rows.

import pandas

list1 = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]
df1 = pandas.DataFrame(list1)
print("df1: \n", df1)

df1.drop([0, 2], axis=0, inplace=True)
df1.drop([1, 2], axis=1, inplace=True)
print("df1 after dropping rows and columns: \n", df1)

df1.reset_index(drop=True, inplace=True)
print("df1 after resetting indexes of rows: \n", df1)

Here, the parameter drop=True indicates that the old indexes won’t be stored in the DataFrame as a new column. Instead, the old indexes will be dropped completely. And the parameter inplace=True indicates that we are not creating any new DataFrame after resetting the indexes of the rows.

The output of the above program will be:

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

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

In the pandas Python library, there is no direct way of resetting the indexes of the columns. Instead, we can first take a transpose of the DataFrame. After that we can reset the indexes of the rows in the transposed DataFrame. And then, we can take …