Introduction
Effectively present data with Python
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What you should know before you start
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Using the exercise files
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1. Data Visualization Tools
Value of data visualization
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Why use a programming language?
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Overview of Jupyter Notebooks
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2. pandas
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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Slicing
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Filtering
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Renaming and deleting columns
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Aggregate functions
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Identifying missing data
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Removing or filling in missing data
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Convert pandas DataFrames to NumPy arrays or dictionaries
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Export pandas DataFrames to CSV and Excel files
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3. Matplotlib
Basics of Matplotlib
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Setting marker type and colors
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MATLAB-style vs. object syntax
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Setting titles, labels, and limits
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Grids
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Legends
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Saving plots to files
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Matplotlib wrappers (pandas and Seaborn)
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4. Advanced Plotting
Heatmaps
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Histograms
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Subplots
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Ex_Files_Python_Data_Visualization.zip
(3.5 MB)
Glossary_Python_for_Data_Visualization.zip
(100 KB)