Python for Finance Cookbook

How it works...

In Step 1, we imported the required libraries. Then, we used the info method of a pandas DataFrame to view information about the columns, such as their type and the number of non-null observations. Another possible way of inspecting the number of missing values per column is to run X.isnull().sum().

Instead of imputing, we could also drop the observations (or even columns) containing missing values. To drop all rows containing any missing value, we could use X_train.dropna(how='any', inplace=True). In our sample case, the number of missing values is not large, however, in a real-life dataset it can be considerable or, alternatively, the dataset can be too small for the analysts to be able to remove observations.

In Step 3 ...