{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ed97617-c927-48b4-857c-c4d75b98fe2c",
   "metadata": {},
   "source": [
    "# Notebook-A1 (Data Wrangling-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a93305d3-03c9-479e-bde3-fb0d06ea8d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8bf62160-0384-4a3f-b25e-11e28c8b8df4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal.length  sepal.width  petal.length  petal.width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset into DataFrame\n",
    "df=pd.read_csv('iris.csv')\n",
    "df.describe() # Print description of DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c813f91-5100-463a-a848-2ef9f46344bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 values:\n",
      "    sepal.length  sepal.width  petal.length  petal.width variety\n",
      "0           5.1          3.5           1.4          0.2  Setosa\n",
      "1           4.9          3.0           1.4          0.2  Setosa\n",
      "2           4.7          3.2           1.3          0.2  Setosa\n",
      "3           4.6          3.1           1.5          0.2  Setosa\n",
      "4           5.0          3.6           1.4          0.2  Setosa\n",
      "Last 5 values:\n",
      "      sepal.length  sepal.width  petal.length  petal.width    variety\n",
      "145           6.7          3.0           5.2          2.3  Virginica\n",
      "146           6.3          2.5           5.0          1.9  Virginica\n",
      "147           6.5          3.0           5.2          2.0  Virginica\n",
      "148           6.2          3.4           5.4          2.3  Virginica\n",
      "149           5.9          3.0           5.1          1.8  Virginica\n"
     ]
    }
   ],
   "source": [
    "# Print first and last 5 values\n",
    "print(\"First 5 values:\\n\", df.head())\n",
    "print (\"Last 5 values:\\n\", df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "78fa6d44-7c39-4306-a5d7-0fd5ff94243a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "Length: 150, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print duplicated values\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5da58b1d-c458-4eb5-b23c-053c02934efd",
   "metadata": {
    "scrolled": true
   },
   "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>sepal.length</th>\n",
       "      <th>sepal.width</th>\n",
       "      <th>petal.length</th>\n",
       "      <th>petal.width</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal.length  sepal.width  petal.length  petal.width  variety\n",
       "0           False        False         False        False    False\n",
       "1           False        False         False        False    False\n",
       "2           False        False         False        False    False\n",
       "3           False        False         False        False    False\n",
       "4           False        False         False        False    False\n",
       "..            ...          ...           ...          ...      ...\n",
       "145         False        False         False        False    False\n",
       "146         False        False         False        False    False\n",
       "147         False        False         False        False    False\n",
       "148         False        False         False        False    False\n",
       "149         False        False         False        False    False\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print null values true/false\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b6face6-d366-4a05-9fd4-baff133e24f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal.length  150 non-null    float64\n",
      " 1   sepal.width   150 non-null    float64\n",
      " 2   petal.length  150 non-null    float64\n",
      " 3   petal.width   150 non-null    float64\n",
      " 4   variety       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Print summary of DataFrame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f08a00a4-c82a-49a9-bbaa-427d5cc4db96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print shape, i.e. rows + columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "186866fe-7614-4a4d-b929-31bd37f80027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "145    False\n",
       "146    False\n",
       "147    False\n",
       "148    False\n",
       "149    False\n",
       "Name: sepal.length, Length: 150, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print null (true/false) values in sepal.length column\n",
    "df[\"sepal.length\"].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5bc1e082-9818-450d-b76d-5dd3a2103e00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal.length  sepal.width  petal.width    variety\n",
      "0             5.1          3.5          0.2     Setosa\n",
      "1             4.9          3.0          0.2     Setosa\n",
      "2             4.7          3.2          0.2     Setosa\n",
      "3             4.6          3.1          0.2     Setosa\n",
      "4             5.0          3.6          0.2     Setosa\n",
      "..            ...          ...          ...        ...\n",
      "145           6.7          3.0          2.3  Virginica\n",
      "146           6.3          2.5          1.9  Virginica\n",
      "147           6.5          3.0          2.0  Virginica\n",
      "148           6.2          3.4          2.3  Virginica\n",
      "149           5.9          3.0          1.8  Virginica\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Delete/Drop petal.length column\n",
    "y = df.drop([\"petal.length\"], axis=1) # axis=1 column. For row, axis=0\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcccff24-c97e-4832-9cc1-cd701a3a9a34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal.length  sepal.width  petal.length  petal.width variety\n",
      "0             5.1          3.5           1.4          0.2       0\n",
      "1             4.9          3.0           1.4          0.2       0\n",
      "2             4.7          3.2           1.3          0.2       0\n",
      "3             4.6          3.1           1.5          0.2       0\n",
      "4             5.0          3.6           1.4          0.2       0\n",
      "..            ...          ...           ...          ...     ...\n",
      "145           6.7          3.0           5.2          2.3       1\n",
      "146           6.3          2.5           5.0          1.9       1\n",
      "147           6.5          3.0           5.2          2.0       1\n",
      "148           6.2          3.4           5.4          2.3       1\n",
      "149           5.9          3.0           5.1          1.8       1\n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# In variety column, replace Setosa with 0 and Virginica with 1\n",
    "df['variety'].replace(['Setosa', 'Virginica'], [0,1], inplace=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "97d22793-657e-4df7-a7f1-01d70886ef57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal.length    0\n",
       "sepal.width     0\n",
       "petal.length    0\n",
       "petal.width     0\n",
       "variety         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print sum of NULL values in each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f0c5c4-a930-4dbc-8300-99fb7cb7c991",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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