2021-02-01 21:43:27 +00:00
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2021-02-03 16:08:06 +00:00
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2021-02-01 21:43:27 +00:00
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{
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"source": [
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2021-02-03 16:08:06 +00:00
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"# Artist Investigations"
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2021-02-01 21:43:27 +00:00
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],
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"cell_type": "markdown",
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{
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"cell_type": "code",
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2021-02-03 16:08:06 +00:00
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"execution_count": 3,
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2021-02-01 21:43:27 +00:00
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"metadata": {},
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"outputs": [
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"output_type": "stream",
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"name": "stdout",
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"text": [
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2021-02-03 16:08:06 +00:00
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"1,706 scrobbles\n4 days spent listening since Nov. 2017\n4.62 minutes/day\n"
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2021-02-01 21:43:27 +00:00
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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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2021-02-03 16:08:06 +00:00
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"mean 0.218702 0.574796 202421.407595 0.678473 0.053497 \n",
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"std 0.215092 0.162472 59794.180372 0.141701 0.186326 \n",
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"min 0.000843 0.256000 48507.000000 0.176000 0.000000 \n",
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2021-02-03 16:08:06 +00:00
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"25% 0.049700 0.451000 154573.000000 0.584000 0.000000 \n",
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"50% 0.161000 0.543000 199053.000000 0.668000 0.000004 \n",
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"75% 0.274000 0.657000 226520.000000 0.805000 0.000648 \n",
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"max 0.946000 0.948000 513707.000000 0.946000 0.901000 \n",
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"\n",
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" key liveness loudness mode speechiness tempo \\\n",
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2021-02-03 16:08:06 +00:00
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"mean 5.482911 0.305708 -8.005934 0.695570 0.289666 110.452840 \n",
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"std 3.492010 0.180859 2.022733 0.460311 0.132537 32.528693 \n",
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"min 0.000000 0.052900 -16.918000 0.000000 0.029600 56.046000 \n",
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"25% 1.000000 0.139000 -9.134000 0.000000 0.203000 87.786000 \n",
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"50% 6.000000 0.303000 -8.320000 1.000000 0.299000 91.973000 \n",
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"75% 8.000000 0.399000 -6.442000 1.000000 0.380000 130.990000 \n",
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2021-02-01 21:43:27 +00:00
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"max 11.000000 0.796000 -2.002000 1.000000 0.749000 188.050000 \n",
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"\n",
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" time_signature valence \n",
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2021-02-03 16:08:06 +00:00
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"mean 3.996203 0.511429 \n",
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"std 0.177908 0.198530 \n",
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"min 3.000000 0.038300 \n",
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"25% 4.000000 0.373000 \n",
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"50% 4.000000 0.516000 \n",
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"75% 4.000000 0.638000 \n",
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"max 5.000000 0.959000 "
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],
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2021-02-03 16:08:06 +00:00
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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>0.218702</td>\n <td>0.574796</td>\n <td>202421.407595</td>\n <td>0.678473</td>\n <td>0.053497</td>\n <td>5.482911</td>\n <td>0.305708</td>\n <td>-8.005934</td>\n <td>0.695570</td>\n <td>0.289666</td>\n <td>110.452840</td>\n <td>3.996203</td>\n <td>0.511429</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.215092</td>\n <td>0.162472</td>\n <td>59794.180372</td>\n <td>0.141701</td>\n <td>0.186326</td>\n <td>3.492010</td>\n <td>0.180859</td>\n <td>2.022733</td>\n <td>0.460311</td>\n <td>0.132537</td>\n <td>32.528693</td>\n <td>0.177908</td>\n <td>0.198530</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000843</td>\n <td>0.256000</td>\n <td>48507.000000</td>\n <td>0.176000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.052900</td>\n <td>-16.918000</td>\n <td>0.000000</td>\n <td>0.029600</td>\n <td>56.046000</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>0.584000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.139000</td>\n <td>-9.134000</td>\n <td>0.000000</td>\n <td>0.203000</td>\n <td>87.786000</td>\n <td>4.000000</td>\n <td>0.373000</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>0.668000</td>\n <td>0.000004</td>\n <td>6.000000</td>\n <td>0.303000</td>\n <td>-8.320000</td>\n <td>1.000000</td>\n <td>0.299000</td>\n <td>91.973000</td>\n <td>4.000000</td>\n <td>0.516000</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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2021-02-01 21:43:27 +00:00
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},
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"metadata": {},
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2021-02-03 16:08:06 +00:00
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"execution_count": 3
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2021-02-01 21:43:27 +00:00
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}
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],
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"source": [
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2021-02-03 16:08:06 +00:00
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"artist_name = \"Freddie Gibbs\".lower()\n",
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"artist_frame = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name] # FILTER SCROBBLES\n",
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2021-02-01 21:43:27 +00:00
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"artist_frame = artist_frame.sort_index(ascending=False) # SORT\n",
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"# artist_frame = artist_frame.loc[:, descriptor_headers] # DESCRIPTORS\n",
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"\n",
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"total_time = artist_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'{artist_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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"artist_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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2021-02-03 16:08:06 +00:00
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"execution_count": 11,
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2021-02-01 21:43:27 +00:00
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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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2021-02-03 16:08:06 +00:00
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|
2021-02-01 21:43:27 +00:00
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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": [
|
2021-02-03 16:08:06 +00:00
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|
|
"filtered_artist = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name]\n",
|
2021-02-01 21:43:27 +00:00
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|
