Let’ see how to combine multiple columns in Pandas using
groupby with dictionary with the help of different examples.
- 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.
- 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.
- Collapse multiple Columns in Pandas
- Using dictionary to remap values in Pandas DataFrame columns
- How to select multiple columns in a pandas dataframe
- How to drop one or multiple columns in Pandas Dataframe
- Python | Combining values from dictionary of list
- Pandas GroupBy
- Python | Pandas dataframe.groupby()
- How to rename columns in Pandas DataFrame
- Difference of two columns in Pandas dataframe
- Python | Pandas DataFrame.columns
- Getting frequency counts of a columns in Pandas DataFrame
- Dealing with Rows and Columns in Pandas DataFrame
- Iterating over rows and columns in Pandas DataFrame
- Conditional operation on Pandas DataFrame columns
- Join two text columns into a single column in Pandas
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