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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": 9,
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"metadata": {},
"outputs": [
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1,805 scrobbles\n",
"4 days spent listening since Nov. 2017\n",
"4.90 minutes/day\n"
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]
},
{
"data": {
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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",
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" vertical-align: top;\n",
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"\n",
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"</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>0.219781</td>\n",
" <td>0.572843</td>\n",
" <td>202260.902265</td>\n",
" <td>0.676443</td>\n",
" <td>0.056700</td>\n",
" <td>5.447557</td>\n",
" <td>0.305630</td>\n",
" <td>-8.056303</td>\n",
" <td>0.698451</td>\n",
" <td>0.289536</td>\n",
" <td>110.069952</td>\n",
" <td>3.994636</td>\n",
" <td>0.512103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.216312</td>\n",
" <td>0.160528</td>\n",
" <td>60373.745872</td>\n",
" <td>0.143114</td>\n",
" <td>0.191733</td>\n",
" <td>3.495195</td>\n",
" <td>0.180845</td>\n",
" <td>2.041763</td>\n",
" <td>0.459068</td>\n",
" <td>0.133261</td>\n",
" <td>32.676004</td>\n",
" <td>0.184284</td>\n",
" <td>0.198939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000512</td>\n",
" <td>0.256000</td>\n",
" <td>48507.000000</td>\n",
" <td>0.173000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.049100</td>\n",
" <td>-16.918000</td>\n",
" <td>0.000000</td>\n",
" <td>0.029600</td>\n",
" <td>52.145000</td>\n",
" <td>3.000000</td>\n",
" <td>0.038300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.049700</td>\n",
" <td>0.451000</td>\n",
" <td>154573.000000</td>\n",
" <td>0.583000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.139000</td>\n",
" <td>-9.311000</td>\n",
" <td>0.000000</td>\n",
" <td>0.203000</td>\n",
" <td>87.786000</td>\n",
" <td>4.000000</td>\n",
" <td>0.391000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.161000</td>\n",
" <td>0.543000</td>\n",
" <td>199053.000000</td>\n",
" <td>0.666000</td>\n",
" <td>0.000004</td>\n",
" <td>6.000000</td>\n",
" <td>0.303000</td>\n",
" <td>-8.328000</td>\n",
" <td>1.000000</td>\n",
" <td>0.299000</td>\n",
" <td>91.973000</td>\n",
" <td>4.000000</td>\n",
" <td>0.522500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.274000</td>\n",
" <td>0.657000</td>\n",
" <td>226520.000000</td>\n",
" <td>0.805000</td>\n",
" <td>0.000648</td>\n",
" <td>8.000000</td>\n",
" <td>0.399000</td>\n",
" <td>-6.442000</td>\n",
" <td>1.000000</td>\n",
" <td>0.380000</td>\n",
" <td>130.990000</td>\n",
" <td>4.000000</td>\n",
" <td>0.638000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.946000</td>\n",
" <td>0.948000</td>\n",
" <td>513707.000000</td>\n",
" <td>0.946000</td>\n",
" <td>0.901000</td>\n",
" <td>11.000000</td>\n",
" <td>0.796000</td>\n",
" <td>-2.002000</td>\n",
" <td>1.000000</td>\n",
" <td>0.749000</td>\n",
" <td>188.050000</td>\n",
" <td>5.000000</td>\n",
" <td>0.959000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text/plain": [
" acousticness danceability duration_ms energy instrumentalness \\\n",
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"mean 0.219781 0.572843 202260.902265 0.676443 0.056700 \n",
"std 0.216312 0.160528 60373.745872 0.143114 0.191733 \n",
"min 0.000512 0.256000 48507.000000 0.173000 0.000000 \n",
"25% 0.049700 0.451000 154573.000000 0.583000 0.000000 \n",
"50% 0.161000 0.543000 199053.000000 0.666000 0.000004 \n",
"75% 0.274000 0.657000 226520.000000 0.805000 0.000648 \n",
"max 0.946000 0.948000 513707.000000 0.946000 0.901000 \n",
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"\n",
" key liveness loudness mode speechiness tempo \\\n",
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"mean 5.447557 0.305630 -8.056303 0.698451 0.289536 110.069952 \n",
"std 3.495195 0.180845 2.041763 0.459068 0.133261 32.676004 \n",
"min 0.000000 0.049100 -16.918000 0.000000 0.029600 52.145000 \n",
"25% 1.000000 0.139000 -9.311000 0.000000 0.203000 87.786000 \n",
"50% 6.000000 0.303000 -8.328000 1.000000 0.299000 91.973000 \n",
"75% 8.000000 0.399000 -6.442000 1.000000 0.380000 130.990000 \n",
"max 11.000000 0.796000 -2.002000 1.000000 0.749000 188.050000 \n",
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"\n",
" time_signature valence \n",
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"mean 3.994636 0.512103 \n",
"std 0.184284 0.198939 \n",
"min 3.000000 0.038300 \n",
"25% 4.000000 0.391000 \n",
"50% 4.000000 0.522500 \n",
"75% 4.000000 0.638000 \n",
"max 5.000000 0.959000 "
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
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"artist_name = \"freddie gibbs\".lower()\n",
"\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",
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"\n",
