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One way to divide a combined column in a dataframe with certain null values into separate columns based on whitespace is to use the str.split() method in Pandas.

Here's an example:

import pandas as pd

# create sample dataframe
df = pd.DataFrame({'combined_column': ['John Smith 25', 'Jane Doe 30', 'Joe    Bloggs    40', 'Bill 45']})

# divide combined_column into separate columns based on whitespace
df[['First Name', 'Last Name', 'Age']] = df['combined_column'].str.split(expand=True)

# drop the original combined_column column
df.drop(columns=['combined_column'], inplace=True)

print(df)

Output:

  First Name Last Name Age
0       John     Smith  25
1       Jane       Doe  30
2        Joe   Bloggs    40
3       Bill        45 NaN

In this example, we used str.split() to separate the combined_column into three separate columns based on whitespace. We then assigned these new columns to the dataframe using double square brackets ([[...]]) and the expand=True argument. Finally, we dropped the original combined_column column using the drop() method.

Note that in the sample dataframe, the last row only contains two values, so creating the Age column for that row resulted in a NaN value.