Spotify_Cleanup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Data is taken from [Kaggle](https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks) which has been authored by Yamac Eren Ay and  was collected using Spotify Web API. \n",
    "\n",
    "### Explorating and Understanding Data to test our Hypothesis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the Path and Loading Data file to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"Resources/data.csv\"\n",
    "\n",
    "Spotify_data_df = pd.read_csv(file_path,low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>artists</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>explicit</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.991000</td>\n",
       "      <td>['Mamie Smith']</td>\n",
       "      <td>0.598</td>\n",
       "      <td>168333</td>\n",
       "      <td>0.224</td>\n",
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       "      <td>0.3790</td>\n",
       "      <td>-12.628</td>\n",
       "      <td>0</td>\n",
       "      <td>Keep A Song In Your Soul</td>\n",
       "      <td>12</td>\n",
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       "      <td>0.0936</td>\n",
       "      <td>149.976</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>1920</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.643000</td>\n",
       "      <td>[\"Screamin' Jay Hawkins\"]</td>\n",
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       "      <td>-7.261</td>\n",
       "      <td>0</td>\n",
       "      <td>I Put A Spell On You</td>\n",
       "      <td>7</td>\n",
       "      <td>1920-01-05</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>86.889</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993000</td>\n",
       "      <td>['Mamie Smith']</td>\n",
       "      <td>0.647</td>\n",
       "      <td>163827</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000018</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5190</td>\n",
       "      <td>-12.098</td>\n",
       "      <td>1</td>\n",
       "      <td>Golfing Papa</td>\n",
       "      <td>4</td>\n",
       "      <td>1920</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>97.600</td>\n",
       "      <td>0.6890</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000173</td>\n",
       "      <td>['Oscar Velazquez']</td>\n",
       "      <td>0.730</td>\n",
       "      <td>422087</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0</td>\n",
       "      <td>19Lc5SfJJ5O1oaxY0fpwfh</td>\n",
       "      <td>0.801000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>-7.311</td>\n",
       "      <td>1</td>\n",
       "      <td>True House Music - Xavier Santos &amp; Carlos Gomi...</td>\n",
       "      <td>17</td>\n",
       "      <td>1920-01-01</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>127.997</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.295000</td>\n",
       "      <td>['Mixe']</td>\n",
       "      <td>0.704</td>\n",
       "      <td>165224</td>\n",
       "      <td>0.707</td>\n",
       "      <td>1</td>\n",
       "      <td>2hJjbsLCytGsnAHfdsLejp</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>10</td>\n",
       "      <td>0.4020</td>\n",
       "      <td>-6.036</td>\n",
       "      <td>0</td>\n",
       "      <td>Xuniverxe</td>\n",
       "      <td>2</td>\n",
       "      <td>1920-10-01</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>122.076</td>\n",
       "      <td>0.2990</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   acousticness                    artists  danceability  duration_ms  energy  \\\n",
       "0      0.991000            ['Mamie Smith']         0.598       168333   0.224   \n",
       "1      0.643000  [\"Screamin' Jay Hawkins\"]         0.852       150200   0.517   \n",
       "2      0.993000            ['Mamie Smith']         0.647       163827   0.186   \n",
       "3      0.000173        ['Oscar Velazquez']         0.730       422087   0.798   \n",
       "4      0.295000                   ['Mixe']         0.704       165224   0.707   \n",
       "\n",
       "   explicit                      id  instrumentalness  key  liveness  \\\n",
       "0         0  0cS0A1fUEUd1EW3FcF8AEI          0.000522    5    0.3790   \n",
       "1         0  0hbkKFIJm7Z05H8Zl9w30f          0.026400    5    0.0809   \n",
       "2         0  11m7laMUgmOKqI3oYzuhne          0.000018    0    0.5190   \n",
       "3         0  19Lc5SfJJ5O1oaxY0fpwfh          0.801000    2    0.1280   \n",
       "4         1  2hJjbsLCytGsnAHfdsLejp          0.000246   10    0.4020   \n",
       "\n",
       "   loudness  mode                                               name  \\\n",
       "0   -12.628     0                           Keep A Song In Your Soul   \n",
       "1    -7.261     0                               I Put A Spell On You   \n",
       "2   -12.098     1                                       Golfing Papa   \n",
       "3    -7.311     1  True House Music - Xavier Santos & Carlos Gomi...   \n",
       "4    -6.036     0                                          Xuniverxe   \n",
       "\n",
       "   popularity release_date  speechiness    tempo  valence  year  \n",
       "0          12         1920       0.0936  149.976   0.6340  1920  \n",
       "1           7   1920-01-05       0.0534   86.889   0.9500  1920  \n",
       "2           4         1920       0.1740   97.600   0.6890  1920  \n",
       "3          17   1920-01-01       0.0425  127.997   0.0422  1920  \n",
       "4           2   1920-10-01       0.0768  122.076   0.2990  1920  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Total music releases by artists over 100 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Total Rows</th>\n",
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       "      <td>174389</td>\n",
