profits_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inroduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this file, we will work with a data that provides different columns such as data, the price of recruitment 'that the client pays', cost of supplier 'that the office had to pay to a third party in order to recruit the maid', and finally the profit. We will analyze the dataframe to show mean profit for each country and also for a given month**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st step: cleaning data, EDA, and feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the data\n",
    "profit_df = pd.read_excel('datasets/Estimated profits of recuriments requests.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial</th>\n",
       "      <th>Request information</th>\n",
       "      <th>Added date</th>\n",
       "      <th>Client name</th>\n",
       "      <th>The Price Of Recruitment</th>\n",
       "      <th>Cost of supplier</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1 (Closed) - Domestic worker</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>1 / HESA SALEH ALAMERY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangladesh - Ready - Muslim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial            Request information               Added date  \\\n",
       "0     1.0  1 (Closed) - Domestic worker   2018-05-06 | 21-08-1439   \n",
       "1     NaN    Bangladesh - Ready - Muslim                      NaN   \n",
       "\n",
       "              Client name  The Price Of Recruitment  Cost of supplier   Profit  \n",
       "0  1 / HESA SALEH ALAMERY                   11000.0               0.0  11000.0  \n",
       "1                     NaN                       NaN               NaN      NaN  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the head of our data\n",
    "profit_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### remove spaces from the columns and replace them with _ symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean column names: convert them to lowercase and replace any space with the symbol _ for easy access to the column\n",
    "profit_df.columns = profit_df.columns.str.lower()\n",
    "profit_df.columns = profit_df.columns.str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since the odd rows have a single value, we'll concat that value to the first column of the even rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterate through the first half of the dataframe\n",
    "for i in range(int(len(profit_df)/2)):\n",
    "    # assign the value of the even row with the value of the odd row to the even rows\n",
    "    # it is kinda aggregation\n",
    "    profit_df.iloc[i*2,1] = profit_df.iloc[i*2,1] + profit_df.iloc[(i*2)+1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial</th>\n",
       "      <th>request_information</th>\n",
       "      <th>added_date</th>\n",
       "      <th>client_name</th>\n",
       "      <th>the_price_of_recruitment</th>\n",
       "      <th>cost_of_supplier</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>1 / HESA SALEH ALAMERY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangladesh - Ready - Muslim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serial                                request_information  \\\n",
       "0     1.0  1 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "1     NaN                        Bangladesh - Ready - Muslim   \n",
       "\n",
       "                added_date             client_name  the_price_of_recruitment  \\\n",
       "0  2018-05-06 | 21-08-1439  1 / HESA SALEH ALAMERY                   11000.0   \n",
       "1                      NaN                     NaN                       NaN   \n",
       "\n",
       "   cost_of_supplier   profit  \n",
       "0               0.0  11000.0  \n",
       "1               NaN      NaN  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the head of our data after cleaning column names\n",
    "profit_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **NOTE: When the profit is 0, then that means it's an alternative maid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a mask which returns a indices when they're even or 0  \n",
    "index_mask = pd.Series(profit_df.index.values).apply(lambda index: index == 0 or index % 2 == 0)\n",
    "# reassign the dataframe with the evevn rows\n",
    "profit_df = profit_df.loc[index_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial</th>\n",
       "      <th>request_information</th>\n",
       "      <th>added_date</th>\n",
       "      <th>client_name</th>\n",
       "      <th>the_price_of_recruitment</th>\n",
       "      <th>cost_of_supplier</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>1 / HESA SALEH ALAMERY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>2 / ALHANOF OBAID ALOMARY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serial                                request_information  \\\n",
       "0     1.0  1 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "1     2.0  2 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "\n",
       "                added_date                client_name  \\\n",
       "0  2018-05-06 | 21-08-1439     1 / HESA SALEH ALAMERY   \n",
       "1  2018-05-06 | 21-08-1439  2 / ALHANOF OBAID ALOMARY   \n",
       "\n",
       "   the_price_of_recruitment  cost_of_supplier   profit  \n",
       "0                   11000.0               0.0  11000.0  \n",
       "1                   11000.0            7400.0   3600.0  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset index\n",
    "profit_df.reset_index(inplace=True, drop=True)\n",
    "# print the head\n",
    "profit_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract country from request_information column\n",
    "def extract_country(value):\n",
