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.
In this exercise, you will load the 'airquality' 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
Cet exercice fait partie du cours
Dealing with Missing Data in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Load the air-quality.csv dataset
airquality = pd.read_csv(___, parse_dates=[___], index_col=___)