a transpose of the resulting DataFrame again. This will reset the indexes of the columns of the original DataFrame.

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)

df2 = df1.T.reset_index(drop=True, inplace=False).T
print("DataFrame after resetting indexes of rows and columns: \n", df2)

So, we are first transposing the df1 DataFrame using df1.T. After that, we are resetting the indexes of the rows of the transposed DataFrame using df1.T.reset_index(drop=True, inplace=False). And then, we are again taking a transpose of the resulting DataFrame using df1.T.reset_index(drop=True, inplace=False).T. Please note that here we are creating a new DataFrame after resetting the indexes. So, the inplace parameter is False here.

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
DataFrame after resetting indexes of rows and columns: 
    0   1   2
0   6   9  10
1  16  19  20