 analytics_bookings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Analytics_bookings\n",
    "- 28 Sept 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "NextPax",
     "Assessment"
    ]
   },
   "source": [
    "---\n",
    "# Assessment details\n",
    "You will find a SQL dump for a table including customer arrivals (reservations). Create and present a report with the following pieces of information:\n",
    "\n",
    "1. arrivals per week\n",
    "2. volume of arrivals per product market (where the accommodation is located)\n",
    "3. benchmark for the last year (if information is available)\n",
    "4. filters in the report for YTD (year to date), MTD (month to date) and / or selected period\n",
    "5. selection for provider (PMS), sales channel\n",
    "6. totals of gross revenue for every selection / filter\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Importing libraries & packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- Invoking & reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('analytics_bookings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>17/9/2021</td>\n",
       "      <td>19/9/2021</td>\n",
       "      <td>700.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.35</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>3/5/2021 04:16:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>16/6/2020</td>\n",
       "      <td>20/6/2020</td>\n",
       "      <td>299.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>18/1/2020 14:52:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>14/8/2020</td>\n",
       "      <td>21/8/2020</td>\n",
       "      <td>945.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>945.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>15/8/2019 09:08:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005a62-12e2-4227-a3c1-3347ef6ea709</td>\n",
       "      <td>6231475</td>\n",
       "      <td>NL</td>\n",
       "      <td>10/9/2021</td>\n",
       "      <td>13/9/2021</td>\n",
       "      <td>193.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>11/8/2021 20:54:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00007861-9d6e-46ed-b777-a569f1d2e698</td>\n",
       "      <td>62411641</td>\n",
       "      <td>US</td>\n",
       "      <td>7/2/2020</td>\n",
       "      <td>10/2/2020</td>\n",
       "      <td>293.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>293.21</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>1/1/2020 23:04:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "3  00005a62-12e2-4227-a3c1-3347ef6ea709           6231475           NL   \n",
       "4  00007861-9d6e-46ed-b777-a569f1d2e698          62411641           US   \n",
       "\n",
       "  arrival_date departure_date   price  commission_base  rent_only currency  \\\n",
       "0    17/9/2021      19/9/2021  700.35              0.0     700.35      USD   \n",
       "1    16/6/2020      20/6/2020  299.00              0.0     299.00      GBP   \n",
       "2    14/8/2020      21/8/2020  945.00              0.0     945.00      GBP   \n",
       "3    10/9/2021      13/9/2021  193.50              0.0     161.00      EUR   \n",
       "4     7/2/2020      10/2/2020  293.21              0.0     293.21      USD   \n",
       "\n",
       "         state  adults  children  babies  pets   channel_id  \\\n",
       "0  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1    cancelled       2         0       0     0       BOO142   \n",
       "2  reservation       2         2       0     0       BOO142   \n",
       "3  reservation       2         0       0     0       HOL303   \n",
       "4  reservation       2         0       0     0  HOM143-VRBO   \n",
       "\n",
       "           created_at  \n",
       "0   3/5/2021 04:16:36  \n",
       "1  18/1/2020 14:52:17  \n",
       "2  15/8/2019 09:08:38  \n",
       "3  11/8/2021 20:54:52  \n",
       "4   1/1/2020 23:04:30  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.drop(columns=['channel_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df['country_code'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Data Cleaning and Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Checking and adjusting datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_uuid         object\n",
       "nextpax_house_id      int64\n",
       "country_code         object\n",
       "arrival_date         object\n",
       "departure_date       object\n",
       "price               float64\n",
       "commission_base     float64\n",
       "rent_only           float64\n",
       "currency             object\n",
       "state                object\n",
       "adults                int64\n",
       "children              int64\n",
       "babies                int64\n",
       "pets                  int64\n",
       "channel_id           object\n",
       "created_at           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['rent_only'] = pd.to_numeric(df[\"rent_only\"], downcast=\"float\")\n",
    "df['price'] = pd.to_numeric(df[\"price\"], downcast=\"float\")\n",
    "df['commission_base'] = pd.to_numeric(df[\"commission_base\"], downcast=\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['arrival_date'] = pd.to_datetime(df['arrival_date'])\n",
    "df['departure_date'] = pd.to_datetime(df['departure_date'])\n",
    "df['created_at'] = pd.to_datetime(df['created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_uuid                object\n",
       "nextpax_house_id             int64\n",
       "country_code                object\n",
       "arrival_date        datetime64[ns]\n",
       "departure_date      datetime64[ns]\n",
       "price                      float32\n",
       "commission_base            float32\n",
       "rent_only                  float32\n",
       "currency                    object\n",
       "state                       object\n",
       "adults                       int64\n",
       "children                     int64\n",
       "babies                       int64\n",
       "pets                         int64\n",
       "channel_id                  object\n",
       "created_at          datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Handling poor quality data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "    - Removing Missing and NA/NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Pandas has three modes of dealing with missing data via calling $fillna()$:\n",
    "- *method='ffill'*: Ffill or forward-fill propagates the last observed non-null value forward until another non-null value is encountered\n",
    "- *method='bfill'*: Bfill or backward-fill propagates the first observed non-null value backward until another non-null value is met\n",
    "- *explicit value*: It is also possible to set an exact value to replace all missings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>700.349976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.349976</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2021-03-05 04:16:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020-01-18 14:52:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>945.000000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2019-08-15 09:08:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005a62-12e2-4227-a3c1-3347ef6ea709</td>\n",
       "      <td>6231475</td>\n",
       "      <td>NL</td>\n",
       "      <td>2021-10-09</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>193.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>2021-11-08 20:54:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00007861-9d6e-46ed-b777-a569f1d2e698</td>\n",
       "      <td>62411641</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>293.209991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>293.209991</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2020-01-01 23:04:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "3  00005a62-12e2-4227-a3c1-3347ef6ea709           6231475           NL   \n",
       "4  00007861-9d6e-46ed-b777-a569f1d2e698          62411641           US   \n",
       "\n",
       "  arrival_date departure_date       price  commission_base   rent_only  \\\n",
       "0   2021-09-17     2021-09-19  700.349976              0.0  700.349976   \n",
       "1   2020-06-16     2020-06-20  299.000000              0.0  299.000000   \n",
       "2   2020-08-14     2020-08-21  945.000000              0.0  945.000000   \n",
       "3   2021-10-09     2021-09-13  193.500000              0.0  161.000000   \n",
       "4   2020-07-02     2020-10-02  293.209991              0.0  293.209991   \n",
       "\n",
       "  currency        state  adults  children  babies  pets   channel_id  \\\n",
       "0      USD  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1      GBP    cancelled       2         0       0     0       BOO142   \n",
       "2      GBP  reservation       2         2       0     0       BOO142   \n",
       "3      EUR  reservation       2         0       0     0       HOL303   \n",
       "4      USD  reservation       2         0       0     0  HOM143-VRBO   \n",
       "\n",
       "           created_at  \n",
       "0 2021-03-05 04:16:36  \n",
       "1 2020-01-18 14:52:17  \n",
       "2 2019-08-15 09:08:38  \n",
       "3 2021-11-08 20:54:52  \n",
       "4 2020-01-01 23:04:30  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.fillna(method='ffill')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# search = (df['country_code']==0)\n",
    "# df.loc[df['country_code']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df = df.astype(object).where(pd.notnull(df),0)\n",
    "# search = (df['country_code']==0)\n",
    "# df.loc[df['country_code']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Handling poor quality data\n",
    "    - Adjusting negetive values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
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       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
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       "      <th>created_at</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97042</th>\n",
       "      <td>3d1bcf5a-890f-4451-9c06-57f5f7d74103</td>\n",
       "      <td>60884162</td>\n",
       "      <td>GB</td>\n",
       "      <td>2021-12-03</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>469.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-429.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2020-02-07 21:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326269</th>\n",
       "      <td>cdb171c1-dd5b-4c51-ad62-d82960f5efc6</td>\n",
       "      <td>608104354</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1045.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2020-09-15 16:03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392299</th>\n",
       "      <td>f71b358d-41aa-4bbf-8346-7592adf10fd7</td>\n",
       "      <td>60884406</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>2020-12-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-345.0</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2020-08-26 16:16:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                booking_uuid  nextpax_house_id country_code  \\\n",
       "97042   3d1bcf5a-890f-4451-9c06-57f5f7d74103          60884162           GB   \n",
       "326269  cdb171c1-dd5b-4c51-ad62-d82960f5efc6         608104354           GB   \n",
       "392299  f71b358d-41aa-4bbf-8346-7592adf10fd7          60884406           GB   \n",
       "\n",
       "       arrival_date departure_date   price  commission_base  rent_only  \\\n",
       "97042    2021-12-03     2021-03-15   469.0              0.0     -429.0   \n",
       "326269   2020-12-21     2020-12-28  1118.0              0.0    -1045.0   \n",
