{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "n_sAxegmac2O"
},
"source": [
"# 19: Data Visualisation with Seaborn"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "TRBvMfEtabAl"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"colab_type": "code",
"id": "RiQVjflwaw9r",
"outputId": "bd16233c-7c2e-417d-e28d-feb22fed89d2"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sepal_length | \n",
" sepal_width | \n",
" petal_length | \n",
" petal_width | \n",
" species | \n",
"
\n",
" \n",
" \n",
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" | 0 | \n",
" 5.1 | \n",
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\n",
" \n",
" | 1 | \n",
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" 3.0 | \n",
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" | 2 | \n",
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" 1.3 | \n",
" 0.2 | \n",
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\n",
" \n",
" | 3 | \n",
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" | 4 | \n",
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"metadata": {},
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"source": [
"sns.pairplot(iris, hue='species')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zuCNYJ6_btlZ"
},
"source": [
"## 2. Categories Viewing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"colab_type": "code",
"id": "zT99eFnnbSp4",
"outputId": "dd9f4421-b85f-4f7e-99d6-a7d5ace29db5"
},
"outputs": [
{
"data": {
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" \n",
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" | 1 | \n",
" 1 | \n",
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