407 lines
265 KiB
Plaintext
407 lines
265 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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},
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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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"# Listening Analysis\n",
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"\n",
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"Combining Spotify & Last.fm data for exploring habits and trends\n",
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"Uses two data sources,\n",
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"\n",
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"1. Last.fm scrobbles\n",
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"2. Spotify audio features\n",
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"\n",
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"The two are joined by searching Last.fm tracks on Spotify to get a Uri, the track name and artist name are provided for the query.\n",
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"These Uris can be used to retrieve Spotify feature descriptors. `all_joined()` gets a BigQuery of that joins the scrobble time series with their audio features and provides this as a panda frame."
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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": 14,
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"metadata": {},
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"outputs": [
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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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"track object\n",
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"album object\n",
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"artist object\n",
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"time object\n",
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"uri object\n",
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"acousticness float64\n",
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"danceability float64\n",
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"duration_ms int64\n",
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"energy float64\n",
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"instrumentalness float64\n",
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"key int64\n",
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"liveness float64\n",
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"loudness float64\n",
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"mode int64\n",
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"speechiness float64\n",
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"tempo float64\n",
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"time_signature int64\n",
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"valence float64\n",
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"dtype: object"
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]
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},
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"metadata": {},
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"execution_count": 14
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}
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],
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"source": [
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"scrobbles.dtypes"
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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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"tags": []
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},
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"outputs": [
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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 0.170649 0.589141 2.422924e+05 0.711968 0.213591 \n",
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"std 0.246679 0.173905 1.220714e+05 0.204289 0.335353 \n",
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"min 0.000000 0.000000 1.578700e+04 0.000000 0.000000 \n",
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"25% 0.004320 0.470000 1.893220e+05 0.586000 0.000000 \n",
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"50% 0.045500 0.599000 2.264410e+05 0.749000 0.001100 \n",
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"75% 0.237000 0.724000 2.787440e+05 0.878000 0.394000 \n",
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"max 0.996000 0.981000 4.995315e+06 0.999000 0.995000 \n",
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"\n",
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" key liveness loudness mode speechiness tempo \\\n",
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"mean 5.328584 0.216903 -7.127309 0.581856 0.146982 124.640429 \n",
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"std 3.673929 0.173524 3.646891 0.493257 0.136440 30.809049 \n",
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"min 0.000000 0.000000 -60.000000 0.000000 0.000000 0.000000 \n",
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"25% 2.000000 0.099900 -8.590000 0.000000 0.047500 97.805000 \n",