       "392299   2020-12-14     2020-12-18     0.0              0.0     -345.0   \n",
       "\n",
       "       currency        state  adults  children  babies  pets channel_id  \\\n",
       "97042       GBP    cancelled       1         0       0     0     HOM143   \n",
       "326269      GBP  reservation       1         0       0     0     HOM143   \n",
       "392299      GBP  reservation       1         0       0     0     HOM143   \n",
       "\n",
       "                created_at  \n",
       "97042  2020-02-07 21:01:00  \n",
       "326269 2020-09-15 16:03:00  \n",
       "392299 2020-08-26 16:16:00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv = df['rent_only'] < 0\n",
    "df[nv]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['rent_only']=df['rent_only'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
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       "Empty DataFrame\n",
       "Columns: [booking_uuid, nextpax_house_id, country_code, arrival_date, departure_date, price, commission_base, rent_only, currency, state, adults, children, babies, pets, channel_id, created_at]\n",
       "Index: []"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "nv = df['rent_only'] < 0\n",
    "df[nv]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
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   "outputs": [
    {
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       "      <th>171666</th>\n",
       "      <td>6c222eb0-e906-4679-89ba-445f250bfe48</td>\n",
       "      <td>6051629</td>\n",
       "      <td>NL</td>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>-16.18</td>\n",
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       "      <td>reservation</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2021-05-25 09:46:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                booking_uuid  nextpax_house_id country_code  \\\n",
       "171666  6c222eb0-e906-4679-89ba-445f250bfe48           6051629           NL   \n",
       "\n",
       "       arrival_date departure_date  price  commission_base  rent_only  \\\n",
       "171666   2021-05-26     2021-05-27 -16.18              0.0  66.419998   \n",
       "\n",
       "       currency        state  adults  children  babies  pets channel_id  \\\n",
       "171666      EUR  reservation       4         0       0     0     BOO142   \n",
       "\n",
       "                created_at  \n",
       "171666 2021-05-25 09:46:05  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv = df['price'] < 0\n",
    "df[nv]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['price']=df['price'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "Empty DataFrame\n",
       "Columns: [booking_uuid, nextpax_house_id, country_code, arrival_date, departure_date, price, commission_base, rent_only, currency, state, adults, children, babies, pets, channel_id, created_at]\n",
       "Index: []"
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "nv = df['price'] < 0\n",
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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Splitting the channel_id ind column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>HOM143-VRBO</td>\n",
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       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
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       "      <td>2019-08-15 09:08:38</td>\n",
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       "      <td>2021-09-13</td>\n",
       "      <td>193.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.000000</td>\n",
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       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>2021-11-08 20:54:52</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00007861-9d6e-46ed-b777-a569f1d2e698</td>\n",
       "      <td>62411641</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>293.209991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>293.209991</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2020-01-01 23:04:30</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "3  00005a62-12e2-4227-a3c1-3347ef6ea709           6231475           NL   \n",
       "4  00007861-9d6e-46ed-b777-a569f1d2e698          62411641           US   \n",
       "\n",
       "  arrival_date departure_date       price  commission_base   rent_only  \\\n",
       "0   2021-09-17     2021-09-19  700.349976              0.0  700.349976   \n",
       "1   2020-06-16     2020-06-20  299.000000              0.0  299.000000   \n",
       "2   2020-08-14     2020-08-21  945.000000              0.0  945.000000   \n",
       "3   2021-10-09     2021-09-13  193.500000              0.0  161.000000   \n",
       "4   2020-07-02     2020-10-02  293.209991              0.0  293.209991   \n",
       "\n",
       "  currency        state  adults  children  babies  pets   channel_id  \\\n",
       "0      USD  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1      GBP    cancelled       2         0       0     0       BOO142   \n",
       "2      GBP  reservation       2         2       0     0       BOO142   \n",
       "3      EUR  reservation       2         0       0     0       HOL303   \n",
       "4      USD  reservation       2         0       0     0  HOM143-VRBO   \n",
       "\n",
       "           created_at channel sub_channel  \n",
       "0 2021-03-05 04:16:36  HOM143        VRBO  \n",
       "1 2020-01-18 14:52:17  BOO142        None  \n",
       "2 2019-08-15 09:08:38  BOO142        None  \n",
       "3 2021-11-08 20:54:52  HOL303        None  \n",
       "4 2020-01-01 23:04:30  HOM143        VRBO  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_channel=df['channel_id'].str.split('-',n=1,expand=True).rename(columns={0:'channel',1:'sub_channel'})\n",
    "df=pd.concat([df, split_channel], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df.channel_id.count(), df.channel.count(),df.sub_channel.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df.channel_id.nunique(), df.channel.nunique(),df.sub_channel.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Setting the decimal places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.options.display.float_format = \"{:,.2f}\".format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Currency Converter to Euro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install currencyconverter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from currency_converter import CurrencyConverter\n",
    "c = CurrencyConverter()\n",
    "def curr(a,cur):\n",
    "    try:\n",
    "        return c.convert(a,cur,'EUR')\n",
    "    except:\n",
    "        return a\n",
    "df[\"rent_only in EUR\"]=df[['rent_only','currency']].apply(lambda x: curr(x.rent_only,x.currency), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# round(curr(1,'USD'),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# round(curr(1,'GBP'),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# round(curr(1,'INR'),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# curr(1,'IDR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>channel</th>\n",
       "      <th>sub_channel</th>\n",
       "      <th>rent_only in EUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>700.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>700.35</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2021-03-05 04:16:36</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>598.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>299.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020-01-18 14:52:17</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>349.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>945.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>945.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2019-08-15 09:08:38</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>1,104.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005a62-12e2-4227-a3c1-3347ef6ea709</td>\n",
       "      <td>6231475</td>\n",
       "      <td>NL</td>\n",
       "      <td>2021-10-09</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>193.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>2021-11-08 20:54:52</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>None</td>\n",
       "      <td>161.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00007861-9d6e-46ed-b777-a569f1d2e698</td>\n",
       "      <td>62411641</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>293.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>293.21</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2020-01-01 23:04:30</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>250.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "3  00005a62-12e2-4227-a3c1-3347ef6ea709           6231475           NL   \n",
       "4  00007861-9d6e-46ed-b777-a569f1d2e698          62411641           US   \n",
       "\n",
       "  arrival_date departure_date  price  commission_base  rent_only currency  \\\n",
       "0   2021-09-17     2021-09-19 700.35             0.00     700.35      USD   \n",
       "1   2020-06-16     2020-06-20 299.00             0.00     299.00      GBP   \n",
       "2   2020-08-14     2020-08-21 945.00             0.00     945.00      GBP   \n",
       "3   2021-10-09     2021-09-13 193.50             0.00     161.00      EUR   \n",
       "4   2020-07-02     2020-10-02 293.21             0.00     293.21      USD   \n",
       "\n",
       "         state  adults  children  babies  pets   channel_id  \\\n",
       "0  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1    cancelled       2         0       0     0       BOO142   \n",
       "2  reservation       2         2       0     0       BOO142   \n",
       "3  reservation       2         0       0     0       HOL303   \n",
       "4  reservation       2         0       0     0  HOM143-VRBO   \n",
       "\n",
       "           created_at channel sub_channel  rent_only in EUR  \n",
       "0 2021-03-05 04:16:36  HOM143        VRBO            598.79  \n",
       "1 2020-01-18 14:52:17  BOO142        None            349.61  \n",
       "2 2019-08-15 09:08:38  BOO142        None          1,104.97  \n",
       "3 2021-11-08 20:54:52  HOL303        None            161.00  \n",
       "4 2020-01-01 23:04:30  HOM143        VRBO            250.69  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# round(wf['rent_only in EUR'].sum(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rent_only'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['rent_only'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sns.boxplot(x=df['commission_base'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "ax.scatter(df['price'], df['rent_only'])\n",
    "ax.set_xlabel('Proportion of rent_only')\n",
    "ax.set_ylabel('Value of the Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Saving a copy of the cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df.to_csv('NextPax_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "# Arrivals per week\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Creating a new dataframe in which the analysis is applied without effecting the cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "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>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>channel</th>\n",
       "      <th>sub_channel</th>\n",
       "      <th>rent_only in EUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>700.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>700.35</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2021-03-05 04:16:36</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>598.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>299.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020-01-18 14:52:17</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>349.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>945.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>945.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2019-08-15 09:08:38</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>1,104.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00005a62-12e2-4227-a3c1-3347ef6ea709</td>\n",
       "      <td>6231475</td>\n",
       "      <td>NL</td>\n",
       "      <td>2021-10-09</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>193.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>2021-11-08 20:54:52</td>\n",
       "      <td>HOL303</td>\n",
       "      <td>None</td>\n",
       "      <td>161.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00007861-9d6e-46ed-b777-a569f1d2e698</td>\n",
       "      <td>62411641</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>293.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>293.21</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2020-01-01 23:04:30</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>250.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "3  00005a62-12e2-4227-a3c1-3347ef6ea709           6231475           NL   \n",
       "4  00007861-9d6e-46ed-b777-a569f1d2e698          62411641           US   \n",
       "\n",
       "  arrival_date departure_date  price  commission_base  rent_only currency  \\\n",
       "0   2021-09-17     2021-09-19 700.35             0.00     700.35      USD   \n",
       "1   2020-06-16     2020-06-20 299.00             0.00     299.00      GBP   \n",
       "2   2020-08-14     2020-08-21 945.00             0.00     945.00      GBP   \n",
       "3   2021-10-09     2021-09-13 193.50             0.00     161.00      EUR   \n",
       "4   2020-07-02     2020-10-02 293.21             0.00     293.21      USD   \n",
       "\n",
       "         state  adults  children  babies  pets   channel_id  \\\n",
       "0  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1    cancelled       2         0       0     0       BOO142   \n",
       "2  reservation       2         2       0     0       BOO142   \n",
       "3  reservation       2         0       0     0       HOL303   \n",
       "4  reservation       2         0       0     0  HOM143-VRBO   \n",
       "\n",
       "           created_at channel sub_channel  rent_only in EUR  \n",
       "0 2021-03-05 04:16:36  HOM143        VRBO            598.79  \n",
       "1 2020-01-18 14:52:17  BOO142        None            349.61  \n",
       "2 2019-08-15 09:08:38  BOO142        None          1,104.97  \n",
       "3 2021-11-08 20:54:52  HOL303        None            161.00  \n",
       "4 2020-01-01 23:04:30  HOM143        VRBO            250.69  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf = df\n",
    "wf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *passengers* per week for all year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>channel</th>\n",
       "      <th>sub_channel</th>\n",
       "      <th>rent_only in EUR</th>\n",
       "      <th>no_Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>700.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>700.35</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2021-03-05 04:16:36</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>598.79</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>299.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020-01-18 14:52:17</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>349.61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>945.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>945.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2019-08-15 09:08:38</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>1,104.97</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "\n",
       "  arrival_date departure_date  price  commission_base  rent_only currency  \\\n",
       "0   2021-09-17     2021-09-19 700.35             0.00     700.35      USD   \n",
       "1   2020-06-16     2020-06-20 299.00             0.00     299.00      GBP   \n",
       "2   2020-08-14     2020-08-21 945.00             0.00     945.00      GBP   \n",
       "\n",
       "         state  adults  children  babies  pets   channel_id  \\\n",
       "0  reservation       8         0       0     0  HOM143-VRBO   \n",
       "1    cancelled       2         0       0     0       BOO142   \n",
       "2  reservation       2         2       0     0       BOO142   \n",
       "\n",
       "           created_at channel sub_channel  rent_only in EUR  no_Passengers  \n",
       "0 2021-03-05 04:16:36  HOM143        VRBO            598.79              8  \n",
       "1 2020-01-18 14:52:17  BOO142        None            349.61              2  \n",
       "2 2019-08-15 09:08:38  BOO142        None          1,104.97              4  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_column = wf[\"adults\"] + wf[\"children\"] + wf[\"babies\"]\n",
    "wf[\"no_Passengers\"] = sum_column\n",
    "wf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>booking_uuid</th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>price</th>\n",
       "      <th>commission_base</th>\n",
       "      <th>rent_only</th>\n",
       "      <th>currency</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>babies</th>\n",
       "      <th>pets</th>\n",
       "      <th>channel_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>channel</th>\n",
       "      <th>sub_channel</th>\n",
       "      <th>rent_only in EUR</th>\n",
       "      <th>no_Passengers</th>\n",
       "      <th>week</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000013c2-3177-46d7-ae98-ff4538b097d6</td>\n",
       "      <td>6248120</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>2021-09-19</td>\n",
       "      <td>700.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>700.35</td>\n",
       "      <td>USD</td>\n",
       "      <td>reservation</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>HOM143-VRBO</td>\n",
       "      <td>2021-03-05 04:16:36</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>VRBO</td>\n",
       "      <td>598.79</td>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0000264b-bc12-4fed-ada6-753c2c2ca2e6</td>\n",
       "      <td>60851394</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>299.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020-01-18 14:52:17</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>349.61</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002c3f-e114-4092-94f5-5d8f92d94ba3</td>\n",
       "      <td>60879939</td>\n",
       "      <td>GB</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>945.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>945.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>reservation</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2019-08-15 09:08:38</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>None</td>\n",
       "      <td>1,104.97</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           booking_uuid  nextpax_house_id country_code  \\\n",
       "0  000013c2-3177-46d7-ae98-ff4538b097d6           6248120           US   \n",
       "1  0000264b-bc12-4fed-ada6-753c2c2ca2e6          60851394           GB   \n",
       "2  00002c3f-e114-4092-94f5-5d8f92d94ba3          60879939           GB   \n",
       "\n",
       "  arrival_date departure_date  price  commission_base  rent_only currency  \\\n",
       "0   2021-09-17     2021-09-19 700.35             0.00     700.35      USD   \n",
       "1   2020-06-16     2020-06-20 299.00             0.00     299.00      GBP   \n",
       "2   2020-08-14     2020-08-21 945.00             0.00     945.00      GBP   \n",
       "\n",
       "         state  ...  babies  pets   channel_id          created_at channel  \\\n",
       "0  reservation  ...       0     0  HOM143-VRBO 2021-03-05 04:16:36  HOM143   \n",
       "1    cancelled  ...       0     0       BOO142 2020-01-18 14:52:17  BOO142   \n",
       "2  reservation  ...       0     0       BOO142 2019-08-15 09:08:38  BOO142   \n",
       "\n",
       "  sub_channel rent_only in EUR no_Passengers  week  year  \n",
       "0        VRBO           598.79             8    37  2021  \n",
       "1        None           349.61             2    25  2020  \n",
       "2        None         1,104.97             4    33  2020  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf['week'] = wf['arrival_date'].apply(lambda x: \"%d\" % (x.week))\n",
    "wf['year'] = wf['arrival_date'].apply(lambda x: \"%d\" % (x.year))\n",
    "wf.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *passengers* per week for all year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>Passengers_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>9748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>24943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>9165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>9290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>10148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2023</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2024</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2025</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2026</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  week  Passengers_w\n",
       "0    2020     1          9748\n",
       "11   2020     2         24943\n",
       "22   2020     3          9165\n",
       "33   2020     4          9290\n",
       "44   2020     5         10148\n",
       "..    ...   ...           ...\n",
       "159  2023    52             9\n",
       "162  2024    52             4\n",
       "163  2025    12             8\n",
       "164  2025    13             1\n",
       "165  2026    35             4\n",
       "\n",
       "[166 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_agr=pd.DataFrame(wf.groupby(['year','week'])['no_Passengers'].sum().reset_index(name=\"Passengers_w\"))\n",
    "pass_agr['week']=pass_agr['week'].astype(int)\n",
    "pass_agr=pass_agr.sort_values(by=['year','week'],ascending=True)\n",
    "pass_agr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *passengers* per week for all year (table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>week</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>9,748.00</td>\n",
       "      <td>24,943.00</td>\n",
       "      <td>9,165.00</td>\n",
       "      <td>9,290.00</td>\n",
       "      <td>10,148.00</td>\n",
       "      <td>13,425.00</td>\n",
       "      <td>20,205.00</td>\n",
       "      <td>18,453.00</td>\n",
       "      <td>15,137.00</td>\n",
       "      <td>21,435.00</td>\n",
       "      <td>...</td>\n",
       "      <td>7,111.00</td>\n",
       "      <td>22,443.00</td>\n",
       "      <td>13,803.00</td>\n",
       "      <td>5,963.00</td>\n",
       "      <td>8,098.00</td>\n",
       "      <td>10,160.00</td>\n",
       "      <td>13,051.00</td>\n",
       "      <td>6,419.00</td>\n",
       "      <td>14,301.00</td>\n",
       "      <td>10,838.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>21,466.00</td>\n",
       "      <td>4,763.00</td>\n",
       "      <td>3,594.00</td>\n",
       "      <td>4,429.00</td>\n",
       "      <td>17,229.00</td>\n",
       "      <td>13,902.00</td>\n",
       "      <td>8,542.00</td>\n",
       "      <td>8,096.00</td>\n",
       "      <td>20,457.00</td>\n",
       "      <td>16,408.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13,065.00</td>\n",
       "      <td>9,673.00</td>\n",
       "      <td>3,554.00</td>\n",
       "      <td>4,451.00</td>\n",
