Sales Data Analysis- Script 2-Analyzing Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Task2:-Analyze-data\" data-toc-modified-id=\"Task2:-Analyze-data-1\">Task2: Analyze data</a></span><ul class=\"toc-item\"><li><span><a href=\"#2.1:-Central-tendency-and-Dispersion:-Customer's-age-distribution\" data-toc-modified-id=\"2.1:-Central-tendency-and-Dispersion:-Customer's-age-distribution-1.1\">2.1: Central tendency and Dispersion: Customer's age distribution</a></span></li><li><span><a href=\"#2.2:-Concentration-analysis-:Lorenz-Curve-and-Gini-coefficient---customer's-age\" data-toc-modified-id=\"2.2:-Concentration-analysis-:Lorenz-Curve-and-Gini-coefficient---customer's-age-1.2\">2.2: Concentration analysis :Lorenz Curve and Gini coefficient - customer's age</a></span></li><li><span><a href=\"#2.3:-Graphic-representations\" data-toc-modified-id=\"2.3:-Graphic-representations-1.3\">2.3: Graphic representations</a></span><ul class=\"toc-item\"><li><span><a href=\"#2.3.1:-Gender-Distribution-of-Customer\" data-toc-modified-id=\"2.3.1:-Gender-Distribution-of-Customer-1.3.1\">2.3.1: Gender Distribution of Customer</a></span></li><li><span><a href=\"#2.3.1:-Boxplot-Product-Category-VS-Price---purpose:-to-check-price-range-per-product-category\" data-toc-modified-id=\"2.3.1:-Boxplot-Product-Category-VS-Price---purpose:-to-check-price-range-per-product-category-1.3.2\">2.3.1: Boxplot Product Category VS Price - purpose: to check price range per product category</a></span></li><li><span><a href=\"#2.3.3:-Times-series-Month-VS-Transaction-Amount\" data-toc-modified-id=\"2.3.3:-Times-series-Month-VS-Transaction-Amount-1.3.3\">2.3.3: Times series Month VS Transaction Amount</a></span></li><li><span><a href=\"#2.3.4:-Bivariate-Analysis-Visualzation:-Age-VS-Average-Price-of-item-purchased---Scatter-Plot\" data-toc-modified-id=\"2.3.4:-Bivariate-Analysis-Visualzation:-Age-VS-Average-Price-of-item-purchased---Scatter-Plot-1.3.4\">2.3.4: Bivariate Analysis Visualzation: Age VS Average Price of item purchased - Scatter Plot</a></span></li><li><span><a href=\"#2.3.5:-Pice-chart-for-Product-Category\" data-toc-modified-id=\"2.3.5:-Pice-chart-for-Product-Category-1.3.5\">2.3.5: Pice chart for Product Category</a></span></li></ul></li></ul></li><li><span><a href=\"#Task3:-Correlations---further-visualizations-and-statistics-(for-calculation-of-correlations-between-variables)\" data-toc-modified-id=\"Task3:-Correlations---further-visualizations-and-statistics-(for-calculation-of-correlations-between-variables)-2\">Task3: Correlations - further visualizations and statistics (for calculation of correlations between variables)</a></span><ul class=\"toc-item\"><li><span><a href=\"#3.1:Correlation-Gender-VS-Categories-of-Products\" data-toc-modified-id=\"3.1:Correlation-Gender-VS-Categories-of-Products-2.1\">3.1:Correlation Gender VS Categories of Products</a></span></li><li><span><a href=\"#3.2:-Correlation-Age-VS-Total-Amount-Purchase\" data-toc-modified-id=\"3.2:-Correlation-Age-VS-Total-Amount-Purchase-2.2\">3.2: Correlation Age VS Total Amount Purchase</a></span></li><li><span><a href=\"#3.3:-Correlaton-Age-VS-Purchase-Frequency\" data-toc-modified-id=\"3.3:-Correlaton-Age-VS-Purchase-Frequency-2.3\">3.3: Correlaton Age VS Purchase Frequency</a></span></li><li><span><a href=\"#3.4-Correlation-Age-VS-Avergage-basket-size(in-number-of-items)\" data-toc-modified-id=\"3.4-Correlation-Age-VS-Avergage-basket-size(in-number-of-items)-2.4\">3.4 Correlation Age VS Avergage basket size(in number of items)</a></span></li><li><span><a href=\"#3.5-Correlation-Age-VS-Category-of-purchase-produducts\" data-toc-modified-id=\"3.5-Correlation-Age-VS-Category-of-purchase-produducts-2.5\">3.5 Correlation Age VS Category of purchase produducts</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* First import packages needed & load CSV files that were cleaned previously in other file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "customers=pd.read_csv('customers_cleaned.csv')\n",
    "products=pd.read_csv('products_cleaned.csv')\n",
    "transactions=pd.read_csv('transactions_cleaned.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Display head of customers, products, transactions data frame to see what are the information included in each dataframe and brain storm idea how to assemble them later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_7839</td>\n",
       "      <td>f</td>\n",
       "      <td>1975</td>\n",
       "      <td>2021</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c_1699</td>\n",
       "      <td>f</td>\n",
       "      <td>1984</td>\n",
       "      <td>2021</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c_5961</td>\n",
       "      <td>f</td>\n",
       "      <td>1962</td>\n",
       "      <td>2021</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c_5320</td>\n",
       "      <td>m</td>\n",
       "      <td>1943</td>\n",
       "      <td>2021</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  client_id sex  birth  year  age\n",
       "0    c_4410   f   1967  2021   54\n",
       "1    c_7839   f   1975  2021   46\n",
       "2    c_1699   f   1984  2021   37\n",
       "3    c_5961   f   1962  2021   59\n",
       "4    c_5320   m   1943  2021   78"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id_prod</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1421</td>\n",
       "      <td>19.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_1368</td>\n",
       "      <td>5.13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_731</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_587</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1507</td>\n",
       "      <td>3.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_prod  price  categ\n",
       "0  0_1421  19.99      0\n",
       "1  0_1368   5.13      0\n",
       "2   0_731  17.99      0\n",
       "3   1_587   4.99      1\n",
       "4  0_1507   3.99      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1483</td>\n",
       "      <td>2021-04-10 18:37:28.723910</td>\n",
       "      <td>s_18746</td>\n",
       "      <td>c_4450</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_226</td>\n",
       "      <td>2022-02-03 01:55:53.276402</td>\n",
       "      <td>s_159142</td>\n",
       "      <td>c_277</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_374</td>\n",
       "      <td>2021-09-23 15:13:46.938559</td>\n",
       "      <td>s_94290</td>\n",
       "      <td>c_4270</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_2186</td>\n",
       "      <td>2021-10-17 03:27:18.783634</td>\n",
       "      <td>s_105936</td>\n",
       "      <td>c_4597</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1351</td>\n",
       "      <td>2021-07-17 20:34:25.800563</td>\n",
       "      <td>s_63642</td>\n",
       "      <td>c_1242</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_prod                        date session_id client_id  month  day\n",
       "0  0_1483  2021-04-10 18:37:28.723910    s_18746    c_4450      4   10\n",
       "1   2_226  2022-02-03 01:55:53.276402   s_159142     c_277      2    3\n",
       "2   1_374  2021-09-23 15:13:46.938559    s_94290    c_4270      9   23\n",
       "3  0_2186  2021-10-17 03:27:18.783634   s_105936    c_4597     10   17\n",
       "4  0_1351  2021-07-17 20:34:25.800563    s_63642    c_1242      7   17"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task2: Analyze data \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Visualization of data in various perspectives. \n",
    "\n",
    "1.Analysis on Customers age: to check central tendency of customer's age using histogram, visualization lorenzo curve and calculate gini coefficient. if we have high gini coefficient that means we have uneven distribution in customer's age \\\n",
    "2.Bivariate analysis on product category VS product price: to check price variance of each product category using boxplot\\   \n",
    "** More visualziation on bivraite analysis is coming on Task 3.\\\n",
    "3.Time series visualization: to see purchase trend over months\\\n",
    "4.Pie chart on product category: to see product distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1: Central tendency and Dispersion: Customer's age distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to check central tendency of customer's age\n",
    "* Variable: customer's age\n",
    "* Method used: histogram and gifi coefficient calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "plt.hist(customers.age, bins = 10)\n",
    "plt.axvline(customers.age.mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "plt.text(customers.age.mean()*1.1, max_ylim*0.9, 'Mean: {:.2f}'.format(customers.age.mean()))\n",
    "plt.xlabel('Customer Age')\n",
    "plt.title('Customer Age Histogram')\n",
    "plt.savefig('Customer Age Histogram.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2: Concentration analysis :Lorenz Curve and Gini coefficient - customer's age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to quantify customer's age concentration\n",
    "* Variable: customer's age\n",
    "* Method used: Lorenz Curve and Gini coefficient formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_7839</td>\n",
       "      <td>f</td>\n",
       "      <td>1975</td>\n",
       "      <td>2021</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c_1699</td>\n",
       "      <td>f</td>\n",
       "      <td>1984</td>\n",
       "      <td>2021</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c_5961</td>\n",
       "      <td>f</td>\n",
       "      <td>1962</td>\n",
       "      <td>2021</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c_5320</td>\n",
       "      <td>m</td>\n",
       "      <td>1943</td>\n",
       "      <td>2021</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  client_id sex  birth  year  age\n",
       "0    c_4410   f   1967  2021   54\n",
       "1    c_7839   f   1975  2021   46\n",
       "2    c_1699   f   1984  2021   37\n",
       "3    c_5961   f   1962  2021   59\n",
       "4    c_5320   m   1943  2021   78"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## initial exploration on customers dataframe\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = customers.iloc[:,4]\n",
    "age = ages.values\n",
    "n = len(age)\n",
    "lorenz = np.cumsum(np.sort(age)) / age.sum()\n",
    "lorenz = np.append([0],lorenz) # The Lorenz Curve begins at 0\n",
    "\n",
    "\n",
    "plt.axes().axis('equal')\n",
    "xaxis = np.linspace(0-1/n,1+1/n,n+1) # There is 1 segment (of size n) for each individual, plus 1 segment at y=0. The first segment starts at 0-1/n and the last one finishes at 1+1/n\n",
    "plt.plot(xaxis,lorenz,drawstyle='steps-post')\n",
    "plt.title('GINI Coefficient Graph on Customer Age')\n",
    "plt.show()\n",
    "plt.savefig('GINI Coefficient Graph on Customer Age.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gini is 0.22594000556853144\n"
     ]
    }
   ],
   "source": [
    "AUC = (lorenz.sum() -lorenz[-1]/2 -lorenz[0]/2)/n # area under the Lorenz Curve. The first segment (lorenz[0]) is halfly below O, so we divide it by 2. We do the same for the mast segment lorenz[-1]\n",
    "S = 0.5 - AUC # area between 1st bisector and the Lorenz Curve\n",
    "gini = 2*S\n",
    "print(\"gini is \"+str(gini))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = ages.to_numpy()\n",
    "arr\n",
    "def gini(arr):\n",
    "    ## first sort\n",
    "    sorted_arr = arr.copy()\n",
    "    sorted_arr.sort()\n",
    "    n = arr.size\n",
    "    coef_ = 2. / n\n",
    "    const_ = (n + 1.) / n\n",
    "    weighted_sum = sum([(i+1)*yi for i, yi in enumerate(sorted_arr)])\n",
    "    return coef_*weighted_sum/(sorted_arr.sum()) - const_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22594000556853144"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3: Graphic representations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.1: Gender Distribution of Customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f</td>\n",
       "      <td>4490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m</td>\n",
       "      <td>4131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex   age\n",
       "0   f  4490\n",
       "1   m  4131"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_gender=customers.groupby('sex')['age'].count()\n",
    "customers_gender = pd.DataFrame(customers_gender).reset_index()\n",
    "customers_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAalElEQVR4nO3de7hdVXnv8e9P7l4QkEhjAgY19RTwQrkItQepWoHSArVS4WDFlpaWg7fW8yi0Hm1V6q1awVvFao21FSltEVCOIjVYW0qMF6ABKSkIRCIg3gA1QHzPH3NsWWz23nMlZO29yP5+nmc9a84x55jzXWGz3jXGmHPMVBWSJM3kYXMdgCRp/JksJEm9TBaSpF4mC0lSL5OFJKmXyUKS1MtkoZFJ8qdJPtaWd0tyZ5It5jquUUjykSRvmuVzXpjk+E10rP+Z5JqB9W8kee6mOHY73qokB2+q42n2mSz0oCT5X0lWtkSwtn2B/eLk/arqxqp6ZFWt3wTnXJ7kd3v22TrJ65Jck+SuJN9ssT3vwZ5/NiSpFvedSW5PcnGSFw7uU1WHVdWyIY/1pJn2qap/raonP9i42/kekDiras+qWr4pjq+5YbLQRkvyR8C7gD8HdgF2A94HHDmHYU04hy6OFwM7ArsDpwOHz2VQkyXZcobNT6uqRwJPBj4CvCfJ62c5BqlTVb58bfALeDRwJ3D0DPv8KfCxtrwEKGDLgfofAtYC3wTeBGzRtr0E+CLwF8B3geuBw9q204D1wI/b+d8zxXmfC/wIWNzzGR4H/CNwWzvHyyfFfjbwUeAOYBWw78D2vYGvtG2fAM4C3jSw/VeBrwHfA/4deOrAtm8ArwGuANZN/JtMiq2AJ00qe0H73I9p68uB323LTwIuAb4PfBv4RCv/QjvWXe3f64XAwcCaFsO3gL+dKJsU46nAVe2/wd8A2w7+95kqXuBE4B7g7na+8weO99y2vA3dj4yb2+tdwDZt20RsrwJupfv7+O25/nv3VbYstNEOBLYF/nkj6y8D7qX7gtkbeB4w2LX0DOAaYGfgbcCHkqSq/gT4V+Cl1XVrvXSKYz8XuKyq1kx38iQPA84HLgcWAc8BXpnkkIHdjqBLAjsA5wHvaXW3Bs6l+5LdCfgH4DcGjv3zwIeB3wceA3wAOC/JNgPHPpaulbNDVd07XZyTfBLYEth/im1vBD5L14paDLwboKoOatuf1v69PtHWf6bF/ni6L/ipHAccAjwR+FngtX0BVtWZwN8Bb2vn+7UpdvsT4ADg6cDT2ucZPPbP0P2YWAScALw3yY5959ZomSy0sR4DfHsDvuh+KskuwGHAK6vqrqq6FfhL4JiB3W6oqg9WN8axDFhI19U1jJ3pfjFPnG+nJN9L8v0kP27F+wELquoNVXV3VV0HfHBSDF+sqk+3GP6W7osNui+6rYB3VdU9VXUO8KWBer8HfKCqLquq9dWNK6xr9SacUVU3VdWPhvxMVNU9dK2GnabYfA/dF//jqurHVfXFnsP9BHh9Va2bIYb3tBi/Q9eiO3bYWHscB7yhqm6tqtuAPwN+a2D7PW37PVX1aboWyiYZT9HGM1loY90O7LyR/d2Pp/uyXdu+xL9H9+v7sQP7/PTLvqp+2BYfuQGxLRyo/52q2gHYh64LZCKGx02cv8Xwx9w/IX1rYPmHwLbt8z4O+GZVDc7CecOkz/eqScfetdWbcNOQn+WnkmwFLAC+M8XmVwMBVrQrj36n53C3VdWPe/YZjPEG7h//g/E47v/vNfnYt0/6EfJDhv9vrxFxYEsb61K6/vOj6AaTN8RNdL+0d96Ylgld//hMLgZelmTxDF1RNwHXV9XSjTj/WmBR6xabiGU34L8Hjn1aVZ02wzE2ZrrnI+m67lY84GBV36Jr0dCuRvtcki9U1eoHcf5dB5Z3oxtfgG784+ETG5L8zAYe+2a6hLpqimNrTNmy0Eapqu8Dr6PrTz4qycOTbJXksCRv66m7lq5//R1Jtk/ysCRPTPKsIU9/C/CEGY7/WeDzwLlJntEuo92K+3cDrQB+kOQ1SbZLskWSvZLsN8T5L6X70n55ki2TPJ/7jyN8EPiDdu4keUSSw5M8asjPdz+tG+044L3AW6vq9in2OTrJ4rb6Xbov7InLlGf895rByUkWJ9mJrtU1Md5xObBnkqcn2ZbuYoBBfef7OPDaJAuS7Ez3d/SxjYhPs8hkoY1WVe8E/ohucPI2ul/UL6Ub/O3zYmBr7rva5hwGuo56nA68IMl3k5wxzT7PBy6g+xL6Ht3VTscBh7bY1wO/RjfIej3dWMBf0w2szqiq7m7Hf0mL/YXAPw1sX0n3K/89bfvqtu+GujzJna3+7wJ/WFWvm2bf/YDL2v7nAa+oquvbtj8FlrUusd/cgPP/PV1Sv6693gRQVf8FvAH4HHAt3ZVrgz4E7NHOd+4Ux30TsJLuarAr6a4qm9UbGrXhcv9uV0mSHsiWhSSpl8lCktTLZCFJ6mWykCT12mzvs9h5551ryZIlcx2GJD2kfPnLX/52VS2YXL7ZJoslS5awcuXKuQ5Dkh5SktwwVbndUJKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXpvtHdwPxpJTPjXXIWhMfeMth891CNKcsGUhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfPs5AegnzmiqYzqmeu2LKQJPUyWUiSeo08WSTZIslXk1zQ1ndKclGSa9v7jgP7nppkdZJrkhwyUL5PkivbtjOSZNRxS5LuMxsti1cAVw+snwJcXFVLgYvbOkn2AI4B9gQOBd6XZItW5/3AicDS9jp0FuKWJDUjTRZJFgOHA389UHwksKwtLwOOGig/q6rWVdX1wGpg/yQLge2r6tKqKuCjA3UkSbNg1C2LdwGvBn4yULZLVa0FaO+PbeWLgJsG9lvTyha15cnlD5DkxCQrk6y87bbbNskHkCSNMFkk+VXg1qr68rBVpiirGcofWFh1ZlXtW1X7LliwYMjTSpL6jPI+i2cCRyT5FWBbYPskHwNuSbKwqta2LqZb2/5rgF0H6i8Gbm7li6colyTNkpG1LKrq1KpaXFVL6Aau/6WqXgScBxzfdjse+GRbPg84Jsk2SXanG8he0bqq7khyQLsK6sUDdSRJs2Au7uB+C3B2khOAG4GjAapqVZKzgauAe4GTq2p9q3MS8BFgO+DC9pIkzZJZSRZVtRxY3pZvB54zzX6nAadNUb4S2Gt0EUqSZuId3JKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqVdvskjyzCSPaMsvSvLOJI8ffWiSpHExTMvi/cAPkzwNeDVwA/DRkUYlSRorwySLe6uqgCOB06vqdOBRow1LkjROthxinzuSnAq8CDgoyRbAVqMNS5I0ToZpWbwQWAecUFXfAhYBbx9pVJKksTJMy+IPq+o1EytVdWOSPUcYkyRpzAzTsvjlKcoO29SBSJLG17QtiyQnAf8beEKSKwY2PQr491EHJkkaHzN1Q/09cCHwZuCUgfI7quo7I41KkjRWpk0WVfV94PvAse0KqF3a/o9M8siqunGWYpQkzbFh7uB+KXALcBHwqfa6YIh62yZZkeTyJKuS/Fkr3ynJRUmube87DtQ5NcnqJNckOWSgfJ8kV7ZtZyTJRnxWSdJGGmaA+5XAk6tqz6p6Sns9dYh664BnV9XTgKcDhyY5gK5L6+KqWgpc3NZJsgdwDLAncCjwvtaige4u8hOBpe116JCfT5K0CQyTLG6i647aINW5s61u1V4Td4Iva+XLgKPa8pHAWVW1rqquB1YD+ydZCGxfVZe2O8k/OlBHkjQLhrnP4jpgeZJP0bUWAKiqd/ZVbC2DLwNPAt5bVZcl2aWq1rZjrE3y2Lb7IuA/BqqvaWX3tOXJ5VOd70S6Fgi77bbbEB9NkjSMYVoWN9KNV2xNd9nsxKtXVa2vqqcDi+laCXvNsPtU4xA1Q/lU5zuzqvatqn0XLFgwTIiSpCH0tiyqamJg+hFVddfGnKSqvpdkOd1Ywy1JFrZWxULg1rbbGmDXgWqLgZtb+eIpyiVJs2SYq6EOTHIVcHVbf1qS9w1Rb0GSHdrydsBzga8D5wHHt92OBz7Zls8DjkmyTZLd6QayV7QuqzuSHNCugnrxQB1J0iwYZsziXcAhdF/mVNXlSQ4aot5CYFkbt3gYcHZVXZDkUuDsJCfQdXEd3Y67KsnZwFXAvcDJVbW+Hesk4CPAdnQ3Cl443MeTJG0KwyQLquqmSbc2rJ9u34E6VwB7T1F+O/CcaeqcBpw2RflKYKbxDknSCA2TLG5K8gtAJdkaeDmtS0qSND8MczXUHwAn012uuobuBruTRxiTJGnMDHM11LeB42YhFknSmJppivJXV9XbkrybKe5rqKqXjzQySdLYmKllMTEusXI2ApEkja+Zpig/v70vm24fSdL8MFM31PlMM60GQFUdMZKIJEljZ6ZuqL+YtSgkSWNtpmRxFbCgqq4aLEyyJ/fN5yRJmgdmus/i3cBUU7cuBk4fTTiSpHE0U7J4SlVdMrmwqj4DDPOkPEnSZmKmZLHVRm6TJG1mZkoW1yb5lcmFSQ6je3qeJGmemGmA+w+BC5L8Jt2jUQH2BQ4EfnXUgUmSxse0LYuq+i/gKcAlwJL2ugR4atsmSZonZpxIsKrWAX8zS7FIksbUMFOUS5LmOZOFJKnXtMkiycXt/a2zF44kaRzNNGaxMMmzgCOSnAXc7yHcVfWVkUYmSRobMyWL1wGn0E3v8c5J2wp49qiCkiSNl5meZ3EOcE6S/1tVb5zFmCRJY2aYZ3C/MckRwEGtaHlVXTDasCRJ46T3aqgkbwZeQTdl+VXAK1qZJGme6G1ZAIcDT6+qnwAkWQZ8FTh1lIFJksbHsPdZ7DCw/OgRxCFJGmPDtCzeDHw1yefpLp89CFsVkjSvDDPA/fEky4H96JLFa6rqW6MOTJI0PoZpWVBVa4HzRhyLJGlMOTeUJKmXyUKS1GvGZJHkYUn+c7aCkSSNpxmTRbu34vIku81SPJKkMTTMAPdCYFWSFcBdE4VVdcTIopIkjZVhksWfjTwKSdJYG+Y+i0uSPB5YWlWfS/JwYIvRhyZJGhfDTCT4e8A5wAda0SLg3BHGJEkaM8NcOnsy8EzgBwBVdS3w2L5KSXZN8vkkVydZleQVrXynJBcluba97zhQ59Qkq5Nck+SQgfJ9klzZtp2RJFOdU5I0GsMki3VVdffESpIt6Z6U1+de4FVV9XPAAcDJSfage/rexVW1FLi4rdO2HQPsCRwKvC/JRHfX+4ETgaXtdegQ55ckbSLDJItLkvwxsF2SXwb+ATi/r1JVrZ14TndV3QFcTdeFdSSwrO22DDiqLR8JnFVV66rqemA1sH+ShcD2VXVpVRXw0YE6kqRZMEyyOAW4DbgS+H3g08BrN+QkSZYAewOXAbu0uaYm5pya6NJaBNw0UG1NK1vUlieXS5JmyTBXQ/2kPfDoMrrup2vaL/yhJHkk8I/AK6vqBzMMN0y1oWYon+pcJ9J1V7Hbbt5HKEmbyjBXQx0O/DdwBvAeYHWSw4Y5eJKt6BLF31XVP7XiW1rXEu391la+Bth1oPpi4OZWvniK8geoqjOrat+q2nfBggXDhChJGsIw3VDvAH6pqg6uqmcBvwT8ZV+ldsXSh4Crq+qdA5vOA45vy8cDnxwoPybJNkl2pxvIXtG6qu5IckA75osH6kiSZsEwd3DfWlWrB9av477WwEyeCfwWcGWSr7WyPwbeApyd5ATgRuBogKpaleRs4Cq6K6lOrqr1rd5JwEeA7YAL20uSNEumTRZJnt8WVyX5NHA23VjB0cCX+g5cVV9k6vEGgOdMU+c04LQpylcCe/WdU5I0GjO1LH5tYPkW4Flt+TZgxwfuLknaXE2bLKrqt2czEEnS+Oods2iDzS8Dlgzu7xTlkjR/DDPAfS7dVU3nAz8ZaTSSpLE0TLL4cVWdMfJIJElja5hkcXqS1wOfBdZNFE7M+yRJ2vwNkyyeQne/xLO5rxuq2rokaR4YJln8OvCEwWnKJUnzyzDTfVwO7DDiOCRJY2yYlsUuwNeTfIn7j1l46awkzRPDJIvXjzwKSdJYG+Z5FpfMRiCSpPE1zB3cd3Dfw4a2BrYC7qqq7UcZmCRpfAzTsnjU4HqSo4D9RxWQJGn8DHM11P1U1bl4j4UkzSvDdEM9f2D1YcC+TPMMbEnS5mmYq6EGn2txL/AN4MiRRCNJGkvDjFn4XAtJmudmeqzq62aoV1X1xhHEI0kaQzO1LO6aouwRwAnAYwCThSTNEzM9VvUdE8tJHgW8Avht4CzgHdPVkyRtfmYcs0iyE/BHwHHAMuDnq+q7sxGYJGl8zDRm8Xbg+cCZwFOq6s5Zi0qSNFZmuinvVcDjgNcCNyf5QXvdkeQHsxOeJGkczDRmscF3d0uSNk8mBElSL5OFJKmXyUKS1MtkIUnqZbKQJPUyWUiSepksJEm9TBaSpF4mC0lSL5OFJKmXyUKS1MtkIUnqZbKQJPUaWbJI8uEktyb5z4GynZJclOTa9r7jwLZTk6xOck2SQwbK90lyZdt2RpKMKmZJ0tRG2bL4CHDopLJTgIurailwcVsnyR7AMcCerc77kmzR6rwfOBFY2l6TjylJGrGRJYuq+gLwnUnFR9I9npX2ftRA+VlVta6qrgdWA/snWQhsX1WXVlUBHx2oI0maJbM9ZrFLVa0FaO+PbeWLgJsG9lvTyha15cnlU0pyYpKVSVbedtttmzRwSZrPxmWAe6pxiJqhfEpVdWZV7VtV+y5YsGCTBSdJ891sJ4tbWtcS7f3WVr4G2HVgv8XAza188RTlkqRZNNvJ4jzg+LZ8PPDJgfJjkmyTZHe6gewVravqjiQHtKugXjxQR5I0S7Yc1YGTfBw4GNg5yRrg9cBbgLOTnADcCBwNUFWrkpwNXAXcC5xcVevboU6iu7JqO+DC9pIkzaKRJYuqOnaaTc+ZZv/TgNOmKF8J7LUJQ5MkbaBxGeCWJI0xk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9TJZSJJ6mSwkSb1MFpKkXiYLSVIvk4UkqZfJQpLUy2QhSeplspAk9XrIJIskhya5JsnqJKfMdTySNJ88JJJFki2A9wKHAXsAxybZY26jkqT54yGRLID9gdVVdV1V3Q2cBRw5xzFJ0ryx5VwHMKRFwE0D62uAZ0zeKcmJwIlt9c4k18xCbPPBzsC35zqIcZC3znUEmoZ/o80m+Bt9/FSFD5VkkSnK6gEFVWcCZ44+nPklycqq2neu45Cm49/o6D1UuqHWALsOrC8Gbp6jWCRp3nmoJIsvAUuT7J5ka+AY4Lw5jkmS5o2HRDdUVd2b5KXAZ4AtgA9X1ao5Dms+sWtP486/0RFL1QO6/iVJup+HSjeUJGkOmSwkSb1MFpuxJOuTfG3gtWSE5/pGkp1HdXzNP0kqyd8OrG+Z5LYkF/TUO7hvH224h8QAtzbaj6rq6XMdhLSR7gL2SrJdVf0I+GXgm3Mc07xly2KeSbJPkkuSfDnJZ5IsbOXLk/xlki8kuTrJfkn+Kcm1Sd40UP/cVndVu2N+qnO8KMmK1pr5QJvbS9oYFwKHt+VjgY9PbEiyf5J/T/LV9v7kyZWTPCLJh5N8qe3nNEEbyWSxedtuoAvqn5NsBbwbeEFV7QN8GDhtYP+7q+og4K+ATwInA3sBL0nymLbP77S6+wIvHygHIMnPAS8EntlaNeuB40b3EbWZOws4Jsm2wFOBywa2fR04qKr2Bl4H/PkU9f8E+Jeq2g/4JeDtSR4x4pg3S3ZDbd7u1w2VZC+6L/+LkkB3z8ragf0nbnS8ElhVVWtbvevo7qC/nS5B/Hrbb1dgaSuf8BxgH+BL7RzbAbdu0k+leaOqrmhjbccCn560+dHAsiRL6ab/2WqKQzwPOCLJ/2nr2wK7AVePJuLNl8lifgldEjhwmu3r2vtPBpYn1rdMcjDwXODAqvphkuV0//NNPseyqjp1UwWtee884C+Ag4HBluwbgc9X1a+3hLJ8iroBfqOqnFT0QbIban65BliQ5ECAJFsl2XMD6j8a+G5LFP8DOGCKfS4GXpDkse0cOyWZchZLaUgfBt5QVVdOKn809w14v2Saup8BXpbWzE2y90ginAdMFvNIexbIC4C3Jrkc+BrwCxtwiP9H18K4gu5X3X9McY6rgNcCn237XQQsfJChax6rqjVVdfoUm94GvDnJv9F1qU7ljXTdU1ck+c+2ro3gdB+SpF62LCRJvUwWkqReJgtJUi+ThSSpl8lCktTLZCFtgCS7JPn7JNe1ObIuHbij/cEc15lSNdZMFtKQ2o1d5wJfqKontDmyjgEWz0Eszr6gWWWykIb3bLrJFv9qoqCqbqiqdyfZIsnb2+ymVyT5ffhpi2F5knOSfD3J3w3cTXxoK/si8PyJY043U2qSlyT5hyTnA5+d1U+uec9fJ9Lw9gS+Ms22E4DvV9V+SbYB/i3JxBf63q3uzcC/Ac9MshL4IF0CWg18YuBYEzOl/k6SHYAVST7Xth0IPLWqvrMJP5fUy2QhbaQk7wV+EbgbuAF4apIXtM2PppuR925gRVWtaXW+BiwB7gSur6prW/nHgInng0w3UyrARSYKzQWThTS8VcBvTKxU1cntUbIrgRuBl1XVZwYrtJl6B2fwXc99/99NN9fOlDOlJnkG3dPjpFnnmIU0vH8Btk1y0kDZw9v7Z4CT2gOmSPKzPQ/Z+Tqwe5IntvVjB7Y5U6rGjslCGlJ1s24eBTwryfVJVgDLgNcAfw1cBXylzW76AWZouVfVj+m6nT7VBrhvGNjsTKkaO846K0nqZctCktTLZCFJ6mWykCT1MllIknqZLCRJvUwWkqReJgtJUq//D1AQw8QnaoGCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [4490,4131]\n",
    "labels = ['Female','Male']\n",
    "plt.xticks(range(len(data)), labels)\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Number of Clients')\n",
    "plt.title('Client Gender Distribution')\n",
    "plt.bar(range(len(data)), data) \n",
    "plt.show()\n",
    "plt.savefig('Gender Distribution of Client.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.1: Boxplot Product Category VS Price - purpose: to check price range per product category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to check out price range of each product \n",
    "* Variable: category and price in products data frame\n",
    "* Method used: boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#products.boxplot(by ='categ', column =['price'],showfliers=False, widths=(0.5, 0.5,0.5)) \n",
    "#plt.savefig('Box Plot for Prodct Category VS Price Range.png')\n",
    "# product category 2 has the highest mean price\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"categ\", y=\"price\", data=products)\n",
    "plt.xlabel(\"Product Category\")\n",
    "plt.ylabel(\"Price Range\")\n",
    "plt.title('Prodct Category VS Price Range')\n",
    "plt.show()\n",
    "plt.savefig('Box Plot for Prodct Category VS Price Range.png')\n",
    "# product category 2 has the highest mean price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We could say that product category is the most expensive one\n"
     ]
    }
   ],
   "source": [
    "print(\"We could say that product category is the most expensive one\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.3: Times series Month VS Transaction Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see total sales transaction amount trend over time, per month \n",
    "* Variable: month and session_id in transactions dataframe\n",
    "* Method used: merge transactions and products dataframe to get price, and afer plt time series visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1483</td>\n",
       "      <td>2021-04-10 18:37:28.723910</td>\n",
       "      <td>s_18746</td>\n",
       "      <td>c_4450</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_226</td>\n",
       "      <td>2022-02-03 01:55:53.276402</td>\n",
       "      <td>s_159142</td>\n",
       "      <td>c_277</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_374</td>\n",
       "      <td>2021-09-23 15:13:46.938559</td>\n",
       "      <td>s_94290</td>\n",
       "      <td>c_4270</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_2186</td>\n",
       "      <td>2021-10-17 03:27:18.783634</td>\n",
       "      <td>s_105936</td>\n",
       "      <td>c_4597</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1351</td>\n",
       "      <td>2021-07-17 20:34:25.800563</td>\n",
       "      <td>s_63642</td>\n",
       "      <td>c_1242</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_prod                        date session_id client_id  month  day\n",
       "0  0_1483  2021-04-10 18:37:28.723910    s_18746    c_4450      4   10\n",
       "1   2_226  2022-02-03 01:55:53.276402   s_159142     c_277      2    3\n",
       "2   1_374  2021-09-23 15:13:46.938559    s_94290    c_4270      9   23\n",
       "3  0_2186  2021-10-17 03:27:18.783634   s_105936    c_4597     10   17\n",
       "4  0_1351  2021-07-17 20:34:25.800563    s_63642    c_1242      7   17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## initial overview of transactions dataframe\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1483</td>\n",
       "      <td>2021-04-10 18:37:28.723910</td>\n",
       "      <td>s_18746</td>\n",
       "      <td>c_4450</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_226</td>\n",
       "      <td>2022-02-03 01:55:53.276402</td>\n",
       "      <td>s_159142</td>\n",
       "      <td>c_277</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>65.75</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_374</td>\n",
       "      <td>2021-09-23 15:13:46.938559</td>\n",
       "      <td>s_94290</td>\n",
       "      <td>c_4270</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>10.71</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_2186</td>\n",
       "      <td>2021-10-17 03:27:18.783634</td>\n",
       "      <td>s_105936</td>\n",
       "      <td>c_4597</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1351</td>\n",
       "      <td>2021-07-17 20:34:25.800563</td>\n",
       "      <td>s_63642</td>\n",
       "      <td>c_1242</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>8.99</td>\n",
       "      <td>0.0</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>336811</th>\n",
       "      <td>1_671</td>\n",
       "      <td>2021-05-28 12:35:46.214839</td>\n",
       "      <td>s_40720</td>\n",
