Exploratory Data Analysis(EDA).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "238120da",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81dfecb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b16babe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(r'C:\\Users\\sir\\Desktop\\Durgesh_7\\22_sep_2023\\iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "750e0b1b",
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\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>146</td>\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>147</td>\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>148</td>\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>149</td>\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>150</td>\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 × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "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",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02657a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44a540d",
   "metadata": {},
   "source": [
    "# Droping Revelent coloumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9fb98e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data.drop(['Id'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a96b31",
   "metadata": {},
   "source": [
    "# Renaming the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dbb60877",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data1.rename(columns={\"SepalLengthCm\":\"SL\",\"SepalWidthCm\":\"SW\",\"PetalLengthCm\":\"PL\",\"PetalWidthCm\":\"PW\",})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a4ad774b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</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>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": [
       "      SL   SW   PL   PW         Species\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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20252515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Species', ylabel='SW'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "## Box plot \n",
    "plt.figure(figsize=(8,4))\n",
    "sns.boxplot(x='Species',y='SW',data=data2,palette='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1de7e62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Species', ylabel='SW'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Box plot \n",
    "plt.figure(figsize=(8,4))\n",
    "sns.boxplot(x='Species',y='SW',data=data2,palette='pink_r')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ee1f6d",
   "metadata": {},
   "source": [
    "# Heap map(coralation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6d4e99e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sir\\AppData\\Local\\Temp\\ipykernel_7856\\308944384.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  c=data2.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "c=data2.corr()\n",
    "sns.heatmap(c,cmap=\"BrBG\",annot=True)"
   ]
  }
 ],
 "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",
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