mirror of
https://github.com/gsi-upm/sitc
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364 lines
8.8 KiB
Plaintext
364 lines
8.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"## [Introduction to Visualization](00_Intro_Visualization.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"# Dataset\n",
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"Seaborn includes several datasets. We can consult the available datasets and load them. \n",
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"\n",
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"The datasets are also available at https://github.com/mwaskom/seaborn-data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['anagrams',\n",
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" 'anscombe',\n",
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" 'attention',\n",
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" 'brain_networks',\n",
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" 'car_crashes',\n",
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" 'diamonds',\n",
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" 'dots',\n",
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" 'dowjones',\n",
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" 'exercise',\n",
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" 'flights',\n",
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" 'fmri',\n",
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" 'geyser',\n",
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" 'glue',\n",
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" 'healthexp',\n",
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" 'iris',\n",
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" 'mpg',\n",
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" 'penguins',\n",
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" 'planets',\n",
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" 'seaice',\n",
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" 'taxis',\n",
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" 'tips',\n",
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" 'titanic']"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sns.get_dataset_names()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>total_bill</th>\n",
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" <th>tip</th>\n",
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" <th>sex</th>\n",
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" <th>smoker</th>\n",
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" <th>day</th>\n",
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" <th>time</th>\n",
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" <th>size</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>16.99</td>\n",
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" <td>1.01</td>\n",
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" <td>Female</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>10.34</td>\n",
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" <td>1.66</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>21.01</td>\n",
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" <td>3.50</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>23.68</td>\n",
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" <td>3.31</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>24.59</td>\n",
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" <td>3.61</td>\n",
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" <td>Female</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>25.29</td>\n",
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" <td>4.71</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>8.77</td>\n",
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" <td>2.00</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>26.88</td>\n",
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" <td>3.12</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>4</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>15.04</td>\n",
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" <td>1.96</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>14.78</td>\n",
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" <td>3.23</td>\n",
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" <td>Male</td>\n",
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" <td>No</td>\n",
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" <td>Sun</td>\n",
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" <td>Dinner</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" total_bill tip sex smoker day time size\n",
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"0 16.99 1.01 Female No Sun Dinner 2\n",
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"1 10.34 1.66 Male No Sun Dinner 3\n",
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"2 21.01 3.50 Male No Sun Dinner 3\n",
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"3 23.68 3.31 Male No Sun Dinner 2\n",
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"4 24.59 3.61 Female No Sun Dinner 4\n",
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"5 25.29 4.71 Male No Sun Dinner 4\n",
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"6 8.77 2.00 Male No Sun Dinner 2\n",
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"7 26.88 3.12 Male No Sun Dinner 4\n",
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"8 15.04 1.96 Male No Sun Dinner 2\n",
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"9 14.78 3.23 Male No Sun Dinner 2"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = sns.load_dataset('tips')\n",
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"df.head(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# References\n",
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"* [Seaborn](http://seaborn.pydata.org/index.html) documentation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"## Licence\n",
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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