       "      <td>9,827.00</td>\n",
       "      <td>17,582.00</td>\n",
       "      <td>2,692.00</td>\n",
       "      <td>5,837.00</td>\n",
       "      <td>4,448.00</td>\n",
       "      <td>2,035.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>730.00</td>\n",
       "      <td>983.00</td>\n",
       "      <td>797.00</td>\n",
       "      <td>990.00</td>\n",
       "      <td>937.00</td>\n",
       "      <td>623.00</td>\n",
       "      <td>1,796.00</td>\n",
       "      <td>1,002.00</td>\n",
       "      <td>888.00</td>\n",
       "      <td>463.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1,129.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1,044.00</td>\n",
       "      <td>283.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>385.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "week        1         2        3        4         5         6         7   \\\n",
       "year                                                                       \n",
       "2020  9,748.00 24,943.00 9,165.00 9,290.00 10,148.00 13,425.00 20,205.00   \n",
       "2021 21,466.00  4,763.00 3,594.00 4,429.00 17,229.00 13,902.00  8,542.00   \n",
       "2022    730.00    983.00   797.00   990.00    937.00    623.00  1,796.00   \n",
       "2023      5.00      2.00        0        0         0         0      2.00   \n",
       "2024         0         0        0        0         0         0         0   \n",
       "2025         0         0        0        0         0         0         0   \n",
       "2026         0         0        0        0         0         0         0   \n",
       "\n",
       "week        8         9         10  ...        44        45        46  \\\n",
       "year                                ...                                 \n",
       "2020 18,453.00 15,137.00 21,435.00  ...  7,111.00 22,443.00 13,803.00   \n",
       "2021  8,096.00 20,457.00 16,408.00  ... 13,065.00  9,673.00  3,554.00   \n",
       "2022  1,002.00    888.00    463.00  ...  1,129.00    140.00         0   \n",
       "2023         0      4.00         0  ...         0         0         0   \n",
       "2024         0         0         0  ...         0         0         0   \n",
       "2025         0         0         0  ...         0         0         0   \n",
       "2026         0         0         0  ...         0         0         0   \n",
       "\n",
       "week       47       48        49        50       51        52        53  \n",
       "year                                                                     \n",
       "2020 5,963.00 8,098.00 10,160.00 13,051.00 6,419.00 14,301.00 10,838.00  \n",
       "2021 4,451.00 9,827.00 17,582.00  2,692.00 5,837.00  4,448.00  2,035.00  \n",
       "2022     4.00 1,044.00    283.00         0     4.00    385.00         0  \n",
       "2023        0        0         0         0        0      9.00         0  \n",
       "2024        0        0         0         0        0      4.00         0  \n",
       "2025        0        0         0         0        0         0         0  \n",
       "2026        0        0         0         0        0         0         0  \n",
       "\n",
       "[7 rows x 53 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_years=pass_agr.pivot_table(values='Passengers_w', \n",
    "                      index='year', columns='week', \n",
    "                      aggfunc='first')\n",
    "pass_years = pass_years.astype(object).where(pd.notnull(pass_years),0)\n",
    "pass_years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *passengers* per week for all year (diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pass_years.T.plot(kind=\"bar\",figsize=(15,10),width=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# No *passengers* per week for 2020 (table)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pass_years.loc['2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# No *passengers* per week for *2020* (daigram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pass_years.loc['2020'].plot(kind=\"bar\",figsize=(15,10),width=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pass_years=pass_years\n",
    "# pass_years\n",
    "# pass_years['2020','2021'].plot(kind=\"bar\",figsize=(15,10),width=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *reservations* per week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wf=df\n",
    "wf['week'] = wf['arrival_date'].apply(lambda x: \"%d\" % (x.week))\n",
    "wf['year'] = wf['arrival_date'].apply(lambda x: \"%d\" % (x.year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>reservations_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>5458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2023</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2024</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2025</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2026</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  week  reservations_w\n",
       "46   2020     1            2301\n",
       "7    2020     2            5458\n",
       "44   2020     3            2355\n",
       "42   2020     4            2462\n",
       "40   2020     5            2666\n",
       "..    ...   ...             ...\n",
       "159  2023    52               1\n",
       "162  2024    52               1\n",
       "163  2025    12               1\n",
       "164  2025    13               1\n",
       "165  2026    35               2\n",
       "\n",
       "[166 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeks_agr=pd.DataFrame(wf.groupby(['year'])['week'].value_counts().reset_index(name=\"reservations_w\"))\n",
    "weeks_agr['week']=weeks_agr['week'].astype(int)\n",
    "weeks_agr=weeks_agr.sort_values(by=['year','week'],ascending=True)\n",
    "weeks_agr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *reservations* per week for all year (table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\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>week</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2,301.00</td>\n",
       "      <td>5,458.00</td>\n",
       "      <td>2,355.00</td>\n",
       "      <td>2,462.00</td>\n",
       "      <td>2,666.00</td>\n",
       "      <td>3,435.00</td>\n",
       "      <td>4,857.00</td>\n",
       "      <td>4,675.00</td>\n",
       "      <td>3,790.00</td>\n",
       "      <td>5,111.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1,809.00</td>\n",
       "      <td>5,152.00</td>\n",
       "      <td>3,145.00</td>\n",
       "      <td>1,390.00</td>\n",
       "      <td>1,626.00</td>\n",
       "      <td>2,390.00</td>\n",
       "      <td>3,411.00</td>\n",
       "      <td>1,385.00</td>\n",
       "      <td>2,934.00</td>\n",
       "      <td>2,278.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5,182.00</td>\n",
       "      <td>1,079.00</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1,152.00</td>\n",
       "      <td>3,930.00</td>\n",
       "      <td>3,533.00</td>\n",
       "      <td>2,125.00</td>\n",
       "      <td>2,010.00</td>\n",
       "      <td>4,760.00</td>\n",
       "      <td>3,915.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3,076.00</td>\n",
       "      <td>2,723.00</td>\n",
       "      <td>728.00</td>\n",
       "      <td>796.00</td>\n",
       "      <td>2,399.00</td>\n",
       "      <td>4,332.00</td>\n",
       "      <td>467.00</td>\n",
       "      <td>1,052.00</td>\n",
       "      <td>776.00</td>\n",
       "      <td>621.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>150.00</td>\n",
       "      <td>169.00</td>\n",
       "      <td>167.00</td>\n",
       "      <td>193.00</td>\n",
       "      <td>176.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>337.00</td>\n",
       "      <td>189.00</td>\n",
       "      <td>176.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>...</td>\n",
       "      <td>199.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>194.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>108.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "week       1        2        3        4        5        6        7        8   \\\n",
       "year                                                                           \n",
       "2020 2,301.00 5,458.00 2,355.00 2,462.00 2,666.00 3,435.00 4,857.00 4,675.00   \n",
       "2021 5,182.00 1,079.00   899.00 1,152.00 3,930.00 3,533.00 2,125.00 2,010.00   \n",
       "2022   150.00   169.00   167.00   193.00   176.00   102.00   337.00   189.00   \n",
       "2023     1.00     1.00        0        0        0        0     1.00        0   \n",
       "2024        0        0        0        0        0        0        0        0   \n",
       "2025        0        0        0        0        0        0        0        0   \n",
       "2026        0        0        0        0        0        0        0        0   \n",
       "\n",
       "week       9        10  ...       44       45       46       47       48  \\\n",
       "year                    ...                                                \n",
       "2020 3,790.00 5,111.00  ... 1,809.00 5,152.00 3,145.00 1,390.00 1,626.00   \n",
       "2021 4,760.00 3,915.00  ... 3,076.00 2,723.00   728.00   796.00 2,399.00   \n",
       "2022   176.00    91.00  ...   199.00    28.00        0     1.00   194.00   \n",
       "2023     1.00        0  ...        0        0        0        0        0   \n",
       "2024        0        0  ...        0        0        0        0        0   \n",
       "2025        0        0  ...        0        0        0        0        0   \n",
       "2026        0        0  ...        0        0        0        0        0   \n",
       "\n",
       "week       49       50       51       52       53  \n",
       "year                                               \n",
       "2020 2,390.00 3,411.00 1,385.00 2,934.00 2,278.00  \n",
       "2021 4,332.00   467.00 1,052.00   776.00   621.00  \n",
       "2022    46.00        0     1.00   108.00        0  \n",
       "2023        0        0        0     1.00        0  \n",
       "2024        0        0        0     1.00        0  \n",
       "2025        0        0        0        0        0  \n",
       "2026        0        0        0        0        0  \n",
       "\n",
       "[7 rows x 53 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeks_agr=weeks_agr.pivot_table(values='reservations_w', \n",
    "                      index='year', columns='week', \n",
    "                      aggfunc='first')\n",
    "weeks_agr = weeks_agr.astype(object).where(pd.notnull(weeks_agr),0)\n",
    "weeks_agr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No *reservations* per week for all year (diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weeks_agr.T.plot(kind=\"bar\",figsize=(15,10),width=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# No *reservations* per week for 2020 (table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# weeks_agr.loc['2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# No *reservations* per week for 2020 (diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# weeks_agr.loc['2020'].plot(kind=\"bar\",figsize=(15,10),width=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "------\n",
    "# Volume of arrivals per product market\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of bookings per product market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>country_code</th>\n",
       "      <th>channel</th>\n",
       "      <th>bookings_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AD</td>\n",
       "      <td>AIR298</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AD</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AD</td>\n",
