pandas.concat of multiple data frames using only common columns

Question

I have multiple pandas data frame objects cost1, cost2, cost3 ....

1. They have different column names (and number of columns) but have some in common.
2. Number of columns is fairly large in each data frame, hence handpicking the common columns manually will be painful.

How can I append rows from all of these data frames into one single data frame while retaining elements from only the common column names?

As of now I have

frames=[cost1,cost2,cost3]

new_combined = pd.concat(frames, ignore_index=True)

This obviously contains columns which are not common across all data frames.

Answer 1

For future readers, Above functionality can be implemented by pandas itself. Pandas can concat dataframe while keeping common columns only, if you provide join='inner' argument in pd.concat. e.g.

pd.concat(frames,join='inner', ignore_index=True)

Answer 2

You can find the common columns with Python's set.intersection:

common_cols = list(set.intersection(*(set(df.columns) for df in frames)))

To concatenate using only the common columns, you can use

pd.concat([df[common_cols] for df in frames], ignore_index=True)