Data Preparation .ipynb

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 "cells": [
  {
   "cell_type": "code",
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    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
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   "source": [
    "###---------LIBRARIES---------------------#####\n",
    "from msilib.schema import Component\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import plotly.express as px  # (version 4.7.0 or higher)\n",
    "import plotly.graph_objects as go\n",
    "import dash_bootstrap_components as dbc\n",
    "from dash import Dash, dcc, html, Input, Output   # pip install dash (version 2.0.0 or higher)\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Setting DataFrame & Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>cust_id</th>\n",
       "      <th>source_duration</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>video_duration</th>\n",
       "      <th>user_created_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>e356</td>\n",
       "      <td>99.058293</td>\n",
       "      <td>396.233171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>475</td>\n",
       "      <td>1355.033724</td>\n",
       "      <td>5420.269792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>4640</td>\n",
       "      <td>63.711967</td>\n",
       "      <td>255.114535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>87a9</td>\n",
       "      <td>64.999300</td>\n",
       "      <td>259.997200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-12-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>e36a</td>\n",
       "      <td>2210.154948</td>\n",
       "      <td>8840.619792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>31d2</td>\n",
       "      <td>1774.940495</td>\n",
       "      <td>5324.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>3fcf</td>\n",
       "      <td>28.658852</td>\n",
       "      <td>100.504688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>5e4f</td>\n",
       "      <td>42659.866670</td>\n",
       "      <td>59163.225000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>fb97</td>\n",
       "      <td>222.320003</td>\n",
       "      <td>889.280013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>6124</td>\n",
       "      <td>103.032194</td>\n",
       "      <td>205.597721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-07-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10901 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cust_id  source_duration  transcoded_duration  video_duration  \\\n",
       "usage_date                                                                 \n",
       "2022-01-01    e356        99.058293           396.233171             NaN   \n",
       "2022-01-01     475      1355.033724          5420.269792             NaN   \n",
       "2022-01-01    4640        63.711967           255.114535             NaN   \n",
       "2022-01-01    87a9        64.999300           259.997200             NaN   \n",
       "2022-01-01    e36a      2210.154948          8840.619792             NaN   \n",
       "...            ...              ...                  ...             ...   \n",
       "2022-10-21    31d2      1774.940495          5324.625000             NaN   \n",
       "2022-10-21    3fcf        28.658852           100.504688             NaN   \n",
       "2022-10-21    5e4f     42659.866670         59163.225000             NaN   \n",
       "2022-10-21    fb97       222.320003           889.280013             NaN   \n",
       "2022-10-21    6124       103.032194           205.597721             NaN   \n",
       "\n",
       "           user_created_date  \n",
       "usage_date                    \n",
       "2022-01-01        2021-08-08  \n",
       "2022-01-01        2021-11-17  \n",
       "2022-01-01        2021-11-18  \n",
       "2022-01-01        2021-12-13  \n",
       "2022-01-01               NaT  \n",
       "...                      ...  \n",
       "2022-10-21               NaT  \n",
       "2022-10-21        2021-06-28  \n",
       "2022-10-21               NaT  \n",
       "2022-10-21        2022-10-19  \n",
       "2022-10-21        2022-07-16  \n",
       "\n",
       "[10901 rows x 5 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFramee\n",
    "df = pd.read_csv('data.csv')\n",
    "df.drop_duplicates(keep = 'first', inplace = True)\n",
    "df.usage_date = df.usage_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d'))\n",
    "df.user_created_date = df.user_created_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d')if x is not np.nan else x)\n",
    "df = df.set_index('usage_date',drop = True)[['cust_id', 'source_duration', 'transcoded_duration', 'video_duration',\"user_created_date\"]].sort_index(ascending = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
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       " ...]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(df.cust_id.to_list()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "           cust_id  source_duration  transcoded_duration  video_duration  \\\n",
       "usage_date                                                                 \n",
       "2022-01-01    e356        99.058293           396.233171             NaN   \n",
       "2022-01-01     475      1355.033724          5420.269792             NaN   \n",
       "2022-01-01    4640        63.711967           255.114535             NaN   \n",
