Iris Flower.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb25abcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13326335",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce745837",
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7467e1",
   "metadata": {},
   "source": [
    "### Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f09d0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4e2e658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a8118a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "900"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f83dae1",
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</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",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\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>43.445368</td>\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>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0e88fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb24701b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad765879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956987c5",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65338b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716676</td>\n",
       "      <td>-0.397729</td>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.899759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>0.716676</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.397729</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.882747</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.899759</td>\n",
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      "text/plain": [
       "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
       "Id             1.000000       0.716676     -0.397729       0.882747   \n",
       "SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n",
       "SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n",
       "PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n",
       "PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n",
       "\n",
       "               PetalWidthCm  \n",
       "Id                 0.899759  \n",
       "SepalLengthCm      0.817954  \n",
       "SepalWidthCm      -0.356544  \n",
       "PetalLengthCm      0.962757  \n",
       "PetalWidthCm       1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a93a8d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(method='pearson').drop(\n",
    "  ['Id'], axis=1).drop(['Id'], axis=0),\n",
    "           annot = True);\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c48534bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coder\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SepalLengthCm'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.boxplot('SepalLengthCm',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "415c4baa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coder\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SepalWidthCm'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEGCAYAAABW0j9MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANm0lEQVR4nO3dfWxdd33H8c8ndiRSaAc4pVRuN4saugEbLY3CWmBjqJUCVDxMoPEHtMAmxIPcUG2AYFIBAZvGNtTWaEMVm+QIxDPaUJV6pBrhoWmLklIaSgq6oCAwUBIX2qKEgpMvf5xjcnt77dx7eu8935z7fkmW7XvP9fn9cux3jo99f3ZECACQx4a6BwAAeDjCDADJEGYASIYwA0AyhBkAkpnsZ+PNmzfHzMzMkIYCAM20b9++wxFxZq/b9xXmmZkZ7d27t/9RAcAYs/3DfrbnUgYAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMn09Tf/0Bzz8/NqtVpD38/S0pIkaXp6euj76tfs7Kzm5ubqHgbwCIR5TLVaLd357QM6dtoTh7qfiSP3S5J+9lCuT7WJI/fVPQRgTbm+WjBSx057oo7+8YuHuo9N9+yUpKHvp1+r4wIy4hozACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAk05gwz8/Pa35+vu5hACjxNVndZN0DGJRWq1X3EAC04WuyusacMQNAUxBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZEYS5uXlZV111VVaXl4exe4AnIKqdGIUbamjXyMJ88LCgvbv368dO3aMYncATkFVOjGKttTRr6GHeXl5WYuLi4oILS4uctYM4BGqdGIUbamrX5PD3sHCwoKOHz8uSTp27Jh27Nihq6++euD7WVpa0tGjR7V9+/aBf+wmarVa2vCbqHsYtdnw6wfUaj3I58sQtVotbdq0qadtq3RiFG0ZVb86nfSM2fYbbe+1vffQoUN97+Dmm2/WysqKJGllZUW7du3qf5QAGq1KJ0bRlrr6ddIz5oi4QdINkrRly5a+T7EuvfRS7dy5UysrK5qcnNRll11WYZgnNz09LUm67rrrhvLxm2b79u3a94N76x5GbY4/5gzNPuUsPl+GqJ/vRqp0YhRtGVW/Og39GvOVV16pDRuK3UxMTOiKK64Y9i4BnGKqdGIUbamrX0MP89TUlLZt2ybb2rZtm6ampoa9SwCnmCqdGEVb6urX0H/4JxX/6xw8eJCzZQBrqtKJUbSljn6NJMxTU1O6/vrrR7ErAKeoKp0YRVvq6BdPyQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMpN1D2BQZmdn6x4CgDZ8TVbXmDDPzc3VPQQAbfiarI5LGQCQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJEOYASAZwgwAyRBmAEiGMANAMoQZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSmax7AKjPxJH7tOmenUPex7IkDX0//Zo4cp+ks+oeBtAVYR5Ts7OzI9nP0tKKJGl6OlsEzxrZvwHQL8I8pubm5uoeAoA1cI0ZAJIhzACQDGEGgGQIMwAkQ5gBIBnCDADJEGYASIYwA0AyhBkAkiHMAJAMYQaAZAgzACRDmAEgGcIMAMkQZgBIhjADQDKEGQCSIcwAkAxhBoBkCDMAJOOI6H1j+5CkH1bc12ZJhys+9lQ3znOXxnv+4zx3abzn3z73P4qIM3t9YF9hfjRs742ILSPZWTLjPHdpvOc/znOXxnv+j2buXMoAgGQIMwAkM8ow3zDCfWUzznOXxnv+4zx3abznX3nuI7vGDADoDZcyACAZwgwAyQw0zLbPtf1l2wds3217e5dtbPt62y3bd9l+9iDHUJce5/4C2/fbvrN8uaaOsQ6a7cfY/obtb5Vzf1+XbRp53KWe59/IY7/K9oTtb9q+sct9jT320knnXum4Tw54jCuS/j4i7rB9uqR9tndFxHfatnmRpKeWL8+R9J/l61NdL3OXpK9FxOU1jG+YHpL0woj4le2Nkr5u+6aIuK1tm6Yed6m3+UvNPPartks6IOmMLvc1+dhL689dqnDcB3rGHBE/jYg7yrcfVDHY6Y7NXiZpRxRuk/R422cPchx16HHujVQey1+V724sXzp/qtzI4y71PP/Gsn2OpJdI+tgamzT22Pcw90qGdo3Z9oykCyXd3nHXtKQftb3/YzUsYOvMXZIuLr/lvcn2M0Y7suEpv527U9LPJe2KiLE67j3MX2rosZd0raR3SDq+xv1NPvbXav25SxWO+1DCbPtxkj4v6W0R8UDn3V0e0pizi5PM/Q4Vz5l/lqR5Sf8z4uENTUQci4gLJJ0jaavtZ3Zs0ujj3sP8G3nsbV8u6ecRsW+9zbrcdsof+x7nXum4DzzM5TW2z0v6RER8ocsmP5Z0btv750j6yaDHUYeTzT0iHlj9ljcidkraaHvziIc5VBHxS0m7JW3ruKuxx73dWvNv8LF/rqSX2j4o6VOSXmj74x3bNPXYn3TuVY/7oH8rw5L+S9KBiPjwGpt9UdIV5U9q/1zS/RHx00GOow69zN32k8vtZHurin//5dGNcjhsn2n78eXbmyRdKumejs0aedyl3ubf1GMfEe+KiHMiYkbSqyX9f0S8pmOzRh77XuZe9bgP+rcynivptZL2l9fbJOndkv5QkiLio5J2SnqxpJakI5JeP+Ax1KWXub9S0pttr0g6KunV0YynXp4tacH2hIpPvM9ExI223yQ1/rhLvc2/qce+qzE69o8wiOPOU7IBIBme+QcAyRBmAEiGMANAMoQZAJIhzACQDGFGZbb/sVxN7a5y5ayBLUxTrsp1Y/m7r4dtP6G8/WzbYft5bdsesj1l+2O2n97lY73O9kfKt1/evo3t3ba7/sFM21ttf9X2d23fU3780wY1R2AthBmV2L5Y0uWSnh0Rf6biSRU/Wv9R/St/5/N2SReXN10i6Zvla9k+X9LhiFiOiL/rsppfp5dLekS8O9k+S9JnJb0zIs6X9CeSFiWdXmUeQD8IM6o6W0UQH5KkiDgcET+xfZHtr9jeZ/v/VlcRK89Mr7W9x/a3y2dBrZ6V7nGxnu2eMrSdblEZ4vL1h/XwUO9p28eW8u3X2/6e7a+oePKPbF8i6aWS/rU8wz+v/BivcrGe8vdsP7+87a2SFiLi1nJ+ERGfi4h7bb/X9oLtL9k+aPuvbX/I9n7biy6emg9URphR1ZcknVvG7D9s/2UZpHlJr4yIiyT9t6QPtj3msRFxiaS3lPdJxVOX/yIiLpR0jaR/6rKvPToR5q0qFoJZXXvhEhXh/r3yP4P3qQjyZSrPkCNij4qnB789Ii6IiO+XD5mMiK2S3ibpPeVtz5S03uI056lY7vFlkj4u6csR8acqnt31knUeB5zUoJ+SjTFRLgp/kaTnS/orSZ+W9AEVQdtVLg8wIal9TYRPlo/9qu0zyvUlTlfxdOanqlhxrNvZ5jckXWj7sZI2lvv+ge1ZFWH+947tnyNpd0QckiTbn5b0tHWms7rg1D5JMz1MX5Juiojf2t5fznOxvH1/Hx8D6Iowo7KIOKZiJbXdZaDeKunuiLh4rYd0ef/9Ks42X+FiHevdXfZzxHZL0htULKMoSbepWH/hSZK+28O+1vNQ+fqYTnxN3C3pIkn/u95jIuK47d+2rX9wXHxd4VHiUgYqsX1+eZa76gIVf7XlzPIHg7K90Q9fGPxvytufp2KFsfsl/YGkpfL+162zy1tUXGq4tXz/VhV/0ue2LovC3C7pBeVvamyU9Kq2+x5Ubz/A+4ikK9t/08T2a2w/uYfHAo8KYUZVj1NxCeI7tu9ScR33GhWraf2L7W9JulMnrg1L0i9s75H0UUl/W972IUn/bPsWFZcE1nKLpKfoRJjvULGu757ODcslJd9bbnuzTpxlS8W6uW8vf9h4Xudj2z7GvSqWcvy38tflDqi4bNP5xw+AgWN1OYyE7d2S/iEi9tY9FiA7zpgBIBnOmAEgGc6YASAZwgwAyRBmAEiGMANAMoQZAJL5HTuKiBGwM1fYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('SepalWidthCm',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65550dbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coder\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PetalLengthCm'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('PetalLengthCm',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1b5859d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\coder\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PetalWidthCm'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('PetalWidthCm',data=df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}