How to Reset Index in a Pandas DataFrame to Ensure Sequential Order

When handling dataframes in Pandas, it's common to encounter situations where index values become inconsistent due to row removals or other operations. To address this, you may want to reset the index to a sequential order, such as [0,1,2,3,4].

Using reset_index() for Index Reset

The preferred method for resetting the index is to use the reset_index() function. This function creates a new dataframe with the reset index as a new column. By default, the original index is saved as a column named 'index'.

Example:

df = df.reset_index()

Removing the Original Index Column

If you don't want the original index to be saved as a column, you can drop it after resetting the index using the drop parameter.

Example:

df = df.reset_index(drop=True)

This will result in a dataframe with a sequentially ordered index.

Note:

The reindex() function does not have the same effect as reset_index(). Reindex() is primarily used for aligning multiple dataframes based on specific indices or rearranging the order of rows and columns.