Reading multiple files to build a DataFrame

It is often convenient to build a large DataFrame by parsing many files as DataFrames and concatenating them all at once. You'll do this here with three files, but, in principle, this approach can be used to combine data from dozens or hundreds of files.

Here, you'll work with DataFrames compiled from The Guardian's Olympic medal dataset.

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'.

Instructions

Iterate over medal_types in the for loop.
Inside the for loop:
- Create file_name using string interpolation with the loop variable medal. This has been done for you. The expression "%s_top5.csv" % medal evaluates as a string with the value of medal replacing %s in the format string.
- Create the list of column names called columns. This has been done for you.
- Read file_name into a DataFrame called medal_df. Specify the keyword arguments header=0, index_col='Country', and names=columns to get the correct row and column Indexes.
- Append medal_df to medals using the list .append() method.
Concatenate the list of DataFrames medals horizontally (using axis='columns') to create a single DataFrame called medals. Print it in its entirety.

Take Hint (-30xp)