pandas_tutorial.ipynb

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   "source": [
    "# Pandas Complete tutorial for data science in 2022\n",
    "\n",
    "This is part of the original article [Pandas Complete tutorial for data science in 2022](https://norochalise.medium.com/pandas-complete-tutorial-for-data-science-in-2022-685a4c6df347)\n"
   ]
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   "source": [
    "## 1. Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vSvx8LjDp6Yh",
   "metadata": {
    "id": "vSvx8LjDp6Yh"
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   "source": [
    "### Import\n",
    "\n",
    "Before moving on to learn pandas first we need to install it and import it. If you install [Anaconda distributions](https://www.anaconda.com/) on your local machine or using [Google Colab](https://research.google.com/colaboratory) then pandas will already be available there, otherwise, you follow this installation process from [pandas official's website](https://pandas.pydata.org/docs/getting_started/install.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "CeSsGkTMiMcI",
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   "source": [
    "# we can set numbers for how many rows and columns will be displayed\n",
    "pd.set_option('display.min_rows', 10) #default will be 10 \n",
    "pd.set_option('display.max_columns', 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cb06b1",
   "metadata": {
    "id": "68cb06b1"
   },
   "source": [
    "## 2. Loading Different Data Formats Into a Pandas Data Frame\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7_X0klbRI24J",
   "metadata": {
    "id": "7_X0klbRI24J"
   },
   "source": [
    "### Reading CSV file\n"
   ]
  },
  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151200</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151201</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151202</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
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       "  Transaction_date  Transaction_ID  Gender   Age Marital_status State_names  \\\n",
       "0         1/1/2019          151200  Female  19.0         Single      Kansas   \n",
       "1         1/1/2019          151201    Male  49.0         Single    Illinois   \n",
       "2         1/1/2019          151202    Male  63.0        Married  New Mexico   \n",
       "\n",
       "  Segment Employees_status Payment_method  Referal  Amount_spent  \n",
       "0   Basic     Unemployment          Other      1.0       2051.36  \n",
       "1   Basic    self-employed           Card      0.0        544.04  \n",
       "2   Basic          workers         PayPal      1.0       1572.60  "
      ]
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# read csv file\n",
    "\n",
    "df = pd.read_csv('dataset/online_store_customer_data.csv')\n",
    "df.head(3)"
   ]
  },
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       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151201</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151202</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>workers</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "         0       1     2     3        4           5         6              7   \\\n",
       "0  1/1/2019  151201  Male  49.0   Single    Illinois     Basic  self-employed   \n",
       "1  1/1/2019  151202  Male  63.0  Married  New Mexico     Basic        workers   \n",
       "2  1/1/2019  151203   NaN  18.0   Single    Virginia  Platinum        workers   \n",
       "\n",
       "       8    9        10  \n",
       "0    Card  0.0   544.04  \n",
       "1  PayPal  1.0  1572.60  \n",
       "2    Card  1.0  1199.79  "
      ]
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   ],
   "source": [
    "# Loading csv file with skip first 2 rows without header\n",
    "df_csv = pd.read_csv('dataset/online_store_customer_data.csv', skiprows=2, header=None)\n",
    "df_csv.head(3)"
   ]
  },
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   "cell_type": "markdown",
   "id": "qNhba27PwpUa",
   "metadata": {
    "id": "qNhba27PwpUa"
   },
   "source": [
    "### Read CSV file from URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ZdhOBOE7xXW0",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
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       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151200</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151201</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151202</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
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       "  Transaction_date  Transaction_ID  Gender   Age Marital_status State_names  \\\n",
       "0         1/1/2019          151200  Female  19.0         Single      Kansas   \n",
       "1         1/1/2019          151201    Male  49.0         Single    Illinois   \n",
       "2         1/1/2019          151202    Male  63.0        Married  New Mexico   \n",
       "\n",
       "  Segment Employees_status Payment_method  Referal  Amount_spent  \n",
       "0   Basic     Unemployment          Other      1.0       2051.36  \n",
       "1   Basic    self-employed           Card      0.0        544.04  \n",
       "2   Basic          workers         PayPal      1.0       1572.60  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read csv file from url\n",
    "url=\"https://raw.githubusercontent.com/norochalise/medium/main/pandas_tutorial/dataset/online_store_customer_data.csv\"\n",
    "df_url = pd.read_csv(url)\n",
    "df_url.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "h6bQJPx5zJEm",
   "metadata": {
    "id": "h6bQJPx5zJEm"
   },
   "source": [
    "### Write CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "uykhkL_7zSMu",
   "metadata": {
    "id": "uykhkL_7zSMu"
   },
   "outputs": [],
   "source": [
    "# saving df_url dataframe to csv file\n",
    "df_url.to_csv('dataset/csv_from_url.csv')\n",
    "df_url.to_csv('dataset/demo_text.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hZCC2_gp1Qcm",
   "metadata": {
    "id": "hZCC2_gp1Qcm"
   },
   "source": [
    "### Read text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "i9Adg1q32Hkj",
   "metadata": {
    "id": "i9Adg1q32Hkj"
   },
   "outputs": [],
   "source": [
    "# read plain text file\n",
    "df_txt = pd.read_csv(\"dataset/demo_text.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "W0hKEl_pJ4M3",
   "metadata": {
    "id": "W0hKEl_pJ4M3"
   },
   "source": [
    "### Read Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "Qb_pWVKFMq4E",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3/10/2019</td>\n",
       "      <td>151378</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Single</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Card</td>\n",
       "      <td>1</td>\n",
       "      <td>2740.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/2/2019</td>\n",
       "      <td>151751</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>Single</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Card</td>\n",
       "      <td>0</td>\n",
       "      <td>977.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6/14/2019</td>\n",
       "      <td>151689</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>Married</td>\n",
       "      <td>Montana</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1</td>\n",
       "      <td>2978.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/5/2019</td>\n",
       "      <td>151849</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>Married</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>Card</td>\n",
       "      <td>1</td>\n",
       "      <td>1157.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction_date  Transaction_ID  Gender  Age Marital_status   State_names  \\\n",
       "0        3/10/2019          151378  Female   25         Single       Wyoming   \n",
       "1         7/2/2019          151751  Female   42         Single      Delaware   \n",
       "2        6/14/2019          151689    Male   38        Married       Montana   \n",
       "3         8/5/2019          151849    Male   24        Married  Rhode Island   \n",
       "\n",
       "    Segment Employees_status Payment_method  Referal  Amount_spent  \n",
       "0  Platinum     Unemployment           Card        1       2740.57  \n",
       "1     Basic     Unemployment           Card        0        977.80  \n",
       "2     Basic          workers         PayPal        1       2978.21  \n",
       "3     Basic          workers           Card        1       1157.79  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read excel file\n",
    "df_excel = pd.read_excel('dataset/excel_file.xlsx', sheet_name='Sheet1')\n",
    "df_excel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4JZevqX3N0Zl",
   "metadata": {
    "id": "4JZevqX3N0Zl"
   },
   "source": [
    "### Write Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "x8soGD0I02RB",
   "metadata": {
    "id": "x8soGD0I02RB"
   },
   "outputs": [],
   "source": [
    "# save dataframe to the excel file\n",
    "df_url.to_csv('demo.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a5b306",
   "metadata": {
    "id": "10a5b306"
   },
   "source": [
    "## 3. Data preprocessing\n",
    "Data preprocessing is the process of making raw data to clean data. This is the most crucial part of data the science. In this section, we will explore data first then we remove unwanted columns, remove duplicates, handle missing data, etc. After this step, we get clean data from raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2098e35f",
   "metadata": {
    "id": "2098e35f"
   },
   "source": [
    "### 3.1 Data Exploring"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aHKv7GbtBGG5",
   "metadata": {
    "id": "aHKv7GbtBGG5"
   },
   "source": [
    "#### Retrieving rows from data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cbc7a3df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "cbc7a3df",
    "outputId": "29db4b44-340c-42fd-c251-509c11391231"
   },
   "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_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151200</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151201</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>151202</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction_date  Transaction_ID  Gender   Age Marital_status State_names  \\\n",
       "0         1/1/2019          151200  Female  19.0         Single      Kansas   \n",
       "1         1/1/2019          151201    Male  49.0         Single    Illinois   \n",
       "2         1/1/2019          151202    Male  63.0        Married  New Mexico   \n",
       "\n",
       "  Segment Employees_status Payment_method  Referal  Amount_spent  \n",
       "0   Basic     Unemployment          Other      1.0       2051.36  \n",
       "1   Basic    self-employed           Card      0.0        544.04  \n",
       "2   Basic          workers         PayPal      1.0       1572.60  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display first 3 rows\n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58d4f7c8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "58d4f7c8",
    "outputId": "584c0da8-d217-4302-9cc0-b21a23903188"
   },
   "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_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2506</th>\n",
       "      <td>4/30/2021</td>\n",
       "      <td>153694</td>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>286.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153695</td>\n",
       "      <td>Female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2508</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153696</td>\n",