       "      <td>c_3454</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "      <td>31.99</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336812</th>\n",
       "      <td>0_759</td>\n",
       "      <td>2021-06-19 00:19:23.917703</td>\n",
       "      <td>s_50568</td>\n",
       "      <td>c_6268</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>22.99</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336813</th>\n",
       "      <td>0_1256</td>\n",
       "      <td>2021-03-16 17:31:59.442007</td>\n",
       "      <td>s_7219</td>\n",
       "      <td>c_4137</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>11.03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336814</th>\n",
       "      <td>2_227</td>\n",
       "      <td>2021-10-30 16:50:15.997750</td>\n",
       "      <td>s_112349</td>\n",
       "      <td>c_5</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>50.99</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336815</th>\n",
       "      <td>0_1417</td>\n",
       "      <td>2021-06-26 14:38:19.732946</td>\n",
       "      <td>s_54117</td>\n",
       "      <td>c_6714</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336816 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod                        date session_id client_id  month  day  \\\n",
       "0       0_1483  2021-04-10 18:37:28.723910    s_18746    c_4450      4   10   \n",
       "1        2_226  2022-02-03 01:55:53.276402   s_159142     c_277      2    3   \n",
       "2        1_374  2021-09-23 15:13:46.938559    s_94290    c_4270      9   23   \n",
       "3       0_2186  2021-10-17 03:27:18.783634   s_105936    c_4597     10   17   \n",
       "4       0_1351  2021-07-17 20:34:25.800563    s_63642    c_1242      7   17   \n",
       "...        ...                         ...        ...       ...    ...  ...   \n",
       "336811   1_671  2021-05-28 12:35:46.214839    s_40720    c_3454      5   28   \n",
       "336812   0_759  2021-06-19 00:19:23.917703    s_50568    c_6268      6   19   \n",
       "336813  0_1256  2021-03-16 17:31:59.442007     s_7219    c_4137      3   16   \n",
       "336814   2_227  2021-10-30 16:50:15.997750   s_112349       c_5     10   30   \n",
       "336815  0_1417  2021-06-26 14:38:19.732946    s_54117    c_6714      6   26   \n",
       "\n",
       "        price  categ  \n",
       "0        4.99    0.0  \n",
       "1       65.75    2.0  \n",
       "2       10.71    1.0  \n",
       "3        4.20    0.0  \n",
       "4        8.99    0.0  \n",
       "...       ...    ...  \n",
       "336811  31.99    1.0  \n",
       "336812  22.99    0.0  \n",
       "336813  11.03    0.0  \n",
       "336814  50.99    2.0  \n",
       "336815  17.99    0.0  \n",
       "\n",
       "[336816 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head()\n",
    "transactions_products=pd.merge(left=transactions, right=products, how='left', on='id_prod')\n",
    "transactions_products\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transactions_products.to_csv('transactions_products.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_transactions=transactions_products.groupby('month')['price'].sum()\n",
    "monthly_transactions = pd.DataFrame(monthly_transactions).reset_index()\n",
    "\n",
    "monthly_transactions.plot(x='month',y='price',figsize=(10,5),linestyle='--', marker='*', markerfacecolor='r',markersize=10)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Amount of Purchase')\n",
    "plt.title('Transactions Over Months')\n",
    "plt.xticks(np.arange(1, 13, 1.0))\n",
    "#plt.yticks(np.arange(1000, 40000, 1000.0))\n",
    "\n",
    "plt.savefig('Transactions Over Months.png')\n",
    "\n",
    "\n",
    "## what is the reasion that we have low amount for mount of October?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>session_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  day  session_id\n",
       "0      1    1        1011\n",
       "1      1    2         951\n",
       "2      1    3         993\n",
       "3      1    4        1011\n",
       "4      1    5         932"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investigation=transactions_products.groupby(['month','day'])['session_id'].count()\n",
    "investigation=investigation.to_frame()\n",
    "investigation=investigation.reset_index()\n",
    "investigation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>session_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  day  session_id\n",
       "181      7    1         876\n",
       "182      7    2         842\n",
       "183      7    3         840\n",
       "184      7    4         822\n",
       "185      7    5         796\n",
       "186      7    6         798\n",
       "187      7    7         837\n",
       "188      7    8         834\n",
       "189      7    9         785\n",
       "190      7   10         766\n",
       "191      7   11         815\n",
       "192      7   12         808\n",
       "193      7   13         806\n",
       "194      7   14         791\n",
       "195      7   15         789\n",
       "196      7   16         852\n",
       "197      7   17         807\n",
       "198      7   18         813\n",
       "199      7   19         772\n",
       "200      7   20         784\n",
       "201      7   21         760\n",
       "202      7   22         762\n",
       "203      7   23         782\n",
       "204      7   24         725\n",
       "205      7   25         747\n",
       "206      7   26         755\n",
       "207      7   27         799\n",
       "208      7   28         778\n",
       "209      7   29         814\n",
       "210      7   30         784\n",
       "211      7   31         777"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investigation.loc[(investigation['month']==7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>session_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  day  session_id\n",
       "273     10    1        1043\n",
       "274     10    2         687\n",
       "275     10    3         677\n",
       "276     10    4         639\n",
       "277     10    5         632\n",
       "278     10    6         726\n",
       "279     10    7         623\n",
       "280     10    8         713\n",
       "281     10    9         675\n",
       "282     10   10         627\n",
       "283     10   11         684\n",
       "284     10   12         661\n",
       "285     10   13         669\n",
       "286     10   14         644\n",
       "287     10   15         670\n",
       "288     10   16         706\n",
       "289     10   17         669\n",
       "290     10   18         645\n",
       "291     10   19         604\n",
       "292     10   20         597\n",
       "293     10   21         653\n",
       "294     10   22         605\n",
       "295     10   23         602\n",
       "296     10   24         628\n",
       "297     10   25         577\n",
       "298     10   26         626\n",
       "299     10   27         579\n",
       "300     10   28         894\n",
       "301     10   29         971\n",
       "302     10   30         920\n",
       "303     10   31         938"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compared to month of September we could see that there are some days ct.\n",
    "investigation.loc[(investigation['month']==10)]\n",
    "\n",
    "\n",
    "## if we compared with month 7, some days in October there are less number of transaction record registed. were there missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Minimum_Date</th>\n",
       "      <th>Maximum_Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-01 00:04:46.203980</td>\n",
       "      <td>2022-01-31 23:59:00.474991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-01 00:02:10.522397</td>\n",
       "      <td>2022-02-28 23:59:58.040472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-01 00:01:07.843138</td>\n",
       "      <td>2021-03-31 23:59:53.214255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01 00:00:04.366652</td>\n",
       "      <td>2021-04-30 23:57:02.590682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-05-01 00:01:49.553055</td>\n",
       "      <td>2021-05-31 23:59:54.413783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-06-01 00:03:16.187805</td>\n",
       "      <td>2021-06-30 23:58:17.847473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-07-01 00:00:03.658679</td>\n",
       "      <td>2021-07-31 23:56:47.244655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-08-01 00:00:00.433085</td>\n",
       "      <td>2021-08-31 23:55:09.566574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-09-01 00:02:22.251475</td>\n",
