Concatenating vertically to get MultiIndexed rows

When stacking a sequence of DataFrames vertically, it is sometimes desirable to construct a MultiIndex to indicate the DataFrame from which each row originated. This can be done by specifying the keys parameter in the call to pd.concat(), which generates a hierarchical index with the labels from keys as the outermost index label. So you don't have to rename the columns of each DataFrame as you load it. Instead, only the Index column needs to be specified.

Here, you'll continue working with DataFrames compiled from The Guardian's Olympic medal dataset. Once again, pandas has been imported as pd and two lists have been pre-loaded: An empty list called medals, and medal_types, which contains the strings 'bronze', 'silver', and 'gold'.

Within the for loop:
- Read file_name into a DataFrame called medal_df. Specify the index to be 'Country'.
- Append medal_df to medals.
Concatenate the list of DataFrames medals into a single DataFrame called medals. Be sure to use the keyword argument keys=['bronze', 'silver', 'gold'] to create a vertically stacked DataFrame with a MultiIndex.
Print the new DataFrame medals. This has been done for you, so hit 'Submit Answer' to see the result!

Take Hint (-30 XP)

Exercise

Concatenating vertically to get MultiIndexed rows

Instructions