266 lines
421 KiB
Plaintext
266 lines
421 KiB
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": "3.8.4-final"
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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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"# Artist Investigations\n",
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"\n",
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"Similar to the playlist 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": 48,
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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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"2,220 scrobbles\n5 days spent listening since Nov. 2017\n6.43 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 0.212988 0.556427 206109.940541 0.689568 0.038141 \n",
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"std 0.195843 0.145240 62979.977621 0.132678 0.159018 \n",
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"min 0.000843 0.256000 48507.000000 0.176000 0.000000 \n",
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"25% 0.049700 0.450000 168009.000000 0.604000 0.000000 \n",
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"50% 0.161000 0.528000 201158.000000 0.668000 0.000002 \n",
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"75% 0.261000 0.604000 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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"mean 5.493694 0.328512 -8.165929 0.677928 0.297323 110.274670 \n",
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"std 3.646806 0.184782 1.846141 0.467376 0.127891 32.589491 \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 0.161000 -9.363000 0.000000 0.203000 88.607000 \n",
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"50% 6.000000 0.325000 -8.336000 1.000000 0.299000 91.973000 \n",
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"75% 8.000000 0.464000 -6.832000 1.000000 0.380000 130.396000 \n",
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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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"mean 3.997297 0.507103 \n",
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"std 0.150085 0.194317 \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 0.504000 \n",
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"75% 4.000000 0.631000 \n",
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"max 5.000000 0.959000 "
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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>0.212988</td>\n <td>0.556427</td>\n <td>206109.940541</td>\n <td>0.689568</td>\n <td>0.038141</td>\n <td>5.493694</td>\n <td>0.328512</td>\n <td>-8.165929</td>\n <td>0.677928</td>\n <td>0.297323</td>\n <td>110.274670</td>\n <td>3.997297</td>\n <td>0.507103</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.195843</td>\n <td>0.145240</td>\n <td>62979.977621</td>\n <td>0.132678</td>\n <td>0.159018</td>\n <td>3.646806</td>\n <td>0.184782</td>\n <td>1.846141</td>\n <td>0.467376</td>\n <td>0.127891</td>\n <td>32.589491</td>\n <td>0.150085</td>\n <td>0.194317</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>0.450000</td>\n <td>168009.000000</td>\n <td>0.604000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.161000</td>\n <td>-9.363000</td>\n <td>0.000000</td>\n <td>0.203000</td>\n <td>88.607000</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>0.528000</td>\n <td>201158.000000</td>\n <td>0.668000</td>\n <td>0.000002</td>\n <td>6.000000</td>\n <td>0.325000</td>\n <td>-8.336000</td>\n <td>1.000000</td>\n <td>0.299000</td>\n <td>91.973000</td>\n <td>4.000000</td>\n <td>0.504000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>0.261000</td>\n <td>0.604000</td>\n <td>226520.000000</td>\n <td>0.805000</td>\n <td>0.000648</td>\n <td>8.000000</td>\n <td>0.464000</td>\n <td>-6.832000</td>\n <td>1.000000</td>\n <td>0.380000</td>\n <td>130.396000</td>\n <td>4.000000</td>\n <td>0.631000</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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},
