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Improving Your Data Visualizations in Python

Средний уровеньУровень навыков

Обновлено 01.2026

Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.

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PythonData Visualization

4 ч

15 видео

54 Упражнения

4,650 XP

19,173

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Описание курса

Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.

Необходимые условия

Python Toolbox Introduction to Data Visualization with Matplotlib Introduction to Data Visualization with Seaborn

1

Highlighting Your Data

How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.

Highlighting data

Hardcoding a highlight

Programmatically creating a highlight

Comparing groups

Comparing with two KDEs

Improving your KDEs

Annotations

A basic text annotation

Arrow annotations

Combining annotations and color

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2

Using Color in Your Visualizations

Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.

Color in visualizations

Getting rid of unnecessary color

Fixing Seaborn's bar charts

Continuous color palettes

Making a custom continuous palette

Customizing a diverging palette heatmap

Adjusting your palette according to context

Categorical palettes

Using a custom categorical palette

Dealing with too many categories

Coloring ordinal categories

Choosing the right variable to encode with color

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3

Showing Uncertainty

Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.

Point estimate intervals

Basic confidence intervals

Annotating confidence intervals

Confidence bands

Making a confidence band

Separating a lot of bands

Cleaning up bands for overlaps

90, 95, and 99% intervals

90 and 95% bands

Using band thickness instead of coloring

Visualizing the bootstrap

The bootstrap histogram

Bootstrapped regressions

Lots of bootstraps with beeswarms

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4

Visualization in the Data Science Workflow

Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.

First explorations

Looking at the farmers market data

Scatter matrix of numeric columns

Digging in with basic transforms

Exploring the patterns

Is latitude related to months open?

What state is the most market-friendly?

Popularity of goods sold by state

Making your visualizations efficient

Stacking to find trends

Using a plot as a legend

Tweaking your plots

Cleaning up the background

Remixing a plot

Enhancing legibility

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Improving Your Data Visualizations in Python

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