       "      <td>Female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Silver</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>708.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2509</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153697</td>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2030.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>New York</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1909.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2511</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153699</td>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>California</td>\n",
       "      <td>Silver</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1073.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Transaction_ID  Gender   Age Marital_status  \\\n",
       "2506        4/30/2021          153694    Male  34.0         Single   \n",
       "2507         5/1/2021          153695  Female  57.0         Single   \n",
       "2508         5/1/2021          153696  Female  36.0        Married   \n",
       "2509         5/1/2021          153697    Male  22.0         Single   \n",
       "2510         5/1/2021          153698     NaN  44.0         Single   \n",
       "2511         5/1/2021          153699    Male  48.0         Single   \n",
       "\n",
       "         State_names   Segment Employees_status Payment_method  Referal  \\\n",
       "2506         Florida   Missing        Employees          Other      1.0   \n",
       "2507  South Carolina  Platinum    self-employed           Card      0.0   \n",
       "2508          Hawaii    Silver    self-employed         PayPal      1.0   \n",
       "2509  South Carolina     Basic          workers         PayPal      1.0   \n",
       "2510        New York     Basic        Employees         PayPal      0.0   \n",
       "2511      California    Silver          workers         PayPal      1.0   \n",
       "\n",
       "      Amount_spent  \n",
       "2506        286.82  \n",
       "2507        150.10  \n",
       "2508        708.88  \n",
       "2509       2030.07  \n",
       "2510       1909.77  \n",
       "2511       1073.15  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display last 6 rows\n",
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1L2Pg_LiChrC",
   "metadata": {
    "id": "1L2Pg_LiChrC"
   },
   "source": [
    "#### Retrieving sample rows from data frame.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36e099c7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "36e099c7",
    "outputId": "4b3209e6-30ba-43a4-94d5-af19e3492b8d"
   },
   "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_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>4/7/2019</td>\n",
       "      <td>151466</td>\n",
       "      <td>Male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Montana</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2681.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2398</th>\n",
       "      <td>3/25/2021</td>\n",
       "      <td>153586</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1167.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>8/22/2020</td>\n",
       "      <td>152963</td>\n",
       "      <td>Male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>867.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2/4/2019</td>\n",
       "      <td>151288</td>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>841.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>6/6/2020</td>\n",
       "      <td>152744</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1972.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1/25/2019</td>\n",
       "      <td>151258</td>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>676.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>12/27/2020</td>\n",
       "      <td>153332</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>workers</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>899.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Transaction_ID  Gender   Age Marital_status  \\\n",
       "278          4/7/2019          151466    Male  43.0        Married   \n",
       "2398        3/25/2021          153586    Male  35.0         Single   \n",
       "1775        8/22/2020          152963    Male  34.0         Single   \n",
       "93           2/4/2019          151288    Male  60.0        Married   \n",
       "1556         6/6/2020          152744  Female  44.0         Single   \n",
       "58          1/25/2019          151258  Female  32.0         Single   \n",
       "2144       12/27/2020          153332    Male  67.0         Single   \n",
       "\n",
       "         State_names   Segment Employees_status Payment_method  Referal  \\\n",
       "278          Montana     Basic     Unemployment         PayPal      1.0   \n",
       "2398       Minnesota  Platinum    self-employed           Card      1.0   \n",
       "1775          Alaska     Basic          workers         PayPal      0.0   \n",
       "93      Rhode Island    Silver        Employees           Card      1.0   \n",
       "1556      New Jersey     Basic     Unemployment         PayPal      0.0   \n",
       "58    South Carolina     Basic        Employees          Other      NaN   \n",
       "2144        Oklahoma  Platinum          workers          Other      1.0   \n",
       "\n",
       "      Amount_spent  \n",
       "278        2681.13  \n",
       "2398       1167.20  \n",
       "1775        867.14  \n",
       "93          841.26  \n",
       "1556       1972.91  \n",
       "58          676.70  \n",
       "2144        899.21  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display random 7 sample rows\n",
    "df.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "A5NQiaXLlmS3",
   "metadata": {
    "id": "A5NQiaXLlmS3"
   },
   "source": [
    "#### Retrieving information about dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74baf9db",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "74baf9db",
    "outputId": "d41a71d4-c32f-411a-9e69-17a9c74397a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2512 entries, 0 to 2511\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Transaction_date  2512 non-null   object \n",
      " 1   Transaction_ID    2512 non-null   int64  \n",
      " 2   Gender            2484 non-null   object \n",
      " 3   Age               2470 non-null   float64\n",
      " 4   Marital_status    2512 non-null   object \n",
      " 5   State_names       2512 non-null   object \n",
      " 6   Segment           2512 non-null   object \n",
      " 7   Employees_status  2486 non-null   object \n",
      " 8   Payment_method    2512 non-null   object \n",
      " 9   Referal           2357 non-null   float64\n",
      " 10  Amount_spent      2270 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(7)\n",
      "memory usage: 216.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40639f0a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "40639f0a",
    "outputId": "8713eeb0-d3c5-4cfd-fa4b-cbbc65e78441"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transaction_date     object\n",
       "Transaction_ID        int64\n",
       "Gender               object\n",
       "Age                 float64\n",
       "Marital_status       object\n",
       "State_names          object\n",
       "Segment              object\n",
       "Employees_status     object\n",
       "Payment_method       object\n",
       "Referal             float64\n",
       "Amount_spent        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display datatypes\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5ad7ad16",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5ad7ad16",
    "outputId": "4d2bb777-3516-441a-cb7d-625b298afddb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     7\n",
       "float64    3\n",
       "int64      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XfPcQQb2IH-u",
   "metadata": {
    "id": "XfPcQQb2IH-u"
   },
   "source": [
    "#### Display number of rows and columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "740fc7ae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "740fc7ae",
    "outputId": "0609e71c-2834-4b0d-c924-f9eab9fb7508"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2512, 11)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdX_PamDE2Ko",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fdX_PamDE2Ko",
    "outputId": "ff017ac2-5be1-410d-b611-a22e0f42d05c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Transaction_date', 'Transaction_ID', 'Gender', 'Age', 'Marital_status',\n",
       "       'State_names', 'Segment', 'Employees_status', 'Payment_method',\n",
       "       'Referal', 'Amount_spent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "T3t93wXoFTeL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T3t93wXoFTeL",
    "outputId": "9f3505ad-97f1-484d-f4a4-0d740f65ca35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19.0\n",
       "1    49.0\n",
       "2    63.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display Age columns first 3 rows data\n",
    "df['Age'].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "x7TKiUXBF4RE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "x7TKiUXBF4RE",
    "outputId": "1ccc0c3a-735a-4db4-8f42-be98bf190ecb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.0</td>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Transaction_date  Gender\n",
       "0  19.0         1/1/2019  Female\n",
       "1  49.0         1/1/2019    Male\n",
       "2  63.0         1/1/2019    Male\n",
       "3  18.0         1/1/2019     NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display first 4 rows of Age, Transaction_date and Gender columns\n",
    "df[['Age', 'Transaction_date', 'Gender']].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Bkh1oFtuoSiS",
   "metadata": {
    "id": "Bkh1oFtuoSiS"
   },
   "source": [
    "#### Retrieving a Range of Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20qnnoofpPfn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "id": "20qnnoofpPfn",
    "outputId": "366a2257-27fa-4f10-ba41-0fdfefc3b609"
   },
   "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_date</th>\n",
       "      <th>Transaction_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>New York</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1909.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2511</th>\n",
       "      <td>5/1/2021</td>\n",
       "      <td>153699</td>\n",
       "      <td>Male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>California</td>\n",
       "      <td>Silver</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1073.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Transaction_ID Gender   Age Marital_status State_names  \\\n",
       "2510         5/1/2021          153698    NaN  44.0         Single    New York   \n",
       "2511         5/1/2021          153699   Male  48.0         Single  California   \n",
       "\n",
       "     Segment Employees_status Payment_method  Referal  Amount_spent  \n",
       "2510   Basic        Employees         PayPal      0.0       1909.77  \n",
       "2511  Silver          workers         PayPal      1.0       1073.15  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for display 2nd to 6th rows\n",
    "df[2:7]\n",
    "\n",
    "# for display starting to 10th\n",
    "df[:11]\n",
    "\n",
    "# for display last two rows\n",
    "df[-2:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03dec553",
   "metadata": {
    "id": "03dec553"
   },
   "source": [
    " ### 3.2 Data Cleaning\n",
    "After the explore our datasets may need to clean them for better analysis. Data coming in from multiple sources so It's possible to have an error in some values. This is where data cleaning becomes extremely important. In this section, we will delete unwanted columns, rename columns, correct appropriate data types, etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HGxEahSYJ0Ft",
   "metadata": {
    "id": "HGxEahSYJ0Ft"
   },
   "source": [
    "#### Delete Columns name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43b62a5f",
   "metadata": {
    "id": "43b62a5f"
   },
   "outputs": [],
   "source": [
    "# Drop unwanted columns\n",
    "df.drop(['Transaction_ID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jtlvWfg7KR_Q",
   "metadata": {
