Pandas DataFrame merging with aggregation

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by vsasikalabe dot icon Updated: Mar 1, 2023

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Create two data frames with different values(df1 &df2). Assign df1 column values by indexing. We can join both data frame values using the append() method. The result is stored in df_both. We have to drop duplicate values by using the drop_duplicates() method. The same indexing value is added using sum() and resetting the value into the data frame. Finally, sort all the elements in the data frame. 

We have three main ways of combining Data Frames, i.e., merge(), join(), and append(). We can use the append or merge method to join two data frames. In the append method, we can add a single element without changing the whole values in the data frame. The merge method allows combining two data frames with the common column. The drop_duplicates() method drops all duplicate rows or values. When looking for duplicates, use the subset parameter if only some specified columns should be considered. You'll use sort_values() to sort the Data Frame based on the values in a single column. This will return a new Data Frame sorted in ascending order by default. DataFrame.groupby() function can help collect identical data into groups. It performs aggregate functions. 

Here is an example of how to do Pandas Data frame merging with aggregation: