{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e716e847",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61f5ff2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45149730",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>species</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>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>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>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>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>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>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>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>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>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>virginica</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    species\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",
       "..            ...          ...           ...          ...        ...\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",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3184be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>species</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>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>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>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>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>setosa</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aed536af",
   "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   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0e963e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "715bf7d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "523cb248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sepal_length', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='sepal_length', data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78444ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='petal_length', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='petal_length',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5a071b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='petal_width', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='petal_width',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f73a014b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sepal_width', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='sepal_width',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3a71b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sepal_length'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='sepal_length', data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0278260e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sepal_width'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='sepal_width',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "13e4443f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='petal_width'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='petal_width',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3affb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='petal_length'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='petal_length',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98d43806",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}