0.190477 0.136551 79915.404232 0.111292 0.000028 \n",
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"min 0.005890 0.301000 130893.000000 0.489000 0.000000 \n",
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"25% 0.110000 0.539000 254200.000000 0.635000 0.000000 \n",
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"50% 0.145000 0.615000 271773.000000 0.729000 0.000000 \n",
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"75% 0.415000 0.752000 291720.000000 0.766000 0.000004 \n",
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"max 0.665000 0.884000 727107.000000 0.896000 0.000115 \n",
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"\n",
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" key liveness loudness mode speechiness tempo \\\n",
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"mean 4.336257 0.254358 -5.601778 0.611111 0.262247 109.402096 \n",
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"std 3.598766 0.185445 1.278614 0.488212 0.135878 28.739742 \n",
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"min 1.000000 0.082700 -9.043000 0.000000 0.063800 79.036000 \n",
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"25% 1.000000 0.129000 -6.562000 0.000000 0.152000 87.948000 \n",
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"50% 3.000000 0.202000 -5.233000 1.000000 0.238000 108.005000 \n",
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"75% 7.000000 0.285000 -4.708000 1.000000 0.325000 111.914000 \n",
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"max 11.000000 0.708000 -4.080000 1.000000 0.750000 176.058000 \n",
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"\n",
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" time_signature valence \n",
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"mean 3.918129 0.451237 \n",
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"std 0.705462 0.151781 \n",
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"min 1.000000 0.270000 \n",
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"25% 4.000000 0.334000 \n",
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"50% 4.000000 0.373000 \n",
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"75% 4.000000 0.558000 \n",
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"max 5.000000 0.897000 "
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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>0.241967</td>\n <td>0.614392</td>\n <td>278004.622807</td>\n <td>0.700585</td>\n <td>0.000010</td>\n <td>4.336257</td>\n <td>0.254358</td>\n <td>-5.601778</td>\n <td>0.611111</td>\n <td>0.262247</td>\n <td>109.402096</td>\n <td>3.918129</td>\n <td>0.451237</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.190477</td>\n <td>0.136551</td>\n <td>79915.404232</td>\n <td>0.111292</td>\n <td>0.000028</td>\n <td>3.598766</td>\n <td>0.185445</td>\n <td>1.278614</td>\n <td>0.488212</td>\n <td>0.135878</td>\n <td>28.739742</td>\n <td>0.705462</td>\n <td>0.151781</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.005890</td>\n <td>0.301000</td>\n <td>130893.000000</td>\n <td>0.489000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.082700</td>\n <td>-9.043000</td>\n <td>0.000000</td>\n <td>0.063800</td>\n <td>79.036000</td>\n <td>1.000000</td>\n <td>0.270000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.110000</td>\n <td>0.539000</td>\n <td>254200.000000</td>\n <td>0.635000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.129000</td>\n <td>-6.562000</td>\n <td>0.000000</td>\n <td>0.152000</td>\n <td>87.948000</td>\n <td>4.000000</td>\n <td>0.334000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>0.145000</td>\n <td>0.615000</td>\n <td>271773.000000</td>\n <td>0.729000</td>\n <td>0.000000</td>\n <td>3.000000</td>\n <td>0.202000</td>\n <td>-5.233000</td>\n <td>1.000000</td>\n <td>0.238000</td>\n <td>108.005000</td>\n <td>4.000000</td>\n <td>0.373000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>0.415000</td>\n <td>0.752000</td>\n <td>291720.000000</td>\n <td>0.766000</td>\n <td>0.000004</td>\n <td>7.000000</td>\n <td>0.285000</td>\n <td>-4.708000</td>\n <td>1.000000</td>\n <td>0.325000</td>\n <td>111.914000</td>\n <td>4.000000</td>\n <td>0.558000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>0.665000</td>\n <td>0.884000</td>\n <td>727107.000000</td>\n <td>0.896000</td>\n <td>0.000115</td>\n <td>11.000000</td>\n <td>0.708000</td>\n <td>-4.080000</td>\n <td>1.000000</td>\n <td>0.750000</td>\n <td>176.058000</td>\n <td>5.000000</td>\n <td>0.897000</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"album_name = \"to pimp a butterfly\".lower()\n",
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"album_frame = scrobbles[scrobbles[\"album\"].str.lower() == album_name]# FILTER SCROBBLES\n",
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"album_frame = album_frame.sort_index(ascending=False) # SORT\n",
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"# album_frame = album_frame.loc[:, descriptor_headers] # DESCRIPTORS\n",
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"\n",
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"total_time = album_frame[\"duration_ms\"].sum() / (1000 * 60) # minutes\n",
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"total_days = total_time / (60 * 24) # days\n",