       "      <td>2021-09-30 23:59:32.752602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-10-01 00:00:23.722317</td>\n",
       "      <td>2021-10-31 23:56:19.757517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-11-01 00:03:51.500183</td>\n",
       "      <td>2021-11-30 23:59:14.350250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-12-01 00:00:07.849056</td>\n",
       "      <td>2021-12-31 23:53:09.887763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Minimum_Date                Maximum_Date\n",
       "month                                                        \n",
       "1      2022-01-01 00:04:46.203980  2022-01-31 23:59:00.474991\n",
       "2      2022-02-01 00:02:10.522397  2022-02-28 23:59:58.040472\n",
       "3      2021-03-01 00:01:07.843138  2021-03-31 23:59:53.214255\n",
       "4      2021-04-01 00:00:04.366652  2021-04-30 23:57:02.590682\n",
       "5      2021-05-01 00:01:49.553055  2021-05-31 23:59:54.413783\n",
       "6      2021-06-01 00:03:16.187805  2021-06-30 23:58:17.847473\n",
       "7      2021-07-01 00:00:03.658679  2021-07-31 23:56:47.244655\n",
       "8      2021-08-01 00:00:00.433085  2021-08-31 23:55:09.566574\n",
       "9      2021-09-01 00:02:22.251475  2021-09-30 23:59:32.752602\n",
       "10     2021-10-01 00:00:23.722317  2021-10-31 23:56:19.757517\n",
       "11     2021-11-01 00:03:51.500183  2021-11-30 23:59:14.350250\n",
       "12     2021-12-01 00:00:07.849056  2021-12-31 23:53:09.887763"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## investigate transactions_products file, to see if there are some data that were not recorded in the month of OCt\n",
    "\n",
    "\n",
    "# using groupby() function on Group column\n",
    "df = transactions_products.groupby(['month'])\n",
    "  \n",
    "# using agg() function on Date column\n",
    "df2 = df.agg(Minimum_Date=('date', np.min), Maximum_Date=('date', np.max))\n",
    "\n",
    "df2\n",
    "\n",
    "## count number of values, in OCT(number of days), \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "1     29316\n",
      "2     29567\n",
      "3     28568\n",
      "4     28415\n",
      "5     28245\n",
      "6     26819\n",
      "7     24716\n",
      "8     25619\n",
      "9     33266\n",
      "10    21584\n",
      "11    28277\n",
      "12    32424\n",
      "Name: session_id, dtype: int64\n",
      "Less number of transaction in the month of 10, probably missing data?\n"
     ]
    }
   ],
   "source": [
    "monthly_transaction_record=transactions_products.groupby('month')['session_id'].count()\n",
    "print(monthly_transaction_record)\n",
    "print(\"Less number of transaction in the month of 10, probably missing data?\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.4: Bivariate Analysis Visualzation: Age VS Average Price of item purchased - Scatter Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see relationship between customer's age and price of item that each age of customer purchased \n",
    "* Variable: age in customers data frame, price in products data frame\n",
    "* Method used: use join function to merge data in each data frame, and alter visualize with scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* in order to have bivrate analysis, merged three individual data frames to have total overview first "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>index</th>\n",
       "      <th>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "      <td>0_1420</td>\n",
       "      <td>2021-03-22 22:31:25.825764</td>\n",
       "      <td>s_10092</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>11.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "      <td>0_1390</td>\n",
       "      <td>2021-09-24 23:35:40.652636</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>19.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "      <td>1_483</td>\n",
       "      <td>2021-03-13 21:35:55.949042</td>\n",
       "      <td>s_5913</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>15.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "      <td>1_267</td>\n",
       "      <td>2021-09-24 23:15:59.919591</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>27.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>2021</td>\n",
       "      <td>54</td>\n",
       "      <td>1_395</td>\n",
       "      <td>2021-09-24 23:57:35.138518</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>28.99</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336708</th>\n",
       "      <td>336708</td>\n",
       "      <td>c_84</td>\n",
       "      <td>f</td>\n",
       "      <td>1982</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>0_1113</td>\n",
       "      <td>2021-04-13 04:10:27.679097</td>\n",
       "      <td>s_19845</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>16.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336709</th>\n",
       "      <td>336709</td>\n",
       "      <td>c_84</td>\n",
       "      <td>f</td>\n",
       "      <td>1982</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>0_1525</td>\n",
       "      <td>2021-10-21 03:58:28.880240</td>\n",
       "      <td>s_107849</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>6.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336710</th>\n",
       "      <td>336710</td>\n",
       "      <td>c_84</td>\n",
       "      <td>f</td>\n",
       "      <td>1982</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>0_1017</td>\n",
       "      <td>2021-09-13 21:40:46.038481</td>\n",
       "      <td>s_89478</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>8.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336711</th>\n",
       "      <td>336711</td>\n",
       "      <td>c_84</td>\n",
       "      <td>f</td>\n",
       "      <td>1982</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>1_158</td>\n",
       "      <td>2021-10-01 16:11:20.841361</td>\n",
       "      <td>s_98493</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>22.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336712</th>\n",
       "      <td>336712</td>\n",
       "      <td>c_84</td>\n",
       "      <td>f</td>\n",
       "      <td>1982</td>\n",
       "      <td>2021</td>\n",
       "      <td>39</td>\n",
       "      <td>1_110</td>\n",
       "      <td>2021-09-13 21:39:46.845376</td>\n",
       "      <td>s_89478</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>54.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336713 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index client_id sex  birth  year  age id_prod  \\\n",
       "0            0    c_4410   f   1967  2021   54  0_1420   \n",
       "1            1    c_4410   f   1967  2021   54  0_1390   \n",
       "2            2    c_4410   f   1967  2021   54   1_483   \n",
       "3            3    c_4410   f   1967  2021   54   1_267   \n",
       "4            4    c_4410   f   1967  2021   54   1_395   \n",
       "...        ...       ...  ..    ...   ...  ...     ...   \n",
       "336708  336708      c_84   f   1982  2021   39  0_1113   \n",
       "336709  336709      c_84   f   1982  2021   39  0_1525   \n",
       "336710  336710      c_84   f   1982  2021   39  0_1017   \n",
       "336711  336711      c_84   f   1982  2021   39   1_158   \n",
       "336712  336712      c_84   f   1982  2021   39   1_110   \n",
       "\n",
       "                              date session_id  month  day  price  categ  \n",
       "0       2021-03-22 22:31:25.825764    s_10092      3   22  11.53      0  \n",
       "1       2021-09-24 23:35:40.652636    s_94984      9   24  19.37      0  \n",
       "2       2021-03-13 21:35:55.949042     s_5913      3   13  15.99      1  \n",
       "3       2021-09-24 23:15:59.919591    s_94984      9   24  27.99      1  \n",
       "4       2021-09-24 23:57:35.138518    s_94984      9   24  28.99      1  \n",
       "...                            ...        ...    ...  ...    ...    ...  \n",
       "336708  2021-04-13 04:10:27.679097    s_19845      4   13  16.99      0  \n",
       "336709  2021-10-21 03:58:28.880240   s_107849     10   21   6.99      0  \n",
       "336710  2021-09-13 21:40:46.038481    s_89478      9   13   8.99      0  \n",
       "336711  2021-10-01 16:11:20.841361    s_98493     10    1  22.17      1  \n",
       "336712  2021-09-13 21:39:46.845376    s_89478      9   13  54.90      1  \n",
       "\n",
       "[336713 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in order to have bivrate analysis, merged three individual data frames to have total overview first \n",
    "\n",
    "transactions_products_info=pd.merge(transactions,products,on='id_prod')\n",
