Hands-On Exploratory Data Analysis with Python

Filling missing values

We can use the fillna() method to replace NaN values with any particular values.

Check the following example:

filledDf = dfx.fillna(0)filledDf

The output of the preceding code is shown in the following screenshot:

Note that in the preceding dataframe, all the NaN values are replaced by 0. Replacing the values with 0 will affect several statistics including mean, sum, and median.

Check the difference in the following two examples:

dfx.mean()

And the output of the preceding code is as follows:

store1 20.0store2 21.0store3 22.0store4 19.0store5 NaNdtype: float64

Now, let's compute the mean from the filled dataframe ...