Heatmaps

Contents

• Introduction Introduction

  • Effectively present data with Python

    1m 3s
  • What you should know before you start

    33s
  • Using the exercise files

    22s
• 1. Data Visualization Tools 1. Data Visualization Tools

  • Value of data visualization

    1m 1s
  • (Locked)
    Why use a programming language?

    3m 23s
  • (Locked)
    Overview of Jupyter Notebooks

    1m 46s
• 2. pandas 2. pandas

  • Introduction to pandas

    52s
  • (Locked)
    Create sample data

    3m 50s
  • (Locked)
    Load sample data

    2m 17s
  • (Locked)
    Basic operations

    1m 57s
  • (Locked)
    Slicing

    4m 12s
  • (Locked)
    Filtering

    5m 39s
  • (Locked)
    Renaming and deleting columns

    3m 16s
  • (Locked)
    Aggregate functions

    2m 39s
  • (Locked)
    Identifying missing data

    3m 41s
  • (Locked)
    Removing or filling in missing data

    5m 3s
  • (Locked)
    Convert pandas DataFrames to NumPy arrays or dictionaries

    1m 15s
  • (Locked)
    Export pandas DataFrames to CSV and Excel files

    1m 28s
• 3. Matplotlib 3. Matplotlib

  • Basics of Matplotlib

    3m 56s
  • (Locked)
    Setting marker type and colors

    1m 52s
  • (Locked)
    MATLAB-style vs. object syntax

    2m 7s
  • (Locked)
    Setting titles, labels, and limits

    4m 21s
  • (Locked)
    Grids

    2m 26s
  • (Locked)
    Legends

    1m 25s
  • (Locked)
    Saving plots to files

    2m 28s
  • (Locked)
    Matplotlib wrappers (pandas and Seaborn)

    5m 6s
• 4. Advanced Plotting 4. Advanced Plotting

  • (Locked)
    Heatmaps

    4m
  • (Locked)
    Histograms

    3m 38s
  • (Locked)
    Subplots

    4m 42s
• Conclusion Conclusion

  • (Locked)
    Next steps

    1m 10s