This replaces the missing value with a number that makes sense.

There are various ways in which imputation can be performed. Some of them are as follows:

• Imputing all the missing values in a dataset with 0:

data.fillna(0)

• Imputing all the missing values with specified text:

data.fillna('text')

• Imputing only the missing values in the body column with 0:

data['body'].fillna(0)

• Imputing with a mean of non-missing values:

data['age'].fillna(data['age'].mean())

• Imputing with a forward fill – this works especially well for time series data. Here, a missing value is replaced with the value in the previous row (period):

data['age'].fillna(method='ffill')

The following is the output:

• Imputing with a backward fill – this works especially well for time series data. Here, a missing value is replaced with the value in the previous row (period). You can control the number of rows that get filled after the first NaN using pad options. Pad=1 means only 1 row will be filled forward or backward:

data['age'].fillna(method='backfill')

The following is the output: