How to Merge DataFrames and Preserve Original Indexes in Python?

Merging DataFrames: Combining Extracted Datasets

In the context of data manipulation, combining multiple data frames is a common requirement. In a given scenario, suppose we have an initial DataFrame D and we extract two subsets A and B from it based on a specific condition:

A = D[D.label == k]
B = D[D.label != k]

The goal is to merge A and B back into a single DataFrame. While the order of data isn't crucial, it's important to preserve the original indexes of A and B as they were derived from D.

Solution: Using the Append Method

One approach to combining data frames is to use the append method. This method allows us to concatenate one or more data frames vertically, effectively stacking them on top of each other. In our case, we can use the code below:

df_merged = df1.append(df2, ignore_index=True)

This will create a new DataFrame called df_merged that contains the combined data from A and B. The ignore_index=True argument ensures that the resulting DataFrame has its own unique set of indexes, independent of the originals.

Keeping Original Indexes

If we want to retain the original indexes of A and B, we can set ignore_index=False in the append method:

df_merged = df1.append(df2, ignore_index=False)

This will preserve the index values of each data frame within the merged result. However, it's important to note that the indexes may become duplicate values in the final DataFrame.

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