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  3. Dealing with Missing Data in Python

Exercise

Visualize missingness

In the previous exercise, you calculated the number of missing values and the percentage of missingness for each column. However, this is usually not enough and preference is to visualize them graphically.

You'll use the misssingno package which is built for visualizing missing values. The airquality DataFrame has already been imported, and the pandas library as pd.

You will visualize the missingness by plotting a bar chart and a nullity matrix of the missing values.

Note that we've used a proprietary display() function instead of plt.show() to make it easier for you to view the output.

Instructions 1/4

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  • 1
    • Plot a bar chart of the missing values in airquality.
  • 2
    • Plot the nullity matrix of airquality.
  • 3
    • Plot the nullity matrix of airquality across a monthly frequency.
  • 4
    • Splice airquality from 'May-1976' to 'Jul-1976' and plot its nullity matrix.