{ "cells": [ { "cell_type": "markdown", "id": "41237e8e-d76b-4939-bfd1-13ed5984a473", "metadata": {}, "source": [ "# Notebook-A10 (Data Visualization-3)" ] }, { "cell_type": "code", "execution_count": 13, "id": "890b5c5d-3b54-4e61-b313-0cb16781f5c1", "metadata": {}, "outputs": [], "source": [ "# Import libraries\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "6cbe6c12-0461-4ef4-9160-70b4dceefdff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | sepal.length | \n", "sepal.width | \n", "petal.length | \n", "petal.width | \n", "variety | \n", "
|---|---|---|---|---|---|
| 0 | \n", "5.1 | \n", "3.5 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "
| 1 | \n", "4.9 | \n", "3.0 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "
| 2 | \n", "4.7 | \n", "3.2 | \n", "1.3 | \n", "0.2 | \n", "Setosa | \n", "
| 3 | \n", "4.6 | \n", "3.1 | \n", "1.5 | \n", "0.2 | \n", "Setosa | \n", "
| 4 | \n", "5.0 | \n", "3.6 | \n", "1.4 | \n", "0.2 | \n", "Setosa | \n", "