spotify_exploring_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>msPlayed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>2021-11-10 11:57</td>\n",
       "      <td>Kolektif İstanbul</td>\n",
       "      <td>Acımadı Yine</td>\n",
       "      <td>198016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2021-09-28 11:08</td>\n",
       "      <td>Joe Dassin</td>\n",
       "      <td>Et si tu n'existais pas</td>\n",
       "      <td>8650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>2021-10-19 15:56</td>\n",
       "      <td>Maanam</td>\n",
       "      <td>Krakowski spleen</td>\n",
       "      <td>234241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2021-02-16 06:32</td>\n",
       "      <td>MFÖ</td>\n",
       "      <td>Ağlamadan</td>\n",
       "      <td>202066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>2021-11-15 05:36</td>\n",
       "      <td>Novus Podcasts</td>\n",
       "      <td>28 Haziran | 🗞️ YKS, Onur yürüyüşü</td>\n",
       "      <td>1735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               endTime         artistName                           trackName  \\\n",
       "3793  2021-11-10 11:57  Kolektif İstanbul                        Acımadı Yine   \n",
       "2999  2021-09-28 11:08         Joe Dassin             Et si tu n'existais pas   \n",
       "3418  2021-10-19 15:56             Maanam                    Krakowski spleen   \n",
       "994   2021-02-16 06:32                MFÖ                           Ağlamadan   \n",
       "3862  2021-11-15 05:36     Novus Podcasts  28 Haziran | 🗞️ YKS, Onur yürüyüşü   \n",
       "\n",
       "      msPlayed  \n",
       "3793    198016  \n",
       "2999      8650  \n",
       "3418    234241  \n",
       "994     202066  \n",
       "3862      1735  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np \n",
    "from datetime import datetime\n",
    "df_stream = pd.read_json('C:\\\\Users\\\\TKA\\Desktop\\\\MyData\\\\StreamingHistory0.json')\n",
    "df_stream.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>WeekdayOrNot</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4005</th>\n",
       "      <td>2021-11-26 19:19:00</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>Whiskey In The Jar</td>\n",
       "      <td>Friday</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>2021-11-26 19:24:00</td>\n",
       "      <td>Jamie Cullum</td>\n",
       "      <td>High And Dry</td>\n",
       "      <td>Friday</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4007</th>\n",
       "      <td>2021-11-26 19:27:00</td>\n",
       "      <td>Goodbye June</td>\n",
       "      <td>Oh No</td>\n",
       "      <td>Friday</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>2021-11-26 19:33:00</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Every You Every Me</td>\n",
       "      <td>Friday</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>2021-11-26 19:37:00</td>\n",
       "      <td>CHVRCHES</td>\n",
       "      <td>Clearest Blue</td>\n",
       "      <td>Friday</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 endTime    artistName           trackName     Day  Hour  \\\n",
       "4005 2021-11-26 19:19:00     Metallica  Whiskey In The Jar  Friday    19   \n",
       "4006 2021-11-26 19:24:00  Jamie Cullum        High And Dry  Friday    19   \n",
       "4007 2021-11-26 19:27:00  Goodbye June               Oh No  Friday    19   \n",
       "4008 2021-11-26 19:33:00       Placebo  Every You Every Me  Friday    19   \n",
       "4009 2021-11-26 19:37:00      CHVRCHES       Clearest Blue  Friday    19   \n",
       "\n",
       "        Month  Year  WeekdayOrNot  Minutes  \n",
       "4005  2021-11  2021             0      5.1  \n",
       "4006  2021-11  2021             0      4.9  \n",
       "4007  2021-11  2021             0      3.7  \n",
       "4008  2021-11  2021             0      5.4  \n",
       "4009  2021-11  2021             0      3.9  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream[\"endTime\"] = pd.to_datetime(df_stream[\"endTime\"]) + pd.Timedelta(hours=3)\n",
    "df_stream[\"Day\"] = pd.to_datetime(df_stream[\"endTime\"], format='%Y-%m-%d %H:%M').dt.day_name()\n",
    "df_stream[\"Hour\"] = pd.to_datetime(df_stream[\"endTime\"], format='%Y-%m-%d %H:%M').dt.hour\n",
    "df_stream['Month'] = pd.to_datetime(df_stream[\"endTime\"], format='%Y-%m-%d %H:%M').dt.strftime('%Y-%m')\n",
    "df_stream[\"Year\"] = pd.to_datetime(df_stream[\"endTime\"], format='%Y-%m-%d %H:%M').dt.year\n",
    "df_stream['WeekdayOrNot'] = np.where(pd.to_datetime(df_stream[\"endTime\"], format='%Y-%m-%d %H:%M').dt.dayofweek < 5,0,1 )\n",
    "df_stream[\"Minutes\"] = df_stream[\"msPlayed\"] / 60000\n",
    "df_stream[\"Minutes\"] = df_stream[\"Minutes\"].round(decimals=1)\n",
    "df_stream.drop([\"msPlayed\"],axis=1, inplace=True)\n",
    "df_stream.iloc[4005:4010]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Session Id with feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can assume that if there is no song has endTime after 15 minutes, that session is closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>WeekdayOrNot</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>PrevEndTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "      <td>DJ Ufuk</td>\n",