    "id": "jtlvWfg7KR_Q"
   },
   "source": [
    "#### Change Columns name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0StjAOVBKdMR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "0StjAOVBKdMR",
    "outputId": "49ac9722-93de-4639-f764-5aa68df94d17"
   },
   "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>Date</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date     Sex   Age Marital_status State_names Segment Employees_status  \\\n",
       "0  1/1/2019  Female  19.0         Single      Kansas   Basic     Unemployment   \n",
       "1  1/1/2019    Male  49.0         Single    Illinois   Basic    self-employed   \n",
       "2  1/1/2019    Male  63.0        Married  New Mexico   Basic          workers   \n",
       "\n",
       "  Payment_method  Referal  Amount_spent  \n",
       "0          Other      1.0       2051.36  \n",
       "1           Card      0.0        544.04  \n",
       "2         PayPal      1.0       1572.60  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new df_col dataframe from df.copy() method. \n",
    "df_col = df.copy()\n",
    "\n",
    "# rename columns name\n",
    "df_col.rename(columns={\"Transaction_date\": \"Date\", \"Gender\": \"Sex\"}, inplace=True)\n",
    "df_col.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HXspyxQwg5wn",
   "metadata": {
    "id": "HXspyxQwg5wn"
   },
   "source": [
    "#### Adding a new column to a DataFrame\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "te1hhuRlhN-l",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "id": "te1hhuRlhN-l",
    "outputId": "2fe189a0-aaf9-45ee-e68c-240a5e59506e"
   },
   "outputs": [],
   "source": [
    "# Add a new ajusted column which value will be amount_spent * 100\n",
    "df_col['new_col'] = df_col['Amount_spent'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2bXmRJsHig4t",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "2bXmRJsHig4t",
    "outputId": "dabdebe3-2614-42b7-9553-57692e0e60b2"
   },
   "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>Date</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "      <th>new_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "      <td>205136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "      <td>54404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "      <td>157260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date     Sex   Age Marital_status State_names Segment Employees_status  \\\n",
       "0  1/1/2019  Female  19.0         Single      Kansas   Basic     Unemployment   \n",
       "1  1/1/2019    Male  49.0         Single    Illinois   Basic    self-employed   \n",
       "2  1/1/2019    Male  63.0        Married  New Mexico   Basic          workers   \n",
       "\n",
       "  Payment_method  Referal  Amount_spent   new_col  \n",
       "0          Other      1.0       2051.36  205136.0  \n",
       "1           Card      0.0        544.04   54404.0  \n",
       "2         PayPal      1.0       1572.60  157260.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_col.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wEGhdvVnKiDy",
   "metadata": {
    "id": "wEGhdvVnKiDy"
   },
   "source": [
    "#### String value change or replace "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "juaJjLGrFvP_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "juaJjLGrFvP_",
    "outputId": "b73be5a5-236a-4e20-aa59-dc2e53a08b4c"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "      <th>new_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "      <td>205136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "      <td>54404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "      <td>157260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date     Sex   Age Marital_status State_names Segment Employees_status  \\\n",
       "0  1/1/2019  Female  19.0         Single      Kansas   Basic     Unemployment   \n",
       "1  1/1/2019    Male  49.0         Single    Illinois   Basic    self-employed   \n",
       "2  1/1/2019    Male  63.0        Married  New Mexico   Basic          workers   \n",
       "\n",
       "  Payment_method  Referal  Amount_spent   new_col  \n",
       "0          Other      1.0       2051.36  205136.0  \n",
       "1           Card      0.0        544.04   54404.0  \n",
       "2         PayPal      1.0       1572.60  157260.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_col.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "qYOI7nb2KpfL",
   "metadata": {
    "id": "qYOI7nb2KpfL"
   },
   "outputs": [],
   "source": [
    "# changing Female to Woman and Male to Man in Sex column.\n",
    "#first argument in loc function is condition and second one is columns name. \n",
    "df_col.loc[df_col.Sex == \"Female\", 'Sex'] = 'Woman' \n",
    "df_col.loc[df_col.Sex == \"Male\", 'Sex'] = 'Man'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "Q7EoRcMSF132",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "Q7EoRcMSF132",
    "outputId": "997a105c-c5ad-4ef9-b738-978d61bcb090"
   },
   "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>Date</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "      <th>new_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Woman</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2051.36</td>\n",
       "      <td>205136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Man</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544.04</td>\n",
       "      <td>54404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Man</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "      <td>157260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Sex   Age Marital_status State_names Segment Employees_status  \\\n",
       "0  1/1/2019  Woman  19.0         Single      Kansas   Basic     Unemployment   \n",
       "1  1/1/2019    Man  49.0         Single    Illinois   Basic    self-employed   \n",
       "2  1/1/2019    Man  63.0        Married  New Mexico   Basic          workers   \n",
       "\n",
       "  Payment_method  Referal  Amount_spent   new_col  \n",
       "0          Other      1.0       2051.36  205136.0  \n",
       "1           Card      0.0        544.04   54404.0  \n",
       "2         PayPal      1.0       1572.60  157260.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_col.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3JWg0IavF531",
   "metadata": {
    "id": "3JWg0IavF531"
   },
   "source": [
    "Now Sex columns values are changed Female to Woman and Male to Man."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "T7sc_D_N267m",
   "metadata": {
    "id": "T7sc_D_N267m"
   },
   "source": [
    "#### Datatypes change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "uINSYaj54XnX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uINSYaj54XnX",
    "outputId": "c445c91f-88cb-4e8c-a055-380a61068a46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2512 entries, 0 to 2511\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Date              2512 non-null   object \n",
      " 1   Sex               2484 non-null   object \n",
      " 2   Age               2470 non-null   float64\n",
      " 3   Marital_status    2512 non-null   object \n",
      " 4   State_names       2512 non-null   object \n",
      " 5   Segment           2512 non-null   object \n",
      " 6   Employees_status  2486 non-null   object \n",
      " 7   Payment_method    2512 non-null   object \n",
      " 8   Referal           2357 non-null   float64\n",
      " 9   Amount_spent      2270 non-null   float64\n",
      " 10  new_col           2270 non-null   float64\n",
      "dtypes: float64(4), object(7)\n",
      "memory usage: 216.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_col.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "IHa8ZUo84cVD",
   "metadata": {
    "id": "IHa8ZUo84cVD"
   },
   "source": [
    "In our `Date` columns, it's object type so now we will convert this to date types, and also we will convert `Referal` columns float64 to float32.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8yfIDgFg4zhY",
   "metadata": {
    "id": "8yfIDgFg4zhY"
   },
   "outputs": [],
   "source": [
    "# change object type to datefime64 format\n",
    "df_col['Date'] = df_col['Date'].astype('datetime64[ns]')\n",
    "\n",
    "# change float64 to float32 of Referal columns\n",
    "df_col['Referal'] = df_col['Referal'].astype('float32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "V6Ux9BVcBJZc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V6Ux9BVcBJZc",
    "outputId": "7a4d7706-d840-4a69-db53-0848466199de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2512 entries, 0 to 2511\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Date              2512 non-null   datetime64[ns]\n",
      " 1   Sex               2484 non-null   object        \n",
      " 2   Age               2470 non-null   float64       \n",
      " 3   Marital_status    2512 non-null   object        \n",
      " 4   State_names       2512 non-null   object        \n",
      " 5   Segment           2512 non-null   object        \n",
      " 6   Employees_status  2486 non-null   object        \n",
      " 7   Payment_method    2512 non-null   object        \n",
      " 8   Referal           2357 non-null   float32       \n",
      " 9   Amount_spent      2270 non-null   float64       \n",
      " 10  new_col           2270 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float32(1), float64(3), object(6)\n",
      "memory usage: 206.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_col.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf97600",
   "metadata": {
    "id": "dcf97600"
   },
   "source": [
    "### 3.3 Remove duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dc549528",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dc549528",
    "outputId": "41e54aab-cae1-4fd1-ebc5-f5e62b338374"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display duplicated entries \n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00fe073d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "00fe073d",
    "outputId": "dccf29c5-2122-4b6b-cceb-e11a1764fa15"
   },
   "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_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1/25/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1397.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1/26/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1277.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1/26/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>515.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1/26/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>790.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1/27/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1218.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2/6/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>433.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2/7/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>workers</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>929.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Silver</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1888.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>685.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2/8/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1264.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Transaction_date  Gender   Age Marital_status     State_names   Segment  \\\n",
       "64         1/25/2019    Male  73.0        Married   West Virginia     Basic   \n",
       "65         1/26/2019    Male  55.0        Married          Kansas     Basic   \n",
       "66         1/26/2019  Female  72.0        Married            Iowa    Silver   \n",
       "67         1/26/2019    Male  15.0        Married  South Carolina     Basic   \n",
       "68         1/27/2019  Female  63.0         Single           Texas      Gold   \n",
       "109         2/6/2019    Male  60.0        Married            Utah    Silver   \n",
       "110         2/7/2019  Female  45.0        Married        Missouri  Platinum   \n",