    "merged=pd.merge(customers,transactions_products_info,on='client_id')\n",
    "merged=merged.fillna(0)\n",
    "merged=merged.reset_index()\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'client_id', 'sex', 'birth', 'year', 'age', 'id_prod', 'date',\n",
       "       'session_id', 'month', 'day', 'price', 'categ'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>age</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "      <th>session_id</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11.53</td>\n",
       "      <td>0</td>\n",
       "      <td>s_10092</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>19.37</td>\n",
       "      <td>0</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>15.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_5913</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>27.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>28.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  month  price  categ session_id sex\n",
       "0   54      3  11.53      0    s_10092   f\n",
       "1   54      9  19.37      0    s_94984   f\n",
       "2   54      3  15.99      1     s_5913   f\n",
       "3   54      9  27.99      1    s_94984   f\n",
       "4   54      9  28.99      1    s_94984   f"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = merged[['age','month','price','categ','session_id','sex']]\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>40.199780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>40.541425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>40.445353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>41.772382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>36.844489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>88</td>\n",
       "      <td>16.147362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>89</td>\n",
       "      <td>16.264717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>90</td>\n",
       "      <td>17.058571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>91</td>\n",
       "      <td>17.637826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>92</td>\n",
       "      <td>15.116628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      price\n",
       "0    17  40.199780\n",
       "1    18  40.541425\n",
       "2    19  40.445353\n",
       "3    20  41.772382\n",
       "4    21  36.844489\n",
       "..  ...        ...\n",
       "71   88  16.147362\n",
       "72   89  16.264717\n",
       "73   90  17.058571\n",
       "74   91  17.637826\n",
       "75   92  15.116628\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_vs_purchase=merged.groupby('age')['price'].mean()\n",
    "age_vs_purchase=age_vs_purchase.to_frame()\n",
    "age_vs_purchase=age_vs_purchase.reset_index()\n",
    "age_vs_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>age</th>\n",
       "      <th>price</th>\n",
       "      <th>age group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>40.199780</td>\n",
       "      <td>24 and under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>40.541425</td>\n",
       "      <td>24 and under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>40.445353</td>\n",
       "      <td>24 and under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>41.772382</td>\n",
       "      <td>24 and under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>36.844489</td>\n",
       "      <td>24 and under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>88</td>\n",
       "      <td>16.147362</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>89</td>\n",
       "      <td>16.264717</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>90</td>\n",
       "      <td>17.058571</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>91</td>\n",
       "      <td>17.637826</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>92</td>\n",
       "      <td>15.116628</td>\n",
       "      <td>65+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      price     age group\n",
       "0    17  40.199780  24 and under\n",
       "1    18  40.541425  24 and under\n",
       "2    19  40.445353  24 and under\n",
       "3    20  41.772382  24 and under\n",
       "4    21  36.844489  24 and under\n",
       "..  ...        ...           ...\n",
       "71   88  16.147362           65+\n",
       "72   89  16.264717           65+\n",
       "73   90  17.058571           65+\n",
       "74   91  17.637826           65+\n",
       "75   92  15.116628           65+\n",
       "\n",
       "[76 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make box plot to see if there is difference in averge item price purchase per age group : \n",
    "# age group segmentation: https://www.pickfu.com/demographic-segmentation#:~:text=Age%20segmentation%20means%20focusing%20on,64%2C%20and%2065%20and%20older.\n",
    "\n",
    "\n",
    "# create a list of our conditions\n",
    "conditions = [\n",
    "    (age_vs_purchase['age'] <= 24),\n",
    "    (age_vs_purchase['age'] > 24) & (age_vs_purchase['age'] <= 34),\n",
    "    (age_vs_purchase['age'] > 34) & (age_vs_purchase['age'] <= 44),\n",
    "    (age_vs_purchase['age'] > 44) & (age_vs_purchase['age'] <= 54),\n",
    "    (age_vs_purchase['age'] > 54) & (age_vs_purchase['age'] <= 64),\n",
    "    (age_vs_purchase['age'] > 64)\n",
    "    ]\n",
    "\n",
    "# create a list of the values we want to assign for each condition\n",
    "values = ['24 and under', '25-34', '35-44', '45-54','55-64','65+']\n",
    "\n",
    "# create a new column and use np.select to assign values to it using our lists as arguments\n",
    "age_vs_purchase['age group'] = np.select(conditions, values)\n",
    "\n",
    "# display updated DataFrame\n",
    "age_vs_purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = sns.boxplot(x=\"age group\", y=\"price\", data=age_vs_purchase)\n",
    "plt.xlabel(\"Age Group\")\n",
    "plt.ylabel(\"Price Range\")\n",
    "plt.title('Age Group VS Purchase Item Price Range')\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('Age Group VS Purchase Item Price Range.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age group 24 and under, 25-34 have the highest item price purchased\n"
     ]
    }
   ],
   "source": [
    "print(\"age group 24 and under, 25-34 have the highest item price purchased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.5: Pice chart for Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "products_categ=products.groupby('categ')['id_prod'].count()\n",
    "products_categ= pd.DataFrame(products_categ)\n",
    "products_categ=products_categ.reset_index()\n",
    "products_categ=products_categ.loc[:,:]\n",
    "products_categ=products_categ['id_prod']\n",
    "products_categ=products_categ.to_numpy()\n",
    "my_labels = 'Categ:0','Categ:1','Categ:2'\n",
    "plt.pie(products_categ,labels=my_labels,autopct='%1.1f%%')\n",
    "plt.title('Product Category Distribution')\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "plt.savefig('Product Category Distribution.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task3: Correlations - further visualizations and statistics (for calculation of correlations between variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1:Correlation Gender VS Categories of Products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to correlation between two qualitative variables\n",
    "* Variable: gender and cateogry of products\n",
    "* Method used:xi_n formula and color-coded contigency table, using seaborn and visualize with heatmap and do Chi- Square Test. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>sex</th>\n",
       "      <th>f</th>\n",
       "      <th>m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categ</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103786</td>\n",
       "      <td>105640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55469</td>\n",
       "      <td>54266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8260</td>\n",
       "      <td>9292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         f       m\n",
       "categ                \n",
       "0      103786  105640\n",
       "1       55469   54266\n",
       "2        8260    9292"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##https://aaronschlegel.me/chi-square-test-independence-contingency-tables.html (reference)\n",
    "## Make observation table - step 1 \n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "from scipy import stats\n",
    "from functools import reduce\n",
    "X = \"categ\"\n",
    "Y = \"sex\"\n",
    "observation = merged[['categ','sex']].pivot_table(index=X,columns=Y,aggfunc=len,margins=True,margins_name=\"Total\")\n",
