Data_Quality_Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab4ca9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4ce3350",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only.</th>\n",
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       "      <td>customer_id</td>\n",
       "      <td>first_name</td>\n",
       "      <td>last_name</td>\n",
       "      <td>gender</td>\n",
       "      <td>past_3_years_bike_related_purchases</td>\n",
       "      <td>DOB</td>\n",
       "      <td>job_title</td>\n",
       "      <td>job_industry_category</td>\n",
       "      <td>wealth_segment</td>\n",
       "      <td>deceased_indicator</td>\n",
       "      <td>...</td>\n",
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       "      <td>Laraine</td>\n",
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       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
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       "      <td>N</td>\n",
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       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16 00:00:00</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20 00:00:00</td>\n",
       "      <td>Recruiting Manager</td>\n",
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       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only.   \\\n",
       "0                                        customer_id                                                                                                                              \n",
       "1                                                  1                                                                                                                              \n",
       "2                                                  2                                                                                                                              \n",
       "3                                                  3                                                                                                                              \n",
       "4                                                  4                                                                                                                              \n",
       "\n",
       "   Unnamed: 1 Unnamed: 2 Unnamed: 3                           Unnamed: 4  \\\n",
       "0  first_name  last_name     gender  past_3_years_bike_related_purchases   \n",
       "1     Laraine  Medendorp          F                                   93   \n",
       "2         Eli    Bockman       Male                                   81   \n",
       "3       Arlin     Dearle       Male                                   61   \n",
       "4      Talbot        NaN       Male                                   33   \n",
       "\n",
       "            Unnamed: 5              Unnamed: 6             Unnamed: 7  \\\n",
       "0                  DOB               job_title  job_industry_category   \n",
       "1  1953-10-12 00:00:00     Executive Secretary                 Health   \n",
       "2  1980-12-16 00:00:00  Administrative Officer     Financial Services   \n",
       "3  1954-01-20 00:00:00      Recruiting Manager               Property   \n",
       "4  1961-10-03 00:00:00                     NaN                     IT   \n",
       "\n",
       "       Unnamed: 8          Unnamed: 9  ... Unnamed: 16 Unnamed: 17  \\\n",
       "0  wealth_segment  deceased_indicator  ...         NaN         NaN   \n",
       "1   Mass Customer                   N  ...         NaN         NaN   \n",
       "2   Mass Customer                   N  ...         NaN         NaN   \n",
       "3   Mass Customer                   N  ...         NaN         NaN   \n",
       "4   Mass Customer                   N  ...         NaN         NaN   \n",
       "\n",
       "  Unnamed: 18  Unnamed: 19  Unnamed: 20  Unnamed: 21  Unnamed: 22  \\\n",
       "0         NaN          NaN          NaN          NaN          NaN   \n",
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       "3         NaN          NaN          NaN          NaN          NaN   \n",
       "4         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 23  Unnamed: 24  Unnamed: 25  \n",
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       "2          NaN          NaN          NaN  \n",
       "3          NaN          NaN          NaN  \n",
       "4          NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd=pd.read_excel('KPMG_VI_New_raw_data_update_final.xlsx',sheet_name='CustomerDemographic')\n",
    "df_cd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5366392d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03 00:00:00</td>\n",
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       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13 00:00:00</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "0 customer_id      first_name  last_name  gender  \\\n",
       "0           1         Laraine  Medendorp       F   \n",
       "1           2             Eli    Bockman    Male   \n",
       "2           3           Arlin     Dearle    Male   \n",
       "3           4          Talbot        NaN    Male   \n",
       "4           5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0 past_3_years_bike_related_purchases                  DOB  \\\n",
       "0                                  93  1953-10-12 00:00:00   \n",
       "1                                  81  1980-12-16 00:00:00   \n",
       "2                                  61  1954-01-20 00:00:00   \n",
       "3                                  33  1961-10-03 00:00:00   \n",
       "4                                  56  1977-05-13 00:00:00   \n",
       "\n",
       "0               job_title job_industry_category     wealth_segment  \\\n",
       "0     Executive Secretary                Health      Mass Customer   \n",
       "1  Administrative Officer    Financial Services      Mass Customer   \n",
       "2      Recruiting Manager              Property      Mass Customer   \n",
       "3                     NaN                    IT      Mass Customer   \n",
       "4           Senior Editor                   NaN  Affluent Customer   \n",
       "\n",
       "0 deceased_indicator  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "0                  N  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1                  N  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2                  N  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3                  N  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4                  N  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd.columns=df_cd.iloc[0,:]  #First row is made as dataframe columns\n",
    "df_cd.drop(0,axis=0,inplace=True)   #First row is dropped from the axis\n",
    "df_cd.reset_index(drop=True,inplace=True)   #Now indices are reset original value.\n",
    "df_cd.head()                              #First five rows are read "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cad5d57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 26 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   customer_id                          4000 non-null   object \n",
      " 1   first_name                           4000 non-null   object \n",
      " 2   last_name                            3875 non-null   object \n",
      " 3   gender                               4000 non-null   object \n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   object \n",
      " 5   DOB                                  3913 non-null   object \n",
      " 6   job_title                            3494 non-null   object \n",
      " 7   job_industry_category                3344 non-null   object \n",
      " 8   wealth_segment                       4000 non-null   object \n",
      " 9   deceased_indicator                   4000 non-null   object \n",
      " 10  default                              3698 non-null   object \n",
      " 11  owns_car                             4000 non-null   object \n",
      " 12  tenure                               3913 non-null   object \n",
      " 13  nan                                  0 non-null      float64\n",
      " 14  nan                                  0 non-null      float64\n",
      " 15  nan                                  0 non-null      float64\n",
      " 16  nan                                  0 non-null      float64\n",
      " 17  nan                                  0 non-null      float64\n",
      " 18  nan                                  0 non-null      float64\n",
      " 19  nan                                  0 non-null      float64\n",
      " 20  nan                                  0 non-null      float64\n",
      " 21  nan                                  0 non-null      float64\n",
      " 22  nan                                  0 non-null      float64\n",
      " 23  nan                                  0 non-null      float64\n",
      " 24  nan                                  0 non-null      float64\n",
      " 25  nan                                  0 non-null      float64\n",
      "dtypes: float64(13), object(13)\n",
      "memory usage: 812.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_cd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e85929a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Laraine</td>\n",
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       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12 00:00:00</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16 00:00:00</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20 00:00:00</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13 00:00:00</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 customer_id      first_name  last_name  gender  \\\n",
       "0           1         Laraine  Medendorp       F   \n",
       "1           2             Eli    Bockman    Male   \n",
       "2           3           Arlin     Dearle    Male   \n",
       "3           4          Talbot        NaN    Male   \n",
       "4           5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0 past_3_years_bike_related_purchases                  DOB  \\\n",
       "0                                  93  1953-10-12 00:00:00   \n",
       "1                                  81  1980-12-16 00:00:00   \n",
       "2                                  61  1954-01-20 00:00:00   \n",
       "3                                  33  1961-10-03 00:00:00   \n",
       "4                                  56  1977-05-13 00:00:00   \n",
       "\n",
       "0               job_title job_industry_category     wealth_segment  \\\n",
       "0     Executive Secretary                Health      Mass Customer   \n",
       "1  Administrative Officer    Financial Services      Mass Customer   \n",
       "2      Recruiting Manager              Property      Mass Customer   \n",
       "3                     NaN                    IT      Mass Customer   \n",
       "4           Senior Editor                   NaN  Affluent Customer   \n",
       "\n",
       "0 deceased_indicator                                            default  \\\n",
       "0                  N                                                 \"'   \n",
       "1                  N                       <script>alert('hi')</script>   \n",
       "2                  N                                2018-02-01 00:00:00   \n",
       "3                  N  () { _; } >_[$($())] { touch /tmp/blns.shellsh...   \n",
       "4                  N                                                NIL   \n",
       "\n",
       "0 owns_car tenure  \n",
       "0      Yes     11  \n",
       "1      Yes     16  \n",
       "2      Yes     15  \n",
       "3       No      7  \n",
       "4      Yes      8  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd=df_cd.iloc[:,0:13]\n",
    "df_cd.head()                 #Unnecesary columns trimmed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "deb656d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7391d023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 13)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b20c3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [customer_id, first_name, last_name, gender, past_3_years_bike_related_purchases, DOB, job_title, job_industry_category, wealth_segment, deceased_indicator, default, owns_car, tenure]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd[df_cd.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0879b4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   customer_id                          4000 non-null   object\n",
      " 1   first_name                           4000 non-null   object\n",
      " 2   last_name                            3875 non-null   object\n",
      " 3   gender                               4000 non-null   object\n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   object\n",
      " 5   DOB                                  3913 non-null   object\n",
