How to do list comprehension with Dataframe when using scalar operation?

Question

I have a list of dataframes. All the dataframes' dtype is <date, int>. I want to multiply 100 with the int column only. So I did -

dfs = [df1, df2, ...]
dfs = [df.select_dtypes(include=['number']) * 100 for df in dfs]

This is dropping the first column from every dataframe. How to keep that intact?

Answer 1

This is because select_dtypes only return a subset of the DataFrame’s columns based on the column dtypes. The easiest way I can think of this to exclude the number dtypes to get the remaining columns and merge it to get the original DataFrame.

[
    pd.concat(
        [
            df.select_dtypes(include=["number"]) * 100,
            df.select_dtypes(exclude=["number"]),
        ],
        axis=1,
    )
    for df in dfs
]