    "    # check if the column has the country name not sri lanka\n",
    "    if len(value.split('-')[1].split()) < 4:\n",
    "        # return the last value in the list which would be the country\n",
    "        return ''.join(value.split('-')[1].split()[-1])\n",
    "    # if the country is located in two indices, then return the last two indices\n",
    "    elif len(value.split('-')[1].split()) == 4:\n",
    "        return ''.join(value.split('-')[1].split()[-2:])\n",
    "# create a new column that would hold the country value and use apply to use the function above\n",
    "profit_df['country'] = profit_df['request_information'].apply(extract_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create another column which would have the percentage of the profit \n",
    "# profit_df['profit_percentage'] =  (profit_df['profit'] - (profit_df['cost_of_supplier']) / profit_df['profit'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial</th>\n",
       "      <th>request_information</th>\n",
       "      <th>added_date</th>\n",
       "      <th>client_name</th>\n",
       "      <th>the_price_of_recruitment</th>\n",
       "      <th>cost_of_supplier</th>\n",
       "      <th>profit</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>1 / HESA SALEH ALAMERY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Bangladesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-06 | 21-08-1439</td>\n",
       "      <td>2 / ALHANOF OBAID ALOMARY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>Bangladesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3 (Closed) - Domestic worker Bangladesh - Read...</td>\n",
       "      <td>2018-05-07 | 22-08-1439</td>\n",
       "      <td>3 / SHEYHA FAHAD ALDOSARY</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Bangladesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serial                                request_information  \\\n",
       "0     1.0  1 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "1     2.0  2 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "2     3.0  3 (Closed) - Domestic worker Bangladesh - Read...   \n",
       "\n",
       "                added_date                client_name  \\\n",
       "0  2018-05-06 | 21-08-1439     1 / HESA SALEH ALAMERY   \n",
       "1  2018-05-06 | 21-08-1439  2 / ALHANOF OBAID ALOMARY   \n",
       "2  2018-05-07 | 22-08-1439  3 / SHEYHA FAHAD ALDOSARY   \n",
       "\n",
       "   the_price_of_recruitment  cost_of_supplier   profit     country  \n",
       "0                   11000.0               0.0  11000.0  Bangladesh  \n",
       "1                   11000.0            7400.0   3600.0  Bangladesh  \n",
       "2                   11000.0               0.0  11000.0  Bangladesh  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract date by months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract date form the added_date column\n",
    "def extraxt_date(value):\n",
    "    return value.split('|')[0]\n",
    "profit_df['date'] = pd.to_datetime(profit_df.added_date.apply(extraxt_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_df['month'] = pd.DatetimeIndex(profit_df['date']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     121\n",
       "7      55\n",
       "9      45\n",
       "8      35\n",
       "10     34\n",
       "6      25\n",
       "11     20\n",
       "12      5\n",
       "1       1\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how many month do we have\n",
    "profit_df.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SriLanka       179\n",
       "Bangladesh      80\n",
       "Philippines     62\n",
       "Indian          20\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the number of our classes 'country in the data'\n",
    "profit_df.country.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the percentage of each country in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set colour\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
    "# set figure size\n",
    "plt.figure(figsize=(6, 6));\n",
    "# plot the chart\n",
    "plt.pie(profit_df.country.value_counts().values, colors=colors, shadow=True, startangle=90, autopct='%.1f%%')\n",
    "# set legend\n",
    "plt.legend(profit_df.country.value_counts().index.tolist(), loc=\"best\")\n",
    "# set the chart to be in the midlle\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "# set title\n",
    "plt.title('Country Percentage in the data')\n",
    "plt.savefig('Country_Percentage_in_the_data.png')\n",
    "# show the plot\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check the profit in a given month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>the_price_of_recruitment</th>\n",
       "      <th>cost_of_supplier</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12985.714286</td>\n",
       "      <td>10888.885714</td>\n",
       "      <td>2096.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       the_price_of_recruitment  cost_of_supplier       profit\n",
       "month                                                         \n",
       "8                  12985.714286      10888.885714  2096.857143"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month = 8\n",
    "df_in_a_month = profit_df[profit_df.month == month]\n",
    "# create a dataframe of the mean of of all numeric values for all countries grouped by months and countries\n",
    "df_month_mean = df_in_a_month.iloc[:, 1:].groupby(['month']).mean()\n",
    "# print the average dataframe\n",
    "df_month_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446500.0"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whole income in the given month ** Note it is not the profit dropping India since we don't have it the other data.\n",
    "df_drop_india = df_in_a_month[df_in_a_month['country'] != 'Indian']\n",