      " 6   job_title                            3494 non-null   object\n",
      " 7   job_industry_category                3344 non-null   object\n",
      " 8   wealth_segment                       4000 non-null   object\n",
      " 9   deceased_indicator                   4000 non-null   object\n",
      " 10  default                              3698 non-null   object\n",
      " 11  owns_car                             4000 non-null   object\n",
      " 12  tenure                               3913 non-null   object\n",
      "dtypes: object(13)\n",
      "memory usage: 406.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_cd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93e4e689",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Norco Bicycles</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only.   \\\n",
       "0                                     transaction_id                                                                                                                              \n",
       "1                                                  1                                                                                                                              \n",
       "2                                                  2                                                                                                                              \n",
       "3                                                  3                                                                                                                              \n",
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       "3          37          402  2017-10-16 00:00:00         False      Approved   \n",
       "4          88         3135  2017-08-31 00:00:00         False      Approved   \n",
       "\n",
       "       Unnamed: 6    Unnamed: 7     Unnamed: 8    Unnamed: 9  ... Unnamed: 16  \\\n",
       "0           brand  product_line  product_class  product_size  ...         NaN   \n",
       "1           Solex      Standard         medium        medium  ...         NaN   \n",
       "2   Trek Bicycles      Standard         medium         large  ...         NaN   \n",
       "3      OHM Cycles      Standard            low        medium  ...         NaN   \n",
       "4  Norco Bicycles      Standard         medium        medium  ...         NaN   \n",
       "\n",
       "  Unnamed: 17 Unnamed: 18  Unnamed: 19  Unnamed: 20  Unnamed: 21  Unnamed: 22  \\\n",
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       "2         NaN         NaN          NaN          NaN          NaN          NaN   \n",
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       "4         NaN         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 23  Unnamed: 24  Unnamed: 25  \n",
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       "1          NaN          NaN          NaN  \n",
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       "3          NaN          NaN          NaN  \n",
       "4          NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr=pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name='Transactions')\n",
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04c4651d",
   "metadata": {},
   "outputs": [
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       "      <td>2017-10-16 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "2              3         37         402  2017-10-16 00:00:00        False   \n",
       "3              4         88        3135  2017-08-31 00:00:00        False   \n",
       "4              5         78         787  2017-10-01 00:00:00         True   \n",
       "\n",
       "0 order_status           brand product_line product_class product_size  ...  \\\n",
       "0     Approved           Solex     Standard        medium       medium  ...   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large  ...   \n",
       "2     Approved      OHM Cycles     Standard           low       medium  ...   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium  ...   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large  ...   \n",
       "\n",
       "0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "4  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.columns=df_tr.iloc[0,:]  #First row is made as dataframe columns\n",
    "df_tr.drop(0,axis=0,inplace=True)   #First row is dropped from the axis\n",
    "df_tr.reset_index(drop=True,inplace=True)   #Now indices are reset original value.\n",
    "df_tr.head()                              #First five rows are read \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9815bdf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 26 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   transaction_id           20000 non-null  object \n",
      " 1   product_id               20000 non-null  object \n",
      " 2   customer_id              20000 non-null  object \n",
      " 3   transaction_date         20000 non-null  object \n",
      " 4   online_order             19640 non-null  object \n",
      " 5   order_status             20000 non-null  object \n",
      " 6   brand                    19803 non-null  object \n",
      " 7   product_line             19803 non-null  object \n",
      " 8   product_class            19803 non-null  object \n",
      " 9   product_size             19803 non-null  object \n",
      " 10  list_price               20000 non-null  object \n",
      " 11  standard_cost            19803 non-null  object \n",
      " 12  product_first_sold_date  19803 non-null  object \n",
      " 13  nan                      0 non-null      float64\n",
      " 14  nan                      0 non-null      float64\n",
      " 15  nan                      0 non-null      float64\n",
      " 16  nan                      0 non-null      float64\n",
      " 17  nan                      0 non-null      float64\n",
      " 18  nan                      0 non-null      float64\n",
      " 19  nan                      0 non-null      float64\n",
      " 20  nan                      0 non-null      float64\n",
      " 21  nan                      0 non-null      float64\n",
      " 22  nan                      0 non-null      float64\n",
      " 23  nan                      0 non-null      float64\n",
      " 24  nan                      0 non-null      float64\n",
      " 25  nan                      0 non-null      float64\n",
      "dtypes: float64(13), object(13)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c4e562f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "\n",
       "0 order_status          brand product_line product_class product_size  \\\n",
       "0     Approved          Solex     Standard        medium       medium   \n",
       "1     Approved  Trek Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr=df_tr.iloc[:,:13]\n",
    "df_tr.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8e2c7f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   transaction_id           20000 non-null  object\n",
      " 1   product_id               20000 non-null  object\n",
      " 2   customer_id              20000 non-null  object\n",
      " 3   transaction_date         20000 non-null  object\n",
      " 4   online_order             19640 non-null  object\n",
      " 5   order_status             20000 non-null  object\n",
      " 6   brand                    19803 non-null  object\n",
      " 7   product_line             19803 non-null  object\n",
      " 8   product_class            19803 non-null  object\n",
      " 9   product_size             19803 non-null  object\n",
      " 10  list_price               20000 non-null  object\n",
      " 11  standard_cost            19803 non-null  object\n",
      " 12  product_first_sold_date  19803 non-null  object\n",
      "dtypes: object(13)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb0e3503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [transaction_id, product_id, customer_id, transaction_date, online_order, order_status, brand, product_line, product_class, product_size, list_price, standard_cost, product_first_sold_date]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr[df_tr.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "483c798a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3494"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3009ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only.</th>\n",
       "      <th>Unnamed: 1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer_id</td>\n",
       "      <td>address</td>\n",
       "      <td>postcode</td>\n",
       "      <td>state</td>\n",
       "      <td>country</td>\n",
       "      <td>property_valuation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only.   \\\n",
       "0                                        customer_id                                                                                                                              \n",
       "1                                                  1                                                                                                                              \n",
       "2                                                  2                                                                                                                              \n",
       "3                                                  4                                                                                                                              \n",
       "4                                                  5                                                                                                                              \n",
       "\n",
       "            Unnamed: 1 Unnamed: 2       Unnamed: 3 Unnamed: 4  \\\n",
       "0              address   postcode            state    country   \n",
       "1   060 Morning Avenue       2016  New South Wales  Australia   \n",
       "2  6 Meadow Vale Court       2153  New South Wales  Australia   \n",
       "3   0 Holy Cross Court       4211              QLD  Australia   \n",
       "4  17979 Del Mar Point       2448  New South Wales  Australia   \n",
       "\n",
       "           Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  ...  \\\n",
       "0  property_valuation         NaN         NaN         NaN         NaN  ...   \n",
       "1                  10         NaN         NaN         NaN         NaN  ...   \n",
       "2                  10         NaN         NaN         NaN         NaN  ...   \n",
       "3                   9         NaN         NaN         NaN         NaN  ...   \n",
       "4                   4         NaN         NaN         NaN         NaN  ...   \n",
       "\n",
       "   Unnamed: 16  Unnamed: 17  Unnamed: 18  Unnamed: 19  Unnamed: 20  \\\n",
       "0          NaN          NaN          NaN          NaN          NaN   \n",
       "1          NaN          NaN          NaN          NaN          NaN   \n",
       "2          NaN          NaN          NaN          NaN          NaN   \n",
       "3          NaN          NaN          NaN          NaN          NaN   \n",
       "4          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 21  Unnamed: 22  Unnamed: 23  Unnamed: 24  Unnamed: 25  \n",
       "0          NaN          NaN          NaN          NaN          NaN  \n",
       "1          NaN          NaN          NaN          NaN          NaN  \n",
       "2          NaN          NaN          NaN          NaN          NaN  \n",
       "3          NaN          NaN          NaN          NaN          NaN  \n",
       "4          NaN          NaN          NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca=pd.read_excel(\"KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name='CustomerAddress')\n",
    "df_ca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0537d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>...</th>\n",
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       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>NaN</th>\n",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0 customer_id              address postcode            state    country  \\\n",
       "0           1   060 Morning Avenue     2016  New South Wales  Australia   \n",
       "1           2  6 Meadow Vale Court     2153  New South Wales  Australia   \n",
       "2           4   0 Holy Cross Court     4211              QLD  Australia   \n",
       "3           5  17979 Del Mar Point     2448  New South Wales  Australia   \n",
       "4           6     9 Oakridge Court     3216              VIC  Australia   \n",
       "\n",
       "0 property_valuation  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  \\\n",
       "0                 10  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1                 10  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2                  9  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3                  4  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4                  9  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "0  NaN  NaN  NaN  NaN  \n",