       "111         2/8/2019    Male  33.0         Single         Arizona    Silver   \n",
       "112         2/8/2019    Male  24.0        Married  South Carolina     Basic   \n",
       "113         2/8/2019  Female  53.0         Single        Colorado     Basic   \n",
       "114         2/8/2019  Female  70.0        Married   New Hampshire   Missing   \n",
       "115         2/8/2019    Male  16.0        Married        Nebraska     Basic   \n",
       "\n",
       "    Employees_status Payment_method  Referal  Amount_spent  \n",
       "64         Employees         PayPal      0.0       1397.09  \n",
       "65         Employees          Other      1.0       1277.64  \n",
       "66      Unemployment         PayPal      NaN        515.77  \n",
       "67     self-employed          Other      1.0        790.10  \n",
       "68         Employees           Card      1.0       1218.56  \n",
       "109     Unemployment          Other      1.0        433.20  \n",
       "110          workers          Other      1.0        929.89  \n",
       "111          workers         PayPal      0.0       2560.26  \n",
       "112     Unemployment          Other      0.0           NaN  \n",
       "113    self-employed          Other      1.0       1888.69  \n",
       "114        Employees           Card      1.0        685.49  \n",
       "115          workers         PayPal      1.0       1264.73  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate rows dispaly, keep arguments will--- 'first', 'last' and False\n",
    "duplicate_value = df.duplicated(keep='first')\n",
    "\n",
    "df.loc[duplicate_value, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6fb4d3eb",
   "metadata": {
    "id": "6fb4d3eb"
   },
   "outputs": [],
   "source": [
    "# dropping ALL duplicate values\n",
    "df.drop_duplicates(keep = 'first', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1e3272",
   "metadata": {
    "id": "de1e3272"
   },
   "source": [
    "### 3.4 Handling missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9y1tYxbzLWw1",
   "metadata": {
    "id": "9y1tYxbzLWw1"
   },
   "source": [
    "Handling missing values in the common task in the data pre-processing part. For many reasons most of the time we will encounter missing values. Without dealing with this we can't do the proper model building. For this section first, we will find out missing values then we decided how to handle them. We can handle this by removing affected columns or rows or replacing appropriate values there."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ff83e8",
   "metadata": {
    "id": "44ff83e8"
   },
   "source": [
    "#### Display missing values information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e6ecbaa3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e6ecbaa3",
    "outputId": "93b02791-c68e-4152-8949-1fd8ba4acc51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amount_spent        241\n",
       "Referal             154\n",
       "Age                  42\n",
       "Gender               28\n",
       "Employees_status     26\n",
       "Transaction_date      0\n",
       "Marital_status        0\n",
       "State_names           0\n",
       "Segment               0\n",
       "Payment_method        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5UgQzwrITZk3",
   "metadata": {
    "id": "5UgQzwrITZk3"
   },
   "source": [
    "#### Delete Nan rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5CV_qntnTpja",
   "metadata": {
    "id": "5CV_qntnTpja"
   },
   "source": [
    "If we have less Nan value then we can delete entire rows by `dropna()` function. For this function, we will add columns name in subset parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3bbb429f",
   "metadata": {
    "id": "3bbb429f"
   },
   "outputs": [],
   "source": [
    "# df copy to df_copy\n",
    "df_new = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "99fe65d5",
   "metadata": {
    "id": "99fe65d5"
   },
   "outputs": [],
   "source": [
    "#Delete Nan rows of Job Columns\n",
    "df_new.dropna(subset = [\"Employees_status\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bxAq9hnUK4R",
   "metadata": {
    "id": "6bxAq9hnUK4R"
   },
   "source": [
    "#### Delete entire columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UIsVP0RAUS75",
   "metadata": {
    "id": "UIsVP0RAUS75"
   },
   "source": [
    "If we have a large number of nan values in particular columns then dropping those columns might be a good decision rather than imputing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4400782c",
   "metadata": {
    "id": "4400782c"
   },
   "outputs": [],
   "source": [
    "df_new.drop(columns=['Amount_spent'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "29243a31",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "29243a31",
    "outputId": "4bfe696f-bf01-4713-a432-7fe57a5c1319"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Referal             153\n",
       "Age                  42\n",
       "Gender               27\n",
       "Transaction_date      0\n",
       "Marital_status        0\n",
       "State_names           0\n",
       "Segment               0\n",
       "Employees_status      0\n",
       "Payment_method        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14-WG_ynUomI",
   "metadata": {
    "id": "14-WG_ynUomI"
   },
   "source": [
    "#### Impute missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jECrC9RYVJX6",
   "metadata": {
    "id": "jECrC9RYVJX6"
   },
   "source": [
    "Sometimes if we delete entire columns that will be not the appropriate approach. Delete columns can affect our model building because we will lose our main features. For imputing we have many approaches so here are some of the most popular techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a4435f",
   "metadata": {},
   "source": [
    "**Method 1** - Impute fixed value like 0, 'Unknown' or 'Missing' etc. We inpute Unknown in Gender columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dfb6c602",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615ac388",
   "metadata": {},
   "source": [
    "**Method 2** - Impute Mean, Median and Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5a25d8d4",
   "metadata": {
    "id": "5a25d8d4"
   },
   "outputs": [],
   "source": [
    "# Impute Mean in Amount_spent columns\n",
    "mean_amount_spent = df['Amount_spent'].mean()\n",
    "df['Amount_spent'].fillna(mean_amount_spent, inplace=True)\n",
    "\n",
    "#Impute Median in Age column\n",
    "median_age = df['Age'].median()\n",
    "df['Age'].fillna(median_age, inplace=True)\n",
    "\n",
    "# Impute Mode in Employees_status column\n",
    "mode_emp = df['Employees_status'].mode().iloc[0]\n",
    "df['Employees_status'].fillna(mode_emp, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ed0805",
   "metadata": {},
   "source": [
    "**Method 3** - Imputing forward fill or backfill by `ffill` and `bfill`. In `ffill` missing value impute from the value of the above row and for `bfill` it's taken from the below rows value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f9f251fd",
   "metadata": {
    "id": "f9f251fd"
   },
   "outputs": [],
   "source": [
    "\n",
    "df['Referal'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "OEFYI75II6PN",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OEFYI75II6PN",
    "outputId": "8c92a2c6-3e53-40c3-ef78-c397a88db10e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AuU7koGzcbPy",
   "metadata": {
    "id": "AuU7koGzcbPy"
   },
   "source": [
    "Now we deal with all missing values with different methods. So now we haven't any null values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2980dcd0",
   "metadata": {
    "id": "2980dcd0"
   },
   "source": [
    "## 4. Memory management"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4hM8Z6ptQwAM",
   "metadata": {
    "id": "4hM8Z6ptQwAM"
   },
   "source": [
    "When we work on large datasets, There we get one big issue is a memory problem. We need too large resources for dealing with this. But there are some methods in pandas to deal with this. Here are some methods or strategies to deal with this problem with help of pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QyO24xKLTEEm",
   "metadata": {
    "id": "QyO24xKLTEEm"
   },
   "source": [
    "### Change datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a8a40362",
   "metadata": {
    "id": "a8a40362"
   },
   "outputs": [],
   "source": [
    "df_memory = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c5587bc4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c5587bc4",
    "outputId": "0f1f1ed0-5a6f-4882-ad91-ce181c3ea4c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Total memory taking df_memory dataframe is : 1.15 MB \n"
     ]
    }
   ],
   "source": [
    "memory_usage = df_memory.memory_usage(deep=True)\n",
    "memory_usage_in_mbs = round(np.sum(memory_usage / 1024 ** 2), 3)\n",
    "print(f\" Total memory taking df_memory dataframe is : {memory_usage_in_mbs:.2f} MB \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YC_ucd2fXzjD",
   "metadata": {
    "id": "YC_ucd2fXzjD"
   },
   "source": [
    "#### Change object to category datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Cclw14OrWEia",
   "metadata": {
    "id": "Cclw14OrWEia"
   },
   "source": [
    "Our data frame is small in size. Which is 1.15 MB. Now We will convert our object datatype to category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c1dfe2d1",
   "metadata": {
    "id": "c1dfe2d1"
   },
   "outputs": [],
   "source": [
    "# Object datatype to category convert\n",
    "df_memory[df_memory.select_dtypes(['object']).columns] = df_memory.select_dtypes(['object']).apply(lambda x: x.astype('category'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3a5daead",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3a5daead",
    "outputId": "82d71b3f-2025-4e52-f871-ffc2253ffe17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2500 entries, 0 to 2511\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   Transaction_date  2500 non-null   category\n",
      " 1   Gender            2500 non-null   category\n",
      " 2   Age               2500 non-null   float64 \n",
      " 3   Marital_status    2500 non-null   category\n",
      " 4   State_names       2500 non-null   category\n",
      " 5   Segment           2500 non-null   category\n",
      " 6   Employees_status  2500 non-null   category\n",
      " 7   Payment_method    2500 non-null   category\n",
      " 8   Referal           2500 non-null   float64 \n",
      " 9   Amount_spent      2500 non-null   float64 \n",
      "dtypes: category(7), float64(3)\n",
      "memory usage: 189.1 KB\n"
     ]
    }
   ],
   "source": [
    "# convert object to category\n",
    "df_memory.info(memory_usage=\"deep\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9NcPyDr0WlDJ",
   "metadata": {
    "id": "9NcPyDr0WlDJ"
   },
   "source": [
    "Now its reduce 1.15 megabytes to 216.6 kb. It's almost reduced 5.5 times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OAhUpeaVYBDX",
   "metadata": {
    "id": "OAhUpeaVYBDX"
   },
   "source": [
    "#### Change int64 or float64 to int 32, 16, or 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JRp-9sgzbK-y",
   "metadata": {
    "id": "JRp-9sgzbK-y"
   },
   "source": [
    "By default, pandas store numeric values to int64 or float64. Which takes more memory. If we have to store small numbers then we can change to 64 to 32, 16, and so on. For example, our Referal columns have only 0 and 1 values so for that we don't need to store at float64. so now we change it to float16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a4208c6e",
   "metadata": {
    "id": "a4208c6e"
   },
   "outputs": [],
   "source": [
    "# Change Referal column datatypes\n",
    "df_memory['Referal'] = df_memory['Referal'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "680accae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "680accae",