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       "2022-01-01    e36a      2210.154948          8840.619792             NaN   \n",
       "...            ...              ...                  ...             ...   \n",
       "2022-10-21    31d2      1774.940495          5324.625000             NaN   \n",
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       "2022-10-21    5e4f     42659.866670         59163.225000             NaN   \n",
       "2022-10-21    fb97       222.320003           889.280013             NaN   \n",
       "2022-10-21    6124       103.032194           205.597721             NaN   \n",
       "\n",
       "           user_created_date  \n",
       "usage_date                    \n",
       "2022-01-01        2021-08-08  \n",
       "2022-01-01        2021-11-17  \n",
       "2022-01-01        2021-11-18  \n",
       "2022-01-01        2021-12-13  \n",
       "2022-01-01               NaT  \n",
       "...                      ...  \n",
       "2022-10-21               NaT  \n",
       "2022-10-21        2021-06-28  \n",
       "2022-10-21               NaT  \n",
       "2022-10-21        2022-10-19  \n",
       "2022-10-21        2022-07-16  \n",
       "\n",
       "[10901 rows x 5 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filters\n",
    "\n",
    "#Date Granularity - Dropdown List\n",
    "date_granularity = {'Day': 'D', 'Month': 'M', 'Year': 'Y'}\n",
    "\n",
    "#DateRange\n",
    "start_date = dt.date(2022,1,1) ; end_date = dt.date(2022,10,21)\n",
    "\n",
    "# Top N - Dropdown List\n",
    "top =  5 #[5,10,20,30,40,50]\n",
    "\n",
    "# Defining a filtered dataset\n",
    "df_filter = df.loc[start_date:end_date].to_period(date_granularity['Day']).reset_index()\n",
    "df_filter = df_filter.reset_index(drop = True)\n",
    "df_filter.usage_date = df_filter.usage_date.apply(lambda x: x.to_timestamp())\n",
    "data_dict = df_filter.to_dict(orient = 'record')\n",
    "df = pd.DataFrame(data_dict)\n",
    "df = df.set_index('usage_date')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source_duration        8.763605e+07\n",
       "transcoded_duration    1.375423e+08\n",
       "video_duration         2.250465e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Metrics Accumulated\n",
    "df_total = df.sum(axis = 0, numeric_only = True)\n",
    "df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>cust_id</th>\n",
       "      <th>source_duration</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>video_duration</th>\n",
       "      <th>user_created_date</th>\n",
       "      <th>new_customer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>e356</td>\n",
       "      <td>99.058293</td>\n",
       "      <td>396.233171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>475</td>\n",
       "      <td>1355.033724</td>\n",
       "      <td>5420.269792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-17</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>4640</td>\n",
       "      <td>63.711967</td>\n",
       "      <td>255.114535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>87a9</td>\n",
       "      <td>64.999300</td>\n",
       "      <td>259.997200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-12-13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>e36a</td>\n",
       "      <td>2210.154948</td>\n",
       "      <td>8840.619792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>31d2</td>\n",
       "      <td>1774.940495</td>\n",
       "      <td>5324.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>3fcf</td>\n",
       "      <td>28.658852</td>\n",
       "      <td>100.504688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>5e4f</td>\n",
       "      <td>42659.866670</td>\n",
       "      <td>59163.225000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>fb97</td>\n",
       "      <td>222.320003</td>\n",
       "      <td>889.280013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-10-19</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>6124</td>\n",
       "      <td>103.032194</td>\n",
       "      <td>205.597721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-07-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10901 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cust_id  source_duration  transcoded_duration  video_duration  \\\n",
       "usage_date                                                                 \n",
       "2022-01-01    e356        99.058293           396.233171             NaN   \n",
       "2022-01-01     475      1355.033724          5420.269792             NaN   \n",
       "2022-01-01    4640        63.711967           255.114535             NaN   \n",
       "2022-01-01    87a9        64.999300           259.997200             NaN   \n",
       "2022-01-01    e36a      2210.154948          8840.619792             NaN   \n",
       "...            ...              ...                  ...             ...   \n",
       "2022-10-21    31d2      1774.940495          5324.625000             NaN   \n",
       "2022-10-21    3fcf        28.658852           100.504688             NaN   \n",
       "2022-10-21    5e4f     42659.866670         59163.225000             NaN   \n",
       "2022-10-21    fb97       222.320003           889.280013             NaN   \n",
       "2022-10-21    6124       103.032194           205.597721             NaN   \n",
       "\n",