       "      <td>EDO334</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AE</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>ZA</td>\n",
       "      <td>GOO328</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>ZA</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>ZA</td>\n",
       "      <td>EXP270</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>ZM</td>\n",
       "      <td>AIR298</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>ZW</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_code channel  bookings_ch\n",
       "0             AD  BOO142           22\n",
       "1             AD  AIR298            2\n",
       "2             AD  HOM143            2\n",
       "3             AD  EDO334            1\n",
       "4             AE  BOO142          105\n",
       "..           ...     ...          ...\n",
       "716           ZA  GOO328            6\n",
       "717           ZA  HOM143            2\n",
       "718           ZA  EXP270            1\n",
       "719           ZM  AIR298            1\n",
       "720           ZW  BOO142            1\n",
       "\n",
       "[721 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch_agr=pd.DataFrame(wf.groupby(['country_code'])['channel'].value_counts().reset_index(name='bookings_ch'))\n",
    "ch_agr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ch_agr=pd.DataFrame(wf.groupby(['country_code'])['channel_id'].value_counts().reset_index(name='bookings_ch'))\n",
    "# ch_agr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of passengers per product market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>country_code</th>\n",
       "      <th>channel</th>\n",
       "      <th>Passengers_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AD</td>\n",
       "      <td>AIR298</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AD</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AD</td>\n",
       "      <td>EDO334</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AE</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>ZA</td>\n",
       "      <td>GOO328</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>ZA</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>ZA</td>\n",
       "      <td>EXP270</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>ZM</td>\n",
       "      <td>AIR298</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>ZW</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_code channel  Passengers_ch\n",
       "0             AD  BOO142             22\n",
       "1             AD  AIR298              2\n",
       "2             AD  HOM143              2\n",
       "3             AD  EDO334              1\n",
       "4             AE  BOO142            105\n",
       "..           ...     ...            ...\n",
       "716           ZA  GOO328              6\n",
       "717           ZA  HOM143              2\n",
       "718           ZA  EXP270              1\n",
       "719           ZM  AIR298              1\n",
       "720           ZW  BOO142              1\n",
       "\n",
       "[721 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_agr=pd.DataFrame(wf.groupby(['country_code'])['channel'].value_counts().reset_index(name='Passengers_ch'))\n",
    "pass_agr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "# Benchmark for the last year\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# years=wf['year'].value_counts().index\n",
    "# year={}\n",
    "# for x in years:\n",
    "# #     print(x)\n",
    "#     year[x]=pd.DataFrame(weeks_agr['week'][x])\n",
    "#     year[x].index = year[x].index.astype(int)\n",
    "#     # print(type(t4.index))\n",
    "#     year[x]=year[x].sort_index().T\n",
    "#     year[x].index=[x]\n",
    "# years_weeks=pd.DataFrame(columns=[x for x in range(1,54)])\n",
    "# def agrr_week(x):\n",
    "#     return pd.concat([years_weeks,year[x]],axis=0)\n",
    "# for x in year:\n",
    "#     years_weeks=agrr_week(x)\n",
    "# years_weeks = years_weeks.astype(object).where(pd.notnull(years_weeks),0)\n",
    "# years_weeks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# weeks_agr=weeks_agr.pivot_table(values='reservations_w', \n",
    "#                       index='year', columns='week', \n",
    "#                       aggfunc='first')\n",
    "# weeks_agr = weeks_agr.astype(object).where(pd.notnull(years_weeks),0)\n",
    "# weeks_agr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# years_weeks.T.plot(kind=\"bar\",figsize=(15,10),width=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# weeks_agr_2020 = pd.Series(weeks_agr.loc['2020'])\n",
    "# weeks_agr_2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of *Reservations* per week for last year (i.e., 2020) (table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "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>week</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2,301.00</td>\n",
       "      <td>5,458.00</td>\n",
       "      <td>2,355.00</td>\n",
       "      <td>2,462.00</td>\n",
       "      <td>2,666.00</td>\n",
       "      <td>3,435.00</td>\n",
       "      <td>4,857.00</td>\n",
       "      <td>4,675.00</td>\n",
       "      <td>3,790.00</td>\n",
       "      <td>5,111.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1,809.00</td>\n",
       "      <td>5,152.00</td>\n",
       "      <td>3,145.00</td>\n",
       "      <td>1,390.00</td>\n",
       "      <td>1,626.00</td>\n",
       "      <td>2,390.00</td>\n",
       "      <td>3,411.00</td>\n",
       "      <td>1,385.00</td>\n",
       "      <td>2,934.00</td>\n",
       "      <td>2,278.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "week       1        2        3        4        5        6        7        8   \\\n",
       "year                                                                           \n",
       "2020 2,301.00 5,458.00 2,355.00 2,462.00 2,666.00 3,435.00 4,857.00 4,675.00   \n",
       "\n",
       "week       9        10  ...       44       45       46       47       48  \\\n",
       "year                    ...                                                \n",
       "2020 3,790.00 5,111.00  ... 1,809.00 5,152.00 3,145.00 1,390.00 1,626.00   \n",
       "\n",
       "week       49       50       51       52       53  \n",
       "year                                               \n",
       "2020 2,390.00 3,411.00 1,385.00 2,934.00 2,278.00  \n",
       "\n",
       "[1 rows x 53 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weeks_agr.loc[['2020']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of Reservations per week for last year (i.e., 2020) (diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weeks_agr.loc['2020'].plot(kind=\"bar\",figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "- No of *passengers* per week for last year (i.e., 2020) (table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>week</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>9,748.00</td>\n",
       "      <td>24,943.00</td>\n",
       "      <td>9,165.00</td>\n",
       "      <td>9,290.00</td>\n",
       "      <td>10,148.00</td>\n",
       "      <td>13,425.00</td>\n",
       "      <td>20,205.00</td>\n",
       "      <td>18,453.00</td>\n",
       "      <td>15,137.00</td>\n",
       "      <td>21,435.00</td>\n",
       "      <td>...</td>\n",
       "      <td>7,111.00</td>\n",
       "      <td>22,443.00</td>\n",
       "      <td>13,803.00</td>\n",
       "      <td>5,963.00</td>\n",
       "      <td>8,098.00</td>\n",
       "      <td>10,160.00</td>\n",
       "      <td>13,051.00</td>\n",
       "      <td>6,419.00</td>\n",
       "      <td>14,301.00</td>\n",
       "      <td>10,838.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "week       1         2        3        4         5         6         7   \\\n",
       "year                                                                      \n",
       "2020 9,748.00 24,943.00 9,165.00 9,290.00 10,148.00 13,425.00 20,205.00   \n",
       "\n",
       "week        8         9         10  ...       44        45        46       47  \\\n",
       "year                                ...                                         \n",
       "2020 18,453.00 15,137.00 21,435.00  ... 7,111.00 22,443.00 13,803.00 5,963.00   \n",
       "\n",
       "week       48        49        50       51        52        53  \n",
       "year                                                            \n",
       "2020 8,098.00 10,160.00 13,051.00 6,419.00 14,301.00 10,838.00  \n",
       "\n",
       "[1 rows x 53 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_years.loc[['2020']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of *passengers* per week for last year (i.e., 2020) (diagram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='week'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pass_years.loc['2020'].plot(kind=\"bar\",figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Filters in the report for YTD (year to date), MTD (month to date) and / or selected period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Example:\n",
    "    - Input:\n",
    "        - Enter the start Date dd/mm/yyyy :  *01/01/2020*\n",
    "        - Enter the end Date dd/mm/yyyy :  *31/12/2020*\n",
    "    - Output:\n",
    "        - country ,channel, & no Passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wf['date'] = wf['arrival_date'].apply(lambda x: \"%d\" % (x.day))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the start Date dd/mm/yyyy :  01/01/2020\n",
      "Enter the end Date dd/mm/yyyy :  31/12/2020\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code</th>\n",
       "      <th>channel</th>\n",
       "      <th>no_Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GB</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GB</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>HOM143</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GB</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
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       "    <tr>\n",
       "      <th>406424</th>\n",
       "      <td>IT</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406426</th>\n",
       "      <td>CH</td>\n",
       "      <td>CAM323</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406427</th>\n",
       "      <td>DK</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406428</th>\n",
       "      <td>AU</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406429</th>\n",
       "      <td>NL</td>\n",
       "      <td>HOT138</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212829 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country_code channel  no_Passengers\n",
       "1                GB  BOO142              2\n",
       "2                GB  BOO142              4\n",
       "4                US  HOM143              2\n",
       "5                GB  HOM143              4\n",
       "6                DE  HOM143              2\n",
       "...             ...     ...            ...\n",
       "406424           IT  BOO142              7\n",
       "406426           CH  CAM323              5\n",
       "406427           DK  BOO142              2\n",
       "406428           AU  BOO142              2\n",
       "406429           NL  HOT138              4\n",
       "\n",
       "[212829 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=input('Enter the start Date dd/mm/yyyy : ')\n",
    "n=input('Enter the end Date dd/mm/yyyy : ')\n",
    "search = (wf['arrival_date'] > pd.Timestamp(d)) & (wf['arrival_date'] <= pd.Timestamp(n))\n",
    "# wf.loc[search]    # for the whole columns.\n",
    "pwf=wf[['country_code','channel','no_Passengers']].loc[search]\n",
    "pwf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "# Selection for provider (PMS), sales channel\n",