    "outputId": "ce03fb73-451a-44bf-e788-ad889fb50cdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2500 entries, 0 to 2511\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   Transaction_date  2500 non-null   category\n",
      " 1   Gender            2500 non-null   category\n",
      " 2   Age               2500 non-null   float64 \n",
      " 3   Marital_status    2500 non-null   category\n",
      " 4   State_names       2500 non-null   category\n",
      " 5   Segment           2500 non-null   category\n",
      " 6   Employees_status  2500 non-null   category\n",
      " 7   Payment_method    2500 non-null   category\n",
      " 8   Referal           2500 non-null   float32 \n",
      " 9   Amount_spent      2500 non-null   float64 \n",
      "dtypes: category(7), float32(1), float64(2)\n",
      "memory usage: 179.3 KB\n"
     ]
    }
   ],
   "source": [
    "# convert object to category\n",
    "df_memory.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tDkU2zSZdBfA",
   "metadata": {
    "id": "tDkU2zSZdBfA"
   },
   "source": [
    "After changing only one column's data types we reduce 216 kb to 179 kb.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XtsqLqzyeJ2q",
   "metadata": {
    "id": "XtsqLqzyeJ2q"
   },
   "source": [
    "**Note: Before changing datatypes please make sure it's consequences.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d6f71f",
   "metadata": {
    "id": "d9d6f71f"
   },
   "source": [
    "## 5. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3b208b",
   "metadata": {
    "id": "4e3b208b"
   },
   "source": [
    "### 5.1. Calculating Basic statistical measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "36f30a11",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "36f30a11",
    "outputId": "c04c4cb2-c6c6-4a16-8f53-f2b31620a05e"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>46.63600</td>\n",
       "      <td>18.020516</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Referal</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.65480</td>\n",
       "      <td>0.475529</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount_spent</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>1419.59178</td>\n",
       "      <td>836.011771</td>\n",
       "      <td>2.09</td>\n",
       "      <td>734.8625</td>\n",
       "      <td>1419.59178</td>\n",
       "      <td>1957.025</td>\n",
       "      <td>2999.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count        mean         std    min       25%         50%  \\\n",
       "Age           2500.0    46.63600   18.020516  15.00   32.0000    47.00000   \n",
       "Referal       2500.0     0.65480    0.475529   0.00    0.0000     1.00000   \n",
       "Amount_spent  2500.0  1419.59178  836.011771   2.09  734.8625  1419.59178   \n",
       "\n",
       "                   75%      max  \n",
       "Age             62.000    78.00  \n",
       "Referal          1.000     1.00  \n",
       "Amount_spent  1957.025  2999.98  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n6s8tjhWLkxS",
   "metadata": {
    "id": "n6s8tjhWLkxS"
   },
   "source": [
    "We know already above code will display only numeric columns basic statistical information. for object or category columns we can use `describe(include=object)` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bedd8b71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "bedd8b71",
    "outputId": "bc70e265-922f-4820-8e0e-965a0325bdff"
   },
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Transaction_date</th>\n",
       "      <td>2500</td>\n",
       "      <td>810</td>\n",
       "      <td>8/29/2020</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>2500</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_status</th>\n",
       "      <td>2500</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_names</th>\n",
       "      <td>2500</td>\n",
       "      <td>50</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>Basic</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employees_status</th>\n",
       "      <td>2500</td>\n",
       "      <td>4</td>\n",
       "      <td>Employees</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_method</th>\n",
       "      <td>2500</td>\n",
       "      <td>3</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique        top  freq\n",
       "Transaction_date  2500    810  8/29/2020    12\n",
       "Gender            2500      3     Female  1351\n",
       "Marital_status    2500      2    Married  1464\n",
       "State_names       2500     50   Illinois    67\n",
       "Segment           2500      5      Basic  1130\n",
       "Employees_status  2500      4  Employees   968\n",
       "Payment_method    2500      3     PayPal  1164"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=object).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f_p8W2lmL-C7",
   "metadata": {
    "id": "f_p8W2lmL-C7"
   },
   "source": [
    "We can calculate the mean, median, mode, maximum values, minimum values of individual columns we simply use these functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c60ff776",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c60ff776",
    "outputId": "a9053dfa-ca0b-4767-e4cd-539688a15aeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Mean of Age : 46.636\n",
      " Median of Age : 47.0\n",
      " Mode of Age : 47.0\n",
      " Standard deviation of Age : 18.02\n",
      " Maximum of Age : 78.0\n",
      " Menimum of Age : 15.0\n"
     ]
    }
   ],
   "source": [
    "# Calculate Mean\n",
    "mean = df['Age'].mean()\n",
    "\n",
    "# Calculate Median\n",
    "median = df['Age'].median()\n",
    "\n",
    "#Calculate Mode\n",
    "mode = df['Age'].mode().iloc[0]\n",
    "\n",
    "# Calculate standard deviation\n",
    "std = df['Age'].std()\n",
    "\n",
    "# Calculate Minimum values\n",
    "minimum = df['Age'].min()\n",
    "\n",
    "# Calculate Maximum values\n",
    "maximum = df.Age.max()\n",
    "\n",
    "print(f\" Mean of Age : {mean}\")\n",
    "print(f\" Median of Age : {median}\")\n",
    "print(f\" Mode of Age : {mode}\")\n",
    "print(f\" Standard deviation of Age : {std:.2f}\")\n",
    "print(f\" Maximum of Age : {maximum}\")\n",
    "print(f\" Menimum of Age : {minimum}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Dd---Bu-Ms-R",
   "metadata": {
    "id": "Dd---Bu-Ms-R"
   },
   "source": [
    "In pandas we can display the correlation of different numeric columns. For this we can use `.corr()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8e89ace8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "8e89ace8",
    "outputId": "2b61173c-4722-4ae8-f73a-75698da9dcae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012042</td>\n",
       "      <td>-0.021030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Referal</th>\n",
       "      <td>0.012042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount_spent</th>\n",
       "      <td>-0.021030</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Age   Referal  Amount_spent\n",
       "Age           1.000000  0.012042     -0.021030\n",
       "Referal       0.012042  1.000000      0.002344\n",
       "Amount_spent -0.021030  0.002344      1.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate correlation\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CZnzwDXQg35f",
   "metadata": {
    "id": "CZnzwDXQg35f"
   },
   "source": [
    "### 5.2 Basic built in function for data analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dUAN1hwfe8m",
   "metadata": {
    "id": "1dUAN1hwfe8m"
   },
   "source": [
    "#### Number of uniqe values in category column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "01b77672",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "01b77672",
    "outputId": "32d4e286-6238-4b8a-d8b2-c710d1943b88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for display how many unique values are there in State_names column\n",
    "df['State_names'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uKCSrDSDI7Bs",
   "metadata": {
    "id": "uKCSrDSDI7Bs"
   },
   "source": [
    "#### Shows all unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5ca391db",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5ca391db",
    "outputId": "71de46ed-5b33-4695-ab26-ec64a5ba4f43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kansas', 'Illinois', 'New Mexico', 'Virginia', 'Connecticut',\n",
       "       'Hawaii', 'Florida', 'Vermont', 'California', 'Colorado', 'Iowa',\n",
       "       'South Carolina', 'New York', 'Maine', 'Maryland', 'Missouri',\n",
       "       'North Dakota', 'Ohio', 'Nebraska', 'Montana', 'Indiana',\n",
       "       'Wisconsin', 'Alabama', 'Arkansas', 'Pennsylvania',\n",
       "       'New Hampshire', 'Washington', 'Texas', 'Kentucky',\n",
       "       'Massachusetts', 'Wyoming', 'Louisiana', 'North Carolina',\n",
       "       'Rhode Island', 'West Virginia', 'Tennessee', 'Oregon', 'Alaska',\n",
       "       'Oklahoma', 'Nevada', 'New Jersey', 'Michigan', 'Utah', 'Arizona',\n",
       "       'South Dakota', 'Georgia', 'Idaho', 'Mississippi', 'Minnesota',\n",
       "       'Delaware'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for display uniqe values of State_names column\n",
    "df['State_names'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9VsTcgZOhakP",
   "metadata": {
    "id": "9VsTcgZOhakP"
   },
   "source": [
    "#### Counts of unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ec290b27",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ec290b27",
    "outputId": "9e1635be-4f0a-495c-8552-f84f7e31be67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female     1351\n",
       "Male       1121\n",
       "Unknown      28\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dVItecqDKUwk",
   "metadata": {
    "id": "dVItecqDKUwk"
   },
   "source": [
    "If we want to show with the percentage of occurrence rather number than we use `normalize=True` argument in `value_counts()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4876aa46",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4876aa46",
    "outputId": "a5ad8292-aaa3-4b8b-bf5e-cd41d0ac0e6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female     0.5404\n",
       "Male       0.4484\n",
       "Unknown    0.0112\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate percentage of each category\n",
    "df['Gender'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "665280f1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "665280f1",
    "outputId": "f28c5a20-206a-4d41-831c-e368135acb60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Illinois         67\n",
       "Georgia          64\n",
       "Massachusetts    63\n",
       "Maine            62\n",
       "Kentucky         59\n",
       "Minnesota        59\n",
       "Delaware         56\n",
       "Missouri         56\n",
       "New York         55\n",
       "New Mexico       55\n",
       "Arkansas         55\n",
       "California       55\n",
       "Arizona          55\n",
       "Nevada           55\n",
       "Vermont          54\n",
       "New Jersey       53\n",
       "Oregon           53\n",
       "Florida          53\n",
       "West Virginia    53\n",
       "Washington       52\n",
       "Name: State_names, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State_names'].value_counts().sort_values(ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "eed29623",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "eed29623",
    "outputId": "31f49d44-ba25-42a1-a2e7-b47e05530a56"
   },
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>7/6/2020</td>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1706.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>233.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>11/2/2020</td>\n",
       "      <td>Female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1954.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Gender   Age Marital_status State_names Segment  \\\n",
       "1639         7/6/2020  Female  28.0         Single     Alabama    Gold   \n",