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"metadata": {},
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"execution_count": 48
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}
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],
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"source": [
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"artist_name = \"Freddie Gibbs\"\n",
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"artist_frame = scrobbles.query(f'artist == \"{artist_name}\"') # FILTER SCROBBLES\n",
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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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"execution_count": 52,
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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_artist = scrobbles.query(f'artist == \"{artist_name}\"')\n",
|
||
|
|
"# resample by day and mean\n",
|
||
|
|
"filtered_artist = filtered_artist.resample(\"1W\").count()\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_artist[\"energy\"].plot()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title(f\"{artist_name} Scrobbles\")\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 51,
|
||
|
|
"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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||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"filtered_artist = scrobbles.query(f'artist == \"{artist_name}\"')\n",
|
||
|
|
"# 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",
|
||
|
|
"filtered_artist.plot()\n",
|
||
|
|
"\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()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"# Artist Listening Time"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 720x480 with 1 Axes>",
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvYAAAHRCAYAAAABoP0zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAABJ0AAASdAHeZh94AACYvElEQVR4nOzdebxVVf3/8ddbc0oFR7SsrzjlmFo/DGfRcsrSSi1zRM0hs1IzTUsltdSy1AbSHEDMsZxnHEBQU8PUnCfEGUEFryigwuf3x1oHNptz7j333nO53Hvfz8fjPA5n7bXXXmffC3z22p+9liICMzMzMzPr2hbo7A6YmZmZmVn7ObA3MzMzM+sGHNibmZmZmXUDDuzNzMzMzLoBB/ZmZmZmZt2AA3szMzMzs27Agb2ZmZmZWTfgwN7MzMzMrBtwYG9mZmZm1g04sDczMzMz6wYc2JuZmZmZdQMO7M26OUkDJYWkgZ3dF2s8SYPyz3dAZ/elrSQNzd+hb2f3pSeRNE7SuM7uR0skLSzpeUm3NKCtkZKiEf2y+YeSxySN7uy+dDYH9mZdSA5+WvMa2Nl9bgtJn5V0lqSnJH0oaaqkVyTdI+k3klbr7D7Wo62BUyFYH9T4Xs11LAfVVUgaMK9+BqXjfl7SjHzs37azrQ4NYnMfR3ZU+wU/AVYHflWlD93i34p6Ff6+hqSv16hT+ffjB/O6f7VIOlDSeZIezD+nkHRqM/VH1vH/24WV+hERwInA5pJ2mxffaX71qc7ugJm1yq+rlB0B9AbOASaXtj0KvAQ8ALzZgf1qGEnrAfcAywCPAxcD7wJ9gK8Ax5O+04ud1cf5zF+AK4BXOrsj7XAccDrwemd3ZD7wA9KgWwD7SzoxIj7poGN9tYPabRhJiwO/BO6IiP+WtrXl34p9gU93fM/nid9Juj0iZnR2R+rwB9L/U5OAN4CWLriGAiNrbPsx6Wd+a7EwIq6X9DTwG0lX52C/x3Fgb9aFRMSgclkele8NnB0R42rs+l7H9arhzib9oz0oIua6kJG0KrDwvO7U/Coi3gbe7ux+tEdEvEkXufDsSJIWBA4AmoB/AIcBOwPXdMTxIqIrXBzvCSxFCvTKzqaV/1ZERFe+AC56AViX9Ptyfif3pR57AE9HxMv5/6whzVWOiKHVyiWtCZwEvAVcX6XKxaRBgq8Cd7ajv12WU3HMurlaOfaVNBFJS+Rb2a/m29iPSvpWrvMpSb/M+a3TJL0o6fBmjrW9pFskvS1peq7/e0lLtaLLm+b3c6ptjIixEfFM6biV27aLSDpV0kuF458kqeqFgKS18q3tVyV9JOktSZfl/zzKdWelrEg6RNLj+Zy8JenvknoX6g7IKRArAyuXbh8PbcW5aJFq5NhL2kLSjZJey+divKQHJJ1UqBPAfvnjS4U+jiu1tYyk0