Sometimes after dropping or filtering rows and columns, we may want to reset the indexes of the rows and/or the columns. In the pandas Python library, we can use the reset_index() function for resetting the indexes of the rows and/or the columns. In this article, we will discuss:

• How to reset the indexes of rows of a DataFrame using the pandas Python library?
• How to reset the indexes of columns of a DataFrame using the pandas Python library?

Let’s say we have the following DataFrame:

df1: 
    0   1   2   3   4
0   1   2   3   4   5
1   6   7   8   9  10
2  11  12  13  14  15
3  16  17  18  19  20

We dropped rows 0, and 2 and columns 1, and 2 from the DataFrame. So, the DataFrame now looks like the following:

df1 after dropping rows and columns: 
    0   3   4
1   6   9  10
3  16  19  20

Now, we want to reset the indexes of the rows and the columns of the DataFrame.

How to reset the indexes of rows of a DataFrame using the pandas Python library?

In the pandas Python library, we can use the DataFrame.reset_index() function to reset the indexes of the …