       "      <td>Nankör Kedi (REMİX)</td>\n",
       "      <td>Monday</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Bırakma Kendini</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>Anıl Piyancı</td>\n",
       "      <td>Varsın</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-08 13:36:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Benimle Kayboldun</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-08 13:40:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Seni Buldum Ya</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2020-12-08 13:36:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              endTime    artistName            trackName      Day  Hour  \\\n",
       "0 2020-12-07 22:25:00       DJ Ufuk  Nankör Kedi (REMİX)   Monday    22   \n",
       "1 2020-12-08 13:33:00   Kaan Boşnak      Bırakma Kendini  Tuesday    13   \n",
       "2 2020-12-08 13:33:00  Anıl Piyancı               Varsın  Tuesday    13   \n",
       "3 2020-12-08 13:36:00   Kaan Boşnak    Benimle Kayboldun  Tuesday    13   \n",
       "4 2020-12-08 13:40:00   Kaan Boşnak       Seni Buldum Ya  Tuesday    13   \n",
       "\n",
       "     Month  Year  WeekdayOrNot  Minutes         PrevEndTime  \n",
       "0  2020-12  2020             0      0.9                 NaT  \n",
       "1  2020-12  2020             0      0.0 2020-12-07 22:25:00  \n",
       "2  2020-12  2020             0      0.0 2020-12-08 13:33:00  \n",
       "3  2020-12  2020             0      3.2 2020-12-08 13:33:00  \n",
       "4  2020-12  2020             0      3.9 2020-12-08 13:36:00  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream[\"PrevEndTime\"] = df_stream[\"endTime\"].shift(1)\n",
    "df_stream.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>WeekdayOrNot</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>PrevEndTime</th>\n",
       "      <th>Session_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>2021-06-07 12:39:00</td>\n",
       "      <td>Måneskin</td>\n",
       "      <td>I WANNA BE YOUR SLAVE</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2021-06-07 12:36:00</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>2021-06-07 12:42:00</td>\n",
       "      <td>Måneskin</td>\n",
       "      <td>ZITTI E BUONI</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2021-06-07 12:39:00</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>2021-06-07 17:16:00</td>\n",
       "      <td>Måneskin</td>\n",
       "      <td>CORALINE</td>\n",
       "      <td>Monday</td>\n",
       "      <td>17</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-06-07 12:42:00</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>2021-06-07 17:19:00</td>\n",
       "      <td>Måneskin</td>\n",
       "      <td>CORALINE</td>\n",
       "      <td>Monday</td>\n",
       "      <td>17</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2021-06-07 17:16:00</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 endTime artistName              trackName     Day  Hour  \\\n",
       "2212 2021-06-07 12:39:00   Måneskin  I WANNA BE YOUR SLAVE  Monday    12   \n",
       "2213 2021-06-07 12:42:00   Måneskin          ZITTI E BUONI  Monday    12   \n",
       "2214 2021-06-07 17:16:00   Måneskin               CORALINE  Monday    17   \n",
       "2215 2021-06-07 17:19:00   Måneskin               CORALINE  Monday    17   \n",
       "\n",
       "        Month  Year  WeekdayOrNot  Minutes         PrevEndTime  Session_Id  \n",
       "2212  2021-06  2021             0      2.9 2021-06-07 12:36:00       260.0  \n",
       "2213  2021-06  2021             0      3.2 2021-06-07 12:39:00       260.0  \n",
       "2214  2021-06  2021             0      0.0 2021-06-07 12:42:00       261.0  \n",
       "2215  2021-06  2021             0      0.2 2021-06-07 17:16:00       261.0  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream[\"PrevEndTime\"] = df_stream[\"endTime\"].shift(1)\n",
    "df_stream.loc[0, 'Session_Id'] = 1\n",
    "for i in range(1, len(df_stream)):   \n",
    "    if df_stream.loc[i-1, 'endTime'] + pd.Timedelta(minutes=30)  < df_stream.loc[i, 'endTime']  :  \n",
    "        df_stream.loc[i, \"Session_Id\"] = df_stream.loc[i-1, 'Session_Id'] + 1\n",
    "    else:\n",
    "        df_stream.loc[i, 'Session_Id'] = df_stream.loc[i-1, 'Session_Id']\n",
    "df_stream.iloc[2212:2216]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'473'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).count()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-12-07 22:25:00')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream.endTime.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'07-DEC-2020'"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(datetime.strptime(str(df_stream.endTime.min()), '%Y-%m-%d %H:%M:%S').strftime('%d-%b-%Y').upper())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Session Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'26'"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(round(df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"})[\"Minutes\"].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session Duration Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fbf8821ac8>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}), x=\"Minutes\", binwidth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Session Duration Distribution with Weekday Attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fbf882d948>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 442.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_stream.groupby([\"Session_Id\",\"WeekdayOrNot\"]).agg({\"Minutes\":\"sum\"}), x=\"Minutes\", binwidth=5, hue=\"WeekdayOrNot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session Intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Session_Id</th>\n",
