Matplotlib

As explained in Everyday Data Visualization

Matplotlib is a Python library for creating static, animated, and interactive visualizations.

Matplotlib is a Python library for creating visualizations. It offers a code-based route for data visualization alongside options such as Seaborn and Plotly. section 1.5.3

Fruit data as a bar chart

A small example turns fruit data into a bar chart with about 18 lines of Python, including nine lines containing the data. The chart uses Matplotlib’s defaults; a horizontal version was left out because the example required substantially more complicated code. section 1.5.3

Figure 1.10 A bar chart created in Python’s Matplotlib library, using its defaults

Inspect the default-styled Matplotlib bar chart built from the fruit data. section 1.5.3

How Everyday Data Visualization covers it

How deep it goes

Demonstrates one default bar chart in roughly 18 lines and stops short of more complicated horizontal bars.

Examples

Uses fruit data and a default-styled bar chart as its concrete example.

What you can do after

Build a basic bar chart in Python with a relatively small amount of code.

How it connects

Connects Python visualization with Seaborn, Plotly, R, ggplot, ggplot2, and Shiny.

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Related concepts

• Pythonused by · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
• seabornused by · Seaborn is a Python library for statistical data visualization that provides a high-level interface to Matplotlib.
• visualizationuses · Visualization is the representation of data, information, or concepts in visual form to support inspection, interpretation, analysis, or communication.
• pandasused by · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
• data explorationused by · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.

This explainer was generated by Manning's knowledge graph from the text of Everyday Data Visualization. The section links point to the parts of the book it draws on.