[Python] Duplicate and rename columns on pandas DataFrame

I guess this must be rather simple, but I'm struggling to find the easy way of doing it.

I have a pandas DataFrame with the columns A to D and need to copy some of the columns to new ones. The trick is that it not just involves renaming, I also need to duplicate the values to new columns as well.

Here is an example of the input:

import pandas as pd

df = pd.DataFrame({
'A': [1,2,3],
'B':['2025-10-01', '2025-10-02', '2025-10-01'],
'C': ['2025-02-10', '2025-02-15', '2025-02-20'],
'D': [0, 5, 4],
'values': [52.3, 60, 70.6]
})

mapping_dict = {
'table_1': {
'id': 'A',
'dt_start': 'B',
'dt_end': 'B',
},
'table_2': {
'id': 'D',
'dt_start': 'C',
'dt_end': 'C',
},
}



I'd like to have as output for table_1 a DataFrame as follows:

id​

dt_start​

dt_end​

values
1​

2025-10-01​

2025-10-01​

52.3
2​

2025-10-02​

2025-10-02​

60
3​

2025-10-01​

2025-10-01​

80.6​

And I guess it is possible to infer the expected output for table_2.

Note that the column values, which is not included in the mapping logic, should remain in the dataframe.

I was able to achieve this by using a for loop, but I feel that should be a natural way of doing this directly on pandas without manually looping over the mapping dict and then dropping the extra columns.

Here is my solution so far:

table_name = 'table_1'

new_df = df.copy()
for new_col, old_col in mapping_dict[table_name].items():
new_df[new_col] = df[old_col]

new_df = new_df.drop(mapping_dict[table_name].values(), axis='columns')


Any help or suggestion will be appreciated!

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