       "0  NaN  NaN  NaN  NaN  \n",
       "1  NaN  NaN  NaN  NaN  \n",
       "2  NaN  NaN  NaN  NaN  \n",
       "3  NaN  NaN  NaN  NaN  \n",
       "4  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.columns=df_ca.iloc[0,:]  #First row is made as dataframe columns\n",
    "df_ca.drop(0,axis=0,inplace=True)   #First row is dropped from the axis\n",
    "df_ca.reset_index(drop=True,inplace=True)   #Now indices are reset original value.\n",
    "df_ca.head()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "891e5a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 26 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   customer_id         3999 non-null   object \n",
      " 1   address             3999 non-null   object \n",
      " 2   postcode            3999 non-null   object \n",
      " 3   state               3999 non-null   object \n",
      " 4   country             3999 non-null   object \n",
      " 5   property_valuation  3999 non-null   object \n",
      " 6   nan                 0 non-null      float64\n",
      " 7   nan                 0 non-null      float64\n",
      " 8   nan                 0 non-null      float64\n",
      " 9   nan                 0 non-null      float64\n",
      " 10  nan                 0 non-null      float64\n",
      " 11  nan                 0 non-null      float64\n",
      " 12  nan                 0 non-null      float64\n",
      " 13  nan                 0 non-null      float64\n",
      " 14  nan                 0 non-null      float64\n",
      " 15  nan                 0 non-null      float64\n",
      " 16  nan                 0 non-null      float64\n",
      " 17  nan                 0 non-null      float64\n",
      " 18  nan                 0 non-null      float64\n",
      " 19  nan                 0 non-null      float64\n",
      " 20  nan                 0 non-null      float64\n",
      " 21  nan                 0 non-null      float64\n",
      " 22  nan                 0 non-null      float64\n",
      " 23  nan                 0 non-null      float64\n",
      " 24  nan                 0 non-null      float64\n",
      " 25  nan                 0 non-null      float64\n",
      "dtypes: float64(20), object(6)\n",
      "memory usage: 812.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ca.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d45a80b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 customer_id              address postcode            state    country  \\\n",
       "0           1   060 Morning Avenue     2016  New South Wales  Australia   \n",
       "1           2  6 Meadow Vale Court     2153  New South Wales  Australia   \n",
       "2           4   0 Holy Cross Court     4211              QLD  Australia   \n",
       "3           5  17979 Del Mar Point     2448  New South Wales  Australia   \n",
       "4           6     9 Oakridge Court     3216              VIC  Australia   \n",
       "\n",
       "0 property_valuation  \n",
       "0                 10  \n",
       "1                 10  \n",
       "2                  9  \n",
       "3                  4  \n",
       "4                  9  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca=df_ca.iloc[:,:6]\n",
    "df_ca.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac17a60",
   "metadata": {},
   "source": [
    "## Checking uniqueness of cust_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1fd0b3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 4000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cd.customer_id.nunique(),len(df_cd)  #So there are unique records for data which is equal to number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f27264c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3494, 20000)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.customer_id.nunique(),len(df_tr)  #There might be multiple transactions by same customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b1b296ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.customer_id.isnull().sum()  #There is no null values in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b08da31a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 3999)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.customer_id.nunique(),len(df_ca)   #There are unique records for each customer in the given df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "62b685d8",
   "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>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [customer_id, address, postcode, state, country, property_valuation]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca[df_ca.duplicated()==True]   #No duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b31afc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can merge the three tables to create a new table on cutomer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a4f9d236",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_left=pd.merge(df_cd,df_ca,how='left')  # So that no data from demographic table is lost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b90a3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 18)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48b37466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12 00:00:00</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16 00:00:00</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20 00:00:00</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13 00:00:00</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 customer_id      first_name  last_name  gender  \\\n",
       "0           1         Laraine  Medendorp       F   \n",
       "1           2             Eli    Bockman    Male   \n",
       "2           3           Arlin     Dearle    Male   \n",
       "3           4          Talbot        NaN    Male   \n",
       "4           5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0 past_3_years_bike_related_purchases                  DOB  \\\n",
       "0                                  93  1953-10-12 00:00:00   \n",
       "1                                  81  1980-12-16 00:00:00   \n",
       "2                                  61  1954-01-20 00:00:00   \n",
       "3                                  33  1961-10-03 00:00:00   \n",
       "4                                  56  1977-05-13 00:00:00   \n",
       "\n",
       "0               job_title job_industry_category     wealth_segment  \\\n",
       "0     Executive Secretary                Health      Mass Customer   \n",
       "1  Administrative Officer    Financial Services      Mass Customer   \n",
       "2      Recruiting Manager              Property      Mass Customer   \n",
       "3                     NaN                    IT      Mass Customer   \n",
       "4           Senior Editor                   NaN  Affluent Customer   \n",
       "\n",
       "0 deceased_indicator                                            default  \\\n",
       "0                  N                                                 \"'   \n",
       "1                  N                       <script>alert('hi')</script>   \n",
       "2                  N                                2018-02-01 00:00:00   \n",
       "3                  N  () { _; } >_[$($())] { touch /tmp/blns.shellsh...   \n",
       "4                  N                                                NIL   \n",
       "\n",
       "0 owns_car tenure              address postcode            state    country  \\\n",
       "0      Yes     11   060 Morning Avenue     2016  New South Wales  Australia   \n",
       "1      Yes     16  6 Meadow Vale Court     2153  New South Wales  Australia   \n",
       "2      Yes     15                  NaN      NaN              NaN        NaN   \n",
       "3       No      7   0 Holy Cross Court     4211              QLD  Australia   \n",
       "4      Yes      8  17979 Del Mar Point     2448  New South Wales  Australia   \n",
       "\n",
       "0 property_valuation  \n",
       "0                 10  \n",
       "1                 10  \n",
       "2                NaN  \n",
       "3                  9  \n",
       "4                  4  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5a5a54c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b0790ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3996, 18)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inner=pd.merge(df_cd,df_ca,how='inner') \n",
    "df_inner.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b3d823d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#It seems like there are 4 such customers whose adress are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3079db0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "customer_id           0\n",
       "address               0\n",
       "postcode              0\n",
       "state                 0\n",
       "country               0\n",
       "property_valuation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "300caf9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 6)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a2e0b747",
   "metadata": {},
   "outputs": [],
   "source": [
    "#So there are 3 records of cutomer which are not present in customer demographic.(3999-3996)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd8b0778",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First find the customer address for 4 cutomers in cd table whose adress is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "57086f7e",
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20 00:00:00</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Fiorenze</td>\n",
       "      <td>Birdall</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>1988-10-11 00:00:00</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ð©ð½</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Deeanne</td>\n",
       "      <td>Durtnell</td>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>1962-12-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ï¼ï¼ï¼</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Olav</td>\n",
       "      <td>Polak</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>1995-02-10 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  customer_id first_name last_name  gender  \\\n",
       "2            3      Arlin    Dearle    Male   \n",
       "9           10   Fiorenze   Birdall  Female   \n",
       "21          22    Deeanne  Durtnell  Female   \n",
       "22          23       Olav     Polak    Male   \n",
       "\n",
       "0  past_3_years_bike_related_purchases                  DOB  \\\n",
       "2                                   61  1954-01-20 00:00:00   \n",
       "9                                   49  1988-10-11 00:00:00   \n",
       "21                                  79  1962-12-10 00:00:00   \n",
       "22                                  43  1995-02-10 00:00:00   \n",
       "\n",
       "0                 job_title job_industry_category  wealth_segment  \\\n",
       "2        Recruiting Manager              Property   Mass Customer   \n",
       "9   Senior Quality Engineer    Financial Services   Mass Customer   \n",
       "21                      NaN                    IT   Mass Customer   \n",
       "22                      NaN                   NaN  High Net Worth   \n",
       "\n",
       "0  deceased_indicator              default owns_car tenure address postcode  \\\n",
       "2                   N  2018-02-01 00:00:00      Yes     15     NaN      NaN   \n",
       "9                   N                 ð©ð½      Yes     20     NaN      NaN   \n",
       "21                  N               ï¼ï¼ï¼       No     11     NaN      NaN   \n",
       "22                  N  2018-02-01 00:00:00      Yes      1     NaN      NaN   \n",
       "\n",
       "0  state country property_valuation  \n",
       "2    NaN     NaN                NaN  \n",
       "9    NaN     NaN                NaN  \n",
       "21   NaN     NaN                NaN  \n",
       "22   NaN     NaN                NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left[(df_left.iloc[:,13:].isnull()==True).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07e44fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#These are the customer in cd table whose adress is missing from the table ca."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d3fdf0a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4000, 4001, 4002, 4003]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el=list()\n",
    "for i in df_ca.customer_id:\n",
    "    if i not in (df_cd.customer_id):\n",
    "        el.append(i)\n",
    "        \n",
    "el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "502179b7",
   "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>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>4000</td>\n",
       "      <td>57042 Village Green Point</td>\n",
       "      <td>4511</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>4001</td>\n",
       "      <td>87 Crescent Oaks Alley</td>\n",