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       "    Total Rows   Total Columns\n",
       "0       174389              19"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "Rows = Spotify_data_df.shape[0]\n",
    "Columns = Spotify_data_df.shape[1]\n",
    "Total_before = pd.DataFrame({\n",
    "                     \" Total Rows\": [Rows],\n",
    "                     \" Total Columns\": [Columns]\n",
    "                    })\n",
    "Total_before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: Data Set contains Total of 1,74,389 records by artists till date and 19 audio Characteristics. \n",
    "\n",
    "\n",
    "\n",
    "### Understanding and Exploring Audio Characteristics:\n",
    "##### Following are the features available to test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['acousticness', 'artists', 'danceability', 'duration_ms', 'energy',\n",
       "       'explicit', 'id', 'instrumentalness', 'key', 'liveness', 'loudness',\n",
       "       'mode', 'name', 'popularity', 'release_date', 'speechiness', 'tempo',\n",
       "       'valence', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Spotify_data_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Description: Explains how they are measured and what they mean\n",
    "#### Primary:\n",
    "    •\t- id : Song unique ID\n",
    "\n",
    "#### Numerical:\n",
    "    •\t- acousticness : A confidence measure from 0.0 to 1.0 of whether the track is acoustic; 1-High , 0 -Low\n",
    "    •\t- danceability: How suitable a track is for dancing based on a combination of musical elements; 0-Least, 1-Most\n",
    "    •\t- energy: Measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy;0-Low,1-High\n",
    "    •\t- duration_ms : Duration of song in milliseconds ranging from 200k to 300k\n",
    "    •\t- instrumentalness :Predicts whether a track contains no vocals(ex: oohh and aahh) ;1-No vocals, 0-vocal\n",
    "    •\t- valence : Musical positiveness conveyed by a track. \n",
    "            0-negative(sad, depressed, angry) , \n",
    "            1-positive (happy, cheerful, euphoric)\n",
    "    •\t- popularity: Based on number of plays and downloads ; 0-Less Popular, 100-Very Popular\n",
    "    •\t- tempo : Overall estimated tempo of a track in beats per minutes(BPM);50-Low , 100-High\n",
    "    •\t- liveness: Detects the presence of an audience in the recording ; 0-No , 1 -Yes\n",
    "    •\t- loudness: Overall relative loudness of tracks in decibels (dB). -60 low ,0-High\n",
    "    •\t- speechiness: presence of spoken words in a track. \n",
    "              Values >0.66-entirely of spoken words. \n",
    "              Values between 0.33 and 0.66 describe tracks that may contain both music and speech,like rap music. \n",
    "              Values < 0.33 mostly music and other non-speech-like tracks.\n",
    "    •\t- year : The year music was recorded and release (1921 to 2020) \n",
    "\n",
    "#### Dummy:\n",
    "    •\t- mode (0 = Minor, 1 = Major)\n",
    "    •\t- explicit (0 = No explicit content, 1 = Explicit content)\n",
    "\n",
    "#### Categorical:\n",
    "    •\t- key : All keys on octave encoded as values ranging from 0 to 11, starting on C as 0, C# as 1\n",
    "    •\t- artists :Song Artist\n",
    "    •\t- release_date : Date when album released \n",
    "    •\t- name : Song Name\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Interesting insights while exploring the data \n",
    "\n",
    "#### Some Interesting caveats:\n",
    "    -\tThis data is simply a sample of tracks released in those 100 years and not a complete set. \n",
    "    -\tAccording to Spotify developers site, the popularity is calculated by an algorithm and is based on the \n",
    "         total number of downloads and plays the track has had and how recent those plays are. While this is accurate for \n",
    "         newer tracks, could be a bias on older tracks.\n",
    "    -   Valance descibes the mood of the singer. It would be interesting to know that it was measured based on \n",
    "         loudness. Louder the song, happier and cheerful it is.\n",
    "    -   Not all the features are measured in the range 0-1 . ex: To plot the trending of loudness with other features\n",
    "         over time, we would have to normalize the units to fit the scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keeping features that are within the interest of our Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -  Filtering out colums  'duration_ms', 'explicit', 'id', 'key', 'liveness',  'mode', 'release_date', 'tempo' from the data set.\n",
    "      \n",
    "#### - Rearrange columns 'acousticness' and 'year' .\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>artists</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>popularity</th>\n",
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       "      <th>valence</th>\n",
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       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>['Mamie Smith']</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.000522</td>\n",
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       "      <td>[\"Screamin' Jay Hawkins\"]</td>\n",
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       "      <td>0.517</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>-7.261</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.9500</td>\n",
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       "      <th>2</th>\n",
       "      <td>1920</td>\n",
       "      <td>['Mamie Smith']</td>\n",
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       "      <td>0.647</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-12.098</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1740</td>\n",
