What is Pandas reset_index?

Pandas reset_index() is a method that resets a data frame’s index. The reset_index() method takes into consideration the index to a list of numbers ranging from 0 to the data length.

The syntax is seen below:

pandas reset_index

Below are the parameters required to use pandas reset_index()

  • level: Default values are int, str, tuple, list or None. Remove only the specified levels from the index. By default, it removes all levels.
  • drop: False is the default boolean value. Avoid inserting indexes into dataframe columns. This returns the index to its original integer value.
  • inplace: False is the default boolean value. Make changes to the DataFrame that is already in place. There is no need to create a new object.
  • col_level: If the columns have many layers, this defines which layer the labels are placed into. It could be int or str, default 0. It is added into the first level by default.
  • col_fill: ” is the default object. Determines how the additional levels are called if the columns have several levels. If there is no value, the index name is reiterated.
  • Returns: DataFrame has been reset.

pandas reset_index Examples

pandas reset_index Examples

The old index is inserted as a column when we reset the index, and a new progressive index is being used:

old index

The drop argument can be used to prevent the old index from being inserted as a column:

reset index

With MultiIndex, one will also be able to use the reset_index() method.

multiIndex python

We will be able to reset a portion of the index’s levels if it has many levels:

Python index levels

If we don’t drop the index, it will be placed at the top level by default. We may put it on a different level:

Python reset index levels

With the argument col_fill, we can indicate which level the index should be entered beneath.


The column will now be generated if we supply a non-existing level for col_fill:

supply a non-existing level for col_fill

Another example is to use data containing zoo animals. For instance, let’s say you’ve sorted some data that will be used in your Machine Learning models, and after the sort, the serial number column becomes messy as shown below:

pandas reset_index Example

Apart from the fact that it is unsightly, incorrect indexing can also cause problems with your visualizations and machine learning models. The idea is that you may need to re-index the rows after performing a change on your dataframe in some circumstances. You may do this with the reset_index() method. Consider the following example:

pandas reset_index
pandas reset_index

Looks nicer right?

Our new dataframe, as you can see, retains the previous indexes as well. To get rid of them, just use the drop = True parameter:

pandas reset_index true parameter
pandas sort values

Troubleshooting the most common Pandas reset_index errors

  1. When Pandas reset_index() does not work, what should you do?

There are several ways around this. Let’s go through some of the solutions:

Solution 1:

By default, reset_index() does not change the DataFrame; instead, it establishes a different DataFrame with the reset index. Use the inplace parameter to change the original: df.reset index(drop=True, inplace=True). Similarly, use df = df.reset index(drop=True) to allocate the output of reset_index().

Solution 2:

This isn’t so much a debugging guide as it is a description of how one should reset the index:

fix Pandas reset_index() does not work, what should you do?

2. TypeError: Cannot reset_index inplace on a Series to create a DataFrame

When you try to reset index on a pandas Series object with inplace=True, you get this error. For example, if you perform anything like this, you’ll get the following error:

TypeError: Cannot reset_index inplace on a Series to create a DataFrame

The cause for this problem is that s is a pandas series by itself, but reset index outputs a dataframe, hence it introduces an incompatibility between the data types before and after the modification.


Instead of using inplace=True, reassign is the answer to this problem.


This does well because it creates a new variable to store the data frame rather than attempting to modify the initial variable s.

Pandas in Python makes working and playing with data analysis principles a breeze. It provides us a sense of comprehensive success to be able to evaluate, modify elements, and return results. When developing programs, it allows us to complete a specified stream.

When we just need to handle explicit rows or columns, Pandas DataFrame rows and columns functionalities come in handy. Obtaining the mark data and printing it for future study reasons is also beneficial.

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