       "      <td>2756</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>4002</td>\n",
       "      <td>8194 Lien Street</td>\n",
       "      <td>4032</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>4003</td>\n",
       "      <td>320 Acker Drive</td>\n",
       "      <td>2251</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0    customer_id                    address postcode state    country  \\\n",
       "3995        4000  57042 Village Green Point     4511   QLD  Australia   \n",
       "3996        4001     87 Crescent Oaks Alley     2756   NSW  Australia   \n",
       "3997        4002           8194 Lien Street     4032   QLD  Australia   \n",
       "3998        4003            320 Acker Drive     2251   NSW  Australia   \n",
       "\n",
       "0    property_valuation  \n",
       "3995                  6  \n",
       "3996                 10  \n",
       "3997                  7  \n",
       "3998                  7  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8047eb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Missing value between tables and extra values from tables are segregated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "00b39906",
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12 00:00:00</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16 00:00:00</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 customer_id first_name  last_name gender  \\\n",
       "0           1    Laraine  Medendorp      F   \n",
       "1           2        Eli    Bockman   Male   \n",
       "\n",
       "0 past_3_years_bike_related_purchases                  DOB  \\\n",
       "0                                  93  1953-10-12 00:00:00   \n",
       "1                                  81  1980-12-16 00:00:00   \n",
       "\n",
       "0               job_title job_industry_category wealth_segment  \\\n",
       "0     Executive Secretary                Health  Mass Customer   \n",
       "1  Administrative Officer    Financial Services  Mass Customer   \n",
       "\n",
       "0 deceased_indicator                       default owns_car tenure  \\\n",
       "0                  N                            \"'      Yes     11   \n",
       "1                  N  <script>alert('hi')</script>      Yes     16   \n",
       "\n",
       "0              address postcode            state    country property_valuation  \n",
       "0   060 Morning Avenue     2016  New South Wales  Australia                 10  \n",
       "1  6 Meadow Vale Court     2153  New South Wales  Australia                 10  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cc60dd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_left.customer_id.isnull().sum()  #No null values in cutomer id and its range between 1 to 4000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "384d7511",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_left.customer_id=df_left.customer_id.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6db74067",
   "metadata": {},
   "outputs": [],
   "source": [
    "## First_Name columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bef5038b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_left.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "49bb22a0",
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>938</td>\n",
       "      <td>Corabelle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>1996-04-06 00:00:00</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Î©âÃ§ââ«ËÂµâ¤â¥Ã·</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>633 Cordelia Alley</td>\n",
       "      <td>3037</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>1905</td>\n",
       "      <td>Corabelle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>1953-09-17 00:00:00</td>\n",
       "      <td>Help Desk Technician</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>00ËÆ$-</td>\n",
       "      <td>No</td>\n",
       "      <td>15</td>\n",
       "      <td>83975 Rowland Place</td>\n",
       "      <td>2166</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0     customer_id first_name last_name  gender  \\\n",
       "937           938  Corabelle       NaN  Female   \n",
       "1904         1905  Corabelle       NaN  Female   \n",
       "\n",
       "0    past_3_years_bike_related_purchases                  DOB  \\\n",
       "937                                   18  1996-04-06 00:00:00   \n",
       "1904                                  21  1953-09-17 00:00:00   \n",
       "\n",
       "0                job_title job_industry_category     wealth_segment  \\\n",
       "937       Technical Writer                Retail      Mass Customer   \n",
       "1904  Help Desk Technician         Manufacturing  Affluent Customer   \n",
       "\n",
       "0    deceased_indicator            default owns_car tenure  \\\n",
       "937                   N  Î©âÃ§ââ«ËÂµâ¤â¥Ã·       No      7   \n",
       "1904                  N             00ËÆ$-       No     15   \n",
       "\n",
       "0                 address postcode state    country property_valuation  \n",
       "937    633 Cordelia Alley     3037   VIC  Australia                  7  \n",
       "1904  83975 Rowland Place     2166   NSW  Australia                  6  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.iloc[:,1:4].duplicated(keep=False)==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9daac81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.first_name.isnull().sum()  #It seems like there is no null values in first_name column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2bcc1b41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['!',\n",
       " '\"',\n",
       " '#',\n",
       " '$',\n",
       " '%',\n",
       " '&',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " '*',\n",
       " '+',\n",
       " ',',\n",
       " '.',\n",
       " '/',\n",
       " ':',\n",
       " ';',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '?',\n",
       " '@',\n",
       " '[',\n",
       " '\\\\',\n",
       " ']',\n",
       " '^',\n",
       " '_',\n",
       " '`',\n",
       " '{',\n",
       " '|',\n",
       " '}',\n",
       " '~']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec_char=list()\n",
    "sc='!\"#$%&\\'()*+,./:;<=>?@[\\]^_`{|}~'   #Except the hypen as it is allowed to present in a name\n",
    "for i in sc:\n",
    "    \n",
    "     spec_char.append(i)     \n",
    "spec_char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "07470743",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_w_sc=list()\n",
    "for i in df.index:\n",
    "    \n",
    "    for j in df.iloc[i,1]:\n",
    "        \n",
    "        if j in spec_char:\n",
    "            if i not in name_w_sc:\n",
    "                name_w_sc.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fffa2e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[248, 629, 2346]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_w_sc  #It looks like these are the first name with special characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "25b318f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248       D'arcy\n",
       "629       D'arcy\n",
       "2346    L;urette\n",
       "Name: first_name, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[name_w_sc,1]   #For first_name column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ebe422d",
   "metadata": {},
   "source": [
    "## last_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2cea9b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.last_name.isnull().sum()  #Missing values in last_name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b310bd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_name_ind=df[df.last_name.isnull()==False].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b7a19ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For last_name column\n",
    "name_w_sc=list()\n",
    "for i in last_name_ind:\n",
    "    \n",
    "    for j in df.iloc[i,2]:\n",
    "        \n",
    "        if j in spec_char:\n",
    "            if i not in name_w_sc:\n",
    "                name_w_sc.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "984b3f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[109,\n",
       " 408,\n",
       " 439,\n",
       " 472,\n",
       " 566,\n",
       " 612,\n",
       " 631,\n",
       " 639,\n",
       " 752,\n",
       " 780,\n",
       " 880,\n",
       " 932,\n",
       " 984,\n",
       " 1002,\n",
       " 1023,\n",
       " 1078,\n",
       " 1318,\n",
       " 1439,\n",
       " 1582,\n",
       " 1764,\n",
       " 1765,\n",
       " 1783,\n",
       " 1786,\n",
       " 1840,\n",
       " 1915,\n",
       " 2107,\n",
       " 2193,\n",
       " 2589,\n",
       " 2638,\n",
       " 3048,\n",
       " 3119,\n",
       " 3130,\n",
       " 3207,\n",
       " 3261,\n",
       " 3300,\n",
       " 3438,\n",
       " 3478,\n",
       " 3496,\n",
       " 3981]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_w_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7bdca319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109     St. Quintin\n",
       "408          O'Kane\n",
       "439        O'Duggan\n",
       "472       O'Shirine\n",
       "566         O'Leahy\n",
       "612      O'Hallagan\n",
       "631      O'Hegertie\n",
       "639        O'Keenan\n",
       "752     St. Quentin\n",
       "780       O'Leagham\n",
       "880         O' Lone\n",
       "932        L'oiseau\n",
       "984      O'Halloran\n",
       "1002       O'Geaney\n",
       "1023        D'Onise\n",
       "1078     L' Anglois\n",
       "1318       St. Paul\n",
       "1439        O'Logan\n",
       "1582      O' Reagan\n",
       "1764        O' Mara\n",
       "1765      O'Moylane\n",
       "1783      O' Timony\n",
       "1786    Godehard.sf\n",
       "1840      D'Ambrogi\n",
       "1915      O'Crowley\n",
       "2107     O'Halligan\n",
       "2193       O'Fergus\n",
       "2589    Tewkesbury.\n",
       "2638        O'Kielt\n",
       "3048      O'Fallone\n",
       "3119      O'Donnell\n",
       "3130      O' Markey\n",
       "3207        O'Meara\n",
       "3261     L'Episcopi\n",
       "3300     O'Halloran\n",
       "3438       O'Gorman\n",
       "3478       O' Ronan\n",
       "3496          O'Day\n",
       "3981       O'Kinedy\n",
       "Name: last_name, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[name_w_sc,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f7435cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4aba85ff",
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12 00:00:00</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16 00:00:00</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20 00:00:00</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13 00:00:00</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0 past_3_years_bike_related_purchases                  DOB  \\\n",
       "0                                  93  1953-10-12 00:00:00   \n",
       "1                                  81  1980-12-16 00:00:00   \n",
       "2                                  61  1954-01-20 00:00:00   \n",
       "3                                  33  1961-10-03 00:00:00   \n",
       "4                                  56  1977-05-13 00:00:00   \n",
       "\n",
       "0               job_title job_industry_category     wealth_segment  \\\n",
       "0     Executive Secretary                Health      Mass Customer   \n",
       "1  Administrative Officer    Financial Services      Mass Customer   \n",
       "2      Recruiting Manager              Property      Mass Customer   \n",
       "3                     NaN                    IT      Mass Customer   \n",
       "4           Senior Editor                   NaN  Affluent Customer   \n",
       "\n",
       "0 deceased_indicator                                            default  \\\n",
       "0                  N                                                 \"'   \n",
       "1                  N                       <script>alert('hi')</script>   \n",
       "2                  N                                2018-02-01 00:00:00   \n",
       "3                  N  () { _; } >_[$($())] { touch /tmp/blns.shellsh...   \n",
       "4                  N                                                NIL   \n",
       "\n",
       "0 owns_car tenure              address postcode            state    country  \\\n",
       "0      Yes     11   060 Morning Avenue     2016  New South Wales  Australia   \n",
       "1      Yes     16  6 Meadow Vale Court     2153  New South Wales  Australia   \n",
       "2      Yes     15                  NaN      NaN              NaN        NaN   \n",
       "3       No      7   0 Holy Cross Court     4211              QLD  Australia   \n",
       "4      Yes      8  17979 Del Mar Point     2448  New South Wales  Australia   \n",
       "\n",
       "0 property_valuation  \n",
       "0                 10  \n",
       "1                 10  \n",
       "2                NaN  \n",
       "3                  9  \n",
       "4                  4  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f68d87",
   "metadata": {},
   "source": [
    "## Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "836a5de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "25d5e98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2037\n",
       "Male      1872\n",
       "U           88\n",
       "Femal        1\n",
       "M            1\n",
       "F            1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7017d1",
   "metadata": {},
   "source": [
    "## past_3_years_bike_related_purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4b9a44fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.past_3_years_bike_related_purchases.isnull().sum()  # No null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "65a66a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.past_3_years_bike_related_purchases=df.past_3_years_bike_related_purchases.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2a208d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='past_3_years_bike_related_purchases'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.past_3_years_bike_related_purchases)  #No extreme values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "53808594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wealth_segment', ylabel='past_3_years_bike_related_purchases'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df.wealth_segment,y=df.past_3_years_bike_related_purchases) #Distribution seems fine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3fe6e93c",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.1</td>\n",
       "      <td>36145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.3</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "2              3         37         402  2017-10-16 00:00:00        False   \n",
       "3              4         88        3135  2017-08-31 00:00:00        False   \n",
       "4              5         78         787  2017-10-01 00:00:00         True   \n",
       "\n",
       "0 order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  \n",
       "2    1793.43        248.82                   36361  \n",
       "3    1198.46         381.1                   36145  \n",
       "4     1765.3        709.48                   42226  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "16e774a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    93\n",
       "Name: past_3_years_bike_related_purchases, dtype: int32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.customer_id==1]['past_3_years_bike_related_purchases'] #this show 93 purchases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "11ff9401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 13)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr[df_tr.customer_id==1].shape   #But there are 11 transcations for this customer\n",
    "#Also there is no quantity mentioned in trasnsaction which is dragging us to dead end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0322f398",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "\n",
       "0 order_status          brand product_line product_class product_size  \\\n",
       "0     Approved          Solex     Standard        medium       medium   \n",
       "1     Approved  Trek Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6dc05519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7163"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr[df_tr.customer_id==1]['product_first_sold_date'].max()-df_tr[df_tr.customer_id==1]['product_first_sold_date'].min()\n",
    "#Also if 'product_first_sold_date' is description of number of days passed after certain date(like date of company first \n",
    "#purchase) , then difference between the minimum and maximum date is more than 3 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d03f4d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 13)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.customer_id==2950]['past_3_years_bike_related_purchases'] #this show 93 purchases.\n",
    "\n",
    "df_tr[df_tr.customer_id==2950].shape   #But there is no record of such transaction in transaction columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26535ea5",
   "metadata": {},
   "source": [
    "## DOB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a267c095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.DOB.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e0b97ec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5494: 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",
      "  self[name] = value\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
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       "      <th>owns_car</th>\n",
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       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0  past_3_years_bike_related_purchases        DOB               job_title  \\\n",
       "0                                   93 1953-10-12     Executive Secretary   \n",
       "1                                   81 1980-12-16  Administrative Officer   \n",
       "2                                   61 1954-01-20      Recruiting Manager   \n",
       "3                                   33 1961-10-03                     NaN   \n",
       "4                                   56 1977-05-13           Senior Editor   \n",
       "\n",
       "0 job_industry_category     wealth_segment deceased_indicator  \\\n",
       "0                Health      Mass Customer                  N   \n",
       "1    Financial Services      Mass Customer                  N   \n",
       "2              Property      Mass Customer                  N   \n",
       "3                    IT      Mass Customer                  N   \n",
       "4                   NaN  Affluent Customer                  N   \n",
       "\n",
       "0                                            default owns_car tenure  \\\n",
       "0                                                 \"'      Yes     11   \n",
       "1                       <script>alert('hi')</script>      Yes     16   \n",
       "2                                2018-02-01 00:00:00      Yes     15   \n",
       "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No      7   \n",
       "4                                                NIL      Yes      8   \n",
       "\n",
       "0              address postcode            state    country property_valuation  \n",
       "0   060 Morning Avenue     2016  New South Wales  Australia                 10  \n",
       "1  6 Meadow Vale Court     2153  New South Wales  Australia                 10  \n",
       "2                  NaN      NaN              NaN        NaN                NaN  \n",
       "3   0 Holy Cross Court     4211              QLD  Australia                  9  \n",
       "4  17979 Del Mar Point     2448  New South Wales  Australia                  4  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dob=df[df.DOB.isnull()==False]\n",
    "df_dob.DOB=pd.to_datetime(df_dob.DOB)\n",
    "\n",
    "df_dob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "236f24e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "  <thead>\n",
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       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Jephthah</td>\n",
       "      <td>Bachmann</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>1843-12-21</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>833 Luster Way</td>\n",
       "      <td>4005</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0   customer_id first_name last_name gender  \\\n",
       "33           34   Jephthah  Bachmann      U   \n",
       "\n",
       "0   past_3_years_bike_related_purchases        DOB        job_title  \\\n",
       "33                                   59 1843-12-21  Legal Assistant   \n",
       "\n",
       "0  job_industry_category     wealth_segment deceased_indicator default  \\\n",
       "33                    IT  Affluent Customer                  N     NaN   \n",
       "\n",
       "0  owns_car tenure         address postcode state    country  \\\n",
       "33       No     20  833 Luster Way     4005   QLD  Australia   \n",
       "\n",
       "0  property_valuation  \n",
       "33                  8  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dob[df_dob.DOB<'1930']  #This DOB seems impractical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6904bfaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2020-1843  #177 age for human seems farfetched."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ec1b32ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>Anselm</td>\n",
       "      <td>Gawne</td>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>ï½ï½¨(Â´âï½â©</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>163 Straubel Trail</td>\n",
       "      <td>2217</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>422</td>\n",
       "      <td>Vito</td>\n",
       "      <td>Norker</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>2002-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ðµ ð ð ð</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>509 Fisk Hill</td>\n",
       "      <td>2031</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>1888</td>\n",
       "      <td>Sibyl</td>\n",
       "      <td>Scholtz</td>\n",
       "      <td>Female</td>\n",
       "      <td>67</td>\n",
       "      <td>2002-01-26</td>\n",
       "      <td>Food Chemist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>9 Emmet Hill</td>\n",
       "      <td>2042</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2857</th>\n",
       "      <td>2858</td>\n",
       "      <td>Benedicto</td>\n",
       "      <td>Radki</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>2002-01-09</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>testâ testâ«</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>0555 Tomscot Plaza</td>\n",
       "      <td>2044</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>3435</td>\n",
       "      <td>Stevena</td>\n",
       "      <td>Allcock</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>2002-01-15</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ÅâÂ´â°ËÃÂ¨ËÃâââ</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>400 Petterle Crossing</td>\n",
       "      <td>4301</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0     customer_id first_name last_name  gender  \\\n",
       "65             66     Anselm     Gawne    Male   \n",
       "421           422       Vito    Norker    Male   \n",
       "1887         1888      Sibyl   Scholtz  Female   \n",
       "2857         2858  Benedicto     Radki    Male   \n",
       "3434         3435    Stevena   Allcock  Female   \n",
       "\n",
       "0     past_3_years_bike_related_purchases        DOB           job_title  \\\n",
       "65                                     46 2002-03-11   Account Executive   \n",
       "421                                    78 2002-01-06                 NaN   \n",
       "1887                                   67 2002-01-26        Food Chemist   \n",
       "2857                                    4 2002-01-09  Recruiting Manager   \n",
       "3434                                   80 2002-01-15       Senior Editor   \n",
       "\n",
       "0    job_industry_category     wealth_segment deceased_indicator  \\\n",
       "65             Argiculture     High Net Worth                  N   \n",
       "421          Manufacturing  Affluent Customer                  N   \n",
       "1887                Health      Mass Customer                  N   \n",
       "2857                   NaN      Mass Customer                  N   \n",
       "3434                   NaN  Affluent Customer                  N   \n",
       "\n",
       "0             default owns_car tenure                address postcode  \\\n",
       "65      ï½ï½¨(Â´âï½â©       No      1     163 Straubel Trail     2217   \n",
       "421          ðµ ð ð ð       No      1          509 Fisk Hill     2031   \n",
       "1887              NaN      Yes      1           9 Emmet Hill     2042   \n",
       "2857     testâ testâ«      Yes      1     0555 Tomscot Plaza     2044   \n",
       "3434  ÅâÂ´â°ËÃÂ¨ËÃâââ       No      1  400 Petterle Crossing     4301   \n",
       "\n",
       "0               state    country property_valuation  \n",
       "65    New South Wales  Australia                 10  \n",
       "421               NSW  Australia                 11  \n",
       "1887              NSW  Australia                 10  \n",
       "2857              NSW  Australia                 10  \n",
       "3434              QLD  Australia                  3  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dob[df_dob.DOB>'2002']  #Some job_title seems far-fetched."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b38c27",
   "metadata": {},
   "source": [
    "## Job_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3a0c61dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_title.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8ce0ed29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Developer III                    7\n",
