Napoleon-Christos Oikonomou has completed

# Introduction to Data Visualization in Python

4 hours
5,000 XP

## Course Description

This course extends your existing Python skills to provide a stronger foundation in data visualization in Python. You’ll get a broader coverage of the Matplotlib library and an overview of seaborn, a package for statistical graphics. Topics covered include customizing graphics, plotting two-dimensional arrays (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and regressions), and working with time series and image data.

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1. 1

### Customizing plots

Free

Following a review of basic plotting with Matplotlib, this chapter delves into customizing plots using Matplotlib. This includes overlaying plots, making subplots, controlling axes, adding legends and annotations, and using different plot styles.

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Plotting multiple graphs
50 xp
Multiple plots on single axis
100 xp
Using axes()
100 xp
Using subplot() (1)
100 xp
Using subplot() (2)
100 xp
Customizing axes
50 xp
Using xlim(), ylim()
100 xp
Using axis()
100 xp
Legends, annotations, and styles
50 xp
Using legend()
100 xp
Using annotate()
100 xp
Modifying styles
100 xp
2. 2

### Plotting 2D arrays

This chapter showcases various techniques for visualizing two-dimensional arrays. This includes the use, presentation, and orientation of grids for representing two-variable functions followed by discussions of pseudocolor plots, contour plots, color maps, two-dimensional histograms, and images.

3. 3

### Statistical plots with Seaborn

This is a high-level tour of the seaborn plotting library for producing statistical graphics in Python. We’ll cover seaborn tools for computing and visualizing linear regressions, as well as tools for visualizing univariate distributions (like strip, swarm, and violin plots) and multivariate distributions (like joint plots, pair plots, and heatmaps). We’ll also discuss grouping categories in plots.

4. 4

### Analyzing time series and images

This chapter ties together the skills gained so far through examining time series data and images. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance image contrast through histogram equalization.

### GroupTraining 2 or more people?

Datasets

Automobile miles per gallonPercentage of bachelor's degrees awarded to women in the USAStocks

Prerequisites

Intermediate Python
Team Anaconda

Data Science Training

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