Let’ see how to combine multiple columns in Pandas using groupby with dictionary with the help of different examples.
Example #1:
# importing pandas as pd import pandas as pd # Creating a dictionary d = {'id':['1', '2', '3'], 'Column 1.1':[14, 15, 16], 'Column 1.2':[10, 10, 10], 'Column 1.3':[1, 4, 5], 'Column 2.1':[1, 2, 3], 'Column 2.2':[10, 10, 10], } # Converting dictionary into a data-frame df = pd.DataFrame(d)print(df) |
Output:
# Creating the groupby dictionary groupby_dict = {'Column 1.1':'Column 1', 'Column 1.2':'Column 1', 'Column 1.3':'Column 1', 'Column 2.1':'Column 2', 'Column 2.2':'Column 2' } # Set the index of df as Column 'id'df = df.set_index('id') # Groupby the groupby_dict created above df = df.groupby(groupby_dict, axis = 1).min()print(df) |
Output:
Explanation
- Here we have grouped Column 1.1, Column 1.2 and Column 1.3 into Column 1 and Column 2.1, Column 2.2 into Column 2.
- Notice that the output in each column is the min value of each row of the columns grouped together. i.e in Column 1, value of first row is the minimum value of Column 1.1 Row 1, Column 1.2 Row 1 and Column 1.3 Row 1.
Example #2:
# importing pandas as pd import pandas as pd # Create dictionary with data dict = { "ID":[1, 2, 3], "Movies":["The Godfather", "Fight Club", "Casablanca"], "Week_1_Viewers":[30, 30, 40], "Week_2_Viewers":[60, 40, 80], "Week_3_Viewers":[40, 20, 20] }; # Convert dictionary to dataframedf = pd.DataFrame(dict);print(df) |
Output:
# Create the groupby_dict groupby_dict = {"Week_1_Viewers":"Total_Viewers", "Week_2_Viewers":"Total_Viewers", "Week_3_Viewers":"Total_Viewers", "Movies":"Movies" } df = df.set_index('ID')df = df.groupby(groupby_dict, axis = 1).sum()print(df) |
Output:
Explanation:
- Here, notice that even though ‘Movies’ isn’t being merged into another column it still has to be present in the groupby_dict, else it won’t be in the final dataframe.
- To calculate the Total_Viewers we have used the .sum() function which sums up all the values of the respective rows.