       "           user_created_date  new_customer  \n",
       "usage_date                                  \n",
       "2022-01-01        2021-08-08         False  \n",
       "2022-01-01        2021-11-17         False  \n",
       "2022-01-01        2021-11-18         False  \n",
       "2022-01-01        2021-12-13         False  \n",
       "2022-01-01               NaT         False  \n",
       "...                      ...           ...  \n",
       "2022-10-21               NaT         False  \n",
       "2022-10-21        2021-06-28         False  \n",
       "2022-10-21               NaT         False  \n",
       "2022-10-21        2022-10-19          True  \n",
       "2022-10-21        2022-07-16         False  \n",
       "\n",
       "[10901 rows x 6 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_potencial = df.copy()\n",
    "df_potencial['new_customer'] = df_potencial.user_created_date.apply(lambda x:\n",
    "                                                                    True if x > df_potencial.index[-1] - dt.timedelta(days = 90)\n",
    "                                                                  else False)\n",
    "df_potencial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter = df_filter.reset_index()\n",
    "df_filter.usage_date = df_filter.usage_date.apply(lambda x: x.to_timestamp())\n",
    "data_dict = df_filter.to_dict(orient = 'record')\n",
    "df = pd.DataFrame(data_dict)\n",
    "df = df.set_index('usage_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_potencial = df.copy()\n",
    "df_potencial['new_customer'] = df_potencial.user_created_date.apply(lambda x:\n",
    "                                                                    True if x > df_potencial.index[-1]- dt.timedelta(days = 90)\n",
    "                                                                   else False)\n",
    "df_new = df_potencial[df_potencial.new_customer == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Metrics Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>source_duration</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>video_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>277516.948135</td>\n",
       "      <td>425663.449481</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>277795.558304</td>\n",
       "      <td>394583.587921</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>260282.767226</td>\n",
       "      <td>364014.982308</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>267185.777589</td>\n",
       "      <td>378787.381206</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>305028.585976</td>\n",
       "      <td>448857.249877</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-17</th>\n",
       "      <td>275351.046731</td>\n",
       "      <td>437041.385764</td>\n",
       "      <td>140.959395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-18</th>\n",
       "      <td>277086.465503</td>\n",
       "      <td>439435.580519</td>\n",
       "      <td>205.737614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>262383.156130</td>\n",
       "      <td>436053.416383</td>\n",
       "      <td>439.125033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>273184.867248</td>\n",
       "      <td>440904.724699</td>\n",
       "      <td>345.237443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>46911.652812</td>\n",
       "      <td>74156.199258</td>\n",
       "      <td>3.870757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            source_duration  transcoded_duration  video_duration\n",
       "usage_date                                                      \n",
       "2022-01-01    277516.948135        425663.449481        0.000000\n",
       "2022-01-02    277795.558304        394583.587921        0.000000\n",
       "2022-01-03    260282.767226        364014.982308        0.000000\n",
       "2022-01-04    267185.777589        378787.381206        0.000000\n",
       "2022-01-05    305028.585976        448857.249877        0.000000\n",
       "...                     ...                  ...             ...\n",
       "2022-10-17    275351.046731        437041.385764      140.959395\n",
       "2022-10-18    277086.465503        439435.580519      205.737614\n",
       "2022-10-19    262383.156130        436053.416383      439.125033\n",
       "2022-10-20    273184.867248        440904.724699      345.237443\n",
       "2022-10-21     46911.652812         74156.199258        3.870757\n",
       "\n",
       "[294 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overtime = df_filter.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n",
    "                                                        'transcoded_duration': np.sum,\n",
    "                                                        'video_duration': np.sum})\n",
    "df_overtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Metrics -   Total Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>source_duration</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>video_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>2.775169e+05</td>\n",
       "      <td>4.256634e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>5.553125e+05</td>\n",
       "      <td>8.202470e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>8.155953e+05</td>\n",
       "      <td>1.184262e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.082781e+06</td>\n",
