DataScience.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8e03fb5-fe61-4a9a-9532-a07d113a71a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b05e03f-cff9-4171-900b-6c93e839abf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd558dd1-0790-48a7-9ea1-e592f814de7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4516dfcd-16a3-4715-aa33-a9eda8f3e023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape of the data frame\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4bb12b1-67e7-48f9-accf-a1b595bd18d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the rows of the data frame\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84ac1994-ac86-46b9-92db-db0e230bbc05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying columns\n",
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "414216b1-aac9-4e1b-9319-5e959b092062",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>sepalwidth</th>\n",
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       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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      ],
      "text/plain": [
       "   sepallength  sepalwidth  petallength  petalwidth        class\n",
       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
       "2          4.7         3.2          1.3         0.2  Iris-setosa\n",
       "3          4.6         3.1          1.5         0.2  Iris-setosa\n",
       "4          5.0         3.6          1.4         0.2  Iris-setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #by default it displays the first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d2a6023-e05b-4b60-9b99-6ca23a1e0157",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
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       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0079ae74-c714-4b48-bfb2-2531c3fe25a6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</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>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</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": [
       "       sepallength  sepalwidth  petallength  petalwidth\n",
       "count   150.000000  150.000000   150.000000  150.000000\n",
       "mean      5.843333    3.054000     3.758667    1.198667\n",
       "std       0.828066    0.433594     1.764420    0.763161\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #Gives descriptive statistics, summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdb1754d-dca3-44b0-9d44-c9d1f24f41c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fca59010-8beb-46ac-a94c-810bb8a82e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepallength    0\n",
       "sepalwidth     0\n",
       "petallength    0\n",
       "petalwidth     0\n",
       "class          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if there are null values\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4825bd5a-4a54-4eb9-affc-281524c285d5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</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>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55d10c7b-f0c6-45a9-aa41-8f487aafd17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.7, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [4.6, 3.1, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.6, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [5.4, 3.9, 1.7, 0.4, 'Iris-setosa'],\n",
       "       [4.6, 3.4, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.0, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [4.4, 2.9, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.4, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.4, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.1, 'Iris-setosa'],\n",
       "       [4.3, 3.0, 1.1, 0.1, 'Iris-setosa'],\n",
       "       [5.8, 4.0, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [5.7, 4.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [5.4, 3.9, 1.3, 0.4, 'Iris-setosa'],\n",
       "       [5.1, 3.5, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.7, 3.8, 1.7, 0.3, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.5, 0.3, 'Iris-setosa'],\n",
       "       [5.4, 3.4, 1.7, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.7, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [4.6, 3.6, 1.0, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.3, 1.7, 0.5, 'Iris-setosa'],\n",
       "       [4.8, 3.4, 1.9, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.0, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.4, 1.6, 0.4, 'Iris-setosa'],\n",
       "       [5.2, 3.5, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.2, 3.4, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.7, 3.2, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.8, 3.1, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [5.4, 3.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [5.2, 4.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.5, 4.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [5.0, 3.2, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [5.5, 3.5, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [4.4, 3.0, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [5.1, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.5, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [4.5, 2.3, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [4.4, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.5, 1.6, 0.6, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.9, 0.4, 'Iris-setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [5.1, 3.8, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [4.6, 3.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [5.3, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [5.0, 3.3, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [7.0, 3.2, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [6.4, 3.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [6.9, 3.1, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [5.5, 2.3, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [6.5, 2.8, 4.6, 1.5, 'Iris-versicolor'],\n",
       "       [5.7, 2.8, 4.5, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 3.3, 4.7, 1.6, 'Iris-versicolor'],\n",
       "       [4.9, 2.4, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [6.6, 2.9, 4.6, 1.3, 'Iris-versicolor'],\n",
       "       [5.2, 2.7, 3.9, 1.4, 'Iris-versicolor'],\n",
       "       [5.0, 2.0, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [5.9, 3.0, 4.2, 1.5, 'Iris-versicolor'],\n",
       "       [6.0, 2.2, 4.0, 1.0, 'Iris-versicolor'],\n",
       "       [6.1, 2.9, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [5.6, 2.9, 3.6, 1.3, 'Iris-versicolor'],\n",
       "       [6.7, 3.1, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [5.6, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.8, 2.7, 4.1, 1.0, 'Iris-versicolor'],\n",
       "       [6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.6, 2.5, 3.9, 1.1, 'Iris-versicolor'],\n",
       "       [5.9, 3.2, 4.8, 1.8, 'Iris-versicolor'],\n",
       "       [6.1, 2.8, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 2.5, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [6.1, 2.8, 4.7, 1.2, 'Iris-versicolor'],\n",
       "       [6.4, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [6.6, 3.0, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [6.8, 2.8, 4.8, 1.4, 'Iris-versicolor'],\n",
       "       [6.7, 3.0, 5.0, 1.7, 'Iris-versicolor'],\n",
       "       [6.0, 2.9, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [5.7, 2.6, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [5.5, 2.4, 3.8, 1.1, 'Iris-versicolor'],\n",
       "       [5.5, 2.4, 3.7, 1.0, 'Iris-versicolor'],\n",
       "       [5.8, 2.7, 3.9, 1.2, 'Iris-versicolor'],\n",
       "       [6.0, 2.7, 5.1, 1.6, 'Iris-versicolor'],\n",
       "       [5.4, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [6.0, 3.4, 4.5, 1.6, 'Iris-versicolor'],\n",
       "       [6.7, 3.1, 4.7, 1.5, 'Iris-versicolor'],\n",
       "       [6.3, 2.3, 4.4, 1.3, 'Iris-versicolor'],\n",
       "       [5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],\n",
       "       [6.1, 3.0, 4.6, 1.4, 'Iris-versicolor'],\n",
       "       [5.8, 2.6, 4.0, 1.2, 'Iris-versicolor'],\n",
       "       [5.0, 2.3, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [5.6, 2.7, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [5.7, 3.0, 4.2, 1.2, 'Iris-versicolor'],\n",
       "       [5.7, 2.9, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [6.2, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [5.1, 2.5, 3.0, 1.1, 'Iris-versicolor'],\n",
       "       [5.7, 2.8, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [6.3, 3.3, 6.0, 2.5, 'Iris-virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'Iris-virginica'],\n",
       "       [7.1, 3.0, 5.9, 2.1, 'Iris-virginica'],\n",
       "       [6.3, 2.9, 5.6, 1.8, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.8, 2.2, 'Iris-virginica'],\n",
       "       [7.6, 3.0, 6.6, 2.1, 'Iris-virginica'],\n",
       "       [4.9, 2.5, 4.5, 1.7, 'Iris-virginica'],\n",
       "       [7.3, 2.9, 6.3, 1.8, 'Iris-virginica'],\n",
       "       [6.7, 2.5, 5.8, 1.8, 'Iris-virginica'],\n",
       "       [7.2, 3.6, 6.1, 2.5, 'Iris-virginica'],\n",
       "       [6.5, 3.2, 5.1, 2.0, 'Iris-virginica'],\n",
       "       [6.4, 2.7, 5.3, 1.9, 'Iris-virginica'],\n",
       "       [6.8, 3.0, 5.5, 2.1, 'Iris-virginica'],\n",
       "       [5.7, 2.5, 5.0, 2.0, 'Iris-virginica'],\n",
       "       [5.8, 2.8, 5.1, 2.4, 'Iris-virginica'],\n",
       "       [6.4, 3.2, 5.3, 2.3, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.5, 1.8, 'Iris-virginica'],\n",
       "       [7.7, 3.8, 6.7, 2.2, 'Iris-virginica'],\n",
       "       [7.7, 2.6, 6.9, 2.3, 'Iris-virginica'],\n",
       "       [6.0, 2.2, 5.0, 1.5, 'Iris-virginica'],\n",
       "       [6.9, 3.2, 5.7, 2.3, 'Iris-virginica'],\n",
       "       [5.6, 2.8, 4.9, 2.0, 'Iris-virginica'],\n",
       "       [7.7, 2.8, 6.7, 2.0, 'Iris-virginica'],\n",
       "       [6.3, 2.7, 4.9, 1.8, 'Iris-virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.1, 'Iris-virginica'],\n",
       "       [7.2, 3.2, 6.0, 1.8, 'Iris-virginica'],\n",
       "       [6.2, 2.8, 4.8, 1.8, 'Iris-virginica'],\n",
       "       [6.1, 3.0, 4.9, 1.8, 'Iris-virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.1, 'Iris-virginica'],\n",
       "       [7.2, 3.0, 5.8, 1.6, 'Iris-virginica'],\n",
       "       [7.4, 2.8, 6.1, 1.9, 'Iris-virginica'],\n",
       "       [7.9, 3.8, 6.4, 2.0, 'Iris-virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.2, 'Iris-virginica'],\n",
       "       [6.3, 2.8, 5.1, 1.5, 'Iris-virginica'],\n",
       "       [6.1, 2.6, 5.6, 1.4, 'Iris-virginica'],\n",
       "       [7.7, 3.0, 6.1, 2.3, 'Iris-virginica'],\n",
       "       [6.3, 3.4, 5.6, 2.4, 'Iris-virginica'],\n",
       "       [6.4, 3.1, 5.5, 1.8, 'Iris-virginica'],\n",
       "       [6.0, 3.0, 4.8, 1.8, 'Iris-virginica'],\n",
       "       [6.9, 3.1, 5.4, 2.1, 'Iris-virginica'],\n",
       "       [6.7, 3.1, 5.6, 2.4, 'Iris-virginica'],\n",
       "       [6.9, 3.1, 5.1, 2.3, 'Iris-virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'Iris-virginica'],\n",
       "       [6.8, 3.2, 5.9, 2.3, 'Iris-virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.5, 'Iris-virginica'],\n",
       "       [6.7, 3.0, 5.2, 2.3, 'Iris-virginica'],\n",
       "       [6.3, 2.5, 5.0, 1.9, 'Iris-virginica'],\n",
       "       [6.5, 3.0, 5.2, 2.0, 'Iris-virginica'],\n",
       "       [6.2, 3.4, 5.4, 2.3, 'Iris-virginica'],\n",
       "       [5.9, 3.0, 5.1, 1.8, 'Iris-virginica']], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ddb86ff-57a6-41ae-b111-d601f3ba076c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepallength', 'sepalwidth', 'petallength', 'petalwidth', 'class'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4b87226-bbbb-4a50-870c-e83aa5727e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "876.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sepallength'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "250eff71-f857-4aad-9320-3fe741ff9e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.843333333333335"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sepallength'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f80f392d-16c5-48ab-8e14-4a085f6042bb",
   "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>sepallength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength\n",
       "0            5.1\n",
       "1            4.9\n",
       "2            4.7\n",
       "3            4.6\n",
       "4            5.0\n",
       "..           ...\n",
       "145          6.7\n",