    "observation=observation.reset_index()\n",
    "observation=observation.set_index('categ')\n",
    "\n",
    "observation = observation[['f', 'm']]\n",
    "observation=observation.iloc[0:3]\n",
    "\n",
    "observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.563511  ,  1.54795888],\n",
       "       [14.0480737 , 13.90833855],\n",
       "       [25.5276867 , 25.27376469]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Make contingency table based on observation table and expected frequence table\n",
    "\n",
    "observation = observation.to_numpy()\n",
    "observation\n",
    "c = np.apply_over_axes(np.sum, observation, 0) # column totals of observations table\n",
    "r = np.apply_over_axes(np.sum, observation, 1) # row totals of observations table\n",
    "exp_freq = reduce(np.multiply, (r, c)) / np.sum(observation)\n",
    "cont_table = np.absolute(observation - exp_freq)\n",
    "cont_table= cont_table ** 2 / exp_freq\n",
    "cont_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cont_table=pd.DataFrame(cont_table)\n",
    "cont_table=cont_table.rename(columns={0: 'F', 1: 'M'})\n",
    "xi_n = cont_table.sum().sum()\n",
    "\n",
    "ax = sns.heatmap(cont_table/xi_n, annot=True) #notation: \"annot\" not \"annote\"\n",
    "bottom, top = ax.get_ylim()\n",
    "plt.xlabel(\"Customer Gender\")\n",
    "plt.ylabel(\"Item Category\")\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "ax.set_title(\"Customer Genver vs Item category Heat Map\")\n",
    "plt.savefig('Heat Map Gender and Item Category Relations.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.019098</td>\n",
       "      <td>0.018908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.171591</td>\n",
       "      <td>0.169885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.311810</td>\n",
       "      <td>0.308709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          F         M\n",
       "0  0.019098  0.018908\n",
       "1  0.171591  0.169885\n",
       "2  0.311810  0.308709"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The larger Xi_n is, the less valid the independence assumption is.\n",
    "(cont_table/xi_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.909764</td>\n",
       "      <td>1.890768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.159140</td>\n",
       "      <td>16.988459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.181012</td>\n",
       "      <td>30.870857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           F          M\n",
       "0   1.909764   1.890768\n",
       "1  17.159140  16.988459\n",
       "2  31.181012  30.870857"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#% contribution to non-independence\n",
    "(cont_table/xi_n)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependent (reject H0)\n",
      "81.86933350990711 is calcaultion of xi_in\n",
      "there is a correlation between the gender of the customer and the category of products purchased\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "\n",
    "stat, p, dof, expected = chi2_contingency(observation)\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "if abs(stat) >= critical:\n",
    "   print('Dependent (reject H0)')\n",
    "else:\n",
    "   print('Independent (fail to reject H0)')\n",
    "    \n",
    "print(str(abs(stat))+' is calcaultion of xi_in')\n",
    "print(\"there is a correlation between the gender of the customer and the category of products purchased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81.86933350990711,\n",
       " 1.6683997257127539e-18,\n",
       " 2,\n",
       " array([[104189.61070704, 105236.38929296],\n",
       "        [ 54593.25456695,  55141.74543305],\n",
       "        [  8732.13472601,   8819.86527399]]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(observation)\n",
    "\n",
    "## very low p value means that relation is signification between variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2: Correlation Age VS Total Amount Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see correlation between csutomer's age and total amount of purchase\n",
    "* Variable: age and price, two quantitative variables\n",
    "* Method used: filter and aggregate variable with groupby , R^2 calculation with .corr and make visualization with scatter plot\n",
    "\n",
    "* How to interpret correlation reference : https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/correlation/interpret-the-results/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>age</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "      <th>session_id</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11.53</td>\n",
       "      <td>0</td>\n",
       "      <td>s_10092</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>19.37</td>\n",
       "      <td>0</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>15.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_5913</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>27.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>28.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  month  price  categ session_id sex\n",
       "0   54      3  11.53      0    s_10092   f\n",
       "1   54      9  19.37      0    s_94984   f\n",
       "2   54      3  15.99      1     s_5913   f\n",
       "3   54      9  27.99      1    s_94984   f\n",
       "4   54      9  28.99      1    s_94984   f"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation calculation as follow\n",
      "            age     price\n",
      "age    1.000000 -0.774658\n",
      "price -0.774658  1.000000\n"
     ]
    }
   ],
   "source": [
    "total_amount_purchase=merged.groupby('age').price.sum()\n",
    "total_amount_purchase=total_amount_purchase.to_frame().reset_index()\n",
    "total_amount_purchase.corr(method='pearson')\n",
    "print(\"Correlation calculation as follow\")\n",
    "print(total_amount_purchase.corr(method='pearson'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "import seaborn as sns\n",
    "sns.scatterplot(x=\"age\", y=\"price\", data=total_amount_purchase)\n",
    "ax = sns.scatterplot(x=\"age\", y=\"price\", data=total_amount_purchase)\n",
    "ax.set_title(\"Age vs Total Purchase Amount\")\n",
    "ax.set_xlabel(\"Client Age\")\n",
    "ax.set_ylabel(\"Total Amount of Purchasee\")\n",
    "plt.savefig('Age vs Total Purchase Amount.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3: Correlaton Age VS Purchase Frequency "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see correlation between csutomer's age and purchase frequency\n",
    "* Variable: age and purchase freqnecy represented by session_id, two quantitative variables\n",
    "* Method used: filter and aggregate variable with groupby , R^2 calculation with .corr and make visualization with scatter plot\n",
    "* How to interpret correlation reference : https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/correlation/interpret-the-results/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation calulation as follow\n",
      "                 age  session_id\n",
      "age         1.000000   -0.548306\n",
      "session_id -0.548306    1.000000\n"
     ]
    }
   ],
   "source": [
    "purchase_frequency=merged.groupby('age').session_id.nunique()\n",
    "purchase_frequency=purchase_frequency.to_frame().reset_index()\n",
    "purchase_frequency.corr(method='pearson')\n",
    "\n",
    "print(\"Correlation calulation as follow\")\n",
    "print(purchase_frequency.corr(method='pearson'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>age</th>\n",
       "      <th>session_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>4182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>1276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>1173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>88</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>89</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>91</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>92</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  session_id\n",
       "0    17        4182\n",
       "1    18        1265\n",
       "2    19        1276\n",
       "3    20        1173\n",
       "4    21        1180\n",
       "..  ...         ...\n",
       "71   88         117\n",
       "72   89         103\n",
       "73   90          49\n",
       "74   91          72\n",
       "75   92          64\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "import seaborn as sns\n",
    "sns.scatterplot(x=\"age\", y=\"session_id\", data=purchase_frequency)\n",
    "ax = sns.scatterplot(x=\"age\", y=\"session_id\", data=purchase_frequency)\n",
    "ax.set_title(\"Age vs Purchase Frequencey\")\n",