       "      <td>1.563049e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>1.387810e+06</td>\n",
       "      <td>2.011907e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-17</th>\n",
       "      <td>8.677648e+07</td>\n",
       "      <td>1.361517e+08</td>\n",
       "      <td>2.249471e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-18</th>\n",
       "      <td>8.705357e+07</td>\n",
       "      <td>1.365912e+08</td>\n",
       "      <td>2.249677e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>8.731595e+07</td>\n",
       "      <td>1.370272e+08</td>\n",
       "      <td>2.250116e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>8.758914e+07</td>\n",
       "      <td>1.374681e+08</td>\n",
       "      <td>2.250461e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>8.763605e+07</td>\n",
       "      <td>1.375423e+08</td>\n",
       "      <td>2.250465e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            source_duration  transcoded_duration  video_duration\n",
       "usage_date                                                      \n",
       "2022-01-01     2.775169e+05         4.256634e+05    0.000000e+00\n",
       "2022-01-02     5.553125e+05         8.202470e+05    0.000000e+00\n",
       "2022-01-03     8.155953e+05         1.184262e+06    0.000000e+00\n",
       "2022-01-04     1.082781e+06         1.563049e+06    0.000000e+00\n",
       "2022-01-05     1.387810e+06         2.011907e+06    0.000000e+00\n",
       "...                     ...                  ...             ...\n",
       "2022-10-17     8.677648e+07         1.361517e+08    2.249471e+06\n",
       "2022-10-18     8.705357e+07         1.365912e+08    2.249677e+06\n",
       "2022-10-19     8.731595e+07         1.370272e+08    2.250116e+06\n",
       "2022-10-20     8.758914e+07         1.374681e+08    2.250461e+06\n",
       "2022-10-21     8.763605e+07         1.375423e+08    2.250465e+06\n",
       "\n",
       "[294 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cumsum = df_filter.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n",
    "                                                      'transcoded_duration': np.sum,\n",
    "                                                      'video_duration': np.sum}).cumsum(axis =0)\n",
    "df_cumsum "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Metrics - Accumulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source_duration        8.763605e+07\n",
       "transcoded_duration    1.375423e+08\n",
       "video_duration         2.250465e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total = df_filter.sum(axis = 0, numeric_only = True)\n",
    "df_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Metrics - Variation Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "    .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>source_duration</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>video_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>0.001004</td>\n",
       "      <td>-0.073015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>-0.063042</td>\n",
       "      <td>-0.077471</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>0.026521</td>\n",
       "      <td>0.040582</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>0.141635</td>\n",
       "      <td>0.184985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>-0.029673</td>\n",
       "      <td>-0.065747</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-17</th>\n",
       "      <td>-0.039343</td>\n",
       "      <td>-0.014368</td>\n",
       "      <td>0.666746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-18</th>\n",
       "      <td>0.006303</td>\n",
       "      <td>0.005478</td>\n",
       "      <td>0.459552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-19</th>\n",
       "      <td>-0.053064</td>\n",
       "      <td>-0.007697</td>\n",
       "      <td>1.134394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-20</th>\n",
       "      <td>0.041168</td>\n",
       "      <td>0.011125</td>\n",
       "      <td>-0.213806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-21</th>\n",
       "      <td>-0.828279</td>\n",
       "      <td>-0.831809</td>\n",
       "      <td>-0.988788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            source_duration  transcoded_duration  video_duration\n",
       "usage_date                                                      \n",
       "2022-01-02         0.001004            -0.073015             NaN\n",
       "2022-01-03        -0.063042            -0.077471             NaN\n",
       "2022-01-04         0.026521             0.040582             NaN\n",
       "2022-01-05         0.141635             0.184985             NaN\n",
       "2022-01-06        -0.029673            -0.065747             NaN\n",
       "...                     ...                  ...             ...\n",
       "2022-10-17        -0.039343            -0.014368        0.666746\n",
       "2022-10-18         0.006303             0.005478        0.459552\n",
       "2022-10-19        -0.053064            -0.007697        1.134394\n",
       "2022-10-20         0.041168             0.011125       -0.213806\n",
       "2022-10-21        -0.828279            -0.831809       -0.988788\n",
       "\n",
       "[293 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pct_change = df_filter.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n",
    "                                    'transcoded_duration': np.sum,\n",
