How to Combine Two DataFrames with Differing Indexes While Maintaining Original Order and Indexes?

Combining Two DataFrames with Differing Indexes

You have a dataframe D and have extracted two dataframes A and B from it:

<code class="python">A = D[D.label == k]
B = D[D.label != k]</code>

Your goal is to combine A and B into a single DataFrame, preserving the original order of data from D while retaining the indexes from D.

Solution via Deprecated Method

While DataFrame.append and Series.append are deprecated in v1.4.0, they can still be used for this task with the argument ignore_index set to True. This will discard the original indexes and reindex the combined dataframe from 0 to n-1.

<code class="python">df_merged = df1.append(df2, ignore_index=True)</code>

Solution with Preserved Indexes

If you want to retain the original indexes, set ignore_index to False. This will append the dataframes vertically and retain their respective indexes.

<code class="python">df_merged = df1.append(df2, ignore_index=False)</code>

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