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"source": [
"# Listening Analysis\n",
"\n",
"Combining Spotify & Last.fm data for exploring habits and trends\n",
"Uses two data sources,\n",
"\n",
"1. Last.fm scrobbles\n",
"2. Spotify audio features\n",
"\n",
"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",
"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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"track object\n",
"album object\n",
"artist object\n",
"uri object\n",
"acousticness float64\n",
"danceability float64\n",
"duration_ms int64\n",
"energy float64\n",
"instrumentalness float64\n",
"key int64\n",
"liveness float64\n",
"loudness float64\n",
"mode int64\n",
"speechiness float64\n",
"tempo float64\n",
"time_signature int64\n",
"valence float64\n",
"dtype: object"
]
},
"metadata": {},
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"execution_count": 3
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}
],
"source": [
"scrobbles.dtypes"
]
},
{
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" acousticness danceability duration_ms energy instrumentalness \\\n",
"mean 0.170649 0.589141 2.422924e+05 0.711968 0.213591 \n",
"std 0.246679 0.173905 1.220714e+05 0.204289 0.335353 \n",
"min 0.000000 0.000000 1.578700e+04 0.000000 0.000000 \n",
"25% 0.004320 0.470000 1.893220e+05 0.586000 0.000000 \n",
"50% 0.045500 0.599000 2.264410e+05 0.749000 0.001100 \n",
"75% 0.237000 0.724000 2.787440e+05 0.878000 0.394000 \n",
"max 0.996000 0.981000 4.995315e+06 0.999000 0.995000 \n",
"\n",
" key liveness loudness mode speechiness tempo \\\n",
"mean 5.328584 0.216903 -7.127309 0.581856 0.146982 124.640429 \n",
"std 3.673929 0.173524 3.646891 0.493257 0.136440 30.809049 \n",
"min 0.000000 0.000000 -60.000000 0.000000 0.000000 0.000000 \n",
"25% 2.000000 0.099900 -8.590000 0.000000 0.047500 97.805000 \n",
"50% 6.000000 0.141000 -6.472000 1.000000 0.080800 124.992000 \n",
"75% 9.000000 0.300000 -4.827000 1.000000 0.223000 143.188000 \n",
"max 11.000000 0.995000 3.108000 1.000000 0.966000 248.028000 \n",
"\n",
" time_signature valence \n",
"mean 3.957806 0.418024 \n",
"std 0.356726 0.236941 \n",
"min 0.000000 0.000000 \n",
"25% 4.000000 0.221000 \n",
"50% 4.000000 0.398000 \n",
"75% 4.000000 0.597000 \n",
"max 5.000000 0.983000 "
],
"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>"
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"scrobbles.describe()[1:]"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
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" track \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 Blackbird - Gorgon City Remix \n",
"2020-12-31 18:28:13+00:00 Lanterns - Dead Man's Chest Remix \n",
"2020-12-31 18:22:07+00:00 ID Check - Original Mix \n",
"2020-12-31 17:52:23+00:00 Up & Down \n",
"2020-12-31 17:00:28+00:00 Cuatro \n",
"... ... \n",
"2017-11-03 03:35:27+00:00 Julia \n",
"2017-11-03 03:28:51+00:00 Site Zero / The Vault \n",
"2017-11-03 02:54:37+00:00 Reminder (feat. How To Dress Well) \n",
"2017-11-03 02:50:23+00:00 Monsoon \n",
"2017-11-03 02:43:01+00:00 Let Go (interlude) \n",
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"\n",
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" album \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 Blackbird EP \n",
"2020-12-31 18:28:13+00:00 Lanterns / Lanterns (Dead Man's Chest Remix) \n",
"2020-12-31 18:22:07+00:00 Toolroom Ibiza 2019 \n",
"2020-12-31 17:52:23+00:00 Emotion EP \n",
"2020-12-31 17:00:28+00:00 Tomahawk EP \n",
"... ... \n",
"2017-11-03 03:35:27+00:00 Void \n",
"2017-11-03 03:28:51+00:00 Void \n",
"2017-11-03 02:54:37+00:00 Void \n",
"2017-11-03 02:50:23+00:00 Void \n",
"2017-11-03 02:43:01+00:00 Void \n",
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"\n",