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"50% 6.000000 0.141000 -6.472000 1.000000 0.080800 124.992000 \n",
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"75% 9.000000 0.300000 -4.827000 1.000000 0.223000 143.188000 \n",
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"max 11.000000 0.995000 3.108000 1.000000 0.966000 248.028000 \n",
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"\n",
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" time_signature valence \n",
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"mean 3.957806 0.418024 \n",
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"std 0.356726 0.236941 \n",
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"min 0.000000 0.000000 \n",
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"25% 4.000000 0.221000 \n",
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"50% 4.000000 0.398000 \n",
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"75% 4.000000 0.597000 \n",
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"max 5.000000 0.983000 "
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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>0.170649</td>\n <td>0.589141</td>\n <td>2.422924e+05</td>\n <td>0.711968</td>\n <td>0.213591</td>\n <td>5.328584</td>\n <td>0.216903</td>\n <td>-7.127309</td>\n <td>0.581856</td>\n <td>0.146982</td>\n <td>124.640429</td>\n <td>3.957806</td>\n <td>0.418024</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.246679</td>\n <td>0.173905</td>\n <td>1.220714e+05</td>\n <td>0.204289</td>\n <td>0.335353</td>\n <td>3.673929</td>\n <td>0.173524</td>\n <td>3.646891</td>\n <td>0.493257</td>\n <td>0.136440</td>\n <td>30.809049</td>\n <td>0.356726</td>\n <td>0.236941</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>1.578700e+04</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>-60.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.004320</td>\n <td>0.470000</td>\n <td>1.893220e+05</td>\n <td>0.586000</td>\n <td>0.000000</td>\n <td>2.000000</td>\n <td>0.099900</td>\n <td>-8.590000</td>\n <td>0.000000</td>\n <td>0.047500</td>\n <td>97.805000</td>\n <td>4.000000</td>\n <td>0.221000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>0.045500</td>\n <td>0.599000</td>\n <td>2.264410e+05</td>\n <td>0.749000</td>\n <td>0.001100</td>\n <td>6.000000</td>\n <td>0.141000</td>\n <td>-6.472000</td>\n <td>1.000000</td>\n <td>0.080800</td>\n <td>124.992000</td>\n <td>4.000000</td>\n <td>0.398000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>0.237000</td>\n <td>0.724000</td>\n <td>2.787440e+05</td>\n <td>0.878000</td>\n <td>0.394000</td>\n <td>9.000000</td>\n <td>0.300000</td>\n <td>-4.827000</td>\n <td>1.000000</td>\n <td>0.223000</td>\n <td>143.188000</td>\n <td>4.000000</td>\n <td>0.597000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>0.996000</td>\n <td>0.981000</td>\n <td>4.995315e+06</td>\n <td>0.999000</td>\n <td>0.995000</td>\n <td>11.000000</td>\n <td>0.995000</td>\n <td>3.108000</td>\n <td>1.000000</td>\n <td>0.966000</td>\n <td>248.028000</td>\n <td>5.000000</td>\n <td>0.983000</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 4
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}
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],
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"source": [
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"scrobbles.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": 15,
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"metadata": {},
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"outputs": [
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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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" track \\\n",
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"34017 Blackbird - Gorgon City Remix \n",
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"81549 Lanterns - Dead Man's Chest Remix \n",
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"46080 ID Check - Original Mix \n",
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"43376 Up & Down \n",
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"73918 Cuatro \n",
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"... ... \n",
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"74391 Julia \n",
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"61000 Site Zero / The Vault \n",
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"32139 Reminder (feat. How To Dress Well) \n",
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"78339 Monsoon \n",
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"59279 Let Go (interlude) \n",
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"\n",
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" album artist \\\n",
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"34017 Blackbird EP Joeski \n",
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"81549 Lanterns / Lanterns (Dead Man's Chest Remix) Tim Reaper \n",
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"46080 Toolroom Ibiza 2019 Ben A \n",
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"43376 Emotion EP Purple Disco Machine \n",
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"73918 Tomahawk EP Mystic State \n",
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"... ... ... \n",
