How to Reset Indexes in Pandas DataFrames: `reset_index()` vs. `reindex()`?

Resetting Indexes in Pandas DataFrames

Dealing with missing or problematic indexes in Pandas dataframes can be frustrating. A common scenario is the need to reset indexes after removing certain rows, resulting in a scattered index sequence. To address this issue, we will explore two different approaches for index resetting in Pandas dataframes.

Method 1: Using reset_index()

The DataFrame.reset_index() method provides a straightforward way to reset indexes. This method allows you to specify whether you want to retain the old index as a column in the dataframe or drop it altogether. To drop the old index, use the following syntax:

df = df.reset_index(drop=True)

Method 2: Using reindex()

The DataFrame.reindex() method can also be used to reset indexes. However, unlike reset_index(), it does not automatically drop the old index. Therefore, you need to manually delete it afterward.

<code class="python">df = df.reindex()
del df['index']</code>

Note: The reindex() method is less commonly used for index resetting because it requires an explicit deletion of the old index.

Conclusion

When resetting indexes in Pandas dataframes, DataFrame.reset_index() is the preferred method. It provides a concise and efficient way to reset and optionally remove the old index. Remember to use the drop=True parameter to automatically discard the old index and avoid any confusion.

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