Following code is given, we need to sum up the medals.

import pandas as pd

# Defining the three dataframes indicating the gold, silver, and bronze medal counts
# of different countries
gold = pd.DataFrame({'Country': ['USA', 'France', 'Russia'],
                         'Medals': [15, 13, 9]}
silver = pd.DataFrame({'Country': ['USA', 'Germany', 'Russia'],
                        'Medals': [29, 20, 16]}
bronze = pd.DataFrame({'Country': ['France', 'USA', 'UK'],
                        'Medals': [40, 28, 27]}

I wrote this working solution. However, it feels very un-pythonic: And I feel there is a better way of approaching this.

df = gold.set_index('Country')["Medals"].add(silver.set_index('Country')["Medals"], fill_value=0).add(bronze.set_index('Country')["Medals"], fill_value=0)
df = pd.DataFrame(df.sort_values(ascending=False))

1 Answer 1


I was provided with this answer by someone:

# Set the index of the dataframes to 'Country' so that you can get the countrywise
# medal count
gold.set_index('Country', inplace = True)
silver.set_index('Country', inplace = True) 
bronze.set_index('Country', inplace = True) 

# Add the three dataframes and set the fill_value argument to zero to avoid getting
# NaN values
total = gold.add(silver, fill_value = 0).add(bronze, fill_value = 0)

# Sort the resultant dataframe in a descending order
total = total.sort_values(by = 'Medals', ascending = False)

# Print the sorted dataframe

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