Introduction
Effectively present data with Python
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Before you start
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Using the exercise files
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1. Data Visualization Overview
Value of data visualization
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Leverage programming languages
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Overview of Jupyter Notebooks
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2. Leverage pandas for Analysis
Introduction to pandas
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Create sample data
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Load sample data
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Basic operations
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Simplify with slicing
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Filter and clean data
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Rename and delete columns
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Aggregate functions
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Identify missing data
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Remove or fill in missing data
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Convert pandas DataFrames
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Export pandas DataFrames
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3. Simplify Visualization with Matplotlib
Basics of Matplotlib
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Set marker type and colors
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MATLAB-style vs. object syntax
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Set titles, labels, and limits
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Add grids
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Create legends
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Save plots to files
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Create plots with Matplotlib wrappers
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4. Customize Visualizations with Matplotlib
Create heatmaps
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Create histograms
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Create subplots
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Ex_Files_Python_for_Data_Vis.zip
(3.8 MB)