    "df_drop_india['the_price_of_recruitment'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Bangladesh      7333.333333\n",
       "Philippines    16250.000000\n",
       "SriLanka       15630.434783\n",
       "Name: the_price_of_recruitment, dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_india.groupby('country')['the_price_of_recruitment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_countries_income = pd.DataFrame(data=[[80663,  325000, 359508]], \n",
    "                                          columns=['Bangladesh', 'Philippines', 'SriLanka'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>Bangladesh</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>SriLanka</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80663</td>\n",
       "      <td>325000</td>\n",
       "      <td>359508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bangladesh  Philippines  SriLanka\n",
       "0       80663       325000    359508"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_countries_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# show the sum of the income per country in a given month\n",
    "pd.DataFrame(df_in_a_month.groupby('country')['the_price_of_recruitment'].sum()).plot(kind='bar')\n",
    "plt.savefig('sum_of_profit_in_a_month.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# show the mean income per country in a given month\n",
    "df_in_a_month.groupby('country')['the_price_of_recruitment'].mean().plot(kind='bar')\n",
    "plt.savefig('mean_of_mean_in_a_month.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of requests in a given month\n",
    "len(df_in_a_month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of the percentage of each country requests in a given month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GKRQKDgAjR45sf+KJJ47Nnz9/6Mcffxw8adKkNo1GM+ja1yuvvFJ55ZVXJt16661x//73v4+9/PLL4ampqZlDhw7tzs3NpaY9FykpzQ8CMBLACACp+DUJJQLwkzC0cxEAIMt2601oKSqqwunJazeAvbrCwkG/dwlxZ4zz87vGdseOHeW5ubn1DoqHnIUdO3aE5ebmJkkdhxRKSvNDISajnls+xNoQO9PjnClyT8zBgkOjUqQq36YdgAHAVgA/2/49qCsspIvpicfx+BoU8X62ZDQWpyajhDM+SAJGo/WMtXYXCQQw2Xbr0dxSVLQNvyasrbrCwgopgiPkbFCCIm6npDTfH+IX7AwA0yE22UlWMxosuZy5a7+QHuJ5nN5zR0tRUS2AHwGsB7BWV1i4S6LYCOkXJSgiuZLSfDmAMRC/QGcAGA9xlBxxnggAl9huaCkqqoEtWUFMWJUSxkYIAEpQRCIlpfkZAGZCTEoFEIduE+lEAVhku6GlqGg3gC9st+91hYVmCWMjPooSFHGZktL8XAALbLdUicMhZ9YzgvDPAFpbiorWAvgMwMe6wsJmSSMjPoMSFHGqktL8HABXgpKSJwsCcLnt9t+WoqL/AXgHwGpdYWGXpJERr+YVCcrf3z+vs7PTMNj9V69erX366acjv/nmm4PLly/X7d69W/PYY4/VODNGX2JLSj01pTSJwyGOpQYwz3Zraykq+hhislqrKyy0SBoZ8TqOT1D7mEOX20A6P+NyG+frmmuuaQHQ4swyfEFJaX4agMWgpORLtACutd3qWoqK3gfwLsQ+K7ruipw3r5oj7kxLY3zwwQdBycnJWfn5+WkffPCBvucxzz77bOh1112XAADvvPOOLicnJz0jIyNzwoQJqZWVlQoAuOuuu2IWLFiQNGbMmLS4uLjsRx55JEKSJ+hmSkrzlSWl+QtLSvM3ANgH4K+g5OSrwgHcBuBbAEdaioqKW4qKMiWOiXg4r0pQQN9LY3R2drLbb7896dNPPz34888/l9XW1ir7euzMmTPbt2/fvm/v3r17rrjiisaHH344qmfbwYMH/TZu3Lj/559/3vvUU0/F9F4U0ZeUlOYnlZTmPwagEsAKiKPwCOmRCOA+ALtbiorWtxQVXdZSVERLzpCz5hV9UPb6WhpDq9Va4+LijD0Tyl5zzTUNy5YtC+/92CNHjqjmzZsXV1dXpzSZTLL4+PiTM0pfeOGFzRqNhms0GktISIi5qqpK0VOOLygpzZcBuBjA7wFcBC/8cUOc4gLbraKlqOg/AJbpCgsbB3gMIQC88Eumv6UxGBu4wnP77bcn3HbbbbX79+/f8/zzz1cYjcaT5+dMS254s5LS/OiS0vy/ATgC4FMAs+GF7xvidIkAHgdQ1VJUVNJSVJQjdUDE/XldDaovI0aM6K6qqlLt3r1bnZWVZVyxYkVIX/u1tbXJExISzADw+uuvh7o2SvdSUpqfDeABAFfAR94nxCU0AJYAWNJSVLQJwLMQr60a3Po2xKf4xC9hf39//txzz1XMmTMnJT8/Py0+Pr7POdMefPDB41dfffXQ/Pz8tNDQUJ8cMltSmj+6pDT/EwA7AFwFSk7EeaYA+ADA4ZaiovtbiopoNhFyClpuw4M5crmNktL8SQD+BuBCRxzPF4X+EnVsesWYWKnj8GBNAP4F4P90hYVtUgdDpEe/jn1cSWn+DIjDw2kkHpFaMIB/ALizpajoSQDP6QoLOyWOiUiIEpSPKinNnwPgQQDjpI6FkF5CARQDuKulqKgYwIu6wsJuiWMiEqAE5WNKSvPnAiiCuMYSIe4sAmKT359biooeA1CiKyw0DvAY4kUoQfmIktL80QCegtgxTYgniQbwHIB7W4qKHgXwKi3/4Rt8YhSfLyspzU8sKc1/h3P+Iyg5Ec8WD+C/APa3FBVdI3UwxPmoBuWlSkrztQD+yjm/gzGmHsyFyoR4iCQAb7cUFd0GYKmusNCpE0oT6XhFDUoul+enp6dnDhs2LGv27NlD2traZGVlZaphw4Zl9bX/nXfeGfPxxx9rAWDMmDFpmzZt8geAgoKClPr6+nOaM+yJJ54If/755yW/uLekNJ+VlObfyDk/AOBexpha6pgIcZIJAH6yzUxx2tRlxPM5vAZVbDA7dLmN+/OUA/46UqvVwr59+/YAwNy5c5Offvrp8Kuvvrqpv/2feeaZ433dv3HjxoPnGue9995bd66PdZSS0vxxnPNnGWOjqcZEfIQM4swUC35e9sE96/Ivff3+PKVPXmTvjbyiBmVv0qRJ7QcPHlQDgNVqxVVXXZWYkpKSNXHixGHt7e0MAObPn5/02muvBfd+bGxsbHZ1dbWirKxMlZycnHX55ZcnpaamZs6aNWtIW1ubrGef3//+97HZ2dkZ2dnZGbt27VID4pIcDz30UCQg1sp69klKShr+5ZdfBgKAxWLBLbfcEjd8+PCM1NTUzCeffDIMACoqKpSjRo1K66kF9uw/WCWl+WElpflvcM43M8ZGn8/5I8QTcTDTtzkXPQWgtNhgnih1PMQxvCpBmc1mfPXVV0HZ2dldAHD06FG/pUuX1h48eHC3Tqezvvnmm6clpf6Ul5f73XrrrXX79+/fo9VqhSeffPJkE0JQUJB1586de2+55ZbaP/7xj/F9Pd5isbCdO3fuffzxxysffvjhGAB45plnwnQ6nXXXrl17d+zYsfeNN94I37dvn+rVV18NmT59esu+ffv27N27d/fYsWMHfXFiSWn+Qs75XgDXMao2ER/1c9rkMpPSTwcgG8C3xQbza8UGc5jUcZHz4xUJymg0ytLT0zOzs7Mz4+LiTHfccUc9AMTGxhonTJjQBQB5eXmd5eXlg+6PiYqKMl144YUdAHDttdc2bN68+WSt5vrrr28EgJtvvrnRYDD0WdtZsGBBEwBMmDCho6qqSgUAa9euDXrvvfdC09PTM/Py8jKampoUe/bs8Rs3blzHu+++G3bXXXfF/PTTT5rg4GBhoPisgllhRmsEgBWMMfogEp9lVKh3bkudaF9rYgBuALC72GBeIE1UxBG8YhSffR+UPZVKZb9EBu/q6hp0Qu5dGbH/WyaT2d/f52SGfn5+HAAUCgWsVisDAM45e/rpp4/Onz+/tff+mzZtKlu1apXuhhtuSF66dOmJ22+/vaG/2LrMjaEdptp4AWZaBI74NIFz/tmoyzX9rKcTAeC9YoN5FYDb7s9T1ro4PHKevKIG5QzV1dWqtWvXBgDAO++8EzJhwoT2nm1vvvlmCAC88sorwXl5eR2DPebMmTNbXnzxxfCe1Xh/+eUXdWtrq2z//v2q2NhY8913312/ePHi+tLSUv++Hm8VTMqmriPD2k01SRwCJSfi80qVuuq6iOSUAXabD2BPscG8yBUxEcfxihqUMwwZMqT71VdfDb3tttsSk5OTjffcc8/JUXpGo5Hl5OSkC4LAVqxYcXiwx/zTn/5UX15ers7Ozs7gnLOQkBDzF198ceirr77SPvvss1EKhYL7+/tbly9ffsT+cZxzdFkawjpMdfEApx8VhADotArdpdOv7XNttz6EAlhebDAvBHDr/XnKaieGRhyEltvoQ1lZmWrOnDnDDhw4sLv3ttjY2OytW7fujY6OdslQVotgVLUZjyVbhO7T+rqOHKzBDuufXREGGQRabsO1vozMqjgy9tLEc3hoM8Qmv3cdHRNxLPo17sa6zI2hTV2Hs/pKToT4shor6g6PvuRckhMA6AG8U2wwv15sMAc4Mi7iWJSg+pCWlmbqq/YEAMeOHdvp7NoT5wJr6a5MbjfVJFGTHiGnEjjn60bOBZOd90fjeojXTY10QFjECejLz82YrV1+DZ0Hs0zWtsG2rRPiU3aqdJWticMdNbVRKoAtxQbzXcUGM11H6GYoQbmRDmN9WHN3eSaHhebPI6QPXVbB+NPkaxw9754KwNMAPi82mCMcfGxyHihBuQHOBdbUWTGk01KbCHD6FUdIP76NzqqxBAZrnHT42QB2FBvMk510fHKWKEFJzGztVjd0Hhxu4R2DnoaJEF90wor6g2PmJji5mCgA64oN5tudXA4ZBK9IUD3LbaSlpWVmZmZmrFmzxuEjc1avXq294IILBrog8BT2S3n0pdPUENrcfSSLw6Kyv/+PtzyAzz766lxDJcTrcM6xbsQczmRyV7QwKAE8Z5vPz88F5ZF+OPxC3ZaiIocutzGYxcjspzpatWpV0AMPPBA3c+bMMkfG4Uicc7R0VSaZebvk60cR4gl2KoOOtiTnOLv21NsNAIYXG8yX35+nrHRx2QReUoOy19LSItfpdBbb/2Xjx49PzczMzEhNTc18++239YB4Ie6QIUOy+lqKY+PGjf6pqamZI0aMSL/lllvi+lr08JtvvvHPy8tLz8jIyMzLy0vfsWOHGgDa29vZnDlzhqSmpmZefPHFQ7q7u0/+2vvwww+DRowYkZ6ZmZl5ybxZI5raToQCwD8e+hcm5V+CgrGXofCBJ0+WseX7bfjN9GswavhFVJsiPq3bKhh/mHyNVBMijwKwrdhgLpCofJ/mFQmqZzbz5OTkrDvuuCOxsLCwGgD8/f2Fzz///OCePXv2bty4cf8DDzwQJwjiROH9LcWxZMmS5BdeeKFi+/bt++RyeZ/TbOTm5nb/9NNP+/bu3bunsLDw2L333hsHAE899VSERqMR9u/fv+ehhx6q3rNnTwAAVFdXKx577LHodd98XbHxp48UeaOy5P997g00NTbji8/W4dutn2Ljjx/hrntvOVnGiZo6rF7zFpa//wL+Ufhv555AQtzYt5EZNVZtSL9N5S4QDmAt9Uu5nlfMxWffxLd27dqAG2+8MXn//v27BUFgd955Z9wPP/wQKJPJUFtbq6qqqlIAfS/FUV9fL+/o6JDNnDmzAxCX1VizZo2+d3mNjY3yhQsXJpeXl/sxxrjZLF4/8d133wUuXbq0FgDGjh3blZqa2gkAGzZsCDh06JB/QcGUTAAwm8wYNXYEtEGBUKtV+NMfHsKMi6bgwtm//kibfcl0yGQypGWkoK6234nNCfFqtVY0HBg3L8ENhrYqIPZLDQVw9/15ygGXxCHnzysSlL0ZM2Z0NDU1KaqrqxWrVq3SNTQ0KHbu3LlXrVbz2NjY7J4lN/paimOw8xLed999sQUFBW1r1qw5VFZWppo2bVpaz7a+Zv03C536qTPGy15+/anTtn21cSW+3fADPvrgf3j1pXfw4RevAQDUKuXJfc53vkRCPBHnHGtzL7a6aGDEYN0JIL7YYF58f56yW+pgvJ1XNPHZMxgMfoIgIDIy0tLS0iIPCwszq9Vq/tlnn2mPHz+uOtNjw8PDrQEBAcK6desCAOCtt97qczaH1tZWeVxcnAkAXnrppZNt45MmTWp/++23QwDg559/9tu/f79/t6UpevTEtLCffzDg8KEKAEBnZxcOHShHe3sHWlvaMOOiKXjk8fuwa+c+B50FQjzfboX2aMuQEe544ex8AOtpxV7n84oaVE8fFCD+6nrxxRfLFQoFlixZ0jh79uyU4cOHZ2RlZXUmJycP+IvnpZdeKr/11lsT/f39hYkTJ7ZptVpr733uu+++miVLliQ/++yzUZMnTz65+OA999xTe9VVVyWnpqZmZmVldeXmDbcypVEfFh6CZ//7KG698c8wGs0AgL889EcEagNw3cLb0d1tAjjHw8X3OfCsEOK5uq2CacvURe6cAMZDnCLpN/fnKQ9IHYy3ouU2emlpaZHpdDoBAB544IGo6upq5WuvvXZWQ0wtVpOisaM8jcktTr2GgpbbcC+03IbjrA1NrTgw8Ypzna3cleoBzL0/T7lF6kC8kdc18Z2v9957T5eenp45bNiwrM2bNwc++uijZ7WwmdHcpW7sPJzp7OREiLeqs/KGA+Pmufqap3MVBnHmiVlSB+KNvKKJz5FuvvnmpptvvrnpXB7b1d3u32auGsZkAp1XQs7RupzZFsgV7jQwYiAaAJ8UG8xX3Z+n/EjqYLwJ1aAcpL2zOajNXJlKyYmQc7dH5l/ZNHRkpNRxnAMVgPeKDeZFUgfiTRyRoARBEDzp145Dcc7R3Fof0mmtHsJkXO7KcgEafk68h9EqmDZPvsaT10FTAHir2GBeInUg3sLU7cngAAAgAElEQVQRCWpXXV2dzheTlJic6sJMrC7R1cmpvcWETl7lqiIJcbrvw4ZVm3Xhnr4EuwzAy8UG8x1SB+INzrs5ymKxLKmpqVlWU1MzHD7WZGg0dWqh6AqGDC5OzhydvApHrK/B1SUT4gz1Ft5UNn5+vNRxOAgD8EyxwRxwf57yMamD8WTnnaDy8/NrAcx1QCweI7dAL0vK9L99+tURj2kC5QynXSnlIpSciJdYl32REQqFt/3AfbTYYLbcn6d8QupAPJW3vSGcLrdALxuSHXDn9EUR/9QEyj29OYIQye1lmsrGYaOipI7DSR4vNpj/IHUQnooS1FnILdDLk4f73zPtqvBHNAFyKWdXJsQrGAXB/P2ka7x9Nennig3mG6UOwhNRghqk3AK9IjJRvfSCKyP+5ucv10gdDyHeYHPI0GPm4IhAqeNwMgZgWbHBvFDqQDwNJahByC3QK7Qhit9fdG3kX/21cm//MBHiEg0W3rRvwnxPmTHifMkAvF1sMPtUf/35ogQ1gNwCvVKpZrde/NuoB4JClZ58jQYhbmVd1oxuKFS+9B2kgHgx70ypA/EUvvTmOGu5BXoZY7hhzpLou8Ni1d7aiUuIy5Uxv6qGtLHRUschATWAD4sN5pFSB+IJKEH1I7dAzwBccdF1kX+KTdEkSR0PId7CJAiW7yYt0kkdh4QCAawuNph9pXnznFGC6t/MKZeH3ZcyIjBD6kAI8SZbgpOrTMFRWqnjkFg0gP8VG8x6qQNxZ5Sg+pBboB+bP0P/t+xJQVQNJ8SBGi28ec/EBVRzEGVCbO4740rfvowSVC+5BfrMjDHaf4ydHTKBMZqqgRBHWpc5rcvHBkYM5AIAr0gdhLuiN4qd3AJ9UkK65h8FV4RPlckYnRtCHGg/1FX16eN9cWDEQBYXG8yPSB2EO6IvYZvcAn2kNljx4MzFkTMVSqaUOh5CvImZBkYM5MFig/l6qYNwN5SgAOQW6HUyOe65eEnULE2A3Nc7bwlxuC36xCpjSDR9ts7spWKDeYzUQbgTn09QuQV6DYA7ZiyKmBEWo46TOh5CvE2TRWjZPWmhtyyl4UxqAB8VG8zUDGrj0wkqt0AvA3BTzmRdQepI7Qip4yHEG63LuKADCpXLFvT0cDEAVtHIPpFPJygAs6KT/WZOuCR0ktSBEOKNDkB1rC5jYozUcXiY8QD+LXUQ7sBnE1RugT5LEyhbPPvGyIkKJaNfK4Q4mFkQLN9OvJr6nc7NbcUG83VSByE1n0xQuQX6CAC3X/K76NH+WkWo1PEQ4o1+1CUcM4bGBkkdhwf7b7HB7NNdDz6XoHIL9H4A/nDBleEjI+L9UqSOh5D+vLBlC8a98ALGv/ACbvrgA3Sbzaft89GuXRj7/PMY98ILWPLBBwCAA/X1KHjpJUx88UX8VFkJALBYrbj0jTfQaTK5JPZmi9C6a+KVsS4pzHtpAKwsNph9duVuhdQBuJJtAthr0kdrx2WO046WOh5C+nO8tRUv/fgjfvzDH6BRKnHDe+9h1a5duCYv7+Q+hxoa8K/vvsNXN90EvUaDuvZ2AMBrW7fi7zNmIEGvx9/XrsVbCxfila1bsTA3F/4q17Rmr0+d0sZVflR7On+pAJ4H4JMr8vpaDWpqUKjiooL5YZMYzWNE3JxVENBtNsNitaLLbEa09tTunDe2bcPNo0dDrxEXeA4PFNfSVMrl6DKb0Wk2QymTobmrC1+WleHq3FyXxH2IK4+dGD6Fak+Oc0OxwXy11EFIwWdqULkF+hQA182+IWqEUi3zlzoeQs4kJigIt0+YgOH//jf8lEpMGzoU01JObZE+2NAAALjolVdgFQTcP3UqZgwbhiWjR+PWjz6CyWrFv+fMwRMbN+LuKVPgit9kZkGwbpp4Fa067Xj/LTaYf7g/T3lE6kBcySdqULkF+mAAS8ddHBIbHqceJnU8hAykuasLX+zbhx133ol9d9+NDpMJK3fsOGUfqyDgUGMjVt9wA5ZdcQWWfvopmru6EK/X4/Mbb8SaJUvgr1Sipq0NqWFh+N2HH+LG99/Hwfp6p8X9kzausjs8nqY0crwgAO8WG8w+U6kAfCBB5RboFQBuDYtVhY4o0BdIHQ8hg7Hh8GEkBgcjLCAASrkcl2RknBzw0CMmKAi/SUuDUi5HUnAwUsLCcLix8ZR9/rF+PR6cNg0v/fgjrszOxl+mTsXjGzc6JeYWi9C2c/JCmo3FecYC+IfUQbiS1ycoADMZQ/qs66PoeifiMeJ0OmytqkKnyQTOOTYeOYLU8PBT9rk4PR3flpcDABo6OnCooQFJwcEnt39XXo5orRZDQ0PRaTZDxhjkMhk6+xgN6Ajrh01q5SqNT/3Cl8C9xQbzNKmDcBWvfjPlFugTAFw59crwWH24MlHqeAgZrFFxcZibmYmCl16CQiZDdnQ0bsjPx6Pr1yMvJga/SU/H9JQUrD90CGOffx5ymQwPz5yJEH+xe5Vzjqc2bcLrCxYAAG7Iz8fNq1bBIgj415w5Do/3CFccr8meSgMjnE8G4JVigzn7/jxlu9TBOBvjnEsdg1PkFujVAB6KG6ZJmntL9EKZnNFcYMSpQn+JOja9YozPfUlbBG59a8Litu6IRFq+3HVeuD9PebvUQTibNzfxXSJXsNgZiyKmUXIixHl+1kZXUnJyuduKDWav71P3ygSVW6BPBXDJhddGDA3UK6KkjocQb9VqFdp3TLra52qNboBBbOrz6ktmvC5B5Rbo/QHcMjQ3QD1keMBEqeMhxJutHzKhmas1tAK1NIYCeFTqIJzJqxKUbSqjBXIFCy64PGwak9FsEYQ4S7mgqK7OnUbDyqW1tNhgniB1EM7iVQkKQDaAaRdcGZ7oH6QIH3BvQsg5sQhc2DB+gZ/UcRDIALxabDCrpQ7EGbwmQeUW6IMA3BwepzYOywucLHU8hHizrQFRR7sik4MH3pO4QBqAe6QOwhm8IkH1zFIOwG/6VeFT5ApGbeKEOEmrVWjfPvkqGhjhXh4oNpgTpA7C0bwiQQHIAjBu5DR9YFisOk3qYAjxZhuSxzZzvwD6Eehe/OGFy8R7fIKyLUB4g1LNmkdO18+SOh5CvNlRQV5zbMRMGhjhni4vNpgvlDoIR/L4BAXgIgCh0xZG5Pj5y+liQUKcxMq58M24BV7ZGe9Fnis2mL1mzlGPTlC5BfpoAHPD41RtQ7LpmidCnGmrf8TRzqghNDDCvaUCuEvqIBzFYxOUbWDEYgCmqQvCp9HACEKcp80qdGyfvChG6jjIoPy12GD2imZYj01QAEYAGJ45VusXmeCXJXUwhHizjYmjGwW/AK9pOvJyAfCSdaM8MkHZZipfDIb6MbNCLpI6HkK8WaUgq6kceVG81HGQs3JdscHs8T/cPTJBAZgBIGTUzOC4QL0iWupgCPFWVs75N2OuoJqT55EB+KfUQZwvj0tQuQX6MADzmAzV2RODLpA6HkK8mUETVtERkxIidRzknFxSbDBPkjqI8+FxCQrAfAB87KyQtACab48Qp+mwCp2lkxZRC4Vne1zqAM6HRyWo3AJ9EoDxMjlqssYHef1iXYRIaUNCfr3VX0vXPXm2CcUG86VSB3GuPCZB2YaVXw6ga9zFoTmaQHmo1DER4q2qBNmJo/mzvW5uNx/1WLHB7JGrintMggIwBECOQsnqM8doqfZEiJMInPNvRl2mkDoO4jCZABZJHcS58IgEZas9zQfQOX5OyAi/AJrSiBBnMfiFHm2PS6MWCu/yl2KD2SO+7+15SsCpADKVataQPlo7RepgCPFWHVaha+vkRVFSx0EcLgNiF4lHcfsEZas9XQGgfeLc0Hy1Rh4kdUyEeKuNcSPqBP8gGhjhnR6UOoCz5fYJCmLmH6bWyJpTR2pppVxCnOS4ldWW5/+GBkZ4rxHFBvNvpA7ibLh1gsot0MsALADQOu7ikDyVnyxQ6pgI8UYC53z9qMtkTObWXwnk/P1V6gDOhru/G7MADAFDY0pu4FipgyHEW21Xhxxti08PkzoO4nTjiw1mj5mBx20TlK32dCWA5hEFumF03RMhztFpFbq2TlkUKXUcxGU8pi/KbRMUgBwACQCaMscGUe2JECfZGJtbZ/XX+UkdB3GZ6cUGc7bUQQyGWyYo28i9SwA0x6dpwkKiVEOljokQb1RtZbVHRl1MAyN8zx+lDmAw3DJBAUiCOHNEU/50PdWeCHECgXO+fuRclw+M+ODvN+OR6bF4ZsGIk/ftXPMB/n1FLh7IV6Nqz7YzPl6wWvHs1aPx+tJ5J+9b8eB1+L8rR+Kr534dA7Cu5FHs2fCp45+Ad7im2GAOljqIgbhrgpoOwKQNVvhFJ2typQ6GEG/0i0p/tDUxy+UDI/IvuQ43Pr/6lPsih2Zh8VPvIWnkwFeSfP/uc4hITj/5d/X+XwAAd7xXinLD9+hua0FrXTWqdv2MzKlzHRu89/AHsETqIAbidgkqt0AfDGA8gNoxs4JHyhVMKXVMhHibLqvQ/dPkayKkKDs5fzL8daf+eI8YkoHwpLQBH9tyogpl3/4Po+f99uR9coUSlu4uCIIAi8UEJpdj7X+LMPP3hQ6P3cvc5u7TH7ljcBMBMJkcwpDhAWOkDoYQb7QxJrvWGqjXSB3H2Vr91N2Yfcc/Yd8sGTEkA7qoBDy/aAxyZl6BhsqD4JwjJj1Pwkg9QhIAt65iutWMxbkFejWA2QBOjJwenK72l+ukjokQb1NjRd3h0XPimdSBnKW9mz5HQEgEYjNH4vDWjadsu+TPT5/8/xt3zMNlf/0Pvln2T1Qf+AUpY2dgzOU3uTpcT/FHAB9LHUR/3K0GlQuxbdSYMVo7TupgCPE2nHOsz5vDmUzuafkJFTs2Y+/G1Xj84mF49y+LcXjrN1j54PWn7LNnw6eIzcyHqasDNYd2Y9Hj78Lw+XKYujolitrtTSs2mDOkDqI/bpOg7IeWJ6RrwnVhShr6SoiD7VTqjrYk5UjS93S+Zv3xUfzlyyO47/MDuPqfb2PIqAuw8NE3Tm63ms34/p3nMeW6u2Hu7gRjYg7mXIDVYpIqbE9wg9QB9MdtEhSAFABxAFqGT9DlSB0MId6m2yoYf5i8SPLpjN79y2K8eMMU1FXsxz9nJePnj1/D7vUf45+zknH0lx/wxtJL8eptFwMAWuuO47U/Dq6bZMt7L2LkJYuh0vgjalgOOOd45so8JOaOh0ZLS8idwbXuuuIu45xLHQMAILdA/wcAw8Fw4uZHku6k/ifiaUJ/iTo2vWJMrNRx9Ofr8PSKQ+MvT5Q6DuKWLr4/T/mF1EH05hY1qNwCfTiAfAB1mWO1iZScCHGsE1bUHxx7KTWbk/7cIHUAfXGLBAUxOXEAQlq+lpr3CHEgzjnW584RPHFgBHGZue44s4TkCco2OGI6gEalmskjE9WZUsdEiDfZpdAebR7imQMjiMuoAVwtdRC9SZ6gACQCCAXQkVugT1UoZTSrMiEO0m0VTD9MXiz5wAjiEW6QOoDe3CFBjQFgBYChOQHUvEeIA30XmV5tCQrxlzoO4hFGFxvMA8835UKSJqjcAr0CwBQA9dpghV9otGqYlPEQ4k3qrLzhwNh5NDCCnI0FUgdgT+oaVCpsM0eMuECfJZMxtxyLT4in4Zxjbc5vLJAraGAEORtXSh2APakT1EQAJgBIyvCn5j1CHGSPPPBo89A8WsadnK1sd2rmkyxB5Rbo/SH2P9WFx6uDgkIV1BRBiAN0WwXTlsnX0MAIclbkMB5JkX224RrVtFlSx9JDytnMMwHIAVgzRmtTe+bNIoScn+/DU6vNujCaMYIMyNR6vDXN75v9Bdr/00fJtqcASAYQCOD/JA4NgLQJaiqADgCIGepHgyMIcYB6C2/cP/5yao0g/bK2VdXFmT/tnBxUohsSuVsPYFSvXUZhH4tDOq+SIj57kiQo26q5mQAqlWomD45QJUsRByHeZm3OLDMNjCD2OOcQ2ipqEy0fdhXolwXHRxwMH8TD5gF43tmxDUSqGlTP+iM8fbQ2kZZ1J+T87ZH5Vzal5MdLHQeRHhcEztsOnRgifGCcGvxKaFTk0bOdSeQy+HCCGgtb815SZgA17xFynoyCYN48ZVGI1HEQ6QiCVWCtZSdS+UpTQchr4WFRNVHncbgp2MeCkM5bHRbgOXB5grIt654FoBoAIuLVlKAIOU/fh6YcM+sjkqSOg7iWYLVY5K27TqSzFdaCkNcj9NGN0Q46tALiOIFPHXS8cw7C1YYAYACs0UP8gjWB8lAJYiDEazRYeFPZ+CtoYISPECxms6Jt+4nhsrf5lNC3IgNj2p21Btl0+GCCygYgAEDayECqPRFyntZmX2iEQiH1RffEiaxmk1Hd9lNtjuItNilkRZS/tivOBcXOcEEZZ+TSBGVbWmMsgEYAiB6ioQRFyHnYxzRVjcNGu+LLiriY1dTd7d+2uTZP9bp8QsiHUaogk6sHwGRiH4tGOq92cbknuboGFQkgGMBRtUam0Ecok1xcPiFewyQI5u8LrtZLHQdxHIuxs1Pbvqku3+811djgTyOVOqvUTbfTAbwtVeGuTlAna0wZY7XJcjmT8kJhQjza5pAhx0zBUUlSx0HOj6W7rV3fsb5hjOYVv/zgryLkeu5Os4DMgA8lqLEA2gEgLkWT5OKyCfEajRbevHcCDYzwVJau5pbQzq+bxgW8EpCr+yZcFoxAqWPqx3QpC3dZgsot0GsApAM4DgDBUSq6oJCQc7Quc3oXFCpq3vMglo76pojuL1smBC4LHB6yOQwh0Ekd0yDEYR9LQzovk6JwV9aghkCcPV1QKJksUK9w1Hh9QnzKfqir6tPH0cAID2Bpr2mIMa5umxRUEpQaZgiB2AfvaWYA8PoElQ3b0u5JWf5R1P9EyNkzCYLluymLPOGXt88SJ2P9pGOK7mV9cvjeUACefq3nVAAvSFGwK5NEDoBmAIhL0dCvP0LOwRZ9UpUxJDpJ6jjIr8TJWMtrkywfdhYEl4TERRwOBzCYCVk9xRipCnZJgrItThgFoBIAwuLUlKAIOUtNFqFlz6Qrqe/WDYiTsR48MVT4wFgQ/EpYVGTl2U7G6kkSsI+FI53XubpgV9Wg4gBw2w36MCUlKELO0rqMaR1QqKh5TyKCYBVkrftqUvkKS0HI62GhUSfOZzJWTzMawBeuLtRVCSoB4vx70Icr/f0C5J7YUUiIZA5AXVWXMYF+2LmYOBnrzhPp7F2hIOTNcH10Y4zUMUlkFLw4QWXBtrxGcnYAfcgIOQtmQbB8O/mqIKnj8BWCxWxWthpOZMmXO3syVk/Se9Vdl3B6gsot0MsApAFoAoDoJD9qQyfkLPygS6gyhsYmSR2HN7OajUa/tp9qc5RvsYnBK101Gasn8c4EBSAMgBqAGQBCIqn/iZDBarIILbsmLqQfdU5gNXV1+bdtqZNwMlZPEo19LBbp/JgrC3VFgoqDrf+JycC0IUpfbcMl5KytTytoh0pNAyMcxGLs6NC2b6of5feaakzwZ+4wGasnGQXA6xLUMAAWAIhK9NPLFUzlgjIJ8XgHoTpWmzWZ+j/Ok6W7tU3f8U2jm07G6klGAfjElQW6IkFlAWgFgIgEtadfUU2IS5gFwfrtxKu0UsfhqSydTS2hXV83jQ94xT9HtyFCFgw6l+fP5f1QTk1QuQV6P4hNfJUAEBKlCnNmeYR4ix+D4iu7w+OSpI7Dk4iTsf6vZWJgSWBW6A9hgEdMxupJMlxdoLNrUNEQl3fnAKALVVANipABNFuE1l2TFtJgokEQJ2P9rG1S0Mu61LAdwfDMyVg9RTz2MTXSudFVBTo7QYVDnMEcABCoV1ANipABfJM6uY2r/Oi6p35Y2yrr4syfdBToXg5OCt/nDZOxegoZgBQAu11VoLMTVBTEGhQAwF9LCYqQMznMlcdqhhfQwAg7nAtcaCuvTbZ81F0Q/HJIbMQRb5uM1ZMMgxclqCQAXQDgr5WrVH4yd101khDJWQRu3ThxIX1GYJuMtfVATQp/3zQ1+JWwiMhjkVLHRAAAqa4szNkJKh5AJwDEDPWj2hMhZ/CTNqayOzwhSeo4pCIIVqusde+JVL5SnIw1+gQtaup+vCNB5RbolRDbhsUlNmJpiDkh/Wm1CG2/TL7K5wZGiJOx/nIiQ/audWrIG5FB0c10Ib97G+bKwpxZgwqF/RIb4UqqQRHSj/UpE1q4SuMT1+pYLWaTqrW0drh8OZ8c+jZNxupZvKMGhV8TFABAG0xDzAnpS7mgqK7OmebVtSfbZKwnchVvyCeFvhfpp+326ufrxaKwj2mRzttcUZgzE1QYAHnPHwFBlKAI6c0icGHD+Cs1UsfhDOJkrJvr8tSvKyYEfxSpCjLRvHfeYRiAUlcU5MwElQDg5AVdao2MrusgpJetgVFHuyKTkqSOw1Esxo6OoPZN9aP8XlWNCf4sSqETKCl5nwR4QYJKhG0En0wOplAxr/yVSMi5arMK7dsnL/L4/hdLd2tbcMe6xjH+r/qN1H9Nk7F6P5cN+XdKgsot0DMAMQAaAUAXpvRnjDFnlEWIp1qfPK6ZqzUe2Rfz62SsywJGBG8MB03G6ks8O0EB8AfgB9syG7pQZYCTyiHEI1UI8urjI2Z4VHKydNQ1RXZ/0TJBu0ybFfpjKGgyVl/l8QkqEHZTHGlDFHR1PCE2Vs6FDeMW+Ekdx2BY2qsbYoyraTJWYs/jE9QpNaaAIAXVoAix2eofebQzakiS1HH0hXMOob2qLt78cccUXQlNxkr64hUJ6mSfk79WTgmKEABtVqFj++Sr3Wq2BM4FzlvLa5OsH3YXBJfQZKxkIB6foAJhl6D8AihBEQIA3ySNaRT8AuKljoMLgoC2A7VDhfeMU4NfDYuIoslYyaB5fII6ZUSPn7+MEhTxeUcFec2xvAslS06CYLXKWvacSGUrLVODXw8PiaqNkioW4tGCsI/5IZ13O7sgZyWoUADmnj/U/rTMBvFtVs6FDWPmq1xdbs9krJmyd4SCkDcjgmJoMlbiEFEAyp1diLMSVDAAU88fKj+qQRHfVqoJP9oRk5LkirLEyVi3nRiueJtNDlkeGRjT4fEXAxO3EwYPT1Ana1BKtczfSeUQ4vbarUJn6eRFTq259DEZq+T9XMSrueQ73VkJSg+7GpRcwdROKocQt7chYVSDoAl0eMKwmrq6Atq+r8tTv64YH/JhlCrIQvPeEVdxydR1Dk9QtmmOdABO9NwnkzFnr9xLiFuqFGQnKvNnOSw5WYzttslYX6PJWImUPDNBAVABUAKw9twhkzt9aXlC3I6Vc/7NmPnn/d63dLW2BXeuaxjj/4pmpH4NTcZK3IHHJih/2E1zpFAxOU0US3yRwS/saEfMsHNKJpbOxpawrq+bxgUsCxgRsikcITQZK3ErLpmqyxkJSmn/h9pPRrUn4nM6xIERZ3WdkTgZ6+ctE7XLtJmhP9FkrMSdeWwNSgG7pd5VlKCID9oQn9dg9dcO2PdkaT9eH2P8rH1KUIkuJewXmoyVeAqPTlAnyZVM5oQyCHE7FovY7HHMymorRs6O76tdm3MOoa2yNt7ySWeB7uXgxPCyMIjXlBDiSbyjiU8mpwRFfIPZwv0Fzvn60ZfLmezXt704GeuR2iTrqu6pwctCYiLLIyQMkxBH8I4alEwGGiBBfINMZtmuDjnaHpeeyAVBQOv+E0P5+6apwa+ER0Qdp8lYiTfxkgRFNSjiIyxaZfv2uOEsrfnBowXBb4SHRNdGSx0TIZ7MGQnqlIQkk9EQc+IbTMMqIke1Lj46IbI6QS4D/TAj3sw08C7nzxkfIga7taBkcvqgEh8hY7J9+uikt2oyjEdqNS75ABMiEY9NUDLYDTO3mLn1DPsS4nXMeo1mrX+aYnO1rtxqBb3/iTcyD7zL+XN6DcrYKdAvSeJ7ZDLZbt3QpJXNw1pqW2R1UodDiIN5dA3qpO4Oq0syLSHuqEOjDflEnhP23XF9ucUKi9TxEOIgHpugTtHVYaUaFPFtMhnbqx+StLJ5WFtNs+zEwA8gxO15bBPfKb8SBSu4YKV+KEI6NdrgT5U5EZuOh1RYLK75gBPiJB5bgzLDbpAEAAhWTh9GQgAwJmNl+qTEFa1pnceb5DVSx0PIOfLYBHVa4FYrp2Y+Qux0+QXoVquyozYcD6kwW1zzYSfEgTw2QZ1WW7JScwYhp2FMhgNibaq7qlFeLXU8hJyFblcU4pIEJVANipB+dfsFBP3PLzd6XXXYUZMZRqnj8WS/fRCImAgMv+T0bU+9CrAMoL7p9G0Vx4D8+cCIy4CsOcB/V4j3G03ArJvF4/3nnV/3/91DgGGPc56Dh2hwRSHOauI7ZXojq4X6oAgZyGFdQsKK9nTT0QbFcalj8VQ3zAO+fPn0+yurgTWbgYR+ZkeMDgc2vwts/wj4cSVQXAIcrwW++g7IzwJ++QR4+X1x3x37AEEA8jKd9zw8QL0rCnFREx/VoAgZDKPaX/uVJidmzfHwo0aqTZ21KaOBEP3p9/+pGHjiHqC/mUFVKkCtEv9vNAGCbZiXUgF0dQMWu7HJf3sWeHipY+P2QB6boE6rQVnMVIMi5GyU6+MTVrRnmMsbFMekjsXTfboeiI0EctPPvF9lNZBzKRA/DbjvJiAmApg5AaipB8ZeBdx7k3is/Cxxmw/jcFETnzNmMzejV4IydQudTiiHEK9mUmsC1yAnMKH6WOXU0BPhapVrVjH1Jp1dwKMvAV8vG3jf+GixKe94LTDvduCKi4DIMOCdp8TtZjNw0c3Apy8AdxUDR6uB6y4F5k5z7nNwQ01Id821rc6oQVkBCLBLUp1tllYnlEOITziqi41/tzNTOFyvrJI6Fk9zqBI4UgXkzgOSpgNVJ4CR84GaM8yOGBMBZKUA32479f7/vAtcPw/Ysh1QKYGV/wIe+a9z43dTLmneA5yQoHZsbOYAjADkPfd1tFgpQRFyHswqP/91/tlx/6uOquwyglokBik7Fdh4mQwAACAASURBVKj9HihfJ97iIoHSVUBU+Kn7VdWIfU0A0NQCfF8KpCX/ur2pBVi9QawxdXYDMpnYn9Xtm72EnpugbLphl6BaG82UoAhxgCpdTPyK7kwcrFNRbaoPV98NjL8KKCsH4qYCr3zQ/75bdwFL/ir+f+8hYOxCsaZVcB1wz2/F5Nbj4f8Af71VTEoXTRIfmz0XuHmBM5+N23JZgmKc84H3Oku5BfqHAOgAtAFAQromfO4tMbc5vCBCfFhMS3XVtJDqEI0a/lLHQnzKq0jnN7miIGfVoOoBqHv+aDhuohoUIQ52XBcdt6J7OCurVVdKHQvxKR7fxHcCgKrnj45Wq9FipmuhCHE0i1Kl2RSYFf9ZTeyxjm60Sx0P8Qkua152VoKqA6C0v8PUTQMlCHGWmqDI2JXG4Yq9J/yOSh0L8XpHXFWQsxJUG8Sh5icZu4Q2J5VFCAFgVar8vtNmJnxSE3e8vQv0eSPOUu6qgpyVoFrRa02o7g6qQRHiCrVBETErTdmqXVSbIs5R7qqCnJmgTtHZRgmKEFcRlEr1Fm1mwkcn4qvbOk//PBJyjuqRzl3W1+nMBHXKsTtaKUER4mr12vDo9yzZfr/UaCqccEUJ8T2HXFmYUxLUjo3NRoizSZyc66/phKnRGWURQs5MUChVPwZlJH5Ul1DT2slapI6HeLQDrizMWTUoQJzt9uS1UFUHuk44sSxCyAAaAsOi3rdk+2+v8a8QOKg+Rc6F1ySoethdC9V0wtxhNtKs5oRISVAolD8HpSeuqkuqbelgzVLHQzyO1ySoE8CpywN0tFpqnVgeIWSQmgNDIt8XcgJLqwPKBYFqU2TQvCZBVaLXxbqtDZSgCHEXXC5XbNOlJX3QkFzX1M6apI6HuD0OoMyVBTozQdWi18W6TbUmSlCEuJmWgOCID3hO0M/VgeWCcOpnlhA7h5DOXXoBuLMT1Ckr69ZWGilBEeKO5HL5dl1q0vsNQxob2phLlvMmHqfU1QU6M0G1QBxqfrKZr2p/FyUoQtxYa4A+7COWG/xjdVC5lWpT5FQGVxfotARlW1m3Avh1rZqOVqvR2Gml6zAIcWNcJpP9oktJer9haFN9q8xlSysQt+dVNSgAOAwg0P6O9hYaKEGIJ2gL0IV+JMsJ2VKtK7daYZU6HiI576lB2RyF3dLvAI3kI8SjyGSyXbqhSSubUlpqW2R1UodDJFOFdO7y19/ZCaoWvWY1b6gx0YwShHiYDv+gkE/kOWHfHdeXW6ywSB0PcTmX154A1ySoU0byVeztdNlqjIQQB5LJ2F79kKSVzcPaTzTLqCXEt7i8/wlwcoLasbG5A+JovpNz8lUf7m4ydtPihYR4qk6NVv+JMif82+rgCosFZqnjIS7hlTUoQBzJF2B/R3OtiRZSI8SDMSZj+3TJiStb0zqqm+TUbO/9tklRqCsS1EH0SlC1R40VLiiXEOJknX4B+s9U2ZEbqkMqzFSb8lZHkM4l6ZpxRYI6jNP7oShBEeIlGJPhgC4pcUVrWtexJnmN1PEQh/tGqoJdkaB6mvNOJqnyvZ21FpPQ7YKyCSEu0u0XEPS5KjtqfXVYhckMk9TxEIfZIFXBTk9QOzY2twOohn0zHwea683UD0WIl2FMhkO6hMQV7enGykbFcanjIQ7h1TUoANgBQGd/R10V9UMR4q2Man/tl345MWurw4+azDBKHQ85Z4ek6n8CXJegynqXVVnWRQmKEC93RBef8G57hrmiQXFM6ljIOZGs9gS4LkFVoNdAicM7O6qtFk6jfgjxcia1JvBrTU7sV9URlUYTqO/Zs2yQsnCFi8ppAdAIQAOgCwAsZi60NpqrgiNUyS6Kwe11tlnw5j+O4tjBLjAGXF+YiN1bWvHdRw0IDBZfqsv+EIPsSae0lqKmvBsv/+XIyb/rjxkx99YYzFgUgVXPHsOu71sQn+aP3z6cBADY8nkDOlusmL4owmXPjZCjurj4FR1hHZNbDxwbEmaOlToeMiiS1qBckqB2bGzmuQX6XwBMhC1BAUBdlbGcEtSvVj5ZhazxQbj1iSGwmAWYugXs3tKKGYsicOF1kf0+LirJDw+9mwEAEKwc987eibwLdOhss+LQjnYUrszEsgePoOpAFyLi1djyWSOWPpfiqqdFyEkmtV/AOmQHlFUfr7wgtCbMTwWN1DGRfh1AOpd0oIurmvgAYA/sFi8EgP2l7S5d396ddbVbsd/QjknzQgEACqUM/tqz//2w96c2hMepERqthkwGWMwcnHOYjQLkCoav3zyBaVeFQ6FkAx+MECep0sXEv9uZyQ/WqWhuTvclae0JcG2COm1QRPnuzhPdndZmF8bgtuqPGaENVuD1v1fgH4v24s2HK2DsEpfg+ea9OhQt3IPXiyrQ0XrmiaR//roJoy/6//buPDqq874b+PfOopFG0mi0g1gkgzHCGAZI7Di2k0lsJ15SJ67TpmnStGlaJ61P3uRN37c5J036dnvf87av0+ZkaZY6iRPHTthsY2ywWQweDEYsAkbsIEAC7aPRXM2+3XvfP57BiEEyEkhz78x8P+fM4fjesfiJo6PvPM99nt9TDQAoLTdj1QNO/MvnTqGuyYayCjO6TkSw4iPOGf9+iK4nXVJq31l+x9xN/bN7YglE9a6HrvGa3gVImqZd/13TwOV2SgC+DyACXFl2+tiXZz/cvMT+gZwUYWBdJyL41y+exjd/sRgLlpVj9dOXUFZuxkf/qB4VTgsgAa/8pA+jw2l88R+ax/0a6ZSKv33oKP5p3e1w1Fqvuf/cP3fjI5+pR/fJKE60BTF3URk+8ZezZ/pbI7ouSyoRuzfROXxbQ2Ke3rUQAPF7ug6tmq6LWnI2gsocAX8YQPXY6+ePRk7lqgYjq24oQXVDCRYsE/uZ3/dgNbpPReGotcJklmAySfjQ79eh63hkwq9xbE8Q81vt44bTxVPiA2pjsw1tm0bwlX9bgN5zcQxe5KIq0l/aaivzVCyd9+pAU280jol/yClXtugdTkBup/gAcaZIydgLpw6ELqaSamyC9xeNqjorqhutGOgSPxMn9wfRtKAUsu/KSvzDO2U0LZz4mfL+LQHc9XDNuPde+UkfPvnXs6GkNaiqGDWbJCAZV6fxuyC6OQOOWXNWJ+4wnxyysdOMvjboXQCQu2Xml50BoEIEowoASlpTh3uTZ2ffUro8x7UYzh9/cx5+8Z0upFMq6ubY8MV/bMbqp3tw6XQUkgTUNtnwJ383HwAg+5J47l8u4ms/EKvxEjEVJ/cF370/1uGdMlqWlsNZLz4bLFhWjn/8zAnMXVSGebfZc/cNEk2CYi0p3W1dOv/MwGDfg1W9jvIyVOhdU5FJwwDPn4AcPoO6zOV2fgPAQgDDl6+9/+PVS+5+pOYzOS2EiAzPlEol7o6fHVjaGB//wSvNhDfRqj2odxFA7qf4AGAvgKs+th9/J3hOVTRFh1qIyMBUq9X2TuXtzRsG5/WHYwjqXU+RMMT0HqBPQF1eFPHuRpxYWEkGhpLndaiFiPKAr7J+9prUstKjA2XdOZ70KUbFG1BejywD6ALgGHu950yMq/mIaEKqxVrS5ljS/PLQ/IFgFKN611Og2vXsXp5NjxEUAOxB1vEbx9tCZ7RcPxAjorzjr6ybtS693H5kwN6tauDvjOn1st4FjKVXQJ1EVnfzkYFkWPaluvQph4jyiWqxWA84Wptf8rUMjUYkdqOZHhqAF/QuYiy9AqofYhXfVYslznVEDutTDhHlo0BFTeM6dXnFoYFyjqZungetWpfeRYylS0BlukrsAXDVrtJDb8on0ylV993LRJQ/NLPZ0u5Y3Pyir2VYDksBvevJY7/Su4Bseo2gAKAj++9PxtX0QFf8mE71EFEekytq6tdpyx0H+yu6VJWjqSkKA1ivdxHZ9AyoLgABAOVjLx7fGzqiSzVElP/MZvPhqtta1vkX+EdCkl/vcvLIerRqhuuBqFtAeT2yCmArsqb5zh4O90aC6SF9qiKiQhAsd9a9KC137uuv7FJUsOHk9f1K7wLGo+cICgAOQqzmu2pFX/fJKEdRRHRzTGZzR9WilnX+hYHhoGn4+v9D0ToPYJfeRYxH14DyemQ/gOMAasdeP7xD7lBVjZ96iOimhcqral82La/Z2+/oUhSwpdq1nkOrMfeg6j2CAoAdyHoOFRhKRfz9yTM61UNEhcZkMh2rurVlbeBW2Rc0+fQux0A0AL/Wu4iJGCGgTgCII+ucqDOHwtwTRUTTKmx31G4wLa/b01/F0ZSww2h7n8bSPaC8HjkBwAOgfuz1jl2jncm4GtanKiIqWCaTdKJqYcuawKLgoGwq9gVZ39e7gPeie0Bl7EXW4YlKWlMvnY4e0qkeIipwEXtl9SvW5fVv91V3pRWk9a5HB50wyMGEEzFKQF0E0IesDudtm0f2q4pWjD84RJQDkmSSTjlvaVkt3xbul82DeteTY9836uKIywwRUJnWR1sBOMdeDwylIn3n4159qiKiYhErq3C+al3W+FZfTXcqjZTe9eSADOBZvYu4HkMEVMZhACoA89iLB7YG9