       "      <th>endTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-12-08 13:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2020-12-12 14:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2020-12-13 17:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2020-12-15 14:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469.0</td>\n",
       "      <td>2021-12-07 11:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470.0</td>\n",
       "      <td>2021-12-07 14:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>471.0</td>\n",
       "      <td>2021-12-07 15:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>472.0</td>\n",
       "      <td>2021-12-08 11:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>473.0</td>\n",
       "      <td>2021-12-08 13:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>473 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Session_Id             endTime\n",
       "0           1.0 2020-12-07 22:25:00\n",
       "1           2.0 2020-12-08 13:55:00\n",
       "2           3.0 2020-12-12 14:11:00\n",
       "3           4.0 2020-12-13 17:28:00\n",
       "4           5.0 2020-12-15 14:39:00\n",
       "..          ...                 ...\n",
       "468       469.0 2021-12-07 11:55:00\n",
       "469       470.0 2021-12-07 14:20:00\n",
       "470       471.0 2021-12-07 15:45:00\n",
       "471       472.0 2021-12-08 11:49:00\n",
       "472       473.0 2021-12-08 13:30:00\n",
       "\n",
       "[473 rows x 2 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream_session = df_stream.groupby(\"Session_Id\").agg({\"endTime\":\"max\"}).reset_index()\n",
    "df_stream_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Session_Id</th>\n",
       "      <th>endTime</th>\n",
       "      <th>Interval</th>\n",
       "      <th>IntervalHour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-12-08 13:55:00</td>\n",
       "      <td>0 days 15:30:00</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2020-12-12 14:11:00</td>\n",
       "      <td>4 days 00:16:00</td>\n",
       "      <td>96.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2020-12-13 17:28:00</td>\n",
       "      <td>1 days 03:17:00</td>\n",
       "      <td>27.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2020-12-15 14:39:00</td>\n",
       "      <td>1 days 21:11:00</td>\n",
       "      <td>45.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469.0</td>\n",
       "      <td>2021-12-07 11:55:00</td>\n",
       "      <td>0 days 01:05:00</td>\n",
       "      <td>1.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470.0</td>\n",
       "      <td>2021-12-07 14:20:00</td>\n",
       "      <td>0 days 02:25:00</td>\n",
       "      <td>2.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>471.0</td>\n",
       "      <td>2021-12-07 15:45:00</td>\n",
       "      <td>0 days 01:25:00</td>\n",
       "      <td>1.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>472.0</td>\n",
       "      <td>2021-12-08 11:49:00</td>\n",
       "      <td>0 days 20:04:00</td>\n",
       "      <td>20.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>473.0</td>\n",
       "      <td>2021-12-08 13:30:00</td>\n",
       "      <td>0 days 01:41:00</td>\n",
       "      <td>1.683333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>473 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Session_Id             endTime        Interval  IntervalHour\n",
       "0           1.0 2020-12-07 22:25:00             NaT           NaN\n",
       "1           2.0 2020-12-08 13:55:00 0 days 15:30:00     15.500000\n",
       "2           3.0 2020-12-12 14:11:00 4 days 00:16:00     96.266667\n",
       "3           4.0 2020-12-13 17:28:00 1 days 03:17:00     27.283333\n",
       "4           5.0 2020-12-15 14:39:00 1 days 21:11:00     45.183333\n",
       "..          ...                 ...             ...           ...\n",
       "468       469.0 2021-12-07 11:55:00 0 days 01:05:00      1.083333\n",
       "469       470.0 2021-12-07 14:20:00 0 days 02:25:00      2.416667\n",
       "470       471.0 2021-12-07 15:45:00 0 days 01:25:00      1.416667\n",
       "471       472.0 2021-12-08 11:49:00 0 days 20:04:00     20.066667\n",
       "472       473.0 2021-12-08 13:30:00 0 days 01:41:00      1.683333\n",
       "\n",
       "[473 rows x 4 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream_session[\"Interval\"] = df_stream_session[\"endTime\"] - df_stream_session[\"endTime\"].shift(1)\n",
    "df_stream_session[\"IntervalHour\"] = df_stream_session[\"Interval\"].dt.total_seconds() / (60 * 60)\n",