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"# resample by day and mean\n",
|
2021-02-03 16:08:06 +00:00
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"filtered_artist = filtered_artist.resample(\"2W\").count()\n",
|
2021-02-01 21:43:27 +00:00
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"\n",
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"filtered_artist[\"energy\"].plot()\n",
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"\n",
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"plt.title(f\"{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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|
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"cell_type": "code",
|
2021-02-03 16:08:06 +00:00
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"execution_count": 5,
|
2021-02-01 21:43:27 +00:00
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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>",
|
2021-02-03 16:08:06 +00:00
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|
2021-02-01 21:43:27 +00:00
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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": [
|
2021-02-03 16:08:06 +00:00
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"filtered_artist = scrobbles[scrobbles[\"artist\"].str.lower() == artist_name]\n",
|
2021-02-01 21:43:27 +00:00
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"# select only descriptor float columns\n",
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"filtered_artist = filtered_artist.loc[:, float_headers]\n",
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|
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"# resample by day and mean\n",
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"filtered_artist = filtered_artist.resample(\"1M\").mean()\n",
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"\n",
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|
|
"# filtered_playlist[\"energy\"].plot()\n",
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"filtered_artist.plot()\n",
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"\n",
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"plt.title(f\"{artist_name} Characteristics Over Time\")\n",
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|
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"plt.legend(loc = \"upper left\", fontsize = \"xx-small\")\n",
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"plt.ylim([0, 1])\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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"source": [
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|
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|
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"# Artist Listening Time"
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],
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"cell_type": "markdown",
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"metadata": {}
|
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|
|
},
|
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|
{
|
|
|
|
|
"cell_type": "code",
|
2021-02-03 16:08:06 +00:00
|
|
|
"execution_count": 48,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"limit = 35\n",
|
|
|
|
|
"artists = fmnet.top_artists(period=fmnet.Range.OVERALL, limit=limit)\n",
|
|
|
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|
"\n",
|
|
|
|
|
"filtered_artists = [scrobbles[scrobbles[\"artist\"] == i.name] for i in artists]\n",
|
|
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|
|
"\n",
|
|
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|
|
"### 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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|
|
},
|
|
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|
|
{
|
|
|
|
|
"source": [
|
|
|
|
|
"For these charts the ordering is left as retrieved from the API, i.e by most played."
|
|
|
|
|
],
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {}
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 49,
|
2021-02-01 21:43:27 +00:00
|
|
|
"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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2021-02-03 16:08:06 +00:00
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"text/plain": "<Figure size 840x1050 with 1 Axes>",
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|
2021-02-01 21:43:27 +00:00
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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": [
|
2021-02-03 16:08:06 +00:00
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"plt.figure(figsize=fig_size)\n",
|
2021-02-01 21:43:27 +00:00
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"plt.barh(np.arange(len(artists))[::-1], np.array(artists_time) / (1000 * 60 * 60) )\n",
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"plt.yticks(np.arange(len(artists))[::-1], labels=[i.name for i in artists])\n",
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"plt.xlabel(\"Time (Hours)\")\n",
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"plt.grid(axis=\"x\")\n",
|
2021-02-03 16:08:06 +00:00
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"plt.title(\"Time Listened (Since Nov 17)\")\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": "code",
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"execution_count": 50,
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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 840x1050 with 1 Axes>",
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"image/png": "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},
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"metadata": {
|
|
|
|
|
"needs_background": "light"
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}
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}
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],
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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) / (artists_track_count * artists_average_track_length) )\n",
|
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|
"plt.yticks(np.arange(len(artists))[::-1], labels=[i.name for i in artists])\n",
|
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|
|
"plt.xlabel(\"Average plays per song\")\n",
|
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|
|
"plt.grid(axis=\"x\")\n",
|
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|
|
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"plt.title(\"Normalised listening time\")\n",
|
2021-02-01 21:43:27 +00:00
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"plt.show()"
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]
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},
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{
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"source": [
|
|
|
|
|
"# Imports & Setup"
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|
],
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"cell_type": "markdown",
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|
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"metadata": {}
|
|
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|
|
},
|
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|
|
{
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|
|
"cell_type": "code",
|
2021-02-03 16:08:06 +00:00
|
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|
"execution_count": 1,
|
2021-02-01 21:43:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"from datetime import datetime\n",
|
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|
"\n",
|
|
|
|
|
"from google.cloud import bigquery\n",
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
"import matplotlib as mpl\n",
|
|
|
|
|
"mpl.rcParams['figure.dpi'] = 120\n",
|
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|
"\n",
|
|
|
|
|
"from analysis.net import get_spotnet, get_fmnet, get_playlist, track_frame\n",
|
|
|
|
|
"from analysis.query import *\n",
|
2021-02-03 16:08:06 +00:00
|
|
|
"from analysis import float_headers, days_since\n",
|
2021-02-01 21:43:27 +00:00
|
|
|
"\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",
|
2021-02-03 16:08:06 +00:00
|
|
|
"execution_count": 2,
|
2021-02-01 21:43:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
2021-02-03 16:08:06 +00:00
|
|
|
"scrobbles = get_query(cache=cache)"
|
2021-02-01 21:43:27 +00:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"source": [
|
|
|
|
|
"## Write Scrobble Frame"
|
|
|
|
|
],
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {}
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
2021-02-03 16:08:06 +00:00
|
|
|
"scrobbles.reset_index().to_csv(cache, sep='\\t')"
|
2021-02-01 21:43:27 +00:00
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
]
|
|
|
|
|
}
|
