Append new level to DataFrame column

Question

Given a DataFrame, how can I add a new level to the columns based on an iterable given by the user? In other words, how do I append a new level?

The question How to simply add a column level to a pandas dataframe shows how to add a new level given a single value, so it doesn’t cover this case.

Here is the expected behaviour:

>>> df = pd.DataFrame(0, columns=["A", "B"], index=range(2))
>>> df
    A   B
0   0   0
1   0   0
>>> append_level(df, ["C", "D"])
    A   B
    C   D
0   0   0
1   0   0

The solution should also work with MultiIndex columns, so

>>> append_level(append_level(df, ["C", "D"]), ["E", "F"])
    A   B
    C   D
    E   F
0   0   0
1   0   0

Asked By: edd313

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Source

Answer 1

def append_level(df, new_level):
    new_df = df.copy()
    new_df.columns = pd.MultiIndex.from_tuples(zip(*zip(*df.columns), new_level))
    return new_df

Answered By: edd313

Answer 2

If the columns is not multiindex, you can just do:

df.columns = pd.MultiIndex.from_arrays([df.columns.tolist(), ['C','D']])

If its multiindex:

if isinstance(df.columns, pd.MultiIndex):
     df.columns = pd.MultiIndex.from_arrays([*df.columns.levels, ['E', 'F']])

The pd.MultiIndex.levels gives a Frozenlist of level values and you need to unpack to form the list of lists as input to from_arrays

Answered By: SomeDude

Append new level to DataFrame column

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