    "df_stream_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.591278248587574"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream_session.IntervalHour.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longest Session Id's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).sort_values(by=[\"Minutes\"], ascending=False).head(1)['Minutes'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the session 455, you listened 233 minutes these artists: {'Imagine Dragons', 'Twenty One Pilots', 'Jamie Cullum', 'Foals', 'Tom Grennan', 'Glass Animals', 'Machine Gun Kelly', 'Faith No More', 'Queen', 'Goodbye June', 'Metallica', 'Owl City', 'Tom Misch', 'alt-J', 'CHVRCHES', 'Placebo', 'Nothing But Thieves', 'The Killers', 'The Last Internationale', 'Pixies', 'Sigrid', 'Frank Carter & The Rattlesnakes', 'Kings of Leon', 'Editors', 'YUNGBLUD', 'Florence + The Machine', 'Black Pumas'}\n"
     ]
    }
   ],
   "source": [
    "longest_session_time = int(df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).sort_values(by=[\"Minutes\"], ascending=False).head(1)['Minutes'].values[0])\n",
    "session_id  = df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).sort_values(by=[\"Minutes\"], ascending=False).head(1).reset_index()[\"Session_Id\"]\n",
    "artist_list = df_stream[df_stream.Session_Id.isin(session_id)].artistName.unique()\n",
    "print(\"In the session \" + str(int(session_id[0])) + \", you listened \" +str(longest_session_time)+ \" minutes these artists: \" + str(set(artist_list)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    455.0\n",
       "Name: Session_Id, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).sort_values(by=[\"Minutes\"], ascending=False).head(1).reset_index()[\"Session_Id\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longest Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>WeekdayOrNot</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>PrevEndTime</th>\n",
       "      <th>Session_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>2021-11-21 16:06:00</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>Spit Out The Bone</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2021-11-20 10:19:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>2021-11-21 16:10:00</td>\n",
       "      <td>Firewater</td>\n",
       "      <td>Electric City</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2021-11-21 16:06:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>2021-11-21 16:16:00</td>\n",
       "      <td>Ken Hensley</td>\n",
       "      <td>Lady in Black</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2021-11-21 16:10:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>2021-11-21 16:21:00</td>\n",
       "      <td>Kolektif İstanbul</td>\n",
       "      <td>Acımadı Yine</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2021-11-21 16:16:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>2021-11-21 16:24:00</td>\n",
       "      <td>THE ROOP</td>\n",
       "      <td>Discoteque</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2021-11-21 16:21:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>2021-11-21 16:27:00</td>\n",
       "      <td>Elena Tsagrinou</td>\n",
       "      <td>El Diablo</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2021-11-21 16:24:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>2021-11-21 16:31:00</td>\n",
       "      <td>Megadeth</td>\n",
       "      <td>Peace Sells - Remastered</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2021-11-21 16:27:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3941</th>\n",
       "      <td>2021-11-21 16:34:00</td>\n",
       "      <td>Megadeth</td>\n",
       "      <td>Architecture Of Aggression</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2021-11-21 16:31:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3942</th>\n",
       "      <td>2021-11-21 16:37:00</td>\n",
       "      <td>Morrissey</td>\n",
       "      <td>I Wish You Lonely</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2021-11-21 16:34:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3943</th>\n",
       "      <td>2021-11-21 16:42:00</td>\n",
       "      <td>Scala &amp; Kolacny Brothers</td>\n",
       "      <td>Le vent nous portera</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2021-11-21 16:37:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3944</th>\n",
       "      <td>2021-11-21 16:47:00</td>\n",
       "      <td>Morrissey</td>\n",
       "      <td>The Girl from Tel-Aviv Who Wouldn't Kneel</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2021-11-21 16:42:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3945</th>\n",
       "      <td>2021-11-21 16:50:00</td>\n",
       "      <td>Sonny J</td>\n",
       "      <td>Handsfree (If You Hold My Hand) - Radio Edit</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2021-11-21 16:47:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3946</th>\n",
       "      <td>2021-11-21 16:54:00</td>\n",
       "      <td>Shocking Blue</td>\n",