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"74391 Void RL Grime \n",
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"61000 Void RL Grime \n",
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"32139 Void RL Grime \n",
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"78339 Void RL Grime \n",
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"59279 Void RL Grime \n",
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"\n",
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" time uri \\\n",
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"34017 2020-12-31 18:35:28+00:00 spotify:track:3eGyeq8R8PscX1d13c9eJP \n",
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"81549 2020-12-31 18:28:13+00:00 spotify:track:3lc7wN7T29s7uRbPZR0hTH \n",
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"46080 2020-12-31 18:22:07+00:00 spotify:track:4x94xmQhUnd59k8oGM7AkG \n",
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"43376 2020-12-31 17:52:23+00:00 spotify:track:11DRarpv190YnCAXt85uFA \n",
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"73918 2020-12-31 17:00:28+00:00 spotify:track:6JBKvAWsMvo68a9pMa9Ujn \n",
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"... ... ... \n",
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"74391 2017-11-03 03:35:27+00:00 spotify:track:4or82pWT9zvQNIoGckZiYb \n",
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"61000 2017-11-03 03:28:51+00:00 spotify:track:762ME2OHjuGo4xTbfZhpok \n",
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"32139 2017-11-03 02:54:37+00:00 spotify:track:2JUdMBlA5JzuemLGzZNDrf \n",
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"78339 2017-11-03 02:50:23+00:00 spotify:track:0jYAtTuRsRdHMuvaOXIAj5 \n",
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"59279 2017-11-03 02:43:01+00:00 spotify:track:39FvWuHBtYQTJNdisJxZIG \n",
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"\n",
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" acousticness danceability duration_ms energy instrumentalness key \\\n",
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"34017 0.000542 0.803 389834 0.857 0.840 4 \n",
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"81549 0.001530 0.537 440255 0.868 0.877 10 \n",
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"46080 0.001720 0.809 372614 0.982 0.911 6 \n",
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"43376 0.032000 0.758 409961 0.913 0.739 5 \n",
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"73918 0.040300 0.621 342866 0.680 0.803 9 \n",
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"... ... ... ... ... ... ... \n",
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"74391 0.003340 0.573 301429 0.932 0.744 9 \n",
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"61000 0.683000 0.289 464015 0.404 0.854 7 \n",
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"32139 0.683000 0.593 260075 0.560 0.109 3 \n",
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"78339 0.034600 0.546 254815 0.850 0.680 10 \n",
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"59279 0.181000 0.361 153346 0.727 0.710 7 \n",
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"\n",
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" liveness loudness mode speechiness tempo time_signature valence \n",
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"34017 0.0787 -7.273 0 0.0449 125.016 4 0.2230 \n",
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"81549 0.5730 -7.319 0 0.0618 157.015 4 0.2650 \n",
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"46080 0.0657 -8.690 0 0.0460 123.992 4 0.8240 \n",
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"43376 0.0304 -6.712 1 0.0518 117.997 4 0.7230 \n",
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"73918 0.2890 -10.943 0 0.0484 139.989 4 0.2190 \n",
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"... ... ... ... ... ... ... ... \n",
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"74391 0.1120 -5.158 0 0.0500 168.008 4 0.1610 \n",
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"61000 0.3280 -12.815 0 0.0352 92.873 4 0.0285 \n",
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"32139 0.1040 -7.059 0 0.0447 113.895 4 0.3630 \n",
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"78339 0.1120 -3.366 0 0.0386 161.996 4 0.3020 \n",
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"59279 0.1980 -8.480 1 0.0519 104.380 4 0.0368 \n",
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"\n",
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"[92217 rows x 18 columns]"
