How to Reset the Index of a Pandas DataFrame After Removing Rows?

Resetting DataFrame Index in Pandas

Question:

You have a Pandas dataframe where rows have been removed, leaving gaps in the index. You want to reset the index to a contiguous sequence, such as [0, 1, 2, 3, 4]. How do you accomplish this?

Answer:

To reset the index of a dataframe, use the DataFrame.reset_index method. This method generates a new dataframe with consecutive integers starting from 0 as the index. By default, the old index is saved as a new column named 'index'.

To prevent saving the old index as a new column, you can use the drop parameter as follows:

<code class="python">df = df.reset_index(drop=True)</code>

This will create a new dataframe with the reset index.

Alternatively, if you don't want to reassign the dataframe, you can use the inplace parameter:

<code class="python">df.reset_index(drop=True, inplace=True)</code>

This will modify the original dataframe in-place, resetting its index without creating a new dataframe.

Note: The DataFrame.reindex method does not reset the index as described in the question. It is used for rearranging or adding missing values in a dataframe while preserving the original index.

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