In this lesson you learned how to extend Matplotlib with Seaborn to create meaningful visualizations from data in DataFrames.

You’ve also learned how Seaborn creates aggregated charts and how to change the way aggregates and error bars are calculated.

Finally, you learned how to aggregate by multiple columns, and how the hue parameter adds a nested categorical variable to a visualization.

To review the seaborn workflow:

1. Ingest data from a CSV file to Pandas DataFrame.
df = pd.read_csv('file_name.csv')
2. Set sns.barplot() with desired values for x, y, and set data equal to your DataFrame.
sns.barplot(data=df, x='X-Values', y='Y-Values')
3. Set desired values for estimator and hue parameters.
sns.barplot(data=df, x='X-Values', y='Y-Values', estimator=len, hue='Value')
4. Render the plot using plt.show().


Examine the Seaborn graphs in script.py. Use this space to practice and modify the graphs.

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