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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>track</th>\n <th>album</th>\n <th>artist</th>\n <th>time</th>\n <th>uri</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>34017</th>\n <td>Blackbird - Gorgon City Remix</td>\n <td>Blackbird EP</td>\n <td>Joeski</td>\n <td>2020-12-31 18:35:28+00:00</td>\n <td>spotify:track:3eGyeq8R8PscX1d13c9eJP</td>\n <td>0.000542</td>\n <td>0.803</td>\n <td>389834</td>\n <td>0.857</td>\n <td>0.840</td>\n <td>4</td>\n <td>0.0787</td>\n <td>-7.273</td>\n <td>0</td>\n <td>0.0449</td>\n <td>125.016</td>\n <td>4</td>\n <td>0.2230</td>\n </tr>\n <tr>\n <th>81549</th>\n <td>Lanterns - Dead Man's Chest Remix</td>\n <td>Lanterns / Lanterns (Dead Man's Chest Remix)</td>\n <td>Tim Reaper</td>\n <td>2020-12-31 18:28:13+00:00</td>\n <td>spotify:track:3lc7wN7T29s7uRbPZR0hTH</td>\n <td>0.001530</td>\n <td>0.537</td>\n <td>440255</td>\n <td>0.868</td>\n <td>0.877</td>\n <td>10</td>\n <td>0.5730</td>\n <td>-7.319</td>\n <td>0</td>\n <td>0.0618</td>\n <td>157.015</td>\n <td>4</td>\n <td>0.2650</td>\n </tr>\n <tr>\n <th>46080</th>\n <td>ID Check - Original Mix</td>\n <td>Toolroom Ibiza 2019</td>\n <td>Ben A</td>\n <td>2020-12-31 18:22:07+00:00</td>\n <td>spotify:track:4x94xmQhUnd59k8oGM7AkG</td>\n <td>0.001720</td>\n <td>0.809</td>\n <td>372614</td>\n <td>0.982</td>\n <td>0.911</td>\n <td>6</td>\n <td>0.0657</td>\n <td>-8.690</td>\n <td>0</td>\n <td>0.0460</td>\n <td>123.992</td>\n <td>4</td>\n <td>0.8240</td>\n </tr>\n <tr>\n <th>43376</th>\n <td>Up & Down</td>\n <td>Emotion EP</td>\n <td>Purple Disco Machine</td>\n <td>2020-12-31 17:52:23+00:00</td>\n <td>spotify:track:11DRarpv190YnCAXt85uFA</td>\n <td>0.032000</td>\n <td>0.758</td>\n <td>409961</td>\n <td>0.913</td>\n <td>0.739</td>\n <td>5</td>\n <td>0.0304</td>\n <td>-6.712</td>\n <td>1</td>\n <td>0.0518</td>\n <td>117.997</td>\n <td>4</td>\n <td>0.7230</td>\n </tr>\n <tr>\n <th>73918</th>\n <td>Cuatro</td>\n <td>Tomahawk EP</td>\n <td>Mystic State</td>\n <td>2020-12-31 17:00:28+00:00</td>\n <td>spotify:track:6JBKvAWsMvo68a9pMa9Ujn</td>\n <td>0.040300</td>\n <td>0.621</td>\n <td>342866</td>\n <td>0.680</td>\n <td>0.803</td>\n <td>9</td>\n <td>0.2890</td>\n <td>-10.943</td>\n <td>0</td>\n <td>0.0484</td>\n <td>139.989</td>\n <td>4</td>\n <td>0.2190</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>74391</th>\n <td>Julia</td>\n <td>Void</td>\n <td>RL Grime</td>\n <td>2017-11-03 03:35:27+00:00</td>\n <td>spotify:track:4or82pWT9zvQNIoGckZiYb</td>\n <td>0.003340</t
|
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},
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"metadata": {},
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"execution_count": 15
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}
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],
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"source": [
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"scrobbles.sort_values(by=\"time\", ascending=False)"
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]
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},
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{
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"source": [
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"# Rap\n",
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"\n",
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"## Descriptor Stats"
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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": 10,
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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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"28 days spent listening since Nov. 2017\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 0.179491 0.654732 217263.266814 0.711093 0.007111 \n",
|
||
|
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"std 0.182540 0.149069 60534.505476 0.142709 0.047975 \n",
|
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|
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"min 0.000063 0.261000 81967.000000 0.274000 0.000000 \n",
|
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"25% 0.039900 0.546000 180893.000000 0.604000 0.000000 \n",
|
||
|
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"50% 0.130000 0.668000 208013.000000 0.720000 0.000000 \n",
|
||
|
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"75% 0.253000 0.759000 245200.000000 0.821000 0.000035 \n",
|
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|
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"max 0.864000 0.975000 774920.000000 0.993000 0.847000 \n",
|
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"\n",
|
||
|
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" key liveness loudness mode speechiness tempo \\\n",
|
||
|
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"mean 5.231103 0.243851 -6.739009 0.653965 0.275643 120.313818 \n",
|
||
|
|
"std 3.752245 0.167658 2.351719 0.475725 0.128600 31.569949 \n",
|
||
|
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"min 0.000000 0.033300 -17.485000 0.000000 0.037800 61.113000 \n",
|
||
|
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"25% 1.000000 0.114000 -8.324000 0.000000 0.186000 91.973000 \n",
|
||
|
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"50% 6.000000 0.184000 -6.553000 1.000000 0.282000 120.051000 \n",
|
||
|
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"75% 8.000000 0.339000 -5.146000 1.000000 0.362000 140.144000 \n",
|
||
|
|
"max 11.000000 0.979000 -1.354000 1.000000 0.827000 207.982000 \n",
|
||
|
|
"\n",
|
||
|
|
" time_signature valence \n",
|
||
|
|
"mean 4.008950 0.465955 \n",
|
||
|
|
"std 0.252267 0.222555 \n",
|
||
|
|
"min 1.000000 0.027200 \n",
|
||
|
|
"25% 4.000000 0.293000 \n",
|
||
|
|
"50% 4.000000 0.457000 \n",
|
||
|
|
"75% 4.000000 0.628000 \n",
|
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|
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"max 5.000000 0.961000 "
|
||
