How to Combine DataFrames in Python: Preserve Indices or Start Fresh?

Concatenating DataFrames

When working with DataFrames, it is often necessary to combine multiple data frames into a single cohesive data structure. This can arise from various scenarios, such as data preprocessing, merging similar datasets, or appending new data.

Combining DataFrames Without Preserving Index

To combine two DataFrames, one can utilize the append method. The syntax is straightforward:

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

When setting ignore_index to True, the resulting DataFrame will have new, sequential indices. This option is suitable when the index order is irrelevant and can simplify further data manipulation.

Combining DataFrames with Preserved Index

In certain scenarios, it may be desirable to maintain the original indices of the individual DataFrames. To achieve this, simply set ignore_index to False:

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

By preserving the indices, traceability to the original DataFrames is retained, facilitating downstream operations such as data exploration or record matching. However, the resulting DataFrame's indices may not be contiguous if the input DataFrames have non-overlapping indices.

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