    "- Note: channel is assumed to be the provider (PMS)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- No of reservations per channel & nextpax_house_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nextpax_house_id</th>\n",
       "      <th>channel</th>\n",
       "      <th>Reservations_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>432</td>\n",
       "      <td>AIR298</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>521</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>581</td>\n",
       "      <td>BOO142</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>581</td>\n",
       "      <td>HOM143</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>107221</th>\n",
       "      <td>640987212</td>\n",
       "      <td>CTR342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107222</th>\n",
       "      <td>640987229</td>\n",
       "      <td>CTR342</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>107223</th>\n",
       "      <td>640989624</td>\n",
       "      <td>CTR342</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>107224</th>\n",
       "      <td>640990025</td>\n",
       "      <td>GOO328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107225</th>\n",
       "      <td>640996064</td>\n",
       "      <td>CTR342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107226 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        nextpax_house_id channel  Reservations_ch\n",
       "0                    432  AIR298                3\n",
       "1                    436  AIR298                6\n",
       "2                    521  BOO142               15\n",
       "3                    581  BOO142               30\n",
       "4                    581  HOM143                1\n",
       "...                  ...     ...              ...\n",
       "107221         640987212  CTR342                1\n",
       "107222         640987229  CTR342                1\n",
       "107223         640989624  CTR342                1\n",
       "107224         640990025  GOO328                1\n",
       "107225         640996064  CTR342                1\n",
       "\n",
       "[107226 rows x 3 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pms=wf.groupby(['nextpax_house_id'])['channel'].value_counts().reset_index(name=\"Reservations_ch\")\n",
    "pms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "# Totals of gross revenue for every selection / filter\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Total Revenue in Euro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# wf['rent_only in EUR'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# round(rev_ch.revenue.sum(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# wf['rent_only'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350084955.87"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(wf['rent_only in EUR'].sum(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per *channel* for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel</th>\n",
       "      <th>year</th>\n",
       "      <th>revenue_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BOO142</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>HOM143</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>HOM143</td>\n",
       "      <td>2020</td>\n",
       "      <td>64,581,135.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BOO142</td>\n",
       "      <td>2021</td>\n",
       "      <td>45,121,611.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AIR298</td>\n",
       "      <td>2021</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>109</th>\n",
       "      <td>TRE327</td>\n",
       "      <td>2022</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>TRA277</td>\n",
       "      <td>2020</td>\n",
       "      <td>296.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CTR342</td>\n",
       "      <td>2022</td>\n",
       "      <td>164.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BOO142</td>\n",
       "      <td>2026</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADR331</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    channel  year    revenue_ch\n",
       "19   BOO142  2020 76,975,095.63\n",
       "62   HOM143  2021 75,076,661.04\n",
       "61   HOM143  2020 64,581,135.53\n",
       "20   BOO142  2021 45,121,611.94\n",
       "5    AIR298  2021 25,660,313.85\n",
       "..      ...   ...           ...\n",
       "109  TRE327  2022        330.00\n",
       "102  TRA277  2020        296.38\n",
       "37   CTR342  2022        164.18\n",
       "23   BOO142  2026          0.00\n",
       "0    ADR331  2020          0.00\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_ch = wf.groupby(['channel','year'])['rent_only in EUR'].sum().reset_index(name=\"revenue_ch\").sort_values(by=['revenue_ch'],ascending=False)\n",
    "rev_ch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per *channel* for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>rev_sum_ch</th>\n",
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       "    <tr>\n",
       "      <th>channel</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BOO142</th>\n",
       "      <td>76,975,095.63</td>\n",
       "      <td>45,121,611.94</td>\n",
       "      <td>2,457,221.41</td>\n",
       "      <td>399.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124,554,328.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOM143</th>\n",
       "      <td>64,581,135.53</td>\n",
       "      <td>75,076,661.04</td>\n",
       "      <td>9,176,195.57</td>\n",
       "      <td>12,137.41</td>\n",
       "      <td>1,454.34</td>\n",
       "      <td>2,340.98</td>\n",
       "      <td>0</td>\n",
       "      <td>148,849,924.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR298</th>\n",
       "      <td>16,320,950.16</td>\n",
       "      <td>25,660,313.85</td>\n",
       "      <td>882,751.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42,864,015.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOT138</th>\n",
       "      <td>4,558,479.28</td>\n",
       "      <td>5,937,889.26</td>\n",
       "      <td>72,868.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10,569,237.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOO135</th>\n",
       "      <td>2,714,131.40</td>\n",
       "      <td>132,796.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,846,927.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRA368</th>\n",
       "      <td>0</td>\n",
       "      <td>603.85</td>\n",
       "      <td>961.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1,565.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URB386</th>\n",
       "      <td>0</td>\n",
       "      <td>14,423.52</td>\n",
       "      <td>8,675.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23,099.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAC372</th>\n",
       "      <td>0</td>\n",
       "      <td>10,616.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10,616.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIN338</th>\n",
       "      <td>0</td>\n",
       "      <td>1,747.06</td>\n",
       "      <td>8,677.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10,424.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YON371</th>\n",
       "      <td>0</td>\n",
       "      <td>2,613.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2,613.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year             2020          2021         2022      2023     2024     2025  \\\n",
       "channel                                                                        \n",
       "BOO142  76,975,095.63 45,121,611.94 2,457,221.41    399.37        0        0   \n",
       "HOM143  64,581,135.53 75,076,661.04 9,176,195.57 12,137.41 1,454.34 2,340.98   \n",
       "AIR298  16,320,950.16 25,660,313.85   882,751.94         0        0        0   \n",
       "HOT138   4,558,479.28  5,937,889.26    72,868.67         0        0        0   \n",
       "HOO135   2,714,131.40    132,796.59            0         0        0        0   \n",
       "...               ...           ...          ...       ...      ...      ...   \n",
       "TRA368              0        603.85       961.78         0        0        0   \n",
       "URB386              0     14,423.52     8,675.60         0        0        0   \n",
       "VAC372              0     10,616.62            0         0        0        0   \n",
       "WIN338              0      1,747.06     8,677.51         0        0        0   \n",
       "YON371              0      2,613.56            0         0        0        0   \n",
       "\n",
       "year    2026     rev_sum_ch  \n",
       "channel                      \n",
       "BOO142  0.00 124,554,328.34  \n",
       "HOM143     0 148,849,924.88  \n",
       "AIR298     0  42,864,015.95  \n",
       "HOT138     0  10,569,237.21  \n",
       "HOO135     0   2,846,927.99  \n",
       "...      ...            ...  \n",
       "TRA368     0       1,565.63  \n",
       "URB386     0      23,099.12  \n",
       "VAC372     0      10,616.62  \n",
       "WIN338     0      10,424.57  \n",
       "YON371     0       2,613.56  \n",
       "\n",
       "[61 rows x 8 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_ch_all=rev_ch.pivot_table(values='revenue_ch', \n",
    "                      index=rev_ch.channel, columns='year', \n",
    "                      aggfunc='first').sort_values(by=['2020'],ascending=False)\n",
    "rev_ch_all['rev_sum_ch']=rev_ch_all.sum(axis=1)\n",
    "\n",
    "rev_ch_all = rev_ch_all.astype(object).where(pd.notnull(rev_ch_all),0)\n",
    "rev_ch_all.sort_values(by=['rev_sum_ch'],ascending=False)\n",
    "rev_ch_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per top 10 channel *for 2020, 2021 & 2022*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='channel'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_ch_all.head(10).plot.bar(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# - Revenue per *to 10 channel_id* for 2020, 2021 & 2022 *without outlies*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_ch_all[['2020','2021','2022']][rev_ch_all.index !='BOO142'].head(10).plot.bar(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#- Revenue per to 10 channel_id for *all years* *without outlies*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_ch_all[rev_ch_all.index !