    "ax.set_xlabel(\"Client Age\")\n",
    "ax.set_ylabel(\"Purchase Frequency\")\n",
    "plt.savefig('Age vs Purchase Frequencey.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Correlation Age VS Avergage basket size(in number of items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see correlation between csutomer's age group and number of item they buy(basket size)\n",
    "* Variable: age group- qualitative variable/ basket size-quantitative variable\n",
    "* Method used: calculate ETA2 and visualize with bar plot\n",
    "* How to interpret ETA Squared: https://www.researchgate.net/figure/nterpretation-of-Eta-Squared-Values_tbl4_330320452"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-46-2fee297d185c>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  merged['age_group'] = np.select(conditions, values)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "      <th>session_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>11.53</td>\n",
       "      <td>0</td>\n",
       "      <td>s_10092</td>\n",
       "      <td>f</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>19.37</td>\n",
       "      <td>0</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>15.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_5913</td>\n",
       "      <td>f</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>27.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>28.99</td>\n",
       "      <td>1</td>\n",
       "      <td>s_94984</td>\n",
       "      <td>f</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  month  price  categ session_id sex age_group\n",
       "0   54      3  11.53      0    s_10092   f     45-54\n",
       "1   54      9  19.37      0    s_94984   f     45-54\n",
       "2   54      3  15.99      1     s_5913   f     45-54\n",
       "3   54      9  27.99      1    s_94984   f     45-54\n",
       "4   54      9  28.99      1    s_94984   f     45-54"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# create a list of our conditions\n",
    "conditions = [\n",
    "    (merged['age'] <= 24),\n",
    "    (merged['age'] > 24) & (merged['age'] <= 34),\n",
    "    (merged['age'] > 34) & (merged['age'] <= 44),\n",
    "    (merged['age'] > 44) & (merged['age'] <= 54),\n",
    "    (merged['age'] > 54) & (merged['age'] <= 64),\n",
    "    (merged['age'] > 64)\n",
    "    ]\n",
    "\n",
    "# create a list of the values we want to assign for each condition\n",
    "values = ['24 and under', '25-34', '35-44', '45-54','55-64','65+']\n",
    "\n",
    "# create a new column and use np.select to assign values to it using our lists as arguments\n",
    "merged['age_group'] = np.select(conditions, values)\n",
    "\n",
    "# display updated DataFrame\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupy age, price, \n",
    "\n",
    "average_basket_size= merged.groupby(['age_group','session_id'])['categ'].count()\n",
    "average_basket_size=average_basket_size.reset_index()\n",
    "average_basket_size=average_basket_size.rename({'categ':'basket_size'}, axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETA squared is 0.08420723244975573\n"
     ]
    }
   ],
   "source": [
    "X = \"age_group\" # qualitative\n",
    "Y = \"basket size\" # quantitative\n",
    "\n",
    "sub_sample = average_basket_size[average_basket_size[\"basket_size\"] > 0] \n",
    "\n",
    "def eta_squared(x,y):\n",
    "    mean_y = y.mean()\n",
    "    categories = []\n",
    "    for category in x.unique():\n",
    "        yi_category = y[x==category]\n",
    "        categories.append({'ni': len(yi_category),\n",
    "                        'mean_category': yi_category.mean()})\n",
    "    TSS = sum([(yj-mean_y)**2 for yj in y])\n",
    "    ESS = sum([c['ni']*(c['mean_category']-mean_y)**2 for c in categories])\n",
    "    return ESS/TSS\n",
    "\n",
    " \n",
    "print(\"ETA squared is \"+str(eta_squared(sub_sample['age_group'],sub_sample['basket_size'])))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = \"age_group\" # qualitative\n",
    "Y = \"basket size\" # quantitative\n",
    "\n",
    "# Only age are retained\n",
    "sub_sample = average_basket_size\n",
    "categories = average_basket_size[X].unique()\n",
    "\n",
    "\n",
    "groups = []\n",
    "for m in categories:\n",
    "    groups.append(sub_sample[sub_sample[\"age_group\"]==m][\"basket_size\"])\n",
    "    \n",
    "# Graph properties (not very important)    \n",
    "medianprops = {'color':\"black\"}\n",
    "meanprops = {'marker':'o', 'markeredgecolor':'black',\n",
    "            'markerfacecolor':'firebrick'}\n",
    "            \n",
    "plt.boxplot(groups, labels=categories, showfliers=False, medianprops=medianprops, \n",
    "            vert=False, patch_artist=True, showmeans=True, meanprops=meanprops)\n",
    "plt.xlabel(\"Basket size\")\n",
    "plt.ylabel(\"Client Age Group\")\n",
    "plt.title('Client Age Group VS Basket Size')\n",
    "plt.show()\n",
    "plt.savefig('Client Age Group VS Basket Size.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x = average_basket_size['age_group']\n",
    "#y = average_basket_size['basket size']\n",
    "#plt.xlabel(\"Age Group\")\n",
    "#plt.ylabel(\"Basket Size\")\n",
    "#plt.bar(x,y) \n",
    "#plt.show()\n",
    "#plt.savefig('Age Group vs Basket Size.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Correlation Age VS Category of purchase produducts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "find correlation between age and category of purchase product: one quantitative and one qualitative variable using eta^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Purpose: to see correlation between customer's age and category of purchased products\n",
    "* Variable: age and categ, one quantitative and one qualitative variable\n",
    "* Method used: filter and aggregate variable with groupby , ETA^2 calculation using formula\n",
    "* How to interpret ETA Squared: https://www.researchgate.net/figure/nterpretation-of-Eta-Squared-Values_tbl4_330320452"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_vs_categ=merged[['age','categ']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETA squared is 0.11886832303683538\n"
     ]
    }
   ],
   "source": [
    "X = \"categ\" # qualitative\n",
    "Y = \"age\" # quantitative\n",
    "\n",
    "sub_sample = age_vs_categ[age_vs_categ[\"age\"] > 0] \n",
    "\n",
    "def eta_squared(x,y):\n",
    "    mean_y = y.mean()\n",
    "    categories = []\n",
    "    for category in x.unique():\n",
    "        yi_category = y[x==category]\n",
    "        categories.append({'ni': len(yi_category),\n",
    "                        'mean_category': yi_category.mean()})\n",
    "    TSS = sum([(yj-mean_y)**2 for yj in y])\n",
    "    ESS = sum([c['ni']*(c['mean_category']-mean_y)**2 for c in categories])\n",
    "    return ESS/TSS\n",
    " \n",
    "print(\"ETA squared is \"+str(eta_squared(sub_sample['categ'],sub_sample['age'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 336713 entries, 0 to 336712\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype\n",
      "---  ------  --------------   -----\n",
      " 0   age     336713 non-null  int64\n",
      " 1   categ   336713 non-null  int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 5.1 MB\n"
     ]
    }
   ],
   "source": [
    "age_vs_categ.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = \"categ\" # qualitative\n",
    "Y = \"age\" # quantitative\n",
    "\n",
    "# Only age are retained\n",
    "sub_sample = age_vs_categ\n",
    "categories = age_vs_categ[X].unique()\n",
    "\n",
    "\n",
    "groups = []\n",
    "for m in categories:\n",
    "    groups.append(sub_sample[sub_sample[\"categ\"]==m][\"age\"])\n",
    "    \n",
    "# Graph properties (not very important)    \n",
    "medianprops = {'color':\"black\"}\n",
    "meanprops = {'marker':'o', 'markeredgecolor':'black',\n",
    "            'markerfacecolor':'firebrick'}\n",
    "            \n",
    "plt.boxplot(groups, labels=categories, showfliers=False, medianprops=medianprops, \n",
    "            vert=False, patch_artist=True, showmeans=True, meanprops=meanprops)\n",
    "plt.xlabel(\"Client Age\")\n",
    "plt.ylabel(\"Product Category Purchased\")\n",
    "plt.title('Age vs Product Category Purchased')\n",
    "plt.show()\n",
    "plt.savefig('Age vs Product Category Purchased.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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Project 5",
   "toc_cell": true,
   "toc_position": {
    "height": "625.426px",
    "left": "43.983px",
    "top": "110.284px",
    "width": "223.97px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}