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" artist \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 Joeski \n",
"2020-12-31 18:28:13+00:00 Tim Reaper \n",
"2020-12-31 18:22:07+00:00 Ben A \n",
"2020-12-31 17:52:23+00:00 Purple Disco Machine \n",
"2020-12-31 17:00:28+00:00 Mystic State \n",
"... ... \n",
"2017-11-03 03:35:27+00:00 RL Grime \n",
"2017-11-03 03:28:51+00:00 RL Grime \n",
"2017-11-03 02:54:37+00:00 RL Grime \n",
"2017-11-03 02:50:23+00:00 RL Grime \n",
"2017-11-03 02:43:01+00:00 RL Grime \n",
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"\n",
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" uri acousticness \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 spotify:track:3eGyeq8R8PscX1d13c9eJP 0.000542 \n",
"2020-12-31 18:28:13+00:00 spotify:track:3lc7wN7T29s7uRbPZR0hTH 0.001530 \n",
"2020-12-31 18:22:07+00:00 spotify:track:4x94xmQhUnd59k8oGM7AkG 0.001720 \n",
"2020-12-31 17:52:23+00:00 spotify:track:11DRarpv190YnCAXt85uFA 0.032000 \n",
"2020-12-31 17:00:28+00:00 spotify:track:6JBKvAWsMvo68a9pMa9Ujn 0.040300 \n",
"... ... ... \n",
"2017-11-03 03:35:27+00:00 spotify:track:4or82pWT9zvQNIoGckZiYb 0.003340 \n",
"2017-11-03 03:28:51+00:00 spotify:track:762ME2OHjuGo4xTbfZhpok 0.683000 \n",
"2017-11-03 02:54:37+00:00 spotify:track:2JUdMBlA5JzuemLGzZNDrf 0.683000 \n",
"2017-11-03 02:50:23+00:00 spotify:track:0jYAtTuRsRdHMuvaOXIAj5 0.034600 \n",
"2017-11-03 02:43:01+00:00 spotify:track:39FvWuHBtYQTJNdisJxZIG 0.181000 \n",
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"\n",
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" danceability duration_ms energy \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 0.803 389834 0.857 \n",
"2020-12-31 18:28:13+00:00 0.537 440255 0.868 \n",
"2020-12-31 18:22:07+00:00 0.809 372614 0.982 \n",
"2020-12-31 17:52:23+00:00 0.758 409961 0.913 \n",
"2020-12-31 17:00:28+00:00 0.621 342866 0.680 \n",
"... ... ... ... \n",
"2017-11-03 03:35:27+00:00 0.573 301429 0.932 \n",
"2017-11-03 03:28:51+00:00 0.289 464015 0.404 \n",
"2017-11-03 02:54:37+00:00 0.593 260075 0.560 \n",
"2017-11-03 02:50:23+00:00 0.546 254815 0.850 \n",
"2017-11-03 02:43:01+00:00 0.361 153346 0.727 \n",
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"\n",
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" instrumentalness key liveness loudness mode \\\n",
"time \n",
"2020-12-31 18:35:28+00:00 0.840 4 0.0787 -7.273 0 \n",
"2020-12-31 18:28:13+00:00 0.877 10 0.5730 -7.319 0 \n",
"2020-12-31 18:22:07+00:00 0.911 6 0.0657 -8.690 0 \n",
"2020-12-31 17:52:23+00:00 0.739 5 0.0304 -6.712 1 \n",
"2020-12-31 17:00:28+00:00 0.803 9 0.2890 -10.943 0 \n",
"... ... ... ... ... ... \n",
"2017-11-03 03:35:27+00:00 0.744 9 0.1120 -5.158 0 \n",
"2017-11-03 03:28:51+00:00 0.854 7 0.3280 -12.815 0 \n",
"2017-11-03 02:54:37+00:00 0.109 3 0.1040 -7.059 0 \n",
"2017-11-03 02:50:23+00:00 0.680 10 0.1120 -3.366 0 \n",
"2017-11-03 02:43:01+00:00 0.710 7 0.1980 -8.480 1 \n",
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"\n",
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" speechiness tempo time_signature valence \n",
"time \n",
"2020-12-31 18:35:28+00:00 0.0449 125.016 4 0.2230 \n",
"2020-12-31 18:28:13+00:00 0.0618 157.015 4 0.2650 \n",
"2020-12-31 18:22:07+00:00 0.0460 123.992 4 0.8240 \n",
"2020-12-31 17:52:23+00:00 0.0518 117.997 4 0.7230 \n",
"2020-12-31 17:00:28+00:00 0.0484 139.989 4 0.2190 \n",
"... ... ... ... ... \n",
"2017-11-03 03:35:27+00:00 0.0500 168.008 4 0.1610 \n",
"2017-11-03 03:28:51+00:00 0.0352 92.873 4 0.0285 \n",
"2017-11-03 02:54:37+00:00 0.0447 113.895 4 0.3630 \n",
"2017-11-03 02:50:23+00:00 0.0386 161.996 4 0.3020 \n",
"2017-11-03 02:43:01+00:00 0.0519 104.380 4 0.0368 \n",
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"\n",
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"[92217 rows x 17 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>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 <tr>\n <th>time</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2020-12-31 18:35:28+00:00</th>\n <td>Blackbird - Gorgon City Remix</td>\n <td>Blackbird EP</td>\n <td>Joeski</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>2020-12-31 18:28:13+00:00</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>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>2020-12-31 18:22:07+00:00</th>\n <td>ID Check - Original Mix</td>\n <td>Toolroom Ibiza 2019</td>\n <td>Ben A</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>2020-12-31 17:52:23+00:00</th>\n <td>Up & Down</td>\n <td>Emotion EP</td>\n <td>Purple Disco Machine</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>2020-12-31 17:00:28+00:00</th>\n <td>Cuatro</td>\n <td>Tomahawk EP</td>\n <td>Mystic State</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 </tr>\n <tr>\n <th>2017-11-03 03:35
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},
"metadata": {},
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"execution_count": 5
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}
],
"source": [
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"scrobbles.sort_index(ascending=False)"
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]
},
{
"source": [
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"# Listening Parameters Over Time"
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],
"cell_type": "markdown",
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{
"cell_type": "code",
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"execution_count": 11,
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{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
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"# select only descriptor float columns\n",
"filtered_scrobbles = scrobbles.loc[:, float_headers]\n",
"# resample by month and mean\n",
"filtered_scrobbles = filtered_scrobbles.resample(\"3W\").mean()\n",
"\n",
"# filtered_scrobbles[\"instrumentalness\"].plot()\n",
"filtered_scrobbles.plot(linewidth=3)\n",
"\n",
"plt.title(f'Listening Characteristics Over Time')\n",
"plt.legend(loc = \"upper right\", fontsize = \"xx-small\")\n",
"plt.ylim([0, 1])\n",
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"plt.grid()\n",
"plt.show()"
]
},
{
"source": [
"# Imports & Setup"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
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"from datetime import datetime\n",
"\n",
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"from google.cloud import bigquery\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
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"mpl.rcParams['figure.dpi'] = 120\n",
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"\n",
"from analysis.net import get_spotnet, 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",
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"import numpy as np\n",
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"import pandas as pd\n",
"\n",
"client = bigquery.Client()\n",
"spotnet = get_spotnet()\n",
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"cache = 'query.csv'\n",
"first_day = datetime(year=2017, month=11, day=3)"
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]
},
{
"source": [
"## Read Scrobble Frame"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
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"scrobbles = get_query()"
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]
},
{
"source": [
"## Write Scrobble Frame"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scrobbles.to_csv(cache, sep='\\t')"
]
}
]
}