       "      <td>Beggin'</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2021-11-21 16:50:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>2021-11-21 16:59:00</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>Karnaval (1)</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>16</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2021-11-21 16:54:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>2021-11-21 17:00:00</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>Çingene Yüreğim</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>17</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2021-11-21 16:59:00</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 endTime                artistName  \\\n",
       "3934 2021-11-21 16:06:00                 Metallica   \n",
       "3935 2021-11-21 16:10:00                 Firewater   \n",
       "3936 2021-11-21 16:16:00               Ken Hensley   \n",
       "3937 2021-11-21 16:21:00         Kolektif İstanbul   \n",
       "3938 2021-11-21 16:24:00                  THE ROOP   \n",
       "3939 2021-11-21 16:27:00           Elena Tsagrinou   \n",
       "3940 2021-11-21 16:31:00                  Megadeth   \n",
       "3941 2021-11-21 16:34:00                  Megadeth   \n",
       "3942 2021-11-21 16:37:00                 Morrissey   \n",
       "3943 2021-11-21 16:42:00  Scala & Kolacny Brothers   \n",
       "3944 2021-11-21 16:47:00                 Morrissey   \n",
       "3945 2021-11-21 16:50:00                   Sonny J   \n",
       "3946 2021-11-21 16:54:00             Shocking Blue   \n",
       "3947 2021-11-21 16:59:00                Pink Floyd   \n",
       "3948 2021-11-21 17:00:00                Pink Floyd   \n",
       "\n",
       "                                         trackName     Day  Hour    Month  \\\n",
       "3934                             Spit Out The Bone  Sunday    16  2021-11   \n",
       "3935                                 Electric City  Sunday    16  2021-11   \n",
       "3936                                 Lady in Black  Sunday    16  2021-11   \n",
       "3937                                  Acımadı Yine  Sunday    16  2021-11   \n",
       "3938                                    Discoteque  Sunday    16  2021-11   \n",
       "3939                                     El Diablo  Sunday    16  2021-11   \n",
       "3940                      Peace Sells - Remastered  Sunday    16  2021-11   \n",
       "3941                    Architecture Of Aggression  Sunday    16  2021-11   \n",
       "3942                             I Wish You Lonely  Sunday    16  2021-11   \n",
       "3943                          Le vent nous portera  Sunday    16  2021-11   \n",
       "3944     The Girl from Tel-Aviv Who Wouldn't Kneel  Sunday    16  2021-11   \n",
       "3945  Handsfree (If You Hold My Hand) - Radio Edit  Sunday    16  2021-11   \n",
       "3946                                       Beggin'  Sunday    16  2021-11   \n",
       "3947                                  Karnaval (1)  Sunday    16  2021-11   \n",
       "3948                               Çingene Yüreğim  Sunday    17  2021-11   \n",
       "\n",
       "      Year  WeekdayOrNot  Minutes         PrevEndTime  Session_Id  \n",
       "3934  2021             1      7.2 2021-11-20 10:19:00       450.0  \n",
       "3935  2021             1      4.6 2021-11-21 16:06:00       450.0  \n",
       "3936  2021             1      5.6 2021-11-21 16:10:00       450.0  \n",
       "3937  2021             1      4.7 2021-11-21 16:16:00       450.0  \n",
       "3938  2021             1      3.0 2021-11-21 16:21:00       450.0  \n",
       "3939  2021             1      3.0 2021-11-21 16:24:00       450.0  \n",
       "3940  2021             1      4.1 2021-11-21 16:27:00       450.0  \n",
       "3941  2021             1      3.7 2021-11-21 16:31:00       450.0  \n",
       "3942  2021             1      3.0 2021-11-21 16:34:00       450.0  \n",
       "3943  2021             1      4.6 2021-11-21 16:37:00       450.0  \n",
       "3944  2021             1      5.0 2021-11-21 16:42:00       450.0  \n",
       "3945  2021             1      3.1 2021-11-21 16:47:00       450.0  \n",
       "3946  2021             1      4.2 2021-11-21 16:50:00       450.0  \n",
       "3947  2021             1      4.7 2021-11-21 16:54:00       450.0  \n",
       "3948  2021             1      1.2 2021-11-21 16:59:00       450.0  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream[df_stream.Session_Id == 450]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Queen', 'Placebo', 'Machine Gun Kelly', 'alt-J',\n",
       "       'Twenty One Pilots', 'Goodbye June', 'Florence + The Machine',\n",
       "       'Black Pumas', 'The Last Internationale', 'The Killers', 'Foals',\n",
       "       'YUNGBLUD', 'Metallica', 'Jamie Cullum', 'CHVRCHES',\n",
       "       'Glass Animals', 'Editors', 'Owl City', 'Imagine Dragons',\n",
       "       'Pixies', 'Nothing But Thieves', 'Tom Grennan',\n",
       "       'Frank Carter & The Rattlesnakes', 'Sigrid', 'Faith No More',\n",
       "       'Kings of Leon', 'Tom Misch'], dtype=object)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_id = df_stream.groupby(\"Session_Id\").agg({\"Minutes\":\"sum\"}).sort_values(by=[\"Minutes\"], ascending=False).head(1).reset_index()[\"Session_Id\"]\n",