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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>0.179491</td>\n <td>0.654732</td>\n <td>217263.266814</td>\n <td>0.711093</td>\n <td>0.007111</td>\n <td>5.231103</td>\n <td>0.243851</td>\n <td>-6.739009</td>\n <td>0.653965</td>\n <td>0.275643</td>\n <td>120.313818</td>\n <td>4.008950</td>\n <td>0.465955</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.182540</td>\n <td>0.149069</td>\n <td>60534.505476</td>\n <td>0.142709</td>\n <td>0.047975</td>\n <td>3.752245</td>\n <td>0.167658</td>\n <td>2.351719</td>\n <td>0.475725</td>\n <td>0.128600</td>\n <td>31.569949</td>\n <td>0.252267</td>\n <td>0.222555</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000063</td>\n <td>0.261000</td>\n <td>81967.000000</td>\n <td>0.274000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.033300</td>\n <td>-17.485000</td>\n <td>0.000000</td>\n <td>0.037800</td>\n <td>61.113000</td>\n <td>1.000000</td>\n <td>0.027200</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.039900</td>\n <td>0.546000</td>\n <td>180893.000000</td>\n <td>0.604000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.114000</td>\n <td>-8.324000</td>\n <td>0.000000</td>\n <td>0.186000</td>\n <td>91.973000</td>\n <td>4.000000</td>\n <td>0.293000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>0.130000</td>\n <td>0.668000</td>\n <td>208013.000000</td>\n <td>0.720000</td>\n <td>0.000000</td>\n <td>6.000000</td>\n <td>0.184000</td>\n <td>-6.553000</td>\n <td>1.000000</td>\n <td>0.282000</td>\n <td>120.051000</td>\n <td>4.000000</td>\n <td>0.457000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>0.253000</td>\n <td>0.759000</td>\n <td>245200.000000</td>\n <td>0.821000</td>\n <td>0.000035</td>\n <td>8.000000</td>\n <td>0.339000</td>\n <td>-5.146000</td>\n <td>1.000000</td>\n <td>0.362000</td>\n <td>140.144000</td>\n <td>4.000000</td>\n <td>0.628000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>0.864000</td>\n <td>0.975000</td>\n <td>774920.000000</td>\n <td>0.993000</td>\n <td>0.847000</td>\n <td>11.000000</td>\n <td>0.979000</td>\n <td>-1.354000</td>\n <td>1.000000</td>\n <td>0.827000</td>\n <td>207.982000</td>\n <td>5.000000</td>\n <td>0.961000</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 10
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"rap = get_playlist(\"RAP\", spotnet)\n",
|
||
|
|
"rap_frame = pd.merge(track_frame(rap.tracks), scrobbles, on=['track', 'artist']) # FILTER SCROBBLES\n",
|
||
|
|
"rap_frame = rap_frame.sort_values(by=\"time\", ascending=False) # SORT\n",
|
||
|
|
"rap_frame = rap_frame.loc[:, descriptor_headers] # DESCRIPTORS\n",
|
||
|
|
"\n",
|
||
|
|
"total_time = rap_frame[\"duration_ms\"].sum() / (1000 * 60 * 60 * 24)\n",
|
||
|
|
"print(f'{total_time:.0f} days spent listening since Nov. 2017')\n",
|
||
|
|
"\n",
|
||
|
|
"rap_frame.describe()[1:]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"# Playlist Comparisons"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"playlist_names = [\"RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\", \"POP\"]\n",
|
||
|
|
"playlists = [get_playlist(i, spotnet) for i in playlist_names]\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_playlists = [pd.merge(track_frame(i.tracks), scrobbles, on=['track', 'artist']) for i in playlists]\n",
|
||
|
|
"filtered_playlists = [i.drop_duplicates(['uri']) for i in filtered_playlists]\n",
|
||
|
|
"filtered_playlists = [i.loc[:, float_headers] for i in filtered_playlists]\n",
|
||
|
|
"\n",
|
||
|
|
"playlist_mean = [i.mean() for i in filtered_playlists]\n",
|
||
|
|
"playlist_std = [i.std() for i in filtered_playlists]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 1 Axes>",
|
||
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|
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x_pos = [i for i, _ in enumerate(float_headers)]\n",
|
||
|
|
"for playlist, mean, std in zip(filtered_playlists, playlist_mean, playlist_std):\n",
|
||
|
|
" plt.plot(x_pos, mean)\n",
|
||
|
|
" \n",
|
||
|
|
"plt.legend(playlist_names)\n",
|
||
|
|
"plt.xticks(x_pos, [i[:6] for i in float_headers])\n",
|
||
|
|
"plt.title('Average Playlist Descriptor')\n",
|
||
|
|
"plt.grid()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"# Imports & Setup"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from google.cloud import bigquery\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import matplotlib as mpl\n",
|
||
|
|
"mpl.rcParams['figure.dpi'] = 200\n",
|
||
|
|
"\n",
|
||
|
|
"from analysis.net import get_spotnet, get_playlist, track_frame\n",
|
||
|
|
"from analysis.query import *\n",
|
||
|
|
"from analysis import descriptor_headers, float_headers\n",
|
||
|
|
"\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"\n",
|
||
|
|
"client = bigquery.Client()\n",
|
||
|
|
"spotnet = get_spotnet()\n",
|
||
|
|
"cache = 'query.csv'"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Read Scrobble Frame"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles = all_joined(limit=-1) # load dataset as panda frame"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles = pd.read_csv(cache, sep='\\t', index_col=0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Write Scrobble Frame"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scrobbles.to_csv(cache, sep='\\t')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|