熊猫用以前的值填充空列名

Question

Newbie here.Try to clean up the following column name presented

1992 | Unnamed: 2 | Unnamed: 3 | 1993 | Unnamed: 4| Unnamed: 5| 1994 | ... | 2015 | Unnamed: 22 | Unnamed: 23|

Next column is

Male | Female | Population | Male | Female | Population ....

Then, the row and beyond are to do with each country's populations

I try the following to clean up all the unnamed column names

df.columns = map(lambda x: np.nan if (re.search(r'\bUnnamed:\s\d{1,2}\b', x)) else x, df.columns)

Then I wanted to naively do a fillna function with pad method...

df_column_names = df_column_names.fillna(method='pad')

obviously that didn't work

Should I just extract the column name, manipulate it as dataframe and use the fillna(method='pad') and then just magically merge it back with the working dataframe?

Seems to be very clunky way to do it.

Answer 1

I think i have an answer. My solution is to convert df.columns above as pd.Series, then run fillna(method='pad) function. As a result, all NaN will be populated with the precedent year value