    "df_stream[df_stream.Session_Id.isin(session_id)].artistName.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Listened Songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>trackName</th>\n",
       "      <th>artistName</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>Queen</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Blackstar</td>\n",
       "      <td>David Bowie</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>I Wish You Lonely</td>\n",
       "      <td>Morrissey</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Aviation</td>\n",
       "      <td>The Last Shadow Puppets</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>La bohème</td>\n",
       "      <td>Charles Aznavour</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              trackName               artistName  Count\n",
       "359   Bohemian Rhapsody                    Queen     33\n",
       "349           Blackstar              David Bowie     30\n",
       "894   I Wish You Lonely                Morrissey     30\n",
       "167            Aviation  The Last Shadow Puppets     28\n",
       "1055          La bohème         Charles Aznavour     26"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_songs = df_stream.groupby([\"trackName\",\"artistName\"]).agg({\"Minutes\":\"count\"}).reset_index()\n",
    "df_top_songs = df_top_songs.rename(columns= {'trackName':'trackName', 'artistName':'artistName', 'Minutes':'Count'})\n",
    "df_top_songs = df_top_songs.sort_values(\"Count\", ascending=False).head(5)\n",
    "df_top_songs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Listened Artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>artistName</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Metallica</td>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Queen</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>The Last Shadow Puppets</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  artistName  Minutes\n",
       "438                Metallica      779\n",
       "535                    Queen      710\n",
       "658  The Last Shadow Puppets      573\n",
       "524               Pink Floyd      434\n",
       "150                Daft Punk      350"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_artists = df_stream.groupby(\"artistName\").agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_top_artists[\"Minutes\"] = df_top_artists[\"Minutes\"].astype(int)\n",
    "df_top_artists = df_top_artists.sort_values(\"Minutes\", ascending=False).head(5)\n",
    "df_top_artists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Listened Hours, Days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\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>Hour</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>162.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>526.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>745.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>925.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>824.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>572.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>933.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>946.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1267.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>920.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1172.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>843.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>543.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>447.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>492.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>195.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hour  Minutes\n",
       "0      0     84.3\n",
       "1      1     16.5\n",
       "2      2      0.0\n",
       "3      3      3.6\n",
       "4      4      0.0\n",
       "5      5      0.0\n",
       "6      6      0.0\n",
       "7      7    162.6\n",
       "8      8    526.8\n",
       "9      9    745.9\n",
       "10    10    925.5\n",
       "11    11    824.3\n",
       "12    12    572.9\n",
       "13    13    933.1\n",
       "14    14    946.5\n",
       "15    15   1267.1\n",
       "16    16    920.7\n",
       "17    17   1172.6\n",
       "18    18    843.3\n",
       "19    19    675.0\n",
       "20    20    543.5\n",
       "21    21    447.2\n",
       "22    22    492.3\n",
       "23    23    195.1"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour = df_stream.groupby(\"Hour\").agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_hour[\"Minutes\"] = df_hour[\"Minutes\"].round(decimals=1)\n",
    "hours = pd.DataFrame(list(range(0,24)))\n",
    "hours.columns = [\"Hour\"]\n",
    "hour_df = hours.merge(df_hour[[\"Hour\",\"Minutes\"]], how='left', on = 'Hour')\n",
    "hour_df[\"Minutes\"] = hour_df[\"Minutes\"].fillna(0)\n",
    "hour_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_peakhour_graph(df,column):\n",
    "    \n",
    "    order_list = [\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
    "    plt.figure(figsize=(14,7))\n",
    "\n",
    "    plt.title(\"Minutes Played by \" + str(column) , fontsize=25)\n",
    "\n",
    "    if column == \"Day\":\n",