    "                                    'video_duration': np.sum}).pct_change().drop(df_filter.index[0],axis = 0).replace(np.inf,0)\n",
    "df_pct_change "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Metrics - Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>cust_id</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>ranking</th>\n",
       "      <th>rank_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e4f</td>\n",
       "      <td>1.085833e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>1-) 5e4f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8863</td>\n",
       "      <td>5.038736e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>2-) 8863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3fcf</td>\n",
       "      <td>4.428012e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>3-) 3fcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e36a</td>\n",
       "      <td>3.215696e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>4-) e36a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31d2</td>\n",
       "      <td>3.103342e+06</td>\n",
       "      <td>5</td>\n",
       "      <td>5-) 31d2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cust_id  transcoded_duration  ranking   rank_id\n",
       "0    5e4f         1.085833e+08        1  1-) 5e4f\n",
       "1    8863         5.038736e+06        2  2-) 8863\n",
       "2    3fcf         4.428012e+06        3  3-) 3fcf\n",
       "3    e36a         3.215696e+06        4  4-) e36a\n",
       "4    31d2         3.103342e+06        5  5-) 31d2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top = 10\n",
    "column_name = 'transcoded_duration'\n",
    "df_rank = df_filter.groupby(by = 'cust_id').agg({'source_duration': np.sum,\n",
    "                                    'transcoded_duration': np.sum,\n",
    "                                    'video_duration': np.sum})[[column_name ]].sort_values(by =column_name,\n",
    "                                                                                                   ascending = False)\n",
    "df_top = df_rank.copy()\n",
    "df_top['ranking'] = range(1,len(df_top.index) +1)\n",
    "df_top = df_top.iloc[:top,:].reset_index().assign(rank_id = lambda x: x['ranking'].apply(lambda x: str(x) + '-) ') + x['cust_id'])\n",
    "df_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>cust_id</th>\n",
       "      <th>transcoded_duration</th>\n",
       "      <th>ranking</th>\n",
       "      <th>rank_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5e4f</td>\n",
       "      <td>1.085833e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>1-) 5e4f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8863</td>\n",
       "      <td>5.038736e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>2-) 8863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3fcf</td>\n",
       "      <td>4.428012e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>3-) 3fcf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e36a</td>\n",
       "      <td>3.215696e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>4-) e36a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31d2</td>\n",
       "      <td>3.103342e+06</td>\n",
       "      <td>5</td>\n",
       "      <td>5-) 31d2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cust_id  transcoded_duration  ranking   rank_id\n",
       "0    5e4f         1.085833e+08        1  1-) 5e4f\n",
       "1    8863         5.038736e+06        2  2-) 8863\n",
       "2    3fcf         4.428012e+06        3  3-) 3fcf\n",
       "3    e36a         3.215696e+06        4  4-) e36a\n",
       "4    31d2         3.103342e+06        5  5-) 31d2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\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>transcoded_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cust_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5e4f</th>\n",
       "      <td>1.085833e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8863</th>\n",
       "      <td>5.038736e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3fcf</th>\n",
       "      <td>4.428012e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e36a</th>\n",
       "      <td>3.215696e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31d2</th>\n",
       "      <td>3.103342e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b301</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b291</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1dd</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1cc</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.10E+10</th>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1829 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          transcoded_duration\n",
       "cust_id                      \n",
       "5e4f             1.085833e+08\n",
       "8863             5.038736e+06\n",
       "3fcf             4.428012e+06\n",
       "e36a             3.215696e+06\n",
       "31d2             3.103342e+06\n",
       "...                       ...\n",
       "b301             0.000000e+00\n",
       "b291             0.000000e+00\n",
       "b1dd             0.000000e+00\n",
       "b1cc             0.000000e+00\n",
       "8.10E+10         0.000000e+00\n",
       "\n",
       "[1829 rows x 1 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rank"
   ]
  }
 ],
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