       "28          1/12/2019    Male  75.0        Married     Alabama   Basic   \n",
       "2001        11/2/2020  Female  47.0        Married     Alabama    Gold   \n",
       "\n",
       "     Employees_status Payment_method  Referal  Amount_spent  \n",
       "1639        Employees         PayPal      0.0       1706.13  \n",
       "28      self-employed         PayPal      1.0        233.05  \n",
       "2001        Employees          Other      0.0       1954.13  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort Values by State_names\n",
    "df.sort_values(by=['State_names']).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_4zUpaMCm6Ig",
   "metadata": {
    "id": "_4zUpaMCm6Ig"
   },
   "source": [
    "For sorting our dataframe by Amount_spent with ascending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9nAJo_njnDTr",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "9nAJo_njnDTr",
    "outputId": "229b54ff-320f-4e58-ec19-335bcdc406c1"
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>4/18/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7/4/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Gold</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>3/25/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Gender   Age Marital_status     State_names Segment  \\\n",
       "2468        4/18/2021  Female  73.0        Married        Michigan    Gold   \n",
       "568          7/4/2019    Male  46.0         Single  South Carolina    Gold   \n",
       "2401        3/25/2021  Female  60.0         Single        Maryland  Silver   \n",
       "\n",
       "     Employees_status Payment_method  Referal  Amount_spent  \n",
       "2468        Employees         PayPal      1.0          2.09  \n",
       "568           workers         PayPal      0.0          2.16  \n",
       "2401        Employees         PayPal      1.0          2.84  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort Values Amount_spent with ascending order\n",
    "df.sort_values(by=['Amount_spent']).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "r0GtAVPrp2BV",
   "metadata": {
    "id": "r0GtAVPrp2BV"
   },
   "source": [
    "For sorting our dataframe by Amount_spent with descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "GYRYIUCHpQU5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "GYRYIUCHpQU5",
    "outputId": "fafcb086-41e6-498f-d6c7-5570b479c8ac"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>workers</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2999.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>6/7/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Gold</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2998.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>2/15/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2997.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Gender   Age Marital_status State_names   Segment  \\\n",
       "17           1/7/2019  Female  77.0        Married  New Mexico  Platinum   \n",
       "485          6/7/2019    Male  65.0        Married     Arizona      Gold   \n",
       "2279        2/15/2021  Female  78.0         Single     Arizona    Silver   \n",
       "\n",
       "     Employees_status Payment_method  Referal  Amount_spent  \n",
       "17            workers           Card      0.0       2999.98  \n",
       "485     self-employed         PayPal      1.0       2998.62  \n",
       "2279        Employees         PayPal      1.0       2997.21  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort Values Amount_spent with descending order\n",
    "df.sort_values(by=['Amount_spent'], ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "TGbW_Qtmn1C2",
   "metadata": {
    "id": "TGbW_Qtmn1C2"
   },
   "source": [
    "Alternatively, We can use `nlargest()` and `nsmallest()` functions for displaying largest and smallest values with desired numbers. for example, If we want to display 4 largest Amount_spent rows then we use this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1580b9fe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "1580b9fe",
    "outputId": "03d3b2be-1bd5-456d-fd9b-bb30a5f890a9"
   },
   "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_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>workers</td>\n",
       "      <td>Card</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2999.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>6/7/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Gold</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2998.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>2/15/2021</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2997.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>7/13/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2997.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Transaction_date  Gender   Age Marital_status     State_names   Segment  \\\n",
       "17           1/7/2019  Female  77.0        Married      New Mexico  Platinum   \n",
       "485          6/7/2019    Male  65.0        Married         Arizona      Gold   \n",
       "2279        2/15/2021  Female  78.0         Single         Arizona    Silver   \n",
       "589         7/13/2019    Male  51.0         Single  North Carolina   Missing   \n",
       "\n",
       "     Employees_status Payment_method  Referal  Amount_spent  \n",
       "17            workers           Card      0.0       2999.98  \n",
       "485     self-employed         PayPal      1.0       2998.62  \n",
       "2279        Employees         PayPal      1.0       2997.21  \n",
       "589         Employees         PayPal      1.0       2997.15  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nlargest\n",
    "df.nlargest(4, 'Amount_spent').head(10) # first argument is how many rows you want to disply and second one is columns name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "T0YVV84jrCXX",
   "metadata": {
    "id": "T0YVV84jrCXX"
   },
   "source": [
    "For 3 smallest Amount_spent rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "458403e0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "458403e0",
    "outputId": "df07fb91-ef11-480c-c658-3421d9e9fd50"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1/14/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>Gold</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>628.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1/23/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>Card</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2690.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1/26/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Basic</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>790.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction_date Gender   Age Marital_status     State_names Segment  \\\n",
       "33        1/14/2019   Male  15.0        Married        Missouri    Gold   \n",
       "54        1/23/2019   Male  15.0        Married        Illinois   Basic   \n",
       "62        1/26/2019   Male  15.0        Married  South Carolina   Basic   \n",
       "\n",
       "   Employees_status Payment_method  Referal  Amount_spent  \n",
       "33    self-employed           Card      1.0        628.93  \n",
       "54        Employees           Card      1.0       2690.18  \n",
       "62    self-employed          Other      1.0        790.10  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nsmallest\n",
    "df.nsmallest(3, 'Age').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LV6wnCzjwgfv",
   "metadata": {
    "id": "LV6wnCzjwgfv"
   },
   "source": [
    "#### Conditional queries on Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9998e789",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "9998e789",
    "outputId": "c6cf03aa-3860-443e-fef7-9701c3be864e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1572.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2922.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1481.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>Male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1149.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction_date  Gender   Age Marital_status  State_names   Segment  \\\n",
       "2         1/1/2019    Male  63.0        Married   New Mexico     Basic   \n",
       "5         1/3/2019    Male  71.0         Single       Hawaii     Basic   \n",
       "6         1/3/2019  Female  34.0        Married   New Mexico  Platinum   \n",
       "7         1/3/2019    Male  37.0        Married  Connecticut     Basic   \n",
       "\n",
       "  Employees_status Payment_method  Referal  Amount_spent  \n",
       "2          workers         PayPal      1.0       1572.60  \n",
       "5        Employees         PayPal      1.0       2922.66  \n",
       "6        Employees         PayPal      1.0       1481.42  \n",
       "7          workers         PayPal      1.0       1149.55  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering - Only show Paypal users\n",
    "condition = df['Payment_method'] == 'PayPal'\n",
    "df[condition].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nSlQRRNrwn8S",
   "metadata": {
    "id": "nSlQRRNrwn8S"
   },
   "source": [
    "We can apply multiple conditional queries like before. For example, if we want to display all Married female people who lived in New York then we use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "YvIpSZM2xiD4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "YvIpSZM2xiD4",
    "outputId": "90428598-504d-4a2d-8808-c0ea210c718e"
   },
   "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_date</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>State_names</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th>Referal</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>3/1/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New York</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1581.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>3/7/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New York</td>\n",
       "      <td>Basic</td>\n",
       "      <td>workers</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2694.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>3/31/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New York</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Employees</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2959.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>4/8/2019</td>\n",
       "      <td>Female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>New York</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Unemployment</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.0</td>\n",
       "      <td>522.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Transaction_date  Gender   Age Marital_status State_names  Segment  \\\n",
       "164         3/1/2019  Female  64.0        Married    New York    Basic   \n",
       "180         3/7/2019  Female  20.0        Married    New York    Basic   \n",
       "254        3/31/2019  Female  78.0        Married    New York  Missing   \n",
       "282         4/8/2019  Female  32.0        Married    New York     Gold   \n",
       "\n",
       "    Employees_status Payment_method  Referal  Amount_spent  \n",
       "164        Employees         PayPal      1.0       1581.77  \n",
       "180          workers         PayPal      1.0       2694.20  \n",
       "254        Employees         PayPal      1.0       2959.54  \n",
       "282     Unemployment          Other      1.0        522.24  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first create 3 condition\n",
    "female_person = df['Gender'] == 'Female'\n",
    "married_person = df['Marital_status'] == 'Married'\n",
    "loc_newyork = df['State_names'] == 'New York'\n",
    "\n",
    "# we passing condition on our dataframe\n",
    "df[female_person & married_person & loc_newyork].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "x0r-TXU244iu",
   "metadata": {
    "id": "x0r-TXU244iu"
   },
   "source": [