    "        ax = sns.barplot(column,\"Minutes\", data = df) \n",
    "    else:\n",
    "        ax = sns.barplot(column,\"Minutes\", data = df) \n",
    "    ax.set_xlabel(str(column),fontsize=25)\n",
    "    ax.set_ylabel(\"Minutes Played\",fontsize=25)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=20)\n",
    "    ax.tick_params(axis='y', which='major', labelsize=20)\n",
    "    \n",
    "    for bar, label in zip(ax.patches, df['Minutes']):\n",
    "        x = bar.get_x()\n",
    "        width = bar.get_width()\n",
    "        height = bar.get_height()\n",
    "        ax.text(x+width/2., height + 0.2, label, color=\"black\", fontsize=14 , ha=\"center\") \n",
    "   \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TKA\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_peakhour_graph(hour_df,\"Hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_day = df_stream.groupby(\"Day\").agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_day[\"Minutes\"] = df_day[\"Minutes\"].round(decimals=1)\n",
    "df_day['Day'] = pd.Categorical(\n",
    "    df_day['Day'],  categories=[\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"], ordered=True\n",
    "         )\n",
    "df_day = df_day.sort_values('Day',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TKA\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_peakhour_graph(df_day,\"Day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TKA\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_month = df_stream.groupby(\"Month\").agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_month[\"Minutes\"] = df_month[\"Minutes\"].round(decimals=1)\n",
    "draw_peakhour_graph(df_month,\"Month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Liked Artist by Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>endTime</th>\n",
       "      <th>artistName</th>\n",
       "      <th>trackName</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>WeekdayOrNot</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>PrevEndTime</th>\n",
       "      <th>Session_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "      <td>DJ Ufuk</td>\n",
       "      <td>Nankör Kedi (REMİX)</td>\n",
       "      <td>Monday</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Bırakma Kendini</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-12-07 22:25:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>Anıl Piyancı</td>\n",
       "      <td>Varsın</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-08 13:36:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Benimle Kayboldun</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2020-12-08 13:33:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-08 13:40:00</td>\n",
       "      <td>Kaan Boşnak</td>\n",
       "      <td>Seni Buldum Ya</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2020-12-08 13:36:00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              endTime    artistName            trackName      Day  Hour  \\\n",
       "0 2020-12-07 22:25:00       DJ Ufuk  Nankör Kedi (REMİX)   Monday    22   \n",
       "1 2020-12-08 13:33:00   Kaan Boşnak      Bırakma Kendini  Tuesday    13   \n",
       "2 2020-12-08 13:33:00  Anıl Piyancı               Varsın  Tuesday    13   \n",
       "3 2020-12-08 13:36:00   Kaan Boşnak    Benimle Kayboldun  Tuesday    13   \n",
       "4 2020-12-08 13:40:00   Kaan Boşnak       Seni Buldum Ya  Tuesday    13   \n",
       "\n",
       "     Month  Year  WeekdayOrNot  Minutes         PrevEndTime  Session_Id  \n",
       "0  2020-12  2020             0      0.9                 NaT         1.0  \n",
       "1  2020-12  2020             0      0.0 2020-12-07 22:25:00         2.0  \n",
       "2  2020-12  2020             0      0.0 2020-12-08 13:33:00         2.0  \n",
       "3  2020-12  2020             0      3.2 2020-12-08 13:33:00         2.0  \n",
       "4  2020-12  2020             0      3.9 2020-12-08 13:36:00         2.0  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stream.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Month</th>\n",
       "      <th>artistName</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>Twenty One Pilots</td>\n",
       "      <td>278.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>2021-11</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>227.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2021-10</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>156.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2021-09</td>\n",
       "      <td>Serge Gainsbourg</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>100.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>189.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>David Bowie</td>\n",
       "      <td>58.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>Queen</td>\n",
       "      <td>150.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>272.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>Queen</td>\n",
       "      <td>118.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>Queen</td>\n",
       "      <td>169.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>142.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>The Last Shadow Puppets</td>\n",