yQ9nX9H3pN0l6TtqvRn1u+bpK3zz+Z9SU2Sbpa0dpV95koHyuc58ra+kq7Iv1fTJI2R9I0a56O3pLPz954m6RlJR0latd7zn+uMyB9PKv38BhTqLSLpF/l34cP8HUdL+m5Lx6hhR+BzwJXA33LZQTX6WDw/X5B0paQJkmZWfgbAVrlusf8jC23MlSqmlM/+E0n/lTQpf69xkq6X9LVcp9I+wFal9gcV2to5/568mX8H31BKkTmsFefkQOAj4Loq29r8b0WpbFbalaQN8+/p5Pzd75G0KVVIWlDSoZLuy38npkp6QdIFktYo1f2UpMPy38Gm3PYjkg6X1JZY7BTgQ+BkpbsadZG0hqRhkl5X+jfvjfy53N9z8znZpUY7/fP2f9Vz3Ii4LSJerrefzTg4vw+JiI+rbL8ivx/YgGN1SR6xN+vZFgLuII16XU8a3fo+cHUO2g4D+pNueU4Hdgf+LGliRFxZbEgpYBxEuhV+EzABWB84Gvi6pE0ioqmOPr1DCm6+ADzUyu9zFbAR8C/gY2CX3Kd+knYu3pqVtANpJHQh4EbSCNjngO8AO0naunzrP/sdsH3eZziwNSn4Wh3YJtcZR0qbOiJ/Pruw/6Ot/E6tlr/bzaSR3xtIKS7LAGuTfqaV0c1fA98CNmDOVK7JhbZWJt0S7wuMBm4DFge+Adwm6ZCIqDZi+A3S+b8VOBdYB/g6sJGkdfKdhnqsTPo9GAtckr/H94DrJX0tIioBOJIWBe4Gvgw8AlxKupv1S2CLOo8Hs4PI/UipHiML28blYy0M3E4Knp8B/kpK8dgNuFLShhFxfCuOCbODlqER8YSkh4HtJK3cTFC0GvAg8Bzp+y4G/I/0sx1IOn/F0exxLfRhKOnfgCeAYcBU4LPA5sAOpFHQR3ObJwEvM+do+kgASQcD5wHjSX9X3ialyKwP7A8MbqEfKF0s9wP+ExEfVqnSnn8rqukHHAP8G7gA+D9gV+Cu/PN8ttC3hUn/zm0LvApcRvr71hf4NnAv8HyuW/k3Znvg2Vx3Gunfjj+T/o3dp5V9fYOU3nJC7vNJzVcHSRuRfn5Lkv5deApYC9gb2CX/ffpPrn4xcAgpdanayHhlQGBoK/vdZpIWyf0JatylyHcEXge+Jkk9Mh0nIvzyy68u/CL9Rx1A3xrbB+btA2vsdyOwSKF8i1z+LvAfYKnCtlVJo2ePlNraOu9zf7F+6fhn1fl9zsz1x5P+s9oS6NXCPiPzPs8BSxfKFyX9Jx3APoXypUm5nm8D65TaWg+YAvy3VD40t/MK8H+F8k8Bo/K2r1Q5x+Pa8DMdlNsb1Iq6AwplV+eyDarUX67G96r1+zMSmAnsUSpfihTgTQVWqPLz/gT4ammf0/K2Y1rqAylAivw6qVR/+1x+S6n8hFx+OaBC+eeBiXnb0Dp/BgOa+xmQngsI4BbgU4XyPsz+u7VpK37mK+Vz9myh7PDczilV6hfPz2+b+3vRzDHn+P0kXQTNBMYAC1apv2zpcwAja7T9MGkwoE9Lv4PN9G+HfIw/19je5n8ravysq/07eUguH1wq/20uv4HCv5952yLA8oXPgyrfo3hegQWBC/O2Xeo8J5W/K18DlsjffQrwmSrH+0GhTMDTuXyvUpvfy+XPAAsUyp/NP8Nlqny/d0npMJ+qp9+l/Qfm453ayv2+n/cb3kK9a3O9dVrbt+7wciqOmR0REdMrHyJiNOmBs6WBYyNicmHbWOA+YD2lfOCKn+T3g4r18z5DSQHgXnX255ek0ZhlSf9B3QNMVkqpOFspb7aWUyJiUuHY00gBGKRc1Ip9SYHpSRHxVKm/T+Tjf0nSOlWOcXIU8nQjPdg4JH/8Sstfb56aWi6I+kfKkbQBaUT66oi4orgt/5xPIl087Vpl9ysi4q5S2d/ze2vO08vAHLNnRMTtpAuscjv7kQLT4yL/D5/rv8qcd00a4QBS8HBUFB5ujYgJpDQJSA/Ctqa9BZlzBPQy0oX0AaW/b0VvUf2h+rYIUgA4nXQe59wY8U4r2/uEdOes3E69v4P/l99rPX/Rnn8rqrkv5s7tvoj0PWb9ruWfxWGkv1+HFv/9BIiI6RExMdddgPSw53jgyCg86Jr//DNysN3KvhIRU0h/Bxdn9u9cLZuSRuf/HRGXltq5knSHYU3SnZmKi5l9F7fom6T/Hy6Njnuwu5rKHa2/N1srnWuY/fvTozgVx6xnmxzVH6B7A1iFNOpW9jrp344VmT2LySak/8B3l7R7lX0WBpaXtGxLwUH+T/JgSSeQRuz6k1Ir+gE/zdu+GxE3Vdn9nipl9wIzgC8VyjbJ7xuo+nSGX8jva5NuVxeNqVL/1fy+dJVtneFSUkrRg5KuJOWL3xcRr7Wyncp56l3jPC2f3+fKm6dx5+nRqD7rx6uF/iGpFykt5dWo/hD5va04ZrMkLUlKvXo9Sjnc2d35/UtVtlVrbwFSTvBMUvoLABHxrqQbSRdOO5FGh8seKweWbRURTfl43wQelXQ1Kf3qwaieCtOcS0mpIk9JuoL0d/O+SsBbp2Xz+6RqG9v5b0U1c/3ORsTHkt5izt/ZtUh3Nx6MiDdaaPMLpPSx54FfSapWZyrV/w7V4wLSwMpASWfngYlqvpzf766x/W5SUP8l0h1ISL+Lp5AumP9aqLt
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"artists = fmnet.top_artists(period=fmnet.Range.OVERALL, limit=20)\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_artists = [scrobbles.query(f'artist == \"{i}\"') for i in artists]\n",
|
||
|
|
"artists_time = [i[\"duration_ms\"].sum() for i in filtered_artists]\n",
|
||
|
|
"\n",
|
||
|
|
"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",
|
||
|
|
"plt.title(\"Time Spent Listening to Artists (Since Nov 17)\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"# Imports & Setup"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"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 descriptor_headers, 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": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles = get_query()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Write Scrobble Frame"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles.to_csv(cache, sep='\\t')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|