vIYDiKaaZJkwllnS/Oa0cXR3oB5QO96ZtgzRuwckc0wAeX1yEGIZ1GNY6/3dsb8w33J0/pURUTFJlZWXrWpZNmsnX213ckUknrXMwPSAH6odxGTYZiAytgKwIqszhJH3pLf0accIipGkmRCp7O5eU2oNX5pxNKvdz3T7CW0apf0LmIyDBVQXo98CaKzRN3Y66cPhi+N+lN58Q9KRIUjXmp3vFG6fPb2/rqLyRQSetczTb6ndwGTZaiAyngNWZ0lAOBEW5CjKCLSxYWq+fN/F16S6vZb+vSu5Sa1oVVr07uIyTJiQJ0GcAlZK/oO7ZBPx8IK2+cTkS6StrKKrWXLm7b2N1xMJJGvB6v+q94FTIXhAiqz5HwDgKqx1zUV2plDob36VEVEJHRXzZ3/u8jtyoVha6/etUxRO1q1V/QuYioMF1AZXoh1+ldN9bVtHjmSjKuGXxpJRIUtZSst325fNueN/sZL8SRietczSf9L7wKmypAB5fXIaQAbkbVYIpXQlHMdYT6LIiJDuFQ1Z97q6O3aOZ+1R+9arqMNrdpmvYuYKkMGVEYbRJdz29iLuzf49yfiakifkoiIrpYqKbXvKF82d/PA7J5YAlG965nAP+hdwI0wbEB5PXIMwOvI2ribiKnp0wdDhjz9kYiKV69j9tzV8aXSGV+J0bbE7EartlXvIm6EYQMqYxfEgVpXdTp/Z6P/UDyiBPQpiYhofGmrrcxTfse81waaeqNxGOV5ed49e7rM0AHl9cgygG0AZo29nk5p6vG24Fu6FEVEdB39jllz1iTuMJ8asl3UuZSdaNV26lzDDTN0QGW8ATGKso692LZ55GhkNF3sh40RkUGlrSWlb1csnb9xYE5vJA69Dl/N29ETkAcBlRlFbULWKEpToR3aKW/TpyoioskZdDTOWZNYZj0+WJrr0dTraNV25/jvnFaGD6iM7QBSyFrR5/WMdgYGk+f1KYmIaHIUq9X2TuXt8zcMzu0LxzDjq5A1DSkAfzPTf89My4uA8nrkMER3icbse3s3jWzjeVFElA98lQ1Na1LLbEcHy7pn8reWJOFHaNVOzdzfkBt5EVAZHgARAPaxF88fjQwMdCU69CmJiGhqVIu1pK1ySfPLQ/MHQlEEp/vraxqGAPzTdH9dPeRNQGX2Ra0B0JB9b9dLvh2KoqVzXxUR0Y3xV9bNWpteVuYdsE/raEqS8G20aqPT9xX1kzcBldEGoB9Znc59Pclg55EwN+8SUV5RLVbrfkdr84u+5sHRiCTf7NfTNBwC8MtpKM0Q8iqgMj36fgugOvvezjW+PZFg2pf7qoiIbk6gorZxnbq84tBAeZeq4YbHU5KEr6FVU6ezNj3lVUBlHIM4dfeqqb50SlP3vjbyKtdLEFE+0sxmS7tjccuLvhafHJZupFPOb9Gq7Zn2wnSUdwGVOS9qDcRiCfPYe6cOhC71nYsf0qUwIqJpIFfUNKzXlle291d0qerkRlOahgiAb85waTmXdwEFAF6P3A1gK4Cm7Hvbfzu0jWdGEVE+08xmy6Gq21rWDy8YHglJI9d7vyTh/6BVy7cDFK8rLwMq4xWIZecVYy+GAul4x9ujW/QpiYho+oxWOOtflJZX7e+v7FJVjPtsSdPgBfB0jkvLibwNKK9HjgD4FYB6ANLYe22bR46ODCTP6VEXEdG0MpnN3qpFLWtHFo74g5J/7C1NgyJJ+Au0FuY2m7wNqIzDmdc1HSZ2rvVt4t4oIioUIXtV3csmV/XefkeXorw7mvoPtGrtuhY2g/I6oDILJl6AWCxRMvZe/4V4gHujiKiQaCaT6VjVrS1rhheGR5MWjyTl50m5k5XXAQUAXo/sA7AOwOzsezvX+PZEuTeKiApMuMJRsTa9/O/RqsX0rmUm5X1AZewE0AOgduzFdEpTd708vEFVC2fjGhERNPzgyVXtb+tdxkwriIDyeuQUxIKJSmTtjeo8Euk7cyictydKEhGNpabVTskkfUvvOnKhIAIKALwe+Rwm2Bv15u+G9gSGkhdyXxUR0fTRNE01WUyff3JVe1zvWnKhYAIqY9y9UZoKbctzgy+nkmpBz9cSUWFTFXz3yVXt+/WuI1cKKqAye6OegdgbddVU33BvMnRwW2CjLoUREd2kdEo9YrZI39G7jlwqqIACAK9HPgrgDQBzsu+1b5dP9ZyNFeyeASIqTEpaC1mspk89uao9pXctuVRwAZXxEoA+AHXZN15/duCNaCg9nPuSiIimTtM0qIr2J0+uar+ody25VpAB5fXICQA/BVAGwDb2XiKmpneu9a1XFU3RpTgioimIhZUfP/XBw0X5eKIgAwoAvB65B8BvIKb6rurVd+FYdPDkgdB2XQojIpqkWFjpsFdavqZ3HXop2IDK2AXgAMZZer5zra/N35/szH1JRETXl0qqIZMJjz65qr1oZ3sKOqC8HlkF8ByAGADHVTc1YNPP+1+KRZQbObmSiGjGaJqmJaLq575635GCO+NpKgo6oADA65FHIZ5H1SJr6XlwJB3b9vzg79IpLalLcURE44iMKt//xv3e1/SuQ28FH1AA4PXIJwBsBDA3+97FUzHfvtdHXtQ0bVJHKxMRzaRYWDlY4bT8D73rMIKiCKiMVwF0YZyzow7vlM+caQ/vyHlFRERjJBNqwFIifeLJVe1scI0iCiivR04C+DEABdnPowBse2Fo90B3/FjOCyMiApBOqcnIaPrhp+4+PKR3LUZRNAEFvHt21A8AVCNrfxQAvPqz/ldCgVRfzgsjoqKmqpo23Jv80jcfPlo0ffYmo6gCCgC8HvkMgGch9kdd9f0nYmp60y8GVifjaliX4oioKPWdi/+/v3/i+At612E0RRdQGW9D9Oubn31juDcZemudbzU7TRBRLvSei23e8OO+ojjfaaqKMqC8HlkDsBbAMYyziffMoXDvkbfkV3NeGBEVFV9PwutZ53s88zuJshRlQAGA1yOnAfwMgIxxmsq+89qIt/NI2JPzwoioKIwOp3qO7w0+sHOtr6g6lE9F0QYUAHg9cgjA9wFYAZRn33/j14NvXTwV3ZfzwoiooEVDyujp9tCDL/zfi369azGyog4oAPB65F4A/wmgASKorrLxv/rf6L8Q9+a8MCIqSMmEmug8En7imW9dOK13LUZX9AEFAF6P3AFgNYB5yP430YCX/7P3FV9v4pQetRFR4VDSmnK+I/LUD7/eycYAk8CAumILgDchVvZddTyHqkB76Ye96wNDyQu6VEZEeU9RNPXEvuB3/v0rZ36pdy35ggGVkel8/gKAfRhn+XkqoSkv/bBvdXAkVdTdhYlo6lRFU4+8JX/Xs3743/SuJZ8woMbIrOz7BYAOjBNSsbCSfPlHfc9Hgmm2IiGiSVEVTT24LfDzva+NfJvLyaeGAZUl07PvJwDOYpzu56FAOr7xp/2/4TlSRHQ9qqpp+94YWbt/S+C/Zz4A0xQwoMbh9chxAD8E0AtgdvZ9f38yvOmZ/ucSMSWU8+KIKC+oqqYe2BJY375d/rLXI8f0ricfMaAm4PXIYQDfAxCAWIJ+lYHuhPzazweejUcVOefFEZGhqaqmHdwa2HBga+AvM/st6QZIPKfvvbnczjoA34bYIzWcfb9+bonjsS/P/oK90nJNNwoiKj6apmkHtwU27ns98GeZE73pBjGgJsHlds6GCCkFYkR1FWe91f74U01fqHBaZuW8OCIyDE3TtPbt8qa2zSNf8Hpkzq7cJAbUJLnczmYA3wIQh+jfd5XyKrPtia/O+VxVnfWa1X9EVPhUVVPbtwc273s98Kdej8xFVNOAATUFLrfzFgD/E4AK4JoeWqV2k/WJ/zbnj2pmlSzMeXFEpJt0Sk29vcH/8vF3gk95PTL7600TBtQUudzOOQD+FuKZlC/7vqVEMj/x1TmfbphnW5Lz4ogo5xIxJbb9haH1F45H/8brka95Tk03jgF1A1xuZwOAb0J0QB/Mvm8yQ3r8qaZPNi0oW5Hz4ogoZyLB9OimXwysG7qY+DuvR77mAyvdHAbUDXK5nTUQ0321APqveYMEPPbk7Iebl9g/kOvaiGjmyb7U0Maf9a0J+tP/2+uR2V1mBjCgboLL7XQA+AZEF/Se8d7z4Oca7l38/ooHJEmSxrtPRPln8GL80qs/6/9VPKr+O5eSzxwG1E1yuZ3lAL4KYDGAi+O9Z+VHnbd94JGaT1usUklOiyOiadd9MnJ287ODP1FS2k/ZIWJmMaCmgcvtLAXwVwBcECF1zT9q8xJ7/cc+3/DHpeXm6lzXR0TT48S+YMeO1b7vAXievfVmHgNqmrjcTiuAvwBwD4AuiKXoV3HUWso++ZXZn3HWl7TktjoiuhmKoqUPbg3sP7A18DSAjZnjeWiGMaCmkcvttAD4QwCPQDSaTWS/x2KVTL/35KxH5i6yvz/X9RHR1MUjSnDr84O7Lp6KfRfALh6ZkTsMqGnmcjslAPcB+BJEW6TgeO9z/0HdnUs/6HjYZJLYsJfIoPx9ie5XnxnYHZbTT3s9slfveooNA2qGuNzO2wB8HeL4+HGXoN5xj+OWez9V+4fWElNZTosjovekaRrOHAq3b39haL+m4T+8HrlT75qKEQNqBrncznoAXwPQBLEM/Zp/7KaFpTUP/2njZ+0OS32u6yOia6VTamLPK/5dR/cEDwH4gdcj9+ldU7FiQM0wl9tph5juuxNihZ+S/Z5Su8n6yJ/PenjOrWWrcl0fEV0RGU37Nv1yYM/QxcR2AL/yeuSI3jUVMwZUDrjcTjOATwF4HKLrxLh7J+58qPr29z3gfMxiNZXmsj4iAvovxE+/9kx/eyKmPg9gC1fq6Y8BlUMut/MuAF8BEMI4R3YAQMN8W9VDX2h8gsd2EOWGqmpKx9ujbbs3+L0AfuT1yCf1rokEBlSOZY7s+DoAO4Bx57ZNZkgf+3zjh291lbslE1skEc2U8Gh6cNvzg229nfFDAH7MbuTGwoDSgcvtrALw5wBWQuyXSo73viV3Vc6/71O1T9js5qpc1kdU6DRV0851RNq2vTB0Tklr2wGs9nrka/Ytkr4YUDpxuZ0mAB8F8HkAEYxzACIAVDgtpY9+qfGxhnmlt+eyPqJCFQsr/rfW+948542EATwLYA833xoTA0pnLrdzPoC/BtAIsRR93Aez932qduWy+6oeMVskay7rIyoUmqbh0unYgTd+PXgiGVf9EM+bLuhdF02MAWUALrezDMBnADwAYABAdLz3zWqxOe//bMNjNY0lC3JZH1G+S8SU4J6N/i0n2kJxADsBrOUScuNjQBlEpkXSSgBPQnSfGJjovfd8snbF8nsdD1lKuByd6Hr6L8S8rz87eCQaUkIAngFwlFN6+YEBZTAut7MOIqRaIab8UuO9r7rRWv6xzzc8ymdTRONLxJTgwW3ym4d3ymEAewH8locL5hcGlAFluqI/DOAPIPZMjbuAAgBW3u+87X0POB8t5Uo/IgBiX1PX8ejeN383dCYRU5MQCyEOcNSUfxhQBpbZM/UliCPlezHBaKrUbrLe/9mGD7cstd/D7uhUzPz9yU7Pet+bfefjdgBHAPza65En/IBHxsaAMrjMQYgfA/BpiPOlxu2MDgDzFpfVuT9d9wkeiEjFJh5RAod2yFsO7ZBlABYAzwN4m+2K8hsDKk+43M4mAH8G8WxqABP08wOAux+tWXbHvY4HOO1HhU5RtPT5jsjunWuHDifjWi2AkxBNXidcZET5gwGVRzJNZ+8B8DmIT4n9mGDflNUmmT/0eN2di1ZVfJjnTVEh8vUmTr21zrd9sDtRDiAM4DcA2jlqKhwMqDzkcjudEAso7gMwCnFy77jKq8w296fr72u+3X632SxZclUj0UyJBtO+g2/KWzp2jYYB2ABsAvC61yOPu3+Q8hcDKo+53M7FAL4IYDbEaGrCXmJ1c0oqP/T7dR9tuqV0BRvQUj6KRxT5RFvwrb2bR85rKuoBdEAsHeeBggWKAZXnXG5nCURPvydwZdovPdH757eW1d/ze7UP1M2xLc5RiUQ3JRlXw6cPhnbt3ujvUFJaI8Sswa8BeLl0vLAxoAqEy+10QOydegji1N4BTPB8ChCd0u/8ePXHHLXWuTkqkWhKUgk12ukN79m9wX8gEVNrAVgBbACwzeuR4zqXRznAgCowLrezAeL03nsAxAEMvuf7P1x167J7Hfc6G7g0nYwhGVcjnd7wnnc2+g/Go6oTQDmAQwDWeD3ye/48U2FhQBUol9vZDLGQYjnElMjIe73/1hXlTavud95bP8e2hM+oSA/JuBruPBLes2ej/2AipjoAOACcBrAOwDlO5xUfBlQByzSgbQXwWQAtAIYhWidNaPaC0uq7Hqq+p2lh2Qqu+qNciATTQ51Hwvv3vxHwJmJqOYBqAN0A1gA4yWAqXgyoIpA5wTEcVAAAB3hJREFUHHElxP6pGojefuH3+n+c9Vb73Z+o+UDzEvud3EdF003TNM3flzxz7J3gvmPvBC8AcEIEUy/EiMnL/UzEgCoimRV/dwJ4HEA9rrOHCgBKy03Wux+tXXXrivIPsjMF3ax0So33nI0dPrgtsH+gKyHjSjD1AFgPoMPrkRVdiyTDYEAVoUxHimUQiylugTgg0Qdgwh8Gs0UyrfhI1W2LVlasrJ1VsojPqWgqoqH0cOeRyL79W0a88YiahhjJV+JKMB1lMFE2BlQRyzyjWgTgExCLKZIQzWjf8xdF7eySipX3O1c0t9pXlFWYa2e+UspHmqZp/v7k2eN7g/uO7g6eh9in1wCgBMApAJsBHONUHk2EAUUAAJfbORfAxwHcCzGSGsQEx3uMteQDlfOX3FW5snF+6VKzRbLOcJmUB8Jyuv/i6ehR767RY/6+ZAiAHUAdxAeftwHsANDLxQ90PQwouorL7ayF6EzxcYiNkSO4zso/ALBXmktWPeC8Y8Gy8pWOGm7+LTbxiCL3nosdPbYn2HHpTGw4c/nyNN4oRL+8fV6PHNStSMo7DCgal8vtrADwfojOFLMg2icNYRKjqvmtZfV33FO1fFaLrdVeaamb2UpJL6mEGh3ojh8/dSB09PTB8KXMZTOARogPN2dxZRpvwvZbRBNhQNF7yjynaoGY+vsQxPODEMTqv+v+8MxdVFbbemdla9PC0tbKastciWsr8pqS1lLDvYnTZ4+EO47tCZ5Lp7TLz4+cEBtrVQC7IabxLnEaj24GA4omzeV2lkKs/nsQwG0Qv4x8EC2Vrqt2dknF7R90LJ63qKzV2WC9xWSSzDNXLU2XWFjxD11KdHafip49tT/UnYyrl0dDFRDTeABwASKUjnIaj6YLA4puiMvtnAXgLoiwqoQIqWFcZwXgZeUOs23pPY5bm1vtrbVNJYssVpNt5qqlqVDSWkr2Jbv6zsfPnmkPd/ZfiI/dK1cKseDBBDHluwPAYa9H9ulRKxU2BhTdFJfbaQGwGMCHIbpVWCCeVw1DLFu/LotVMi10lTfNW2xvqZ9ja66qs8xjYOVWNJQe9vUkO7tPRjtP7g92pRLa2A8aVoiN3VaI6V0PgAPgFB7NMAYUTRuX22kDcCuAVQDuBlAGMQ04ArEZeFJMZki3LC2f1bzE3lw/z9bsrLc2s93S9FFVTY0GlSHZl+wZupToOX800p3p6jBWGcT0nRnig8ZeAG0AOrmhlnKFAUUzItOtogWAC2KBRXXmlgxgas8oJKC51d7Qcru9uWG+rdlZb51nKzM7prPeQpaIKaHR4XSPvz/R03c+3nPhaKQ/HlWzV2OaIBY6VEIsfgkA2A/gGEQoTXhaM9FMYUDRjMusBGwCsBTAfQDmZW4lIAJryofPOWosZXMXlTXUz7M1VDeUNDpqLI12h6XBYpVKpq3wPJRKqNFIUPEFhpK9gxcTPV3HIz3DvcmJ9rFdHiWZIELpJEQonQUwyOk70hsDinIusxl4AURgLYf45A6IZ1cypjAdeBUJmDXf5py9sKyhrqmk0VlnbaiotjSWVZhrTSbJNB21G4GS1lLxiDISCab9oUDaP+pL+Yf7ksP9F+L+UCD9XmFfCrEUvAwikEZwZZR0nqfUktEwoEhXmdFVNcR04BKIwKqH+AWqQnQhiGASe64mIpkg1TSWlNfMKqmqqrM4KpxWR3mV2VFWYXaUlZsdNrvJUVJqqjTCsndN1bRUUoumEmokmVAjyZgaiUeVcNCfHhkZTPoHuhPDvp5EcBL/GmaIMKrI/LcEMW13MvPqBDDEURIZGQOKDMfldlYBaIbYa7UCYnpQxZUH9pHMa/oe1ktAdYO1vHZ2SVVltbXCZjfZSkpNthKbyVZSaiq1lkg2S4lUYjJLFrNFsprNksVkkawmk2QxmWHWVKiaqimqlvlTffdPRVM19d0/FSiKoqUTUTUajyjRaEiJREbTkZCcjowOpyLBkXTsBqJYguh354BYaadBjEY7ARwH0AVxzlKQgUT5hAFFhudyO8sh2i01QIy0FkI8x7LiSnDFIEIripsYbRmcBSKIygDYcOX7lAD0QYyMzkAcYTHE1XaU7xhQlJcypwTXQITWLIhnWgsh+sBdbr9z+blTIutl1OMdJIjQtUKEkB0ihDSI7yUBMRK6BOAixF4zPwA/V9lRIWJAUUHJnBpcC6AKYsrLiSujrzqIULPgSkhJmZcCMS2W/ac65jXRiEQa8zJN8N8WiD6GlwNIG/MCroRpCOK5Wz+AbohjT/yZV5hTdFRMGFBUVDKLMi4/r8l+lWe97BChcvllhQibsVNrwJVnPsqYV3bYBSFWKAYgVs+FceVZWjTzZ4yH9xFdwYAimqRMuJkhRkMaRPCoDBWimcGAIiIiQyqYzYtERFRYGFBERGRIDCgiIjIkBhQRERkSA4qIiAyJAUVERIbEgCIiIkNiQBERkSExoIiIyJAYUEREZEgMKCIiMiQGFBERGRIDioiIDIkBRUREhsSAIiIiQ2JAERGRITGgiIjIkBhQRERkSAwoIiIyJAYUEREZ0v8Hj7+ZZqCIlJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set color\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
    "# set figure size\n",
    "plt.figure(figsize=(6, 6));\n",
    "# plot the chart\n",
    "plt.pie(df_in_a_month.country.value_counts().values, colors=colors, shadow=True, startangle=90, autopct='%.1f%%')\n",
    "# set legend\n",
    "plt.legend(df_in_a_month.country.value_counts().index.tolist(), loc=\"best\")\n",
    "# set the chart to be in the middle\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "# set chart title\n",
    "plt.title('Country Percentage Requests in Aug')\n",
    "plt.savefig('Country_Percentage_Requests_in_Aug.png')\n",
    "# show the plot\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "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>the_price_of_recruitment</th>\n",
       "      <th>cost_of_supplier</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>9725.000000</td>\n",
       "      <td>6303.750000</td>\n",
       "      <td>3421.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indian</th>\n",
       "      <td>1735.000000</td>\n",
       "      <td>223.600000</td>\n",
       "      <td>1511.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>14735.483871</td>\n",
       "      <td>9677.935484</td>\n",
       "      <td>5057.548387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SriLanka</th>\n",
       "      <td>17010.279330</td>\n",
       "      <td>12084.173184</td>\n",
       "      <td>4926.206704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             the_price_of_recruitment  cost_of_supplier       profit\n",
       "country                                                             \n",
       "Bangladesh                9725.000000       6303.750000  3421.250000\n",
       "Indian                    1735.000000        223.600000  1511.400000\n",
       "Philippines              14735.483871       9677.935484  5057.548387\n",
       "SriLanka                 17010.279330      12084.173184  4926.206704"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe of the mean of of all numeric values for all countries grouped by months and countries\n",
    "df_mean = profit_df.iloc[:, 1:-2].groupby(['country']).mean()\n",
    "# print the average dataframe\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization of the profit, cost of supplier, cot of the client in a given month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the mean of all columns for all every country\n",
    "df_mean.plot(kind='bar', figsize=(12, 8))\n",
    "plt.title('Average client payments, office costs, and profits');\n",
    "plt.savefig('Average_client_payments_office_costs_and_profits.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}