       "      <td>323.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month               artistName  Minutes  Rank\n",
       "1245  2021-12        Twenty One Pilots    278.8   1.0\n",
       "1136  2021-11                Metallica    227.8   1.0\n",
       "996   2021-10               Pink Floyd    156.1   1.0\n",
       "838   2021-09         Serge Gainsbourg     76.6   1.0\n",
       "708   2021-08                Metallica    100.8   1.0\n",
       "688   2021-07               Pink Floyd    189.7   1.0\n",
       "586   2021-06              David Bowie     58.6   1.0\n",
       "530   2021-05                    Queen    150.7   1.0\n",
       "391   2021-04                Daft Punk    272.5   1.0\n",
       "360   2021-03                    Queen    118.8   1.0\n",
       "270   2021-02                    Queen    169.7   1.0\n",
       "111   2021-01                Metallica    142.1   1.0\n",
       "20    2020-12  The Last Shadow Puppets    323.0   1.0"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_stream.groupby([\"Month\",\"artistName\"]).agg({\"Minutes\":\"sum\"}).reset_index().sort_values([\"Month\",\"artistName\"],ascending=False)\n",
    "data[\"Rank\"] = data.groupby(\"Month\").rank(ascending=False, method='first')[\"Minutes\"]\n",
    "data = data.loc[data.Rank==1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_most_liked_month(df):\n",
    "    plt.figure(figsize=(14,7))\n",
    "\n",
    "    plt.title(\"Most Liked Artist For Monthly Basis\",fontsize=20)\n",
    "    \n",
    "    ax = sns.barplot('Month',\"Minutes\", data = data)  \n",
    "\n",
    "    ax.set_xlabel(\"Month\",fontsize=15)\n",
    "    ax.set_ylabel(\"Minutes\",fontsize=15)\n",
    "    ax.tick_params(axis='x', which='major', labelsize=15)\n",
    "    ax.tick_params(axis='y', which='major', labelsize=15)\n",
    "\n",
    "    for bar, label in zip(ax.patches, data['artistName'].loc[::-1]):\n",
    "        x = bar.get_x()\n",
    "        width = bar.get_width()\n",
    "        height = bar.get_height()\n",
    "        ax.text(x+width/2., height + 0.2, label, backgroundcolor=\"yellow\", color=\"black\", fontsize=11 , ha=\"center\") \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TKA\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_most_liked_month(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TKA\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "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>Month</th>\n",
       "      <th>artistName</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>The Last Shadow Puppets</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>Queen</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>The Last Shadow Puppets</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>Metallica</td>\n",
       "      <td>202.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>Pink Floyd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>Queen</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2021-12</td>\n",
       "      <td>Daft Punk</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Month               artistName  Minutes\n",
       "0   2020-12  The Last Shadow Puppets    323.0\n",
       "1   2020-12                Metallica      0.0\n",
       "2   2020-12               Pink Floyd      0.0\n",
       "3   2020-12                    Queen      0.0\n",
       "4   2020-12                Daft Punk      0.0\n",
       "..      ...                      ...      ...\n",
       "60  2021-12  The Last Shadow Puppets      0.0\n",
       "61  2021-12                Metallica    202.7\n",
       "62  2021-12               Pink Floyd      0.0\n",
       "63  2021-12                    Queen      0.0\n",
       "64  2021-12                Daft Punk      4.7\n",
       "\n",
       "[65 rows x 3 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top = df_stream.groupby(\"artistName\").agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_top[\"Minutes\"] = df_top[\"Minutes\"].astype(int)\n",
    "df_top10 = df_top.sort_values(\"Minutes\", ascending=False).head(5)\n",
    "df_artist1 = df_stream.groupby([\"Month\",\"artistName\"]).agg({\"Minutes\":\"sum\"}).reset_index()\n",
    "df_artist2 = df_artist1[df_artist1.artistName.isin(df_top10.artistName)]\n",
    "df_artist2[\"key\"] = df_artist2[\"Month\"] + df_artist2[\"artistName\"]\n",
    "months = pd.DataFrame(df_artist2[\"Month\"].unique())\n",
    "artists = pd.DataFrame(df_artist2[\"artistName\"].unique() )\n",
    "months['key'] = 0\n",
    "artists['key'] = 0\n",
    "months_and_artists = months.merge(artists, how='outer', on = 'key')\n",
    "months_and_artists.drop('key',1, inplace=True)\n",
    "months_and_artists.columns = [\"Month\",\"artistName\"]\n",
    "months_and_artists[\"key\"] = months_and_artists[\"Month\"] + months_and_artists[\"artistName\"]\n",
    "months_and_artists = pd.merge(months_and_artists, df_artist2[[\"key\",\"Minutes\"]], how='left', on ='key')\n",
    "months_and_artists.drop('key',1, inplace=True)\n",
    "months_and_artists[\"Minutes\"] = months_and_artists[\"Minutes\"].fillna(0)\n",
    "months_and_artists"
   ]
  }
 ],
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