       "Budget/Accounting Analyst II     7\n",
       "Media Manager II                 7\n",
       "Programmer IV                    6\n",
       "Office Assistant IV              6\n",
       "Web Developer II                 6\n",
       "Account Representative III       6\n",
       "Administrative Assistant I       6\n",
       "Budget/Accounting Analyst III    6\n",
       "Web Designer III                 6\n",
       "Software Engineer I              6\n",
       "Web Developer IV                 6\n",
       "Web Developer I                  6\n",
       "Programmer Analyst I             6\n",
       "Software Engineer IV             6\n",
       "Programmer I                     6\n",
       "Human Resources Assistant II     6\n",
       "Safety Technician IV             6\n",
       "Media Manager I                  6\n",
       "Statistician III                 6\n",
       "Developer IV                     5\n",
       "Automation Specialist III        5\n",
       "Research Assistant I             5\n",
       "Biostatistician III              5\n",
       "Research Assistant IV            5\n",
       "Staff Accountant IV              5\n",
       "Systems Administrator IV         4\n",
       "Database Administrator II        4\n",
       "Programmer Analyst IV            4\n",
       "Computer Systems Analyst III     4\n",
       "Human Resources Assistant IV     4\n",
       "Database Administrator I         4\n",
       "Geologist II                     4\n",
       "Staff Accountant I               4\n",
       "Administrative Assistant II      4\n",
       "Account Representative II        4\n",
       "Research Assistant III           3\n",
       "Health Coach III                 3\n",
       "Health Coach I                   3\n",
       "Developer I                      1\n",
       "Name: job_title, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_title.value_counts().tail(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e978fb31",
   "metadata": {},
   "outputs": [],
   "source": [
    "## job_industry_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4247bae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "656"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_industry_category.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "00ec48d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manufacturing         799\n",
       "Financial Services    774\n",
       "Health                602\n",
       "Retail                358\n",
       "Property              267\n",
       "IT                    223\n",
       "Entertainment         136\n",
       "Argiculture           113\n",
       "Telecommunications     72\n",
       "Name: job_industry_category, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.job_industry_category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e59441",
   "metadata": {},
   "source": [
    "## wealth_segment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2c8c7e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.wealth_segment.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0049ff62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mass Customer        2000\n",
       "High Net Worth       1021\n",
       "Affluent Customer     979\n",
       "Name: wealth_segment, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.wealth_segment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8362f9",
   "metadata": {},
   "source": [
    "## deceased_indicator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5343b2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.deceased_indicator.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "faa71f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    3998\n",
       "Y       2\n",
       "Name: deceased_indicator, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.deceased_indicator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1d17b108",
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>753</td>\n",
       "      <td>Josy</td>\n",
       "      <td>St. Quentin</td>\n",
       "      <td>Female</td>\n",
       "      <td>82</td>\n",
       "      <td>1970-03-07 00:00:00</td>\n",
       "      <td>Food Chemist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>Y</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6</td>\n",
       "      <td>37 Dorton Street</td>\n",
       "      <td>2594</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3789</th>\n",
       "      <td>3790</td>\n",
       "      <td>Kurtis</td>\n",
       "      <td>Morson</td>\n",
       "      <td>Male</td>\n",
       "      <td>91</td>\n",
       "      <td>1959-05-31 00:00:00</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>Y</td>\n",
       "      <td>ÌÌºÍÌ¹Ì¯ÍTÌ±Ì¤ÍÌ¥ÍÍhÍÌ²eÍÍÌ¼ÌÌÌ¼Ì£Í ÍÌÌ±Ì ÍÍÍN...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9</td>\n",
       "      <td>8681 Dennis Pass</td>\n",
       "      <td>3215</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0     customer_id first_name    last_name  gender  \\\n",
       "752           753       Josy  St. Quentin  Female   \n",
       "3789         3790     Kurtis       Morson    Male   \n",
       "\n",
       "0     past_3_years_bike_related_purchases                  DOB      job_title  \\\n",
       "752                                    82  1970-03-07 00:00:00   Food Chemist   \n",
       "3789                                   91  1959-05-31 00:00:00  Senior Editor   \n",
       "\n",
       "0    job_industry_category     wealth_segment deceased_indicator  \\\n",
       "752                 Health  Affluent Customer                  Y   \n",
       "3789                Retail      Mass Customer                  Y   \n",
       "\n",
       "0                                               default owns_car tenure  \\\n",
       "752                                                -0.5      Yes      6   \n",
       "3789  ÌÌºÍÌ¹Ì¯ÍTÌ±Ì¤ÍÌ¥ÍÍhÍÌ²eÍÍÌ¼ÌÌÌ¼Ì£Í ÍÌÌ±Ì ÍÍÍN...      Yes      9   \n",
       "\n",
       "0              address postcode state    country property_valuation  \n",
       "752   37 Dorton Street     2594   NSW  Australia                  3  \n",
       "3789  8681 Dennis Pass     3215   VIC  Australia                  3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.deceased_indicator=='Y']  #After dropping these two rows for future analysis this become a 0 variance column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0985dbdf",
   "metadata": {},
   "source": [
    "## default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3292af44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.default.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e1470188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100                                       113\n",
       "1                                         112\n",
       "-1                                        111\n",
       "-100                                       99\n",
       "Ù¡Ù¢Ù£                                     53\n",
       "                                         ... \n",
       "<img src=x onerror=alert('hi') />          31\n",
       "/dev/null; touch /tmp/blns.fail ; echo     30\n",
       "âªâªtestâª                                 29\n",
       "ì¸ëë°í ë¥´                                 27\n",
       ",ãã»:*:ã»ãâ( â» Ï â» )ãã»:*:ã»ãâ           25\n",
       "Name: default, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "14b0d7ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4000 entries, 0 to 3999\n",
      "Data columns (total 18 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   customer_id                          4000 non-null   int32 \n",
      " 1   first_name                           4000 non-null   object\n",
      " 2   last_name                            3875 non-null   object\n",
      " 3   gender                               4000 non-null   object\n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   int32 \n",
      " 5   DOB                                  3913 non-null   object\n",
      " 6   job_title                            3494 non-null   object\n",
      " 7   job_industry_category                3344 non-null   object\n",
      " 8   wealth_segment                       4000 non-null   object\n",
      " 9   deceased_indicator                   4000 non-null   object\n",
      " 10  default                              3698 non-null   object\n",
      " 11  owns_car                             4000 non-null   object\n",
      " 12  tenure                               3913 non-null   object\n",
      " 13  address                              3996 non-null   object\n",
      " 14  postcode                             3996 non-null   object\n",
      " 15  state                                3996 non-null   object\n",
      " 16  country                              3996 non-null   object\n",
      " 17  property_valuation                   3996 non-null   object\n",
      "dtypes: int32(2), object(16)\n",
      "memory usage: 691.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e891f9",
   "metadata": {},
   "source": [
    "## owns_car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "04ee5736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.owns_car.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ec893951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    2024\n",
       "No     1976\n",
       "Name: owns_car, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.owns_car.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c123a8d",
   "metadata": {},
   "source": [
    "## tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "35374755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dde1a6e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     235\n",
       "5     228\n",
       "11    221\n",
       "10    218\n",
       "16    215\n",
       "8     211\n",
       "18    208\n",
       "12    202\n",
       "9     200\n",
       "14    200\n",
       "6     192\n",
       "4     191\n",
       "13    191\n",
       "17    182\n",
       "15    179\n",
       "1     166\n",
       "3     160\n",
       "19    159\n",
       "2     150\n",
       "20     96\n",
       "22     55\n",
       "21     54\n",
       "Name: tenure, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "656e1a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3913 entries, 0 to 3999\n",
      "Data columns (total 18 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          3913 non-null   int32         \n",
      " 1   first_name                           3913 non-null   object        \n",
      " 2   last_name                            3792 non-null   object        \n",
      " 3   gender                               3913 non-null   object        \n",
      " 4   past_3_years_bike_related_purchases  3913 non-null   int32         \n",
      " 5   DOB                                  3913 non-null   datetime64[ns]\n",
      " 6   job_title                            3416 non-null   object        \n",
      " 7   job_industry_category                3257 non-null   object        \n",
      " 8   wealth_segment                       3913 non-null   object        \n",
      " 9   deceased_indicator                   3913 non-null   object        \n",
      " 10  default                              3698 non-null   object        \n",
      " 11  owns_car                             3913 non-null   object        \n",
      " 12  tenure                               3913 non-null   int32         \n",
      " 13  address                              3909 non-null   object        \n",
      " 14  postcode                             3909 non-null   object        \n",
      " 15  state                                3909 non-null   object        \n",
      " 16  country                              3909 non-null   object        \n",
      " 17  property_valuation                   3909 non-null   object        \n",
      "dtypes: datetime64[ns](1), int32(3), object(14)\n",
      "memory usage: 535.0+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5494: 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",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "df_dob.tenure=df_dob.tenure.astype('int')\n",
    "df_dob.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "33acecee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-94-f896aac21684>:1: 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",
      "  df_dob['yearof']=df_dob.DOB.apply(lambda x:x.year)\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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>yearof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>1953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>1977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0  customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "0  past_3_years_bike_related_purchases        DOB               job_title  \\\n",
       "0                                   93 1953-10-12     Executive Secretary   \n",