='BOO142'].head(10).plot.bar(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Mean Revenue per year based on channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2AAAAKICAYAAAAW8pn/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAe+ElEQVR4nO3de9DlB13f8c/XLAjlIpUsqATZ1glYilxXhKKUWzGKQqn3ESqCZNqKIm3VOHbqqK1l2hlG2lFqiliZigoqXmDKxQsKDlA2AZGAUYpBU4hZAhSCSgC//eM5gc3ukn1C93x/v+fs6zXzTM7zO2eTb+Y7zybv/f3O71R3BwAAgO37rKUHAAAAOFcIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIVsLsKp6QVVdW1Vv2+frv7Gq3l5VV1TVi7Y1FwAAwFJqW58DVlUPT3J9khd2933O8NoLk7w4yaO6+wNVdZfuvnYrgwEAACxka2fAuvv3krz/xGNV9UVV9YqquqyqXltVX7x56ulJfqK7P7D5teILAADYOdPvAbs0yXd194OS/OskP7k5fs8k96yq36+qN1TVRcNzAQAAbN2hqX9QVd0+yT9I8pKquvHwZ58wx4VJHpHkgiSvrar7dPcHp+YDAADYtrEAy97Ztg929/1P89zVSd7Q3R9L8qdVdWX2guxNg/MBAABs1dgliN39oezF1TckSe253+bpX03yyM3x87N3SeK7pmYDAACYsM3b0P98ktcnuVdVXV1VT0vyrUmeVlV/kOSKJE/YvPyVSa6rqrcn+Z0k39vd121rNgAAgCVs7Tb0AAAA3NT0XRABAADOWfu6CUdV3SnJ85PcJ0kneWp3v/7Tvf7888/vI0eOnI35AAAADpzLLrvsfd19+OTj+70L4nOTvKK7v76qbp3kb93ci48cOZJjx459BmMCAAAcfFX17tMdP2OAVdUdkzw8yVOSpLtvSHLD2RwOAADgXLCf94D93STHk/xMVb25qp5fVbc7+UVVdXFVHauqY8ePHz/rgwIAABx0+wmwQ0kemOR53f2AJB9JcsnJL+ruS7v7aHcfPXz4lEsdAQAAznn7CbCrk1zd3W/cfP9L2QsyAAAAboEzBlh3X5Pkz6vqXptDj07y9q1OBQAAsIP2exfE70ryc5s7IL4rybdvbyQAAIDdtK8A6+63JDm63VEAAAB2237eAwYAAMBZIMAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABhyaOkBAACAdTpyycuXHuGsuurZj1t6BGfAAAAApggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgyKH9vKiqrkry4SSfSPLx7j66zaEAAAB20b4CbOOR3f2+rU0CAACw41yCCAAAMGS/AdZJXlVVl1XVxad7QVVdXFXHqurY8ePHz96EAAAAO2K/Afaw7n5gkq9K8p1V9fCTX9Ddl3b30e4+evjw4bM6JAAAwC7YV4B193s2f702yUuTPHibQwEAAOyiMwZYVd2uqu5w4+Mkj03ytm0PBgAAsGv2cxfEuyZ5aVXd+PoXdfcrtjoVAADADjpjgHX3u5Lcb2AWAACAneY29AAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABD9h1gVXVeVb25ql62zYEAAAB21S05A/bMJO/Y1iAAAAC7bl8BVlUXJHlckudvdxwAAIDdtd8zYD+e5PuS/M2ne0FVXVxVx6rq2PHjx8/GbAAAADvljAFWVV+T5NruvuzmXtfdl3b30e4+evjw4bM2IAAAwK7YzxmwhyV5fFVdleQXkjyqqv7HVqcCAADYQWcMsO7+ge6+oLuPJPnmJL/d3U/a+mQAAAA7xueAAQAADDl0S17c3a9J8pqtTAIAALDjnAEDAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGHLGAKuq21TV/6qqP6iqK6rqhycGAwAA2DWH9vGajyZ5VHdfX1W3SvK6qvqf3f2GLc8GAACwU84YYN3dSa7ffHurzVdvcygAAIBdtK/3gFXVeVX1liTXJnl1d7/xNK+5uKqOVdWx48ePn+UxAQAADr59BVh3f6K775/kgiQPrqr7nOY1l3b30e4+evjw4bM8JgAAwMF3i+6C2N0fTPKaJBdtYxgAAIBdtp+7IB6uqjttHt82yWOS/NGW5wIAANg5+7kL4ucn+dmqOi97wfbi7n7ZdscCAADYPfu5C+JbkzxgYBYAAICddoveAwYAAMBnToABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwJAzfhDzmh255OVLj3BWXfXsxy09AgAAsEXOgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMOLT0Au+vIJS9feoSz7qpnP27pEQAAOMCcAQMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYcsYAq6q7V9XvVNU7quqKqnrmxGAAAAC75tA+XvPxJP+quy+vqjskuayqXt3db9/ybAAAADvljGfAuvu93X355vGHk7wjyd22PRgAAMCuuUXvAauqI0kekOSNp3nu4qo6VlXHjh8/fpbGAwAA2B37DrCqun2SX07yPd39oZOf7+5Lu/todx89fPjw2ZwRAABgJ+wrwKrqVtmLr5/r7l/Z7kgAAAC7aT93QawkP53kHd39nO2PBAAAsJv2cwbsYUmenORRVfWWzddXb3kuAACAnXPG29B39+uS1MAsAAAAO+0W3QURAACAz5wAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABgiwAAAAIYIMAAAgCECDAAAYIgAAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAYIsAAAACGCDAAAIAhAgwAAGCIAAMAABhyxgCrqhdU1bVV9baJgQAAAHbVfs6A/fckF215DgAAgJ13xgDr7t9L8v6BWQAAAHbaWXsPWFVdXFXHqurY8ePHz9bfFgAAYGectQDr7ku7+2h3Hz18+PDZ+tsCAADsDHdBBAAAGCLAAAAAhuznNvQ/n+T1Se5VVVdX1dO2PxYAAMDuOXSmF3T3t0wMAgAAsOtcgggAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwJBDSw8ALOfIJS9feoSz6qpnP27pEQAAbpYzYAAAAEMEGAAAwBABBgAAMESAAQAADBFgAAAAQwQYAADAEAEGAAAwRIABAAAMEWAAAABDBBgAAMAQAQYAADBEgAEAAAwRYAAAAEMEGAAAwBABBgAAMESAAQAADNlXgFXVRVV1ZVW9s6ou2fZQAAAAu+iMAVZV5yX5iSRfleTeSb6lqu697cEAAAB2zX7OgD04yTu7+13dfUOSX0jyhO2OBQAAsHuqu2/+BVVfn+Si7v6OzfdPTvJl3f2Mk153cZKLN9/eK8mVZ3/cxZyf5H1LD8HNsqN1s5/1s6N1s5/1s6P1s6N128X93KO7D5988NA+fmGd5tgp1dbdlya59DMYbPWq6lh3H116Dj49O1o3+1k/O1o3+1k/O1o/O1q3c2k/+7kE8eokdz/h+wuSvGc74wAAAOyu/QTYm5JcWFV/p6puneSbk/z6dscCAADYPWe8BLG7P15Vz0jyyiTnJXlBd1+x9cnWZScvrdwxdrRu9rN+drRu9rN+drR+drRu58x+zngTDgAAAM6OfX0QMwAAAP//BBgAAMAQAQYAADBEgAEAAAwRYKdRVV9ZVc+rql+vql/bPL5o6bm4eVX1b5eegT2bn6GnVdWRk44/daGROEHt+caq+obN40dX1X+uqn9RVf67sEJV9dtLz8CnVNX5J33/pM3P0MVVVUvNxZ6qemJVfe7m8eGqemFV/WFV/WJVXbD0fJyqqs6rqi+oqi+88WvpmbbJXRBPUlU/nuSeSV6YvQ+hTvY+fPqfJvmT7n7mQqNxBlX1Z9290z+wB0FV/ViSL09yeZKvTfLj3f1fNs9d3t0PXHI+kqr6ySR3SXLrJB9K8tlJfiPJVyf5C7/PLauq3nryoez9d+nKJOnu+44PxU2c+HtZVf2bJF+R5EVJvibJ1d39rCXnO9dV1du7+96bx7+Y5A1JXpLkMUm+tbv/0ZLzcVNV9V1JfijJXyT5m83h3uXf6wTYSarqj7v7nqc5Xkn+uLsvXGAsNqrqQ5/uqSS37e4zfrYd21VVf5jkAZvPELxT9v6n5MruflZVvbm7H7DshFTVH3b3l1TVrZJck+Tzu/uGqjqU5M3d/SULj3hOq6pfz14Y/7skf5W9399em70/2Eh3v3u56UiSE38vq6rLk3xFd39k8zN1uZ+hZVXVld19r83jy7r7QSc895buvv9iw3GKqnpnki/r7uuWnmWKS01O9ddV9eDTHP/SJH89PQyn+GCSC7v7jid93SHJexeejT2HuvvjSdLdH8zeWbA7VtVLsnfGheXduJ+PJXlTd9+w+f7jST6x5GAk3f34JL+cvQ8lvV93X5XkY939bvG1GretqgdU1YOSnNfdH0k++TPlZ2h5r6mqH6mq224e/+MkqapHJvm/i07G6fx5zrG9OFtwqqckeV5V3SGfugTx7tn708inLDQTn/LCJPfI3mnqk71oeBZO739X1T/s7t9Nku7+RJKnVdW/S/J1y47GxjVVdfvuvr67P/n+1qr6vCQ3LDgXG9390qp6VZIfrarviD+8WJv3JnnO5vH7q+rzu/u9VXXnbP6Ag0U9I8kPZnPZbpJnVdVHsnep9ZMXm4qbqKp/uXn4ruyF8suTfPTG57v7Oaf9hTvAJYifxuZ/RO6WvUs/ru7uaxYeCQ6EzZ84prv/6jTP3a27/8/8VOxHVd0uye26+9qlZ+FTqup+SR7a3f916Vm4eVV1XpLP7u6/XHoW9lTV52Tvyoxz5vK2g6Kqfujmnu/uH56aZZoA+zSq6labSwlOPHZ+d79vqZn4FPtZPztaPztaN/tZPztaN/thrbwH7CRV9ciqujrJe6rqVSfdRvtVC43Fhv2snx2tnx2tm/2snx2tm/0cLFX16s1Nu278/m9X1SsXHGnrBNip/mOSr+zuw9l7A/Srq+ohm+d8tsfy7Gf97Gj97Gjd7Gf97Gjd7OdgOby5aVeSpLs/kL2PStlZbsJxqlt39xVJ0t2/VFXvSPIrVXVJEtdrLs9+1s+O1s+O1s1+1s+O1s1+DpZPVNUXdvefJUlV3SM7vicBdqqPVdXn3XjTje6+oqoeneRlSb5o2dGI/RwEdrR+drRu9rN+drRu9nOw/GCS11XV726+f3iSixecZ+tcgniqS5Lc9cQD3X11kkckefYSA3ET9rN+drR+drRu9rN+drRu9nOAdPcrkjwwyS8meXGSB3X3J98DVlV/f6nZtsVdEAEAgFWqqsu7+4FLz3E2OQN2kqr6nKp6dlX9UVVdt/l6x+bYnZae71xnP+tnR+tnR+tmP+tnR+tmPztn526cIsBO9eIkH0jyiO6+c3ffOckjN8desuhkJPZzENjR+tnRutnP+tnRutnPbtm5y/VcgniSqrqyu+91S59jhv2snx2tnx2tm/2snx2tm/3sFpcgnhveXVXfV1WffPNmVd21qr4/yZ8vOBd77Gf97Gj97Gjd7Gf97Gjd7Ge33LD0AGebADvVNyW5c5LfraoPVNX7k7wmyecm+cYlByOJ/RwEdrR+drRu9rN+drRu9nPAVNV9q+rxVfVPbvy68bnufsjN/dqDyCWIp1FVX5zkgiRv6O7rTzh+0eZWmSzIftbPjtbPjtbNftbPjtbNfg6OqnpBkvsmuSLJ32wOd3c/dbmptssZsJNU1Xcn+bUkz0jytqp6wglP/9gyU3Ej+1k/O1o/O1o3+1k/O1o3+zlwHtLdR7v727r72zdfOxtfSXJo6QFW6OnZ+wC466vqSJJfqqoj3f3c7OBtMA8g+1k/O1o/O1o3+1k/O1o3+zlYXl9V9+7uty89yBQBdqrzbjxV3d1XVdUjsveDe4/4oV0D+1k/O1o/O1o3+1k/O1o3+zlYfjZ7EXZNko9mb0fd3fdddqztcQniqa6pqvvf+M3mB/hrkpyf5EuWGopPsp/1s6P1s6N1s5/1s6N1s5+D5QVJnpzkoiRfm71dfe2iE22Zm3CcpKouSPLx7r7mNM89rLt/f4Gx2LCf9bOj9bOjdbOf9bOjdbOfg6Wqfru7H7X0HJMEGAAAsIiq+skkd0ryG9m7BDFJ0t2/stRM2+Y9YAAAwFJum73weuwJxzrJzgaYM2AAAABDnAEDAAAWUVU/k70zXjexy58FJsAAAIClvOyEx7dJ8sQk71lolhEuQQQAAFahqj4ryW/u8p0RfQ4YAACwFhcm+cKlh9gmlyACAACLqKoP56bvAbsmyfcvNM4IlyACAAAMcQkiAACwiKp6WFXdbvP4SVX1nKq6x9JzbZMAAwAAlvK8JH9ZVfdL8n1J3p3khcuOtF0CDAAAWMrHe+89UU9I8tzufm6SOyw801a5CQcAALCUD1fVDyR5UpKHV9V5SW618Exb5QwYAACwlG9K8tEkT+vua5LcLcl/Wnak7XIXRAAAYJWq6vXd/dCl5zibnAEDAADW6jZLD3C2CTAAAGCtdu5yPQEGAAAwRIABAABrVUsPcLYJMAAAYBFV9ayquuBmXvLksWGGCDAAAGApd0zyyqp6bVV9Z1Xd9cQnu/ttC821NW5DDwAALKqq7pu9zwT7uiRXd/djFh5pa5wBAwAAlnZtkmuSXJfkLgvPslUCDAAAWERV/fOqek2S30pyfpKnd/d9l51quw4tPQAAAHDOukeS7+nutyw9yBTvAQMAABZTVV+e5MLu/pmqOpzk9t39p0vPtS0CDAAAWERV/VCSo0nu1d33rKovSPKS7n7YwqNtjfeAAQAAS3likscn+UiSdPd7ktxh0Ym2TIABAABLuaH3LsnrJKmq2y08z9YJMAAAYFxVVZKXVdVPJblTVT09yW8m+W/LTrZd3gMGAAAsoqouT/L9SR6bpJK8srtfvexU2+U29AAAwFJen+SD3f29Sw8yxRkwAABgEVX19iT3TPLubG7EkSS7/GHMAgwAAFhEVd3jdMe7+93Ts0wRYAAAAEPcBREAAGCIAAMAABgiwAAAAIYIMADOWVV13tIzAHBuEWAAHAhV9aNV9cwTvv/3VfXdVfW9VfWmqnprVf3wCc//alVdVlVXVNXFJxy/vqp+pKremOShw/8aAJzjBBgAB8VPJ/m2JKmqz0ryzUn+IsmFSR6c5P5JHlRVD9+8/qnd/aAkR5N8d1XdeXP8dkne1t1f1t2vG5wfAHJo6QEAYD+6+6qquq6qHpDkrknenORLkzx28zhJbp+9IPu97EXXEzfH7745fl2STyT55cnZAeBGAgyAg+T5SZ6S5POSvCDJo5P8h+7+qRNfVFWPSPKYJA/t7r+sqtckuc3m6b/u7k8MzQsAN+ESRAAOkpcmuSh7Z75eufl6alXdPkmq6m5VdZckn5PkA5v4+uIkD1lqYAA4kTNgABwY3X1DVf1Okg9uzmK9qqr+XpLXV1WSXJ/kSUlekeSfVdVbk1yZ5A1LzQwAJ6ruXnoGANiXzc03Lk/yDd39J0vPAwC3lEsQATgQqureSd6Z5LfEFwAHlTNgAAAAQ5wBAwAAGCLAAAAAhggwAACAIQIMAABgiAADAAAY8v8AB2XsJUOY/XgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_ch_all.mean().plot(kind=\"bar\",figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "<!-- - comparison of Revenues for top 10 per 2020, 2021, & 2022 *without outliers* -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_ch_all[['2020','2021','2022']][rev_ch_all.index !='BOO142'].head(10).plot(kind = 'line',figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- General discription of the Revenue per *channel*\n",
    "    - The *describe()* function computes a summary of statistics pertaining to the DataFrame columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- General discription of the Revenue per *channel* for all years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "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>revenue_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>128.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2,735,038.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11,876,393.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3,059.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24,781.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>285,295.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76,975,095.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         revenue_ch\n",
       "count        128.00\n",
       "mean   2,735,038.72\n",
       "std   11,876,393.09\n",
       "min            0.00\n",
       "25%        3,059.34\n",
       "50%       24,781.78\n",
       "75%      285,295.94\n",
       "max   76,975,095.63"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_ch.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYZMUBVlXrquofq+p90xwIAABgXu3LEbCnJdk6rUEAAADm3YoCrKoOT/KAJG+Y7jgAAADza6VHwF6Z5JlJvj+9UQAAAObbsgFWVT+X5OLuPneZ7U6qqs1VtXlhYWHVBgQAAJgXKzkCdo8kD6qqbUnenuS4qnrrnht19yndvam7N23YsGGVxwQAANj/LRtg3f3s7j68uzcmOTHJh7v7kVOfDAAAYM64DxgAAMAg6/dl4+7+SJKPTGUSAACAOecIGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgkGUDrKoOqaqzq+pTVXV+VT1/xGAAAADzZv0KtvnPJMd196VVdVCSj1fVB7r7E1OeDQAAYK4sG2Dd3UkuXXx40OJHT3MoAACAebSia8Cqal1VbUlycZK/6u6zpjoVAADAHFpRgHX3Fd19VJLDkxxbVT+55zZVdVJVba6qzQsLC6s8JgAAwP5vn1ZB7O6vJ/lIkhOWeO2U7t7U3Zs2bNiwOtMBAADMkZWsgrihqm60+Pl1ktwnyT9NeS4AAIC5s5JVEG+e5M1VtS6TYHtnd79vumMBAADMn5WsgvjpJEcPmAUAAGCu7dM1YAAAAFxzAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYJBlA6yqfqSq/qaqtlbV+VX1tBGDAQAAzJv1K9jm8iT/q7s/WVXXT3JuVf1Vd392yrMBAADMlWWPgHX3v3f3Jxc//1aSrUluMe3BAAAA5s0+XQNWVRuTHJ3krKlMAwAAMMdWHGBVdb0k70ryG939zSVeP6mqNlfV5oWFhdWcEQAAYC6sKMCq6qBM4utPu/vdS23T3ad096bu3rRhw4bVnBEAAGAurGQVxEryxiRbu/t3pz8SAADAfFrJEbB7JHlUkuOqasvix/2nPBcAAMDcWXYZ+u7+eJIaMAsAAMBc26dVEAEAALjmBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAg62c9ADA7G09+/6xHWFXbXvyAWY8AAHC1HAEDAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADDIsgFWVadW1cVV9ZkRAwEAAMyrlRwB++MkJ0x5DgAAgLm3bIB190eTfG3ALAAAAHNt1a4Bq6qTqmpzVW1eWFhYrbcFAACYG6sWYN19Sndv6u5NGzZsWK23BQAAmBtWQQQAABhEgAEAAAyykmXo35bkH5Lcuqq2V9Xjpz8WAADA/Fm/3Abd/YgRgwAAAMw7pyACAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGEWAAAACDCDAAAIBBBBgAAMAgAgwAAGAQAQYAADCIAAMAABhEgAEAAAwiwAAAAAYRYAAAAIMIMAAAgEEEGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAg6yf9QDMr40nv3/WI6y6bS9+wKxHAABgP+YIGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADCLAAAAABhFgAAAAgwgwAACAQQQYAADAIAIMAABgEAEGAAAwiAADAAAYRIABAAAMIsAAAAAGWVGAVdUJVXVBVf1zVZ087aEAAADm0bIBVlXrkvxhkvsluW2SR1TVbac9GAAAwLxZv4Jtjk3yz939+SSpqrcneXCSz05zMAAA5tvGk98/6xFW3bYXP2DWI7DGrSTAbpHk33Z5vD3JXaYzDgDA6pm3H/D9cA/7v+ruq9+g6mFJ7tvdT1h8/Kgkx3b3U/bY7qQkJy0+vHWSC1Z/3Jm5aZKvznoIrpZ9tLbZP2uffbS22T9rn3209tlHa9s87p9bdveGPZ9cyRGw7Ul+ZJfHhyf50p4bdfcpSU65xuOtYVW1ubs3zXoO9s4+Wtvsn7XPPlrb7J+1zz5a++yjte1A2j8rWQXxnCQ/XlVHVtW1k5yY5L3THQsAAGD+LHsErLsvr6pfT/L/kqxLcmp3nz/1yQAAAObMSk5BTHf/ZZK/nPIsa9lcnlo5Z+yjtc3+Wfvso7XN/ln77KO1zz5a2w6Y/bPsIhwAAACsjpVcAwYAAMAqEGAAAACDCDAAAGC4qjpkieduOotZRnIN2BKq6kPdfe/lnmO2quruSTZml8VkuvtPZjYQSZKqelWSvf7F0t1PHTgO7PeqakOSpyW5TpLXdvc/z3gk9mAfrW32z9pVVecleWJ3f2Lx8S8k+Z3uvtVsJ5uuFa2CeKBYrPBDk9y0qm6cpBZfukGS/zazwbiKqnpLkh9LsiXJFYtPdxIBNnubF//3Hklum+Qdi48fluTcmUzEkqrqIUlekuRmmfx9V0m6u28w08HY0yuSvDWTv+PeluTOsx2HJdhHa5v9s3b9zySnVtVHMvlZ+7Akx810ogEE2O6elOQ3MvkX4NxcGWDfTPKHM5qJpW1Kctt2CHfN6e43J0lVPTbJz3T3ZYuP/yjJmTMcjat6aZIHdvfWWQ/Clarqg0le2N0fW3zq2km2ZfLD48Gzmosr2Udrm/2z/+ju86rqhUnekuRbSe7Z3dtnPNbUOQVxCVX1lO5+1aznYO+q6rQkT+3uf5/1LCytqi5Icrfu/tri4xsn+UR333q2k7FDVf1dd99j1nOwu6q6YZLnZPLLwOdkcr32czM5fer3uvvjMxyP2Edrnf2z/6iqN2ZyRtPjktwqySuTvLq75/rAhwDbC9cXrW1V9TdJjkpydpL/3PF8dz9oVjOxu6p6XJLnJfmbxaf+e5Lnd/cfz2omdldVv5/kh5Ockd2/j949q5m4UlX9aJIXJrkoyQu6+xszHok92Edrm/2z9lXVbyZ55Y4zmhbj+Xe7+/GznWy6BNgS9nZ9kcUD1o6q+u9LPd/dfzt6Fvauqn44yV0WH57V3V+e5TzsrqretMTT3d2/PHwYdlr8ofFXk1yW5NWZ/Pfofyd5X5LXdPcVV/PlDGAfrW32D2udAFtCVW2N64vgB2I1UbhmquqsJL+d5LpJnrbje6aqHpPk0b6HZs8+Wtvsn/1HVf14kt/JZNGunUvSd/ePzmyoASzCsbTPZHJajuuL1qiqumuSVyW5TSYX165L8m2rt82e1UTXvqp6Zne/dG+3DHC0f+YOSfKFTH54PHTHk9395qp658ymYlf20dpm/+w/3pTJ9Xm/l+RnMrkWrK72K+aAAFvaTZN8tqpcX7R2vTrJiUlOy2RFxEcn+fGZTsQOS60m2pmsbvTq2Y3FLnaserg5V3PPNmbmV5O8LMl/JfmVXV/o7u/OZCL2ZB+tbfbP/uM63f2hqqru/mKS51XVxzKJsrnlFMQluL5o7auqzd29qao+3d13WHzu77v77rOejYmq+j+ZXFj7zap6TpJjMrkI+pMzHo1FVXXnTE7T2ZgrfyHXO76nAGCaqurvkvx0ktOTfDiTBVNePO8rJgsw9ktV9dEk90nyhiRfzuR00cd29x1nOhg77YjjqvqpJC/K5EaYv93dd1nmSxlk8VYBz0hyXpLv73h+8beQzEhVndDdH1z8/IZJfjeTG8d+JslvdvdXZjkfO/fLyUl+PpMbmSfJxUn+PJMfHr8+m8lIfA/tTxZ/Ebg1yY2SvCCTyxVe2t1nzXKuabvWrAdYi6rqW1X1zcWP71XVFVX1zVnPxW4elcm/v7+e5NtJfiTJL8x0Iva0Y5WpByT5o+7+80yu12PtWOju93b3F7r7izs+Zj0UedEun78ik18wPTDJOUleN5OJ2NM7k3w9k5vNH9bdh2Vy/cp/ZHJqPLPle2j/0ZnchPm9mVxScqskr5/pRAM4ArYCVfXzSY7t7t+e9Sxcqaquk+SI7r5g1rNwVVX1vkxOJbhPkjsl+W6Ssx2lXDuq6t5JHpHkQ3EfsDWjqj7Z3ccsfr6lu4/a5bXdHjMbVXXB3k6RurrXGMP30P7jQD0TwyIcK9DdZ1TVybOegytV1QOTvDyTIypHVtVRSf6vhVLWlIcnOSHJy7v761V180z+kmXteFySn0hyUK78D18nEWCzdbOq+q1MFrC5weLF6Tt+W+rMlbXhi1X1zCRv3nE6W1X9UJLHJvm3WQ5GEt9D+5OF7n7vrIcYTYAtoaoessvDa2VySNShwrXleUmOTfKRJOnuLVW1cYbzsIfu/k52+UG+u/89bu2w1tyxu28/6yG4itcnuf7i52/OZGXehcUbm2+Z1VDs5hczuQbsbxfDq5N8JZPTqB4+y8FI4ntof/LcqnpDDrAzMQTY0h64y+eXJ9mW5MGzGYW9uLy7v1E197eKgGn6RFXdtrs/O+tBuFJ3P7+qfiLJLZKc1d2XLj7/5ar6s9lOR5J0938kedbiR6rqpzP5peB53f21Wc5Gksm1eO/p7t2ORnb3lzO5bQ1rxwF5JoZrwNgvVdUbM/ltycmZLL7x1CQHdfevXO0XAjtV1dYkP5bJDUv/M4v3bLMM/WxV1VMyWWBoa5KjkjxtcRGb3a5tYXaq6uzuPnbx8yckeXKSM5Icn+QvuvvFMxzvgFdV38hkga5/SfK2JKd198Jsp2IpVXXegXgmhvNgl1BVh1fVe6rq4qr6SlW9q6oOn/Vc7OYpSW6XyQ+Nf5bkG0meNtOJYP9zQiY3MD8+kyP/P5fdzwBgNk5Kcqfu/vkk90rynKra8febw/5rw0G7fP6kJMd39/Mz+V76pdmMxC4+n+TwTJY1v1OSz1bVB6vqMVV1/av/Ugb7RFXddtZDjOYUxKW9KZMf6h+2+PiRi8/97MwmYk+3XfxYv/jx4CQPSuI397BC877K1H5s3S6nHW6rqnslOb2qbhkBtlZcq6punMkvsmvH0ZXu/nZVXT7b0cjkSP73k5yZ5MyqOijJ/TJZ9fXlSTbMcjh281NJHlNVB9SZGE5BXMJSS5RatnRtWVy29OmZ3FTxgFm2FJh/VfXhJL/V3Vt2eW59klOT/FJ3r5vVbExU1bZM/ttTmVyvcvfFa/Sul+Tjfl6Yrar6x+4+ei+vXae7vzt6Jpa2+Iulq5j3n+ccAVvaV6vqkZmcN5xMfmNyyQzn4aoWuvsvZj0EwBQ8OpMFoHbq7suTPLqq3ER2DejujXt56ftJ/sfAUVjaL+7tBfG1tsx7aO2NI2BLqKojkrw6yd0y+c3W3yd5anf/60wHYyc3kAUAYH/kCNjSXpDkMYvLzKaqbpLJOcO/PNOp2NUBuWwpAAD7NwG2tDvsiK8k6e6vVdWS5xIzM24gCwDAfscy9EvbsbpRkp1HwMTq2nJALlsKAMD+TVQs7RVJ/r6qTs/ktLaHJ3nhbEdiDwfksqUAAOzfLMKxF4tHV47L5Af7D3X3Z2c8Ers4UJctBQBg/ybAAAAABnENGAAAwCACDAAAYBABBgAAMIgAAwAAGESAAQAADPL/Ae5b0Ln7pjxyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_ch.describe().plot(figsize=(15,10),kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "- General discription of the Revenue per *channel* for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_ch_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_ch_all.describe().plot(figsize=(15,10),kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per *Country* for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "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>country_code</th>\n",
       "      <th>year</th>\n",
       "      <th>revenue_con</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AU</td>\n",
       "      <td>2020</td>\n",
       "      <td>198,683,440.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>US</td>\n",
       "      <td>2020</td>\n",
       "      <td>120,512,712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>US</td>\n",
       "      <td>2021</td>\n",
       "      <td>92,238,456.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>GB</td>\n",
       "      <td>2020</td>\n",
       "      <td>65,347,608.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>RU</td>\n",
       "      <td>2020</td>\n",
       "      <td>50,856,012.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>PF</td>\n",
       "      <td>2020</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>HR</td>\n",
       "      <td>2022</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>GH</td>\n",
       "      <td>2021</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>BZ</td>\n",
       "      <td>2020</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>NL</td>\n",
       "      <td>2026</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_code  year    revenue_con\n",
       "22            AU  2020 198,683,440.00\n",
       "385           US  2020 120,512,712.00\n",
       "386           US  2021  92,238,456.00\n",
       "138           GB  2020  65,347,608.00\n",
       "332           RU  2020  50,856,012.00\n",
       "..           ...   ...            ...\n",
       "303           PF  2020          92.00\n",
       "173           HR  2022          79.00\n",
       "151           GH  2021          77.00\n",
       "63            BZ  2020          38.00\n",
       "289           NL  2026           0.00\n",
       "\n",
       "[417 rows x 3 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_count=wf.groupby(['country_code','year'])['rent_only'].sum().reset_index(name=\"revenue_con\").sort_values(by=['revenue_con'],ascending=False)\n",
    "rev_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_count.sort_values(by=['revenue'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per *Country* for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "tags": []
   },
   "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>year</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>rev_sum_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>120,512,712.00</td>\n",
       "      <td>92,238,456.00</td>\n",
       "      <td>10,567,533.00</td>\n",
       "      <td>14,663.02</td>\n",
       "      <td>1,701.00</td>\n",
       "      <td>2,738.01</td>\n",
       "      <td>0</td>\n",
       "      <td>223,337,803.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>198,683,440.00</td>\n",
       "      <td>142,938.02</td>\n",
       "      <td>13,164.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>198,839,542.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>65,347,608.00</td>\n",
       "      <td>10,960,174.00</td>\n",
       "      <td>2,022,490.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78,330,272.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU</th>\n",
       "      <td>50,856,012.00</td>\n",
       "      <td>344,390.81</td>\n",
       "      <td>15,336.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51,215,738.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>27,427,628.00</td>\n",
       "      <td>20,818,742.00</td>\n",
       "      <td>1,268,654.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49,515,024.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VC</th>\n",
       "      <td>0</td>\n",
       "      <td>217.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>217.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BI</th>\n",
       "      <td>0</td>\n",
       "      <td>188.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>188.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BF</th>\n",
       "      <td>0</td>\n",
       "      <td>180.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MR</th>\n",
       "      <td>0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BQ</th>\n",
       "      <td>0</td>\n",
       "      <td>113.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                   2020          2021          2022      2023     2024  \\\n",
       "country_code                                                                 \n",
       "US           120,512,712.00 92,238,456.00 10,567,533.00 14,663.02 1,701.00   \n",
       "AU           198,683,440.00    142,938.02     13,164.00         0        0   \n",
       "GB            65,347,608.00 10,960,174.00  2,022,490.25         0        0   \n",
       "RU            50,856,012.00    344,390.81     15,336.01         0        0   \n",
       "DE            27,427,628.00 20,818,742.00  1,268,654.75         0        0   \n",
       "...                     ...           ...           ...       ...      ...   \n",
       "VC                        0        217.80             0         0        0   \n",
       "BI                        0        188.00             0         0        0   \n",
       "BF                        0        180.00             0         0        0   \n",
       "MR                        0        120.00             0         0        0   \n",
       "BQ                        0        113.40             0         0        0   \n",
       "\n",
       "year             2025 2026  rev_sum_count  \n",
       "country_code                               \n",
       "US           2,738.01    0 223,337,803.03  \n",
       "AU                  0    0 198,839,542.02  \n",
       "GB                  0    0  78,330,272.25  \n",
       "RU                  0    0  51,215,738.82  \n",
       "DE                  0    0  49,515,024.75  \n",
       "...               ...  ...            ...  \n",
       "VC                  0    0         217.80  \n",
       "BI                  0    0         188.00  \n",
       "BF                  0    0         180.00  \n",
       "MR                  0    0         120.00  \n",
       "BQ                  0    0         113.40  \n",
       "\n",
       "[202 rows x 8 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_count_all=rev_count.pivot_table(values='revenue_con', \n",
    "                      index=rev_count.country_code, columns='year', \n",
    "                      aggfunc='first')\n",
    "rev_count_all = rev_count_all.astype(object).where(pd.notnull(rev_count_all),0)\n",
    "\n",
    "rev_count_all['rev_sum_count']=rev_count_all.sum(axis=1)\n",
    "rev_count_all = rev_count_all.sort_values(by=['rev_sum_count'],ascending=False)\n",
    "rev_count_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# x=rev_count_all.T.sum().reset_index(name='sumation').sort_values(by=['sumation'],ascending=False)\n",
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_count_all.head(10).T.sum().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Revenue per *to 10 Countries* for 2020, 2021 & 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country_code'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_count_all.head(10).plot.bar(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- Mean Revenue per year based on *countries*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_count_all.mean().plot(kind=\"bar\",figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- comparison of Revenues for top 10 countries for 2020, 2021, & 2022 *without outliers*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country_code'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_count_all[['2020','2021','2022']].head().plot(kind = 'line',figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "- General discription of the Revenue per *country*\n",
    "    - The *describe()* function computes a summary of statistics pertaining to the DataFrame columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>revenue_con</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>417.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1,727,890.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13,002,069.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1,138.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6,466.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53,132.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>198,683,440.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         revenue_con\n",
       "count         417.00\n",
       "mean    1,727,890.75\n",
       "std    13,002,069.00\n",
       "min             0.00\n",
       "25%         1,138.00\n",
       "50%         6,466.02\n",
       "75%        53,132.37\n",
       "max   198,683,440.00"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_count.describe().plot(kind='bar',figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_count_all.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rev_count_all.describe().plot(kind='bar',figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sns.boxplot(x=wf['rent_only'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import sys\n",
    "# print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Thank you !\n",
    "- Yousef Qasem"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}