    "### 5.3 Summarizing or grouping data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NtTwyDD77VG0",
   "metadata": {
    "id": "NtTwyDD77VG0"
   },
   "source": [
    "#### Groupby\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2o84INb7AsFp",
   "metadata": {
    "id": "2o84INb7AsFp"
   },
   "source": [
    "**Grouping by one column:** For example, if we want to find `maximum` values of `Age` and `Amount_spent` by `Gender` then we can use this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2p_-CTIG-hSk",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "2p_-CTIG-hSk",
    "outputId": "a869040b-7a8a-436d-bb36-ace6274b0b2e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Amount_spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>78.0</td>\n",
       "      <td>2999.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>78.0</td>\n",
       "      <td>2998.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>72.0</td>\n",
       "      <td>2909.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age  Amount_spent\n",
       "Gender                     \n",
       "Female   78.0       2999.98\n",
       "Male     78.0       2998.62\n",
       "Unknown  72.0       2909.85"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age', 'Amount_spent']].groupby(df['Gender']).max()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7WCCLbwfCy_q",
   "metadata": {
    "id": "7WCCLbwfCy_q"
   },
   "source": [
    "To find `mean`, `count`, and `max` values of `Age` and `Amount_spent` by `Gender` then we can use `agg()` function with `groupby()` ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2b47c9d9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "2b47c9d9",
    "outputId": "d1841bb4-b8cc-4b2c-b128-dfd43fca356a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Amount_spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1351</td>\n",
       "      <td>46.816432</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1351</td>\n",
       "      <td>1429.471760</td>\n",
       "      <td>2999.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1121</td>\n",
       "      <td>46.525424</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1121</td>\n",
       "      <td>1409.420962</td>\n",
       "      <td>2998.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>28</td>\n",
       "      <td>42.357143</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1350.078699</td>\n",
       "      <td>2909.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age                  Amount_spent                      \n",
       "        count       mean   max        count         mean      max\n",
       "Gender                                                           \n",
       "Female   1351  46.816432  78.0         1351  1429.471760  2999.98\n",
       "Male     1121  46.525424  78.0         1121  1409.420962  2998.62\n",
       "Unknown    28  42.357143  72.0           28  1350.078699  2909.85"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by one columns\n",
    "state_gender_res = df[['Age','Gender','Amount_spent']].groupby(['Gender']).agg(['count', 'mean', 'max'])\n",
    "state_gender_res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9umxlA7YDjvD",
   "metadata": {
    "id": "9umxlA7YDjvD"
   },
   "source": [
    "**Grouping by multiple columns:** To find total count, maximum and minimum values of Amount_spent by State_names, Gender, and Payment_method then we can pass these columns names under `groupby()` function and add `.agg()` with `count`, `mean`, `max` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "667947eb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "id": "667947eb",
    "outputId": "73bcc08e-0ed0-48bb-97db-3f827c9fe709"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Amount_spent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Payment_method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Alabama</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
       "      <th>Card</th>\n",
       "      <td>8</td>\n",
       "      <td>413.11</td>\n",
       "      <td>2749.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>6</td>\n",
       "      <td>851.25</td>\n",
       "      <td>2789.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PayPal</th>\n",
       "      <td>6</td>\n",
       "      <td>77.90</td>\n",
       "      <td>2520.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
       "      <th>Card</th>\n",
       "      <td>6</td>\n",
       "      <td>221.17</td>\n",
       "      <td>2735.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>4</td>\n",
       "      <td>459.47</td>\n",
       "      <td>1691.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PayPal</th>\n",
       "      <td>11</td>\n",
       "      <td>87.88</td>\n",
       "      <td>2876.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <th>PayPal</th>\n",
       "      <td>1</td>\n",
       "      <td>1716.37</td>\n",
       "      <td>1716.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alaska</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
       "      <th>Card</th>\n",
       "      <td>6</td>\n",
       "      <td>141.50</td>\n",
       "      <td>1988.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>8</td>\n",
       "      <td>489.16</td>\n",
       "      <td>2970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PayPal</th>\n",
       "      <td>10</td>\n",
       "      <td>462.96</td>\n",
       "      <td>2615.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Card</th>\n",
       "      <td>1</td>\n",
       "      <td>2497.31</td>\n",
       "      <td>2497.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>8</td>\n",
       "      <td>588.88</td>\n",
       "      <td>2977.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Amount_spent                  \n",
       "                                          count      min      max\n",
       "State_names Gender  Payment_method                               \n",
       "Alabama     Female  Card                      8   413.11  2749.37\n",
       "                    Other                     6   851.25  2789.52\n",
       "                    PayPal                    6    77.90  2520.85\n",
       "            Male    Card                      6   221.17  2735.65\n",
       "                    Other                     4   459.47  1691.62\n",
       "                    PayPal                   11    87.88  2876.36\n",
       "            Unknown PayPal                    1  1716.37  1716.37\n",
       "Alaska      Female  Card                      6   141.50  1988.38\n",
       "                    Other                     8   489.16  2970.00\n",
       "                    PayPal                   10   462.96  2615.89\n",
       "            Male    Card                      1  2497.31  2497.31\n",
       "                    Other                     8   588.88  2977.82"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group By multiple columns\n",
    "state_gender_res = df[['State_names','Gender','Payment_method','Amount_spent']].groupby([ 'State_names','Gender', 'Payment_method']).agg(['count', 'min', 'max'])\n",
    "state_gender_res.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BTEMA10_JfrV",
   "metadata": {
    "id": "BTEMA10_JfrV"
   },
   "source": [
    "#### Cross Tabulation (Crosstab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bNNkQ9-eRclS",
   "metadata": {
    "id": "bNNkQ9-eRclS"
   },
   "source": [
    "For creating a simple crosstab between Maritatal_status and Payment_method columns we just use `crosstab()` with both column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "_9rixwbc5VEe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "_9rixwbc5VEe",
    "outputId": "487a2393-48ac-474f-c985-7fd3f6a25f44"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Payment_method</th>\n",
       "      <th>Card</th>\n",
       "      <th>Other</th>\n",
       "      <th>PayPal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>440</td>\n",
       "      <td>357</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>307</td>\n",
       "      <td>232</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Payment_method  Card  Other  PayPal\n",
       "Marital_status                     \n",
       "Married          440    357     667\n",
       "Single           307    232     497"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Marital_status, df.Payment_method)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OK_gu5KFSETb",
   "metadata": {
    "id": "OK_gu5KFSETb"
   },
   "source": [
    "We can include subtotals by `margins` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "UNpAxzbLSMBJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "UNpAxzbLSMBJ",
    "outputId": "ef309136-ece9-4989-b294-f0648f70a879"
   },
   "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>Payment_method</th>\n",
       "      <th>Card</th>\n",
       "      <th>Other</th>\n",
       "      <th>PayPal</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>440</td>\n",
       "      <td>357</td>\n",
       "      <td>667</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>307</td>\n",
       "      <td>232</td>\n",
       "      <td>497</td>\n",
       "      <td>1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>747</td>\n",
       "      <td>589</td>\n",
       "      <td>1164</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Payment_method  Card  Other  PayPal  Total\n",
       "Marital_status                            \n",
       "Married          440    357     667   1464\n",
       "Single           307    232     497   1036\n",
       "Total            747    589    1164   2500"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Marital_status, df.Payment_method, margins=True, margins_name=\"Total\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rDLBsIoZVRUK",
   "metadata": {
    "id": "rDLBsIoZVRUK"
   },
   "source": [
    "If We want a display with percentage than `normalize=True` parameter help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "rM6o2CocVeYG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239
    },
    "id": "rM6o2CocVeYG",
    "outputId": "ca351fca-c0e5-46bf-aac3-8b05ed1a8859"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Payment_method</th>\n",
       "      <th>Card</th>\n",
       "      <th>Other</th>\n",
       "      <th>PayPal</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>0.1760</td>\n",
       "      <td>0.1428</td>\n",
       "      <td>0.2668</td>\n",
       "      <td>0.5856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>0.1228</td>\n",
       "      <td>0.0928</td>\n",
       "      <td>0.1988</td>\n",
       "      <td>0.4144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.2988</td>\n",
       "      <td>0.2356</td>\n",
       "      <td>0.4656</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Payment_method    Card   Other  PayPal   Total\n",
       "Marital_status                                \n",
       "Married         0.1760  0.1428  0.2668  0.5856\n",
       "Single          0.1228  0.0928  0.1988  0.4144\n",
       "Total           0.2988  0.2356  0.4656  1.0000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Marital_status, df.Payment_method, normalize=True, margins=True, margins_name=\"Total\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "D3GJPV8aVxsD",
   "metadata": {
    "id": "D3GJPV8aVxsD"
   },
   "source": [
    "In this crosstab features, we can pass multiple columns names for grouping and analyzing data. For instance, If we want to see how the `Payment_method` and `Employees_status` are distributed by `Marital_status` then we will pass these columns' names in `crosstab()` function and it will show below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "uIHl15rL6fzm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "uIHl15rL6fzm",
    "outputId": "bd6a1d6c-33c9-48c5-f616-9f103157da86"
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Payment_method</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Card</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Other</th>\n",
