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sitc/ml21/visualization/04_Hierarchical_Charts.ipynb

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Added visualization notebooks
2024-04-03 20:53:02 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## [Introduction to Visualization](00_Intro_Visualization.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Hierarchical charts\n",
"Charts are used to show the parts of a variable.\n",
"\n",
"Most popular: pie chart, donut chart, and bar chart."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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"<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",
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"\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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16.99</td>\n",
" <td>1.01</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.34</td>\n",
" <td>1.66</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>21.01</td>\n",
" <td>3.50</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
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"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"df = sns.load_dataset('tips')\n",
"df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We can represent them with **pandas** or **mathplotlib**.\n",
"\n",
"A pie plot is a proportional representation of the numerical data in a column."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='tip'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.tip.groupby(df.sex, observed=False).sum().plot(kind='pie') # If observed False: show all values for categorical groupers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"For other graph types, we can use *Dash plot.ly*.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Male', 'Female']\n",
"[157, 87]\n"
]
}
],
"source": [
"import plotly as py\n",
"import plotly.graph_objects as go\n",
"\n",
"labels = list(df['sex'].value_counts().index)\n",
"values = list(df['sex'].value_counts().values)\n",
"\n",
"print(labels)\n",
"print(values)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
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}
],
"source": [
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"fig.show()"
]
},
{
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"execution_count": 8,
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Donut chart\n",
"fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# References\n",
"* [Data Preprocessing for Machine learning in Python, GeeksForGeeks](https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/)\n",
"* [Plotly](https://plot.ly/python/pie-charts/) library"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## Licence\n",
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
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