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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": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
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"output_type": "stream",
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"text": [
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"1,233 scrobbles\n",
"4 days spent listening since Nov. 2017\n",
"4.46 minutes/day\n"
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]
},
{
"data": {
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"text/html": [
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"<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",
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" <td>0.046042</td>\n",
" <td>0.714379</td>\n",
" <td>270745.820490</td>\n",
" <td>0.800026</td>\n",
" <td>0.346944</td>\n",
" <td>5.617338</td>\n",
" <td>0.134218</td>\n",
" <td>-7.078945</td>\n",
" <td>0.441331</td>\n",
" <td>0.054055</td>\n",
" <td>122.854020</td>\n",
" <td>3.995622</td>\n",
" <td>0.498696</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
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" <td>0.084004</td>\n",
" <td>0.076841</td>\n",
" <td>77567.562426</td>\n",
" <td>0.096553</td>\n",
" <td>0.379047</td>\n",
" <td>3.463314</td>\n",
" <td>0.133318</td>\n",
" <td>1.108589</td>\n",
" <td>0.496764</td>\n",
" <td>0.022931</td>\n",
" <td>4.799479</td>\n",
" <td>0.066053</td>\n",
" <td>0.190035</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
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" <td>0.000098</td>\n",
" <td>0.345000</td>\n",
" <td>179080.000000</td>\n",
" <td>0.257000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.030300</td>\n",
" <td>-12.378000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.027700</td>\n",
" <td>69.942000</td>\n",
" <td>3.000000</td>\n",
" <td>0.039900</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
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" <td>0.005240</td>\n",
" <td>0.671000</td>\n",
" <td>216841.000000</td>\n",
" <td>0.744000</td>\n",
" <td>0.000643</td>\n",
" <td>3.000000</td>\n",
" <td>0.067600</td>\n",
" <td>-7.636000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.041100</td>\n",
" <td>121.982000</td>\n",
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" <td>4.000000</td>\n",
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" <td>0.327000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <td>0.013200</td>\n",
" <td>0.712000</td>\n",
" <td>235200.000000</td>\n",
" <td>0.798000</td>\n",
" <td>0.122000</td>\n",
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" <td>6.000000</td>\n",
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" <td>0.088000</td>\n",
" <td>-6.865000</td>\n",
" <td>0.000000</td>\n",
" <td>0.048600</td>\n",
" <td>123.951000</td>\n",
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" <td>4.000000</td>\n",
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" <td>0.520000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
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" <td>0.043800</td>\n",
" <td>0.781000</td>\n",
" <td>291779.000000</td>\n",
" <td>0.880000</td>\n",
" <td>0.862000</td>\n",
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" <td>9.000000</td>\n",
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" <td>0.117000</td>\n",
" <td>-6.246000</td>\n",
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" <td>1.000000</td>\n",
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" <td>0.060800</td>\n",
" <td>124.998000</td>\n",
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" <td>4.000000</td>\n",
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" <td>0.668000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
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" <td>0.735000</td>\n",
" <td>0.843000</td>\n",
" <td>472444.000000</td>\n",
" <td>0.976000</td>\n",
" <td>0.919000</td>\n",
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" <td>11.000000</td>\n",
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" <td>0.676000</td>\n",
" <td>-4.063000</td>\n",
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" <td>1.000000</td>\n",
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" <td>0.151000</td>\n",
" <td>176.994000</td>\n",
" <td>4.000000</td>\n",
" <td>0.853000</td>\n",
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" </tr>\n",
" </tbody>\n",
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],
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"text/plain": [
" acousticness danceability duration_ms energy instrumentalness \\\n",
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"mean 0.046042 0.714379 270745.820490 0.800026 0.346944 \n",
"std 0.084004 0.076841 77567.562426 0.096553 0.379047 \n",
"min 0.000098 0.345000 179080.000000 0.257000 0.000000 \n",
"25% 0.005240 0.671000 216841.000000 0.744000 0.000643 \n",
"50% 0.013200 0.712000 235200.000000 0.798000 0.122000 \n",
"75% 0.043800 0.781000 291779.000000 0.880000 0.862000 \n",
"max 0.735000 0.843000 472444.000000 0.976000 0.919000 \n",
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"\n",
" key liveness loudness mode speechiness tempo \\\n",
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"mean 5.617338 0.134218 -7.078945 0.441331 0.054055 122.854020 \n",
"std 3.463314 0.133318 1.108589 0.496764 0.022931 4.799479 \n",
"min 0.000000 0.030300 -12.378000 0.000000 0.027700 69.942000 \n",
"25% 3.000000 0.067600 -7.636000 0.000000 0.041100 121.982000 \n",
"50% 6.000000 0.088000 -6.865000 0.000000 0.048600 123.951000 \n",
"75% 9.000000 0.117000 -6.246000 1.000000 0.060800 124.998000 \n",
"max 11.000000 0.676000 -4.063000 1.000000 0.151000 176.994000 \n",
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"\n",
" time_signature valence \n",
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"mean 3.995622 0.498696 \n",
"std 0.066053 0.190035 \n",
"min 3.000000 0.039900 \n",
"25% 4.000000 0.327000 \n",
"50% 4.000000 0.520000 \n",
"75% 4.000000 0.668000 \n",
"max 4.000000 0.853000 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
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"artist_name = \"gorgon city\".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": 5,
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"metadata": {},
"outputs": [
{
"data": {
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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": 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": [
"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": 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": [
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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": 8,
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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": 9,
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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": 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": [
"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": 2,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"query.csv not found, pulling\n"
]
}
],
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"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",
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