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Added visualization notebooks
This commit is contained in:
Carlos A. Iglesias
GitHub
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commit
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185
ml21/visualization/00_Intro_Visualization.ipynb
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ml21/visualization/00_Intro_Visualization.ipynb
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{
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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": "slide"
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}
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},
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"source": [
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"# Introduction to Visualization\n",
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" \n",
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"In this session, we will get more insight regarding how to visualize data.\n",
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"\n",
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"# Objectives\n",
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"\n",
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"The main objectives of this session are:\n",
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"* Understanding how to visualize data\n",
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"* Understanding the purpose of different charts \n",
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"* Experimenting with several environments for visualizing data\n",
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"\n"
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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": "slide"
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}
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},
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"source": [
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"# Seaborn\n",
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"\n",
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"Seaborn is a library that visualizes data in Python. The main characteristics are:\n",
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"\n",
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"* A dataset-oriented API for examining relationships between multiple variables\n",
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"* Specialized support for using categorical variables to show observations or aggregate statistics\n",
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"* Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data\n",
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"* Automatic estimation and plotting of linear regression models for different kinds of dependent variables\n",
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"* Convenient views of the overall structure of complex datasets\n",
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"* High-level abstractions for structuring multi-plot grids that let you quickly build complex visualizations\n",
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"* Concise control over matplotlib figure styling with several built-in themes\n",
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"* Tools for choosing color palettes that faithfully reveal patterns in your data\n"
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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": "slide"
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}
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},
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"source": [
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"## Install\n",
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"Use:\n",
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"\n",
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"**conda install seaborn**\n",
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"\n",
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"or \n",
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"\n",
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"**pip install seaborn**"
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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": "slide"
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}
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},
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"source": [
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"# Table of Contents"
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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": "fragment"
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}
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},
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"source": [
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"1. [Home](00_Intro_Visualization.ipynb)\n",
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"2. [Dataset](01_Dataset.ipynb)\n",
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"3. [Comparison Charts](02_Comparison_Charts.ipynb)\n",
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" 1. [More Comparison Charts](02_01_More_Comparison_Charts.ipynb)\n",
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"4. [Distribution Charts](03_Distribution_Charts.ipynb)\n",
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"5. [Hierarchical charts](04_Hierarchical_Charts.ipynb)\n",
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"6. [Relational charts](05_Relational_Charts.ipynb)\n",
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"7. [Spatial charts](06_Spatial_Charts.ipynb)\n",
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"8. [Temporal charts](07_Temporal_Charts.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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"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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"metadata": {
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"datacleaner": {
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"position": {
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"top": "50px"
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},
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"python": {
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"varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])"
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},
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"window_display": false
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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},
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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"autocomplete": true,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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"current_citInitial": 1,
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"eqLabelWithNumbers": true,
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"eqNumInitial": 1,
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"hotkeys": {
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"equation": "Ctrl-E",
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"itemize": "Ctrl-I"
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},
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"labels_anchors": false,
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"latex_user_defs": false,
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"report_style_numbering": false,
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"user_envs_cfg": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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363
ml21/visualization/01_Dataset.ipynb
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363
ml21/visualization/01_Dataset.ipynb
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@ -0,0 +1,363 @@
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{
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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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||||
}
|
||||
},
|
||||
"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."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"datacleaner": {
|
||||
"position": {
|
||||
"top": "50px"
|
||||
},
|
||||
"python": {
|
||||
"varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])"
|
||||
},
|
||||
"window_display": false
|
||||
},
|
||||
"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": "3.10.13"
|
||||
},
|
||||
"latex_envs": {
|
||||
"LaTeX_envs_menu_present": true,
|
||||
"autocomplete": true,
|
||||
"bibliofile": "biblio.bib",
|
||||
"cite_by": "apalike",
|
||||
"current_citInitial": 1,
|
||||
"eqLabelWithNumbers": true,
|
||||
"eqNumInitial": 1,
|
||||
"hotkeys": {
|
||||
"equation": "Ctrl-E",
|
||||
"itemize": "Ctrl-I"
|
||||
},
|
||||
"labels_anchors": false,
|
||||
"latex_user_defs": false,
|
||||
"report_style_numbering": false,
|
||||
"user_envs_cfg": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
3192
ml21/visualization/02_01_More_Comparison_Charts.ipynb
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ml21/visualization/02_01_More_Comparison_Charts.ipynb
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561
ml21/visualization/02_Comparison_Charts.ipynb
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561
ml21/visualization/02_Comparison_Charts.ipynb
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1235
ml21/visualization/03_Distribution_Charts.ipynb
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1235
ml21/visualization/03_Distribution_Charts.ipynb
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2126
ml21/visualization/04_Hierarchical_Charts.ipynb
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2126
ml21/visualization/04_Hierarchical_Charts.ipynb
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500
ml21/visualization/05_Relational_Charts.ipynb
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500
ml21/visualization/05_Relational_Charts.ipynb
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689
ml21/visualization/06_Spatial_Charts.ipynb
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689
ml21/visualization/06_Spatial_Charts.ipynb
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451
ml21/visualization/07_Temporal_Charts.ipynb
Normal file
451
ml21/visualization/07_Temporal_Charts.ipynb
Normal file
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Loading…
Reference in New Issue
Block a user