       "146          6.3\n",
       "147          6.5\n",
       "148          6.2\n",
       "149          5.9\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['sepallength']] #double brackets produces a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a77afdb-d7a5-416c-84ca-440e8fb5c8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth            class\n",
       "18           5.7         3.8          1.7         0.3      Iris-setosa\n",
       "132          6.4         2.8          5.6         2.2   Iris-virginica\n",
       "134          6.1         2.6          5.6         1.4   Iris-virginica\n",
       "54           6.5         2.8          4.6         1.5  Iris-versicolor"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "341104ed-602a-4b37-a56a-a138c2cd812e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth            class\n",
       "57           4.9         2.4          3.3         1.0  Iris-versicolor\n",
       "95           5.7         3.0          4.2         1.2  Iris-versicolor\n",
       "127          6.1         3.0          4.9         1.8   Iris-virginica\n",
       "137          6.4         3.1          5.5         1.8   Iris-virginica"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed=42\n",
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ecec769-5626-48a8-a867-cec40e5bfb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallength  sepalwidth  petallength  petalwidth        class\n",
       "0          5.1         3.5          1.4         0.2  Iris-setosa\n",
       "1          4.9         3.0          1.4         0.2  Iris-setosa\n",
       "2          4.7         3.2          1.3         0.2  Iris-setosa"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#splicing rows\n",
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5780146b-1dc3-431c-9933-d45a813d5fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallength  sepalwidth  petallength  petalwidth\n",
       "0          5.1         3.5          1.4         0.2\n",
       "1          4.9         3.0          1.4         0.2\n",
       "2          4.7         3.2          1.3         0.2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1713fd1f-d1b2-4500-9eb0-a0a236e84565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iris-setosa'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7199dead-40cf-46d3-9902-5c22e8639e95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: class, Length: 150, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4109938b-d481-4c30-a752-d37ddfb3149a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9c40d247-3ce7-44c5-9516-6906a4867c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming columns\n",
    "df.rename(columns = {'new_col1':'sepallength', 'new_col2':'class'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "87ed1b48-363b-4819-88d6-9691dda01cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>sepallength</th>\n",
       "      <th>sepalwidth</th>\n",
       "      <th>petallength</th>\n",
       "      <th>petalwidth</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</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>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepallength  sepalwidth  petallength  petalwidth           class\n",
       "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
       "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
       "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
       "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
       "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
       "..           ...         ...          ...         ...             ...\n",
       "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
       "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
       "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
       "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
       "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2d7448b6-245f-42cd-9aae-ae9d69365c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking outliers using boxplot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "233007f5-8282-4b46-9267-090b075fdd99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e42c1d0e-045a-430d-8079-89db93d5f9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"class\", y=\"petallength\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e139ee5a-75a1-4552-bd45-40b3a12e9d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='petalwidth'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box plot\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,2,1) #grid paper\n",
    "sns.boxplot(x='class' ,y='sepallength' ,data=df)\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(x='class',y='sepallength' ,data=df)\n",
    "plt.subplot(2,2,3)\n",
    "sns.boxplot(x='class', y='petallength' , data=df)\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(x='class', y='petalwidth' ,data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2be126b5-0789-4998-89e7-b332000c2f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e0204b2e-a9d0-43d7-8477-c2eedd3d651c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c60f0b57-29d5-4caf-b091-33c707c1331f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: class, Length: 150, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df['class']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f99d810c-7cb6-4342-9957-7f56adc263f4",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('sepallength', 'sepalwidth', 'petallength', 'petalwidth')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('sepallength', 'sepalwidth', 'petallength', 'petalwidth')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_5980/1915042093.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sepallength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'sepalwidth'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'petallength'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'petalwidth'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ('sepallength', 'sepalwidth', 'petallength', 'petalwidth')"
     ]
    }
   ],
   "source": [
    "x=df['sepallength','sepalwidth','petallength','petalwidth']\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14710f7c-6659-40e0-badf-8e23ce3ba123",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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