Sorting a Pandas Dataframe by Multiple Columns

Sorting a Pandas dataframe by multiple columns is a common operation in data analysis. Consider a dataframe with columns 'a', 'b', and 'c'. To sort this dataframe by column 'b' in ascending order and column 'c' in descending order, follow these steps:

Starting from Pandas version 0.17.0, the sort method has been deprecated in favