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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"import spotipy\n",
"import spotipy.oauth2 as oauth2\n",
"from spotipy.oauth2 import SpotifyOAuth,SpotifyClientCredentials\n",
"import yaml\n",
"import re\n",
"from tqdm import tqdm\n",
"import multiprocessing as mp\n",
"import time\n",
"import random\n",
"import datetime\n",
"\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"stream= open(\"spotify/spotify2.yaml\")\n",
"spotify_details = yaml.safe_load(stream)\n",
"auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'],\n",
" client_secret=spotify_details['client_secret'])\n",
"sp = spotipy.client.Spotify(auth_manager=auth_manager)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df=pd.read_csv('Data/1mV3.csv')\n",
"artist_features=pd.read_csv('Data/artist_features.csv')\n",
"audio_features=pd.read_csv('Data/audio_features.csv')\n",
"track_features=pd.read_csv('Data/track_features.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merging all dataframes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(df,audio_features, left_on = \"track_uri\", right_on= \"id\",how = 'outer')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(df,track_features, left_on = \"track_uri\", right_on= \"Track_uri\",how = 'outer')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge(df,artist_features, left_on = \"artist_uri\", right_on= \"Artist_uri\",how = 'outer')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handling missing data "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"track_uri 0\n",
"artist_uri 0\n",
"album_uri 0\n",
"danceability 101\n",
"energy 101\n",
"key 101\n",
"loudness 101\n",
"mode 101\n",
"speechiness 101\n",
"acousticness 101\n",
"instrumentalness 101\n",
"liveness 101\n",
"valence 101\n",
"tempo 101\n",
"type 101\n",
"id 101\n",
"uri 101\n",
"track_href 101\n",
"analysis_url 101\n",
"duration_ms 101\n",
"time_signature 101\n",
"Track_uri 576\n",
"Track_release_date 576\n",
"Track_pop 576\n",
"Artist_uri 0\n",
"Artist_pop 0\n",
"Artist_genres 0\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling audio_features missing From extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"missing_t_uri=df.track_uri[df.id.isna()]\n",
"missing_t_uri=missing_t_uri.unique()\n",
"random.shuffle(missing_t_uri)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = open('data/audio_features.csv','a')\n",
"for i in tqdm(range(0,len(missing_t_uri),1)):\n",
" try:\n",
" track_feature = sp.audio_features(missing_t_uri[i:i+1])\n",
" track_df = pd.DataFrame(track_feature)\n",
" csv_data = track_df.to_csv(header=False,index=False)\n",
" f.write(csv_data)\n",
" except Exception as e:\n",
" r = open(\"extract_log0.txt\", \"a\")\n",
" r.write(datetime.datetime.now().strftime(\"%d.%b %Y %H:%M:%S\")+\": \"+str(e)+'\\n')\n",
" r.close()\n",
" time.sleep(1)\n",
" continue\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling track_features missing From extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"missing_t_uri=df.track_uri[df.Track_uri.isna()]\n",
"missing_t_uri=missing_t_uri.unique()\n",
"random.shuffle(missing_t_uri)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = open('data/track_features.csv','a')\n",
"for i in tqdm(range(0,len(missing_t_uri),1)):\n",
" try:\n",
" track_features = sp.tracks(missing_t_uri[i:i+1])\n",
" for x in range(1):\n",
" track_pop=pd.DataFrame([missing_t_uri[i+x]])\n",
" track_pop['release_date']=track_features['tracks'][x]['album']['release_date']\n",
" track_pop['pop'] = track_features['tracks'][x][\"popularity\"]\n",
" csv_data = track_pop.to_csv(header=False,index=False)\n",
" f.write(csv_data)\n",
" except Exception as e:\n",
" r = open(\"extract_log.txt\", \"a\")\n",
" r.write(datetime.datetime.now().strftime(\"%d.%b %Y %H:%M:%S\")+\": \"+str(e)+'\\n')\n",
" r.close()\n",
" time.sleep(1)\n",
" continue\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Droping Unwanted Columns Save Space"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There were still 101 from audio_features and 576 from track_features extraction that were missing from the soptify api, so I had to drop them."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.dropna(axis=0,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isna().sum().sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key',\n",
" 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',\n",
" 'liveness', 'valence', 'tempo', 'type', 'id', 'uri', 'track_href',\n",
" 'analysis_url', 'duration_ms', 'time_signature', 'Track_uri',\n",
" 'Track_release_date', 'Track_pop', 'Artist_uri', 'Artist_pop',\n",
" 'Artist_genres'],\n",
" dtype='object')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df.drop(columns=['Track_uri','Artist_uri','type','id','uri','track_href','analysis_url'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>track_uri</th>\n",
" <th>artist_uri</th>\n",
" <th>album_uri</th>\n",
" <th>danceability</th>\n",
" <th>energy</th>\n",
" <th>key</th>\n",
" <th>loudness</th>\n",
" <th>mode</th>\n",
" <th>speechiness</th>\n",
" <th>acousticness</th>\n",
" <th>instrumentalness</th>\n",
" <th>liveness</th>\n",
" <th>valence</th>\n",
" <th>tempo</th>\n",
" <th>duration_ms</th>\n",
" <th>time_signature</th>\n",
" <th>Track_release_date</th>\n",
" <th>Track_pop</th>\n",
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" <td>0.813</td>\n",
" <td>4.0</td>\n",
" <td>-7.105</td>\n",
" <td>0.0</td>\n",
" <td>0.121</td>\n",
" <td>0.0311</td>\n",
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" <td>0.0471</td>\n",
" <td>0.81</td>\n",
" <td>125.461</td>\n",
" <td>226864.0</td>\n",
" <td>4.0</td>\n",
" <td>2005-07-04</td>\n",
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"text/plain": [
" track_uri artist_uri album_uri \\\n",
"0 0UaMYEvWZi0ZqiDOoHU3YI 2wIVse2owClT7go1WT98tk 6vV5UrXcfyQD1wu4Qo2I9K \n",
"\n",
" danceability energy key loudness mode speechiness acousticness \\\n",
"0 0.904 0.813 4.0 -7.105 0.0 0.121 0.0311 \n",
"\n",
" instrumentalness liveness valence tempo duration_ms time_signature \\\n",
"0 0.00697 0.0471 0.81 125.461 226864.0 4.0 \n",
"\n",
" Track_release_date Track_pop Artist_pop \\\n",
"0 2005-07-04 67.0 71 \n",
"\n",
" Artist_genres \n",
"0 dance_pop hip_hop hip_pop pop_rap r&b rap urba... "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create five point buckets for track and artist popularity .\n",
"\n",
"and 50 point buckets for the track release date."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df['Track_pop'] = df['Track_pop'].apply(lambda x: int(x/5))\n",
"df['Artist_pop'] = df['Artist_pop'].apply(lambda x: int(x/5))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df['Track_release_date'] = df['Track_release_date'].apply(lambda x: x.split('-')[0])\n",
"df['Track_release_date']=df['Track_release_date'].astype('int16')\n",
"df['Track_release_date'] = df['Track_release_date'].apply(lambda x: int(x/50))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" track_uri artist_uri album_uri \\\n",
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"0 0.904 0.813 4.0 -7.105 0.0 0.121 0.0311 \n",
"\n",
" instrumentalness liveness valence tempo duration_ms time_signature \\\n",
"0 0.00697 0.0471 0.81 125.461 226864.0 4.0 \n",
"\n",
" Track_release_date Track_pop Artist_pop \\\n",
"0 40 13 14 \n",
"\n",
" Artist_genres \n",
"0 dance_pop hip_hop hip_pop pop_rap r&b rap urba... "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('data/1M_unique_processed_data.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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|