       "1                                   81 1980-12-16  Administrative Officer   \n",
       "2                                   61 1954-01-20      Recruiting Manager   \n",
       "3                                   33 1961-10-03                     NaN   \n",
       "4                                   56 1977-05-13           Senior Editor   \n",
       "\n",
       "0 job_industry_category     wealth_segment deceased_indicator  \\\n",
       "0                Health      Mass Customer                  N   \n",
       "1    Financial Services      Mass Customer                  N   \n",
       "2              Property      Mass Customer                  N   \n",
       "3                    IT      Mass Customer                  N   \n",
       "4                   NaN  Affluent Customer                  N   \n",
       "\n",
       "0                                            default owns_car  tenure  \\\n",
       "0                                                 \"'      Yes      11   \n",
       "1                       <script>alert('hi')</script>      Yes      16   \n",
       "2                                2018-02-01 00:00:00      Yes      15   \n",
       "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No       7   \n",
       "4                                                NIL      Yes       8   \n",
       "\n",
       "0              address postcode            state    country  \\\n",
       "0   060 Morning Avenue     2016  New South Wales  Australia   \n",
       "1  6 Meadow Vale Court     2153  New South Wales  Australia   \n",
       "2                  NaN      NaN              NaN        NaN   \n",
       "3   0 Holy Cross Court     4211              QLD  Australia   \n",
       "4  17979 Del Mar Point     2448  New South Wales  Australia   \n",
       "\n",
       "0 property_valuation  yearof  \n",
       "0                 10    1953  \n",
       "1                 10    1980  \n",
       "2                NaN    1954  \n",
       "3                  9    1961  \n",
       "4                  4    1977  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dob['yearof']=df_dob.DOB.apply(lambda x:x.year)\n",
    "df_dob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "88bb1333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='yearof'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,figsize=(15,8))\n",
    "df_dob.groupby('yearof')['tenure'].mean().sort_values().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fa16e079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4000 entries, 0 to 3999\n",
      "Data columns (total 18 columns):\n",
      " #   Column                               Non-Null Count  Dtype \n",
      "---  ------                               --------------  ----- \n",
      " 0   customer_id                          4000 non-null   int32 \n",
      " 1   first_name                           4000 non-null   object\n",
      " 2   last_name                            3875 non-null   object\n",
      " 3   gender                               4000 non-null   object\n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   int32 \n",
      " 5   DOB                                  3913 non-null   object\n",
      " 6   job_title                            3494 non-null   object\n",
      " 7   job_industry_category                3344 non-null   object\n",
      " 8   wealth_segment                       4000 non-null   object\n",
      " 9   deceased_indicator                   4000 non-null   object\n",
      " 10  default                              3698 non-null   object\n",
      " 11  owns_car                             4000 non-null   object\n",
      " 12  tenure                               3913 non-null   object\n",
      " 13  address                              3996 non-null   object\n",
      " 14  postcode                             3996 non-null   object\n",
      " 15  state                                3996 non-null   object\n",
      " 16  country                              3996 non-null   object\n",
      " 17  property_valuation                   3996 non-null   object\n",
      "dtypes: int32(2), object(16)\n",
      "memory usage: 691.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52480eea",
   "metadata": {},
   "source": [
    "## Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c043734d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.address.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "935022b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              060 Morning Avenue\n",
       "1             6 Meadow Vale Court\n",
       "2                             NaN\n",
       "3              0 Holy Cross Court\n",
       "4             17979 Del Mar Point\n",
       "                  ...            \n",
       "3995           0 Transport Center\n",
       "3996          4 Dovetail Crossing\n",
       "3997         736 Roxbury Junction\n",
       "3998              1482 Hauk Trail\n",
       "3999    57042 Village Green Point\n",
       "Name: address, Length: 4000, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2c892b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    1,    3,    4,    5,    6,    7,    8,   10,   11,\n",
       "            ...\n",
       "            3990, 3991, 3992, 3993, 3994, 3995, 3996, 3997, 3998, 3999],\n",
       "           dtype='int64', length=3996)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_ind=df[df.address.isnull()==False].index\n",
    "add_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8f924fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_w_sc=list()\n",
    "for i in add_ind:\n",
    "    \n",
    "    for j in df.iloc[i,13]:\n",
    "        \n",
    "        if j in spec_char:\n",
    "            if i not in name_w_sc:\n",
    "                name_w_sc.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "66870700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_w_sc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e81d17",
   "metadata": {},
   "source": [
    "## postcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c63081b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.postcode.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5e64d3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc=list()\n",
    "for i in df.index:\n",
    "    if ((df.iloc[i,14]/1000)>=10) or ((df.iloc[i,14]/1000)<1):\n",
    "        \n",
    "        nc.append(i)\n",
    "nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c6fbe29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the postcodes are 4 digit code only"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bccd1092",
   "metadata": {},
   "source": [
    "## State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b716269c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fed0a7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW                2052\n",
       "VIC                 939\n",
       "QLD                 837\n",
       "New South Wales      86\n",
       "Victoria             82\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe901e6",
   "metadata": {},
   "source": [
    "## Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cc1825d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.country.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a8236e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Australia    3996\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.country.value_counts()  #No variance column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0cdf775",
   "metadata": {},
   "source": [
    "## property_valuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "590f291e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.property_valuation.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "10b6f861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     647\n",
       "8     646\n",
       "10    576\n",
       "7     491\n",
       "11    281\n",
       "6     238\n",
       "5     225\n",
       "4     214\n",
       "12    195\n",
       "3     186\n",
       "1     154\n",
       "2     143\n",
       "Name: property_valuation, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.property_valuation.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc6d66e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "913f482b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b6c1f221",
   "metadata": {},
   "outputs": [],
   "source": [
    "##End of df_left table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68efd1d3",
   "metadata": {},
   "source": [
    "# df_tr table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fc163a75",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.1</td>\n",
       "      <td>36145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.3</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "2              3         37         402  2017-10-16 00:00:00        False   \n",
       "3              4         88        3135  2017-08-31 00:00:00        False   \n",
       "4              5         78         787  2017-10-01 00:00:00         True   \n",
       "\n",
       "0 order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  \n",
       "2    1793.43        248.82                   36361  \n",
       "3    1198.46         381.1                   36145  \n",
       "4     1765.3        709.48                   42226  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104628ae",
   "metadata": {},
   "source": [
    "## transaction_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dbd1248c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_tr.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "48463658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "transaction_id               0\n",
       "product_id                   0\n",
       "customer_id                  0\n",
       "transaction_date             0\n",
       "online_order               360\n",
       "order_status                 0\n",
       "brand                      197\n",
       "product_line               197\n",
       "product_class              197\n",
       "product_size               197\n",
       "list_price                   0\n",
       "standard_cost              197\n",
       "product_first_sold_date    197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7cc16b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 20000)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.transaction_id.nunique(),len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8231feeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "## transaction date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8b45c76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           20000 non-null  object        \n",
      " 1   product_id               20000 non-null  object        \n",
      " 2   customer_id              20000 non-null  object        \n",
      " 3   transaction_date         20000 non-null  datetime64[ns]\n",
      " 4   online_order             19640 non-null  object        \n",
      " 5   order_status             20000 non-null  object        \n",
      " 6   brand                    19803 non-null  object        \n",
      " 7   product_line             19803 non-null  object        \n",
      " 8   product_class            19803 non-null  object        \n",
      " 9   product_size             19803 non-null  object        \n",
      " 10  list_price               20000 non-null  object        \n",
      " 11  standard_cost            19803 non-null  object        \n",
      " 12  product_first_sold_date  19803 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(12)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.transaction_date=pd.to_datetime(df.transaction_date)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cdf33447",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [transaction_id, product_id, customer_id, transaction_date, online_order, order_status, brand, product_line, product_class, product_size, list_price, standard_cost, product_first_sold_date]\n",
       "Index: []"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.transaction_date<'2017']  #Only 2017 data is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "38a109f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     9829\n",
       "False    9811\n",
       "Name: online_order, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.online_order.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f6d550dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Approved     19821\n",
       "Cancelled      179\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.order_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c5a09eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42      2017-03-17\n",
       "138     2017-03-25\n",
       "249     2017-11-25\n",
       "254     2017-04-01\n",
       "583     2017-09-13\n",
       "           ...    \n",
       "19405   2017-05-29\n",
       "19409   2017-01-15\n",
       "19822   2017-12-02\n",
       "19957   2017-06-04\n",
