Analyzing missingness percentage

Before jumping into treating missing data, it is essential to analyze the various factors surrounding missing data. The elementary step in analyzing the data is to analyze the amount of missingness, that is the number of values missing for a variable. In this exercise, you'll calculate the total number of missing values per column and also find out the percentage of missing values per column. The 'airquality' dataset which contains weather data collected from various sensors has been loaded for you.

In this exercise, you will load the dataset by parsing the Date column and then calculate the sum of missing values and the degree of missingness in percent on the nullity DataFrame

Load 'air-quality.csv' into a pandas DataFrame while parsing the 'Date' column and setting it to the index column as well.

Take Hint (-7 XP)

Exercise

Analyzing missingness percentage

Instructions 1/4