       "      <th colspan=\"4\" halign=\"left\">PayPal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Employees_status</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>self-employed</th>\n",
       "      <th>workers</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>self-employed</th>\n",
       "      <th>workers</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>self-employed</th>\n",
       "      <th>workers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>185</td>\n",
       "      <td>38</td>\n",
       "      <td>81</td>\n",
       "      <td>136</td>\n",
       "      <td>127</td>\n",
       "      <td>39</td>\n",
       "      <td>78</td>\n",
       "      <td>113</td>\n",
       "      <td>265</td>\n",
       "      <td>66</td>\n",
       "      <td>120</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>117</td>\n",
       "      <td>37</td>\n",
       "      <td>57</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>17</td>\n",
       "      <td>43</td>\n",
       "      <td>76</td>\n",
       "      <td>178</td>\n",
       "      <td>55</td>\n",
       "      <td>106</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Payment_method        Card                                        Other  \\\n",
       "Employees_status Employees Unemployment self-employed workers Employees   \n",
       "Marital_status                                                            \n",
       "Married                185           38            81     136       127   \n",
       "Single                 117           37            57      96        96   \n",
       "\n",
       "Payment_method                                         PayPal               \\\n",
       "Employees_status Unemployment self-employed workers Employees Unemployment   \n",
       "Marital_status                                                               \n",
       "Married                    39            78     113       265           66   \n",
       "Single                     17            43      76       178           55   \n",
       "\n",
       "Payment_method                          \n",
       "Employees_status self-employed workers  \n",
       "Marital_status                          \n",
       "Married                    120     216  \n",
       "Single                     106     158  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Marital_status, [df.Payment_method, df.Employees_status])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7fbc9c",
   "metadata": {
    "id": "2c7fbc9c"
   },
   "source": [
    "## 6. Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uOkFj7ixq9d2",
   "metadata": {
    "id": "uOkFj7ixq9d2"
   },
   "source": [
    "Visualization is the key to data analysis. The most popular python package for visualization are matplotlib and seaborn but sometimes pandas will be handy for you. Pandas also provide some visualization plots easily. For the basic analysis part, it will be easy to use. For this section, we are exploring some different types of plots using pandas. Here are the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GeYsxRfly4Oq",
   "metadata": {
    "id": "GeYsxRfly4Oq"
   },
   "source": [
    "### 6.1 Line plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "x4GV-jQS3Wf3",
   "metadata": {
    "id": "x4GV-jQS3Wf3"
   },
   "source": [
    "For creating a line plot in pandas we use `.plot()` with two columns name for the argument. For example, we create a line plot from one dummy dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5uTSu01EmH-p",
   "metadata": {
    "id": "5uTSu01EmH-p"
   },
   "outputs": [],
   "source": [
    "dict_line = {\n",
    "    'year': [2016, 2017, 2018, 2019, 2020, 2021],\n",
    "    'price': [200, 250, 260, 220, 280, 300]\n",
    "}\n",
    "df_line = pd.DataFrame(dict_line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "Q8Oo5OrpnSQe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "Q8Oo5OrpnSQe",
    "outputId": "25b6e6f7-f592-4e5e-fcb9-12c8c0750c04"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use plot() method on the dataframe\n",
    "df_line.plot('year', 'price');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "setZs0vH35tj",
   "metadata": {
    "id": "setZs0vH35tj"
   },
   "source": [
    "The above line chart shows prices over a different time. It shows like price trend."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ki2MpNQ6bQUW",
   "metadata": {
    "id": "ki2MpNQ6bQUW"
   },
   "source": [
    "### 6.2 Bar plot\n",
    "\n",
    "A bar plot is also known as a bar chart shows quantitative or qualitative values for different category items. In a bar plot data are represented in the form of bars. Bars length or height are used to represent the quantitative value for each item. Bar plot can be plotted horizontally or vertically. For creating these plots look below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0o6W3oobIA9",
   "metadata": {
    "id": "c0o6W3oobIA9"
   },
   "source": [
    "**For horizontal bar:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ykQ0Vz8GZr63",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "ykQ0Vz8GZr63",
    "outputId": "6f5bc5e8-7044-48b6-dbe0-1011768c844f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Employees_status'].value_counts().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hUW2_IyhbNtQ",
   "metadata": {
    "id": "hUW2_IyhbNtQ"
   },
   "source": [
    "**For vertical bar:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0RFfg-TzbTB5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "0RFfg-TzbTB5",
    "outputId": "351e0a40-76f0-48a1-a21a-3dcb2ddb323b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Employees_status'].value_counts().plot(kind='barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZdCUNgj9iKHo",
   "metadata": {
    "id": "ZdCUNgj9iKHo"
   },
   "source": [
    "### 6.3 Pie plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "EG1XHZHLbiFX",
   "metadata": {
    "id": "EG1XHZHLbiFX"
   },
   "source": [
    "A pie plot is also known as a pie chart. A pie plot is a circular graph that represents the total value with its components. The area of a circle represents the total value and the different sectors of the circle represent the different parts. In this plot, the data are expressed as percentages. Each component is expressed as a percentage of the total value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DYGE4SaNcZLo",
   "metadata": {
    "id": "DYGE4SaNcZLo"
   },
   "source": [
    "In pandas for creating pie plot. We use `kind=pie` in `plot()` function in dataframes columns or series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "jf61Zg3GiPFp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "jf61Zg3GiPFp",
    "outputId": "2ee80f09-b3b7-4b63-a33e-b577633ae034"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Segment'].value_counts().plot(\n",
    "    kind='pie');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utOuo_y6biXn",
   "metadata": {
    "id": "utOuo_y6biXn"
   },
   "source": [
    "### 6.4 Box Plot\n",
    "A box plot is also known as a box and whisker plot. This plot is used to show the distribution of a variable based on its quartiles. Box plot displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum. It will also be popular to identify outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FK-M1zjEeWoB",
   "metadata": {
    "id": "FK-M1zjEeWoB"
   },
   "source": [
    "We can plot this by one column or multiple columns. For multiple columns, we need to pass columns name in `y` variable as a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "Oh9c2_0xblDZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "Oh9c2_0xblDZ",
    "outputId": "91dee7e1-e2cf-4427-ccef-12065779ccfe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=['Amount_spent'], kind='box');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LsLAK0twwF5h",
   "metadata": {
    "id": "LsLAK0twwF5h"
   },
   "source": [
    "In a boxplot, we can plot the distribution of categorical variables against a numerical variable and compare them. Let's plot it with the Employees_status and Amount_spent columns with pandas `boxplot()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "CMdCBcUjtY85",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "CMdCBcUjtY85",
    "outputId": "07df85a0-9701-4139-fb5c-dcb7d69d1c11"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning) \n",
    "fig, ax = plt.subplots(figsize=(6,6)) \n",
    "\n",
    "df.boxplot(by ='Employees_status', column =['Amount_spent'],ax=ax, grid = False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XlioEpoHf5tx",
   "metadata": {
    "id": "XlioEpoHf5tx"
   },
   "source": [
    "### 6.5 Histogram\n",
    "\n",
    "A histogram shows the frequency and distribution of quantitative measurement across grouped values for data items. It is commonly used in statistics to show how many of a certain type of variable occurs within a specific range or bucket. Below we will plot a histogram for looking Age distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "tIfoa79re8ca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "tIfoa79re8ca",
    "outputId": "c4f92206-6f0a-4f85-9912-86a32e74ca3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(\n",
    "    y='Age',\n",
    "    kind='hist',\n",
    "    bins=10\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zlw8AfaLgCIp",
   "metadata": {
    "id": "zlw8AfaLgCIp"
   },
   "source": [
    "### 6.6 KDE plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bsv4rB3rh9kY",
   "metadata": {
    "id": "bsv4rB3rh9kY"
   },
   "source": [
    "A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram. KDE represents the data using a continuous probability density curve in one or more dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "YW8XyrPugFnL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "YW8XyrPugFnL",
    "outputId": "852ab177-ecab-4060-f5de-67a7e56bd836"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(\n",
    "    y='Age',\n",
    "    xlim=(0, 100),\n",
    "    kind='kde'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BDnue_BWhLmF",
   "metadata": {
    "id": "BDnue_BWhLmF"
   },
   "source": [
    "### 6.7 Scatterplot\n",
    "A scatterplot is used to observe and show relationships between two quantitative variables for different category items. Each member of the dataset gets plotted as a point whose x-y coordinates relate to its values for the two variables. Below we will plot a scatterplot to display relationships between Age and Amount_spent columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "zF0yBKd8hObb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "zF0yBKd8hObb",
    "outputId": "035238ef-1a69-4561-d4cd-9039dfab7a27"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(\n",
    "    x='Age',\n",
    "    y='Amount_spent',\n",
    "    kind='scatter'\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9414a18",
   "metadata": {
    "id": "8f734a86"
   },
   "source": [
    "## 7. Reference\n",
    "\n",
    "\n",
    "1. [Pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html)\n",
    "2. [Pandas 1.x Cookbook](https://www.packtpub.com/product/pandas-1-x-cookbook-second-edition/9781839213106)\n",
    "3. [The Data Wrangling Workshop](https://www.packtpub.com/product/the-data-wrangling-workshop-second-edition/9781839215001) \n",
    "4. [Python for Data Analysis](https://www.oreilly.com/library/view/python-for-data/9781449323592/)\n",
    "5. [Data Analysis with Python: Zero to Pandas - Jovian YouTube Channel](https://www.youtube.com/watch?v=BaV4PRXYNIY&list=PLyMom0n-MBrpzC91Uo560S4VbsiLYtCwo)\n",
    "6. [Best practices with pandas - Data School YouTube Channel](https://www.youtube.com/watch?v=hl-TGI4550M&list=PL5-da3qGB5IBITZj_dYSFqnd_15JgqwA6)\n",
    "7. [Pandas Tutorials - Corey Schafer YouTube Channel](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)\n",
    "8. [Pandas Crosstab Explained](https://pbpython.com/pandas-crosstab.html)"
   ]
  }
 ],
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