       "19989   2017-01-27\n",
       "Name: transaction_date, Length: 179, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.order_status=='Cancelled']['transaction_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c589b580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Standard    14176\n",
       "Road         3970\n",
       "Touring      1234\n",
       "Mountain      423\n",
       "Name: product_line, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.product_line.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cb53b33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium    12990\n",
       "large      3976\n",
       "small      2837\n",
       "Name: product_size, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.product_size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bb46f1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium    13826\n",
       "high       3013\n",
       "low        2964\n",
       "Name: product_class, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.product_class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6cff11ef",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.1</td>\n",
       "      <td>36145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.3</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id transaction_date online_order  \\\n",
       "0              1          2        2950       2017-02-25        False   \n",
       "1              2          3        3120       2017-05-21         True   \n",
       "2              3         37         402       2017-10-16        False   \n",
       "3              4         88        3135       2017-08-31        False   \n",
       "4              5         78         787       2017-10-01         True   \n",
       "\n",
       "0 order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  \n",
       "2    1793.43        248.82                   36361  \n",
       "3    1198.46         381.1                   36145  \n",
       "4     1765.3        709.48                   42226  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  # More insights about product line, class and size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "825ca420",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shakti\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='list_price'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.list_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7ab021bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product_class', ylabel='list_price'>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['product_class'],y=df.list_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "fe168396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product_size', ylabel='list_price'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['product_size'],y=df.list_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a16cfa49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='product_line', ylabel='list_price'>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['product_line'],y=df.list_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "fd52530f",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.1</td>\n",
       "      <td>36145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.3</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0 transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "0              1          2        2950  2017-02-25 00:00:00        False   \n",
       "1              2          3        3120  2017-05-21 00:00:00         True   \n",
       "2              3         37         402  2017-10-16 00:00:00        False   \n",
       "3              4         88        3135  2017-08-31 00:00:00        False   \n",
       "4              5         78         787  2017-10-01 00:00:00         True   \n",
       "\n",
       "0 order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "0 list_price standard_cost product_first_sold_date  \n",
       "0      71.49         53.62                   41245  \n",
       "1    2091.47        388.92                   41701  \n",
       "2    1793.43        248.82                   36361  \n",
       "3    1198.46         381.1                   36145  \n",
       "4     1765.3        709.48                   42226  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "01a5b267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0\n",
       "transaction_id               0\n",
       "product_id                   0\n",
       "customer_id                  0\n",
       "transaction_date             0\n",
       "online_order               360\n",
       "order_status                 0\n",
       "brand                      197\n",
       "product_line               197\n",
       "product_class              197\n",
       "product_size               197\n",
       "list_price                   0\n",
       "standard_cost              197\n",
       "product_first_sold_date    197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "005ac05a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tr.iloc[:,-1:].astype('float').boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "87ca2353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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McivwFNNP/lzpkzuSNFpDh35V/W8Gj9N//jDbbAY2D9umNB/rNm1Z+kbuXto2TjzmFUu6f7VnwTdypSPRzmvfseRtrNu0ZSTtSIvJr2GQpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGjLy0E9yYZKnk+xIsmnU7UtSy0Ya+klWAX8CvA04Hbgkyemj7IMkteyoEbd3DrCjqp4FSHILcBHw1Ij7IR0iyfy3+cP5t1NV899IWiSjDv01wDf73k8Bv3TwSkk2AhsBxsbG6PV6I+mc2nbffffNa/09e/Zw3HHHzbsdz2ctp1GH/qBLqUMue6pqEpgEGB8fr4mJiSXuljR/vV4Pz02tNKO+kTsFnNr3fi2wa8R9kKRmjTr0/xZYn+S0JK8ENgB3jrgPktSskQ7vVNW+JO8H/hewCrihqp4cZR8kqWWjHtOnqj4PfH7U7UqS/ESuJDXF0Jekhhj6ktSQHOmfDkzyd8DXl7sf0gCrgb9f7k5IM/iZqjr54OIRH/rSkSrJ1qoaX+5+SPPh8I4kNcTQl6SGGPrS8CaXuwPSfDmmL0kN8Upfkhpi6EtSQwx9SWqIoa+fCEmuSvKqRdzfziSrF7D9ryf548XqzwxtLOoxqw2Gvn5SXAUsWwAmWbUMzV7FMh6zViZDXytOkmOTbEnyeJLtSa4BXgvcl+S+bp3rk2xN8mSSD/ZtuzPJB5M8kmRbkn/e1V+T5AtJHk3yZ/T9tGeSzyZ5uNvXxr76niR/kORLwBuT/EaSryb5G+BNsxzDWJLbu2N4PMm/7Or/uTum7UmumuF4fzXJbx58zNKcVJWT04qagH8PfLzv/YnATmB1X+3V3esqoAe8vnu/E/hP3fx/BD7RzV8H/H43/w6mf7t59UH7OgbYDryme1/Au7v5U4BvACcDrwT+D/DHhzmGTwNX9fXxROBsYBtwLHAc8CTwi4OOt+9YVs/238vJqX/ySl8r0TbgrUn+MMmbq+p7A9Z5d5JHgEeBM4DT+5Z9pnt9GFjXzb8F+HOAqtoCfLdv/d9M8jjwINO/8by+q+8HbuvmfwnoVdXfVdX/YzrUD+dfA9d37e3vjuFfAbdX1UtVtafr55vneLzSnBj6WnGq6qv841Xxf0vy+/3Lk5wG/Bfg/Kp6PbAF+Kd9q+ztXvfz8l+PO+STikkmgLcCb6yqNzD9j8iBff3fqtp/uO3nKYOKsx2vNB+GvlacJK8FflBVfw58GPgXwIvA8d0qJwAvAd9LMga8bQ67vR/4D93+3wac1NVPBL5bVT/oxv/PnWH7LwET3b2BVwDvmqW9e4EruvZWJTmh68PFSV6V5FjgncAXZzheDjpmaU5G/hu50iL4BeCPkvwD8COmw/ONwF1JdlfVeUkeZXpM/Fmmx9dn80Hg5m5I6G+YHp8HuBu4PMkTwNNMD/Ecoqp2J/kA8ACwG3iE6bH6mfwWMJnkvUz/xXFFVT2Q5EbgoW6dT1TVo0kuGHC8MP3dPz8+5jkco+R370hSSxzekaSGOLwjLaEkv8uh4/t/WVWbl6M/ksM7ktQQh3ckqSGGviQ1xNCXpIYY+pLUkP8P99pTpGWmlOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tr.iloc[:,-2:-1].astype('float').boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7c9fdd9b",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>3473</td>\n",
       "      <td>2017-12-18 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>41848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>60</td>\n",
       "      <td>2135</td>\n",
       "      <td>2017-01-26 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>41047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>292</td>\n",
       "      <td>64</td>\n",
       "      <td>3380</td>\n",
       "      <td>2017-10-28 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>42172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>413</td>\n",
       "      <td>60</td>\n",
       "      <td>720</td>\n",
       "      <td>2017-09-09 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>41848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>699</td>\n",
       "      <td>60</td>\n",
       "      <td>1535</td>\n",
       "      <td>2017-08-13 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>40779</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>19190</th>\n",
       "      <td>19191</td>\n",
       "      <td>60</td>\n",
       "      <td>3411</td>\n",
       "      <td>2017-06-23 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>40410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19551</th>\n",
       "      <td>19552</td>\n",
       "      <td>64</td>\n",
       "      <td>844</td>\n",
       "      <td>2017-08-09 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>40779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19705</th>\n",
       "      <td>19706</td>\n",
       "      <td>60</td>\n",
       "      <td>54</td>\n",
       "      <td>2017-10-24 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>42172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19722</th>\n",
       "      <td>19723</td>\n",
       "      <td>60</td>\n",
       "      <td>1841</td>\n",
       "      <td>2017-01-03 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>40779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19749</th>\n",
       "      <td>19750</td>\n",
       "      <td>60</td>\n",
       "      <td>2640</td>\n",
       "      <td>2017-10-23 00:00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>small</td>\n",
       "      <td>1977.36</td>\n",
       "      <td>1759.85</td>\n",
       "      <td>40779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0     transaction_id product_id customer_id     transaction_date online_order  \\\n",
       "59                60         60        3473  2017-12-18 00:00:00        False   \n",
       "142              143         60        2135  2017-01-26 00:00:00         True   \n",
       "291              292         64        3380  2017-10-28 00:00:00        False   \n",
       "412              413         60         720  2017-09-09 00:00:00        False   \n",
       "698              699         60        1535  2017-08-13 00:00:00        False   \n",
       "...              ...        ...         ...                  ...          ...   \n",
       "19190          19191         60        3411  2017-06-23 00:00:00        False   \n",
       "19551          19552         64         844  2017-08-09 00:00:00        False   \n",
       "19705          19706         60          54  2017-10-24 00:00:00        False   \n",
       "19722          19723         60        1841  2017-01-03 00:00:00         True   \n",
       "19749          19750         60        2640  2017-10-23 00:00:00        False   \n",
       "\n",
       "0     order_status           brand product_line product_class product_size  \\\n",
       "59        Approved  Giant Bicycles     Standard          high        small   \n",
       "142       Approved  Giant Bicycles     Standard          high        small   \n",
       "291       Approved  Giant Bicycles     Standard          high        small   \n",
       "412       Approved  Giant Bicycles     Standard          high        small   \n",
       "698       Approved  Giant Bicycles     Standard          high        small   \n",
       "...            ...             ...          ...           ...          ...   \n",
       "19190     Approved  Giant Bicycles     Standard          high        small   \n",
       "19551     Approved  Giant Bicycles     Standard          high        small   \n",
       "19705     Approved  Giant Bicycles     Standard          high        small   \n",
       "19722     Approved  Giant Bicycles     Standard          high        small   \n",
       "19749     Approved  Giant Bicycles     Standard          high        small   \n",
       "\n",
       "0     list_price standard_cost product_first_sold_date  \n",
       "59       1977.36       1759.85                   41848  \n",
       "142      1977.36       1759.85                   41047  \n",
       "291      1977.36       1759.85                   42172  \n",
       "412      1977.36       1759.85                   41848  \n",
       "698      1977.36       1759.85                   40779  \n",
       "...          ...           ...                     ...  \n",
       "19190    1977.36       1759.85                   40410  \n",
       "19551    1977.36       1759.85                   40779  \n",
       "19705    1977.36       1759.85                   42172  \n",
       "19722    1977.36       1759.85                   40779  \n",
       "19749    1977.36       1759.85                   40779  \n",
       "\n",
       "[195 rows x 13 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr[df_tr.standard_cost>1610.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "8ce13cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tr.iloc[:,-3:-2].astype('float').boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "fa3d0944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium    12990\n",
       "large      3976\n",
       "small      2837\n",
       "Name: product_size, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tr.product_size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3a667a5",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}