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"print(f'{album_frame.count()[0]:,d} scrobbles')\n",
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"print(f'{total_days:.0f} days spent listening since Nov. 2017')\n",
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"print(f'{total_time / days_since(first_day).days:.2f} minutes/day')\n",
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"\n",
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"album_frame.describe()[1:]"
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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": 4,
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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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},
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||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"filtered_album = scrobbles[scrobbles[\"album\"].str.lower() == album_name]\n",
|
||
|
|
"# resample by day and mean\n",
|
||
|
|
"filtered_album = filtered_album.resample(\"3W\").count()\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_album[\"energy\"].plot()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(f\"{album_name} Scrobbles\")\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"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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||
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|
"metadata": {
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||
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|
"needs_background": "light"
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||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"filtered_album = scrobbles[scrobbles[\"album\"].str.lower() == album_name]\n",
|
||
|
|
"# select only descriptor float columns\n",
|
||
|
|
"filtered_album = filtered_album.loc[:, float_headers]\n",
|
||
|
|
"# resample by day and mean\n",
|
||
|
|
"filtered_album = filtered_album.resample(\"1M\").mean()\n",
|
||
|
|
"\n",
|
||
|
|
"# filtered_album[\"energy\"].plot()\n",
|
||
|
|
"filtered_album.plot()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(f\"{album_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": [
|
||
|
|
"# Album Listening Time"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"limit = 50\n",
|
||
|
|
"albums = fmnet.top_albums(period=fmnet.Range.OVERALL, limit=limit)\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_albums = [scrobbles[scrobbles[\"album\"] == i.name] for i in albums]\n",
|
||
|
|
"\n",
|
||
|
|
"### STATS ###\n",
|
||
|
|
"# total time listening to artists \n",
|
||
|
|
"albums_time = [i[\"duration_ms\"].sum() for i in filtered_albums]\n",
|
||
|
|
"# total number of tracks\n",
|
||
|
|
"albums_track_count = np.array([i.drop_duplicates([\"uri\"]).count()[0] for i in filtered_albums])\n",
|
||
|
|
"# average length of artist's tracks (ms)\n",
|
||
|
|
"albums_average_track_length = np.array([i.drop_duplicates([\"uri\"])[\"duration_ms\"].mean() for i in filtered_albums])\n",
|
||
|
|
"\n",
|
||
|
|
"fig_size = (7,(5/20)*limit)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"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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},
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"metadata": {
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||
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"needs_background": "light"
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||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=fig_size)\n",
|
||
|
|
"plt.barh(np.arange(len(albums))[::-1], np.array(albums_time) / (1000 * 60 * 60) )\n",
|
||
|
|
"plt.yticks(np.arange(len(albums))[::-1], labels=[f'{i.name}, {i.artist[\"name\"]}' for i in albums])\n",
|
||
|
|
"plt.xlabel(\"Time (Hours)\")\n",
|
||
|
|
"plt.grid(axis=\"x\")\n",
|
||
|
|
"plt.title(\"Time Listened (Since Nov 17)\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
|
||
|
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"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
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"data": {
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||
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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": [
|
||
|
|
"plt.figure(figsize=fig_size)\n",
|
||
|
|
"plt.barh(np.arange(len(albums))[::-1], np.array(albums_time) / (albums_track_count * albums_average_track_length) )\n",
|
||
|
|
"plt.yticks(np.arange(len(albums))[::-1], labels=[f'{i.name}, {i.artist[\"name\"]}' for i in albums])\n",
|
||
|
|
"plt.xlabel(\"Average plays per song\")\n",
|
||
|
|
"plt.grid(axis=\"x\")\n",
|
||
|
|
"plt.title(\"Normalised listening time\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"# Imports & Setup"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"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",
|
||
|
|
"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)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Read Scrobble Frame"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles = get_query(cache=cache)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Write Scrobble Frame"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles.reset_index().to_csv(cache, sep='\\t')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|