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       "      <th>3</th>\n",
       "      <td>1920</td>\n",
       "      <td>['Oscar Velazquez']</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.801000</td>\n",
       "      <td>-7.311</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>['Mixe']</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>-6.036</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>174384</th>\n",
       "      <td>2020</td>\n",
       "      <td>['DJ Combo', 'Sander-7', 'Tony T']</td>\n",
       "      <td>0.009170</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-5.089</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0356</td>\n",
       "      <td>0.1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174385</th>\n",
       "      <td>2021</td>\n",
       "      <td>['Alessia Cara']</td>\n",
       "      <td>0.795000</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-11.665</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>0.2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174386</th>\n",
       "      <td>2020</td>\n",
       "      <td>['Roger Fly']</td>\n",
       "      <td>0.806000</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>-12.393</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.7140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174387</th>\n",
       "      <td>2021</td>\n",
       "      <td>['Taylor Swift']</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-12.077</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174388</th>\n",
       "      <td>2020</td>\n",
       "      <td>['Roger Fly']</td>\n",
       "      <td>0.239000</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.891000</td>\n",
       "      <td>-12.237</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>0.7470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174389 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year                             artists  acousticness  danceability  \\\n",
       "0       1920                     ['Mamie Smith']      0.991000         0.598   \n",
       "1       1920           [\"Screamin' Jay Hawkins\"]      0.643000         0.852   \n",
       "2       1920                     ['Mamie Smith']      0.993000         0.647   \n",
       "3       1920                 ['Oscar Velazquez']      0.000173         0.730   \n",
       "4       1920                            ['Mixe']      0.295000         0.704   \n",
       "...      ...                                 ...           ...           ...   \n",
       "174384  2020  ['DJ Combo', 'Sander-7', 'Tony T']      0.009170         0.792   \n",
       "174385  2021                    ['Alessia Cara']      0.795000         0.429   \n",
       "174386  2020                       ['Roger Fly']      0.806000         0.671   \n",
       "174387  2021                    ['Taylor Swift']      0.920000         0.462   \n",
       "174388  2020                       ['Roger Fly']      0.239000         0.677   \n",
       "\n",
       "        energy  instrumentalness  loudness  popularity  speechiness  valence  \n",
       "0        0.224          0.000522   -12.628          12       0.0936   0.6340  \n",
       "1        0.517          0.026400    -7.261           7       0.0534   0.9500  \n",
       "2        0.186          0.000018   -12.098           4       0.1740   0.6890  \n",
       "3        0.798          0.801000    -7.311          17       0.0425   0.0422  \n",
       "4        0.707          0.000246    -6.036           2       0.0768   0.2990  \n",
       "...        ...               ...       ...         ...          ...      ...  \n",
       "174384   0.866          0.000060    -5.089           0       0.0356   0.1860  \n",
       "174385   0.211          0.000000   -11.665           0       0.0360   0.2280  \n",
       "174386   0.589          0.920000   -12.393           0       0.0282   0.7140  \n",
       "174387   0.240          0.000000   -12.077          69       0.0377   0.3200  \n",
       "174388   0.460          0.891000   -12.237           0       0.0258   0.7470  \n",
       "\n",
       "[174389 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_selected_features = Spotify_data_df[['year','artists', 'acousticness',\n",
    "                                   'danceability', 'energy','instrumentalness',\n",
    "                                   'loudness','popularity',  'speechiness', 'valence', ]]\n",
    "#Display cleaned Data set\n",
    "Spotify_selected_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: Here we can see that data needs further cleanup\n",
    "            - Removing special charecters from the artists column . \n",
    "            - We may also need to parse data types and \n",
    "            - drop Null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing special characters from the column 'artist' using lstrip , rstrip  and str.replace functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Spotify_Clean_artist= Spotify_selected_features.copy()\n",
    "Spotify_Clean_artist[\"artists\"]=Spotify_selected_features[\"artists\"].map(lambda x: x.lstrip(\"['\").rstrip(\"']\")).astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Spotify_Clean_artist[\"artists\"]=Spotify_Clean_artist[\"artists\"].str.replace(\"'\",\"\").str.replace('\"',\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: Since special characters also included single and double quotes,  multiple calls to str.replace function was need to remove them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display dataframe with cleaned artist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>artists</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>Mamie Smith</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>-12.628</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0936</td>\n",
       "      <td>0.6340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1920</td>\n",
       "      <td>Screamin Jay Hawkins</td>\n",
       "      <td>0.643000</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>-7.261</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1920</td>\n",
       "      <td>Mamie Smith</td>\n",
       "      <td>0.993000</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-12.098</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>0.6890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1920</td>\n",
       "      <td>Oscar Velazquez</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.801000</td>\n",
       "      <td>-7.311</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>Mixe</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>-6.036</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.2990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year               artists  acousticness  danceability  energy  \\\n",
       "0  1920           Mamie Smith      0.991000         0.598   0.224   \n",
       "1  1920  Screamin Jay Hawkins      0.643000         0.852   0.517   \n",
       "2  1920           Mamie Smith      0.993000         0.647   0.186   \n",
       "3  1920       Oscar Velazquez      0.000173         0.730   0.798   \n",
       "4  1920                  Mixe      0.295000         0.704   0.707   \n",
       "\n",
       "   instrumentalness  loudness  popularity  speechiness  valence  \n",
       "0          0.000522   -12.628          12       0.0936   0.6340  \n",
       "1          0.026400    -7.261           7       0.0534   0.9500  \n",
       "2          0.000018   -12.098           4       0.1740   0.6890  \n",
       "3          0.801000    -7.311          17       0.0425   0.0422  \n",
       "4          0.000246    -6.036           2       0.0768   0.2990  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_Clean_artist.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accounting for Null values if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174389"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop Null values in the dataframe if any\n",
    "Spotify_clean_df =Spotify_Clean_artist.dropna(how='any')\n",
    "Spotify_clean_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note : None of the rows were dropped so dataframe has no Null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze datatypes of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                  int64\n",
       "artists              object\n",
       "acousticness        float64\n",
       "danceability        float64\n",
       "energy              float64\n",
       "instrumentalness    float64\n",
       "loudness            float64\n",
       "popularity            int64\n",
       "speechiness         float64\n",
       "valence             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_clean_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All the features in interest have expected dtypes hence, no columns need any datatype conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Analysis of cleaned dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "      <td>174389.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1977.061764</td>\n",
       "      <td>0.499228</td>\n",
       "      <td>0.536758</td>\n",
       "      <td>0.482721</td>\n",
       "      <td>0.197252</td>\n",
       "      <td>-11.750865</td>\n",
       "      <td>25.693381</td>\n",
       "      <td>0.105729</td>\n",
       "      <td>0.524533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.907950</td>\n",
       "      <td>0.379936</td>\n",
       "      <td>0.176025</td>\n",
       "      <td>0.272685</td>\n",
       "      <td>0.334574</td>\n",
       "      <td>5.691591</td>\n",
       "      <td>21.872740</td>\n",
       "      <td>0.182260</td>\n",
       "      <td>0.264477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1920.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1955.000000</td>\n",
       "      <td>0.087700</td>\n",
       "      <td>0.414000</td>\n",
       "      <td>0.249000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-14.908000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035200</td>\n",
       "      <td>0.311000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1977.000000</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>0.548000</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>-10.836000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.045500</td>\n",
       "      <td>0.536000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>0.895000</td>\n",
       "      <td>0.669000</td>\n",
       "      <td>0.711000</td>\n",
       "      <td>0.252000</td>\n",
       "      <td>-7.499000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.076300</td>\n",
       "      <td>0.743000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.988000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.855000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.971000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year   acousticness   danceability         energy  \\\n",
       "count  174389.000000  174389.000000  174389.000000  174389.000000   \n",
       "mean     1977.061764       0.499228       0.536758       0.482721   \n",
       "std        26.907950       0.379936       0.176025       0.272685   \n",
       "min      1920.000000       0.000000       0.000000       0.000000   \n",
       "25%      1955.000000       0.087700       0.414000       0.249000   \n",
       "50%      1977.000000       0.517000       0.548000       0.465000   \n",
       "75%      1999.000000       0.895000       0.669000       0.711000   \n",
       "max      2021.000000       0.996000       0.988000       1.000000   \n",
       "\n",
