Assn_10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "159b0487",
   "metadata": {
    "id": "159b0487"
   },
   "source": [
    "# Assignment No 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec49876e",
   "metadata": {
    "id": "ec49876e"
   },
   "source": [
    "# Problem Statement: \n",
    "Data Visualization III\n",
    "Download    the    Iris    flower    dataset    or    any    other    dataset    into    a    DataFrame.    (e.g., https://archive.ics.uci.edu/ml/datasets/Iris ). Scan the dataset and give the inference as:\n",
    "1. List down the features and their types (e.g., numeric, nominal) available in the dataset.\n",
    "2. Create a histogram for each feature in the dataset to illustrate the feature distributions.\n",
    "3. Create a box plot for each feature in the dataset.\n",
    "4. Compare distributions and identify outliers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dea2ab17",
   "metadata": {
    "id": "dea2ab17"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74fb3eed",
   "metadata": {
    "id": "74fb3eed"
   },
   "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",
       "  </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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"iris.csv\")\n",
    "dataset.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b5238bab",
   "metadata": {
    "id": "b5238bab",
    "outputId": "cfc55c6b-32b0-4ac0-9c00-213a56d2d155"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PetalWidthCm', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(2,2,figsize=(16,9))\n",
    "sns.histplot(dataset['SepalLengthCm'], ax = axes[0,0])\n",
    "sns.histplot(dataset['SepalWidthCm'], ax = axes[0,1])\n",
    "sns.histplot(dataset['PetalLengthCm'], ax = axes[1,0])\n",
    "sns.histplot(dataset['PetalWidthCm'], ax = axes[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d332c25c",
   "metadata": {
    "id": "d332c25c",
    "outputId": "45297775-7226-4c70-baf8-4ca6295a2b77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Species', ylabel='PetalWidthCm'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(2,2,figsize=(16,9))\n",
    "sns.boxplot(y='SepalLengthCm', x='Species', data=dataset, ax=axes[0,0])\n",
    "sns.boxplot(y='SepalWidthCm', x='Species', data=dataset, ax=axes[0,1])\n",
    "sns.boxplot(y='PetalLengthCm', x='Species', data=dataset, ax=axes[1,0])\n",
    "sns.boxplot(y='PetalWidthCm', x='Species', data=dataset, ax=axes[1,1])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}