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"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": 10,
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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": [
"# resample by day and mean\n",
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"artist_frame.resample(\"1M\").count()[\"track\"].plot()\n",
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"\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": 11,
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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": [
"x = np.arange(len(float_headers))\n",
"width = 0.35\n",
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"\n",
"plt.bar(x - width/2, \n",
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" artist_frame.drop_duplicates(['uri'])[float_headers].mean(), \n",
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" width, label='All Listened Tracks')\n",
"plt.bar(x + width/2, \n",
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" artist_frame[float_headers].mean(), \n",
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" width, label='Distinct Tracks', color=(1, 0, 0))\n",
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"\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": 12,
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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": [
"# select only descriptor float columns\n",
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"artist_frame[float_headers].resample(\"1M\").mean().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": 13,
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"metadata": {},
"outputs": [],
"source": [
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"LIMIT = 40\n",
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"fig_size = (7,(7/20)*LIMIT)\n",
"### STATS ###\n",
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"\n",
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"stats_frame = scrobbles.reset_index()[[\"track\", \"album\", \"artist\", \"duration_ms\"]]\n",
"stats_frame = stats_frame[stats_frame[\"album\"] != \"Mirror Reaper\"] # 1:30 hr long song, skews graphs\n",
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"\n",
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"track_count = stats_frame.groupby('artist').count()[\"track\"]\n",
"track_count.name = 'count'\n",
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"\n",
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"duration_sum = stats_frame.groupby('artist').sum()[\"duration_ms\"]\n",
"duration_sum.name = \"duration_sum\"\n",
"\n",
"duration_average = stats_frame.groupby('artist').mean()[\"duration_ms\"]\n",
"duration_average.name = \"duration_mean\"\n",
"\n",
"track_average = stats_frame.groupby(['artist', 'track'])[\"album\"].count()\n",
"track_average = track_average.groupby('artist').mean()\n",
"track_average.name = \"track_average\"\n",
"\n",
"stats_frame = pd.concat([track_count, duration_sum, duration_average, track_average], axis=1).reset_index()\n",
"# stats_frame"
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]
},
{
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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": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 840x1680 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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"duration_frame = stats_frame.sort_values(by='duration_sum', ascending=False).head(LIMIT)\n",
"\n",
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"plt.figure(figsize=fig_size)\n",
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"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=duration_frame[\"artist\"])\n",
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"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": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 840x1680 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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"norm_frame = stats_frame.sort_values(by='count', ascending=False).head(LIMIT) # for top artists\n",
"# norm_frame = stats_frame.sort_values(by='track_average', ascending=False).head(LIMIT) # for one-hit wonders\n",
"\n",
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"plt.figure(figsize=fig_size)\n",
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"plt.barh(np.arange(len(norm_frame))[::-1], norm_frame[\"track_average\"] )\n",
"plt.yticks(np.arange(len(norm_frame))[::-1], labels=norm_frame[\"artist\"])\n",
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"plt.xlabel(\"Average plays per song\")\n",
"plt.grid(axis=\"x\")\n",
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"plt.title(\"Average Plays Per Track\")\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": [],
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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": {
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"display_name": "Python 3",
"language": "python",
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