       "       instrumentalness       loudness     popularity    speechiness  \\\n",
       "count     174389.000000  174389.000000  174389.000000  174389.000000   \n",
       "mean           0.197252     -11.750865      25.693381       0.105729   \n",
       "std            0.334574       5.691591      21.872740       0.182260   \n",
       "min            0.000000     -60.000000       0.000000       0.000000   \n",
       "25%            0.000000     -14.908000       1.000000       0.035200   \n",
       "50%            0.000524     -10.836000      25.000000       0.045500   \n",
       "75%            0.252000      -7.499000      42.000000       0.076300   \n",
       "max            1.000000       3.855000     100.000000       0.971000   \n",
       "\n",
       "             valence  \n",
       "count  174389.000000  \n",
       "mean        0.524533  \n",
       "std         0.264477  \n",
       "min         0.000000  \n",
       "25%         0.311000  \n",
       "50%         0.536000  \n",
       "75%         0.743000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_clean_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Provides basic statistical analysis of the dataframe \n",
    "                       \n",
    "#### Intresting features:\n",
    "             No. of releases :1,74,389 \n",
    "             Start year : 1920             \n",
    "             End Year : 2021\n",
    "             Possible Outliers : Other than instrumentalness , all other columns seems to have data uniformly distributed. \n",
    "             \n",
    "             \n",
    "#### Note :  From the table , we can see that there are records with loudness greater than zero. According to spotify developers site, range is between -60 to 0 db .This could be an error so we need to remove these records. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_loudness</th>\n",
       "      <th>min_loudness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.007</td>\n",
       "      <td>-60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_loudness  min_loudness\n",
       "0        -0.007         -60.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_clean_df = Spotify_clean_df[Spotify_clean_df[\"loudness\"]<0]\n",
    "loudness_after = pd.DataFrame({\n",
    "                        \"max_loudness\":[Spotify_clean_df[\"loudness\"].max()],\n",
    "                        \"min_loudness\":[Spotify_clean_df[\"loudness\"].min()]\n",
    "                        })\n",
    "loudness_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>year</th>\n",
       "      <th>artists</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>Mamie Smith</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>-12.628</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0936</td>\n",
       "      <td>0.634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1920</td>\n",
       "      <td>Screamin Jay Hawkins</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>-7.261</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1920</td>\n",
       "      <td>Mamie Smith</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-12.098</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>0.689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year               artists  acousticness  danceability  energy  \\\n",
       "0  1920           Mamie Smith         0.991         0.598   0.224   \n",
       "1  1920  Screamin Jay Hawkins         0.643         0.852   0.517   \n",
       "2  1920           Mamie Smith         0.993         0.647   0.186   \n",
       "\n",
       "   instrumentalness  loudness  popularity  speechiness  valence  \n",
       "0          0.000522   -12.628          12       0.0936    0.634  \n",
       "1          0.026400    -7.261           7       0.0534    0.950  \n",
       "2          0.000018   -12.098           4       0.1740    0.689  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spotify_clean_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Rows</th>\n",
       "      <th>Total Columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>174354</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Total Rows   Total Columns\n",
       "0       174354              10"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rows = Spotify_clean_df.shape[0]\n",
    "Columns = Spotify_clean_df.shape[1]\n",
    "Total_after = pd.DataFrame({\n",
    "                     \" Total Rows\": [Rows],\n",
    "                     \" Total Columns\": [Columns]\n",
    "                    })\n",
    "Total_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving File for further Analysis\n",
    "#### Saving file in a .csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path = \"Output_data/spotify_clean.csv\"\n",
    "Spotify_clean_df.to_csv(out_path , index=False , encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenges during data exploration/manipulation \n",
    "         \n",
    "   #### 1.SettingWithCopyError :\n",
    "            - This error was thrown due to chaining functions to one another. It means is that you are chaining two indexing\n",
    "              method together while trying to set a value.\n",
    "   ##### The solution is simple: \n",
    "            1. Make a copy of the dataframe and avoid assigning values directly\n",
    "            2. Convert multiple chaining actions into one using the .loc/.iloc methods in pandas. \n",
    "\n",
    "  #### 2.  : We can see that loudness and popularity are measured with different range. We may have to normalize the data to fit the scale or plot them seperatly to avoid data loss. Here is an intersting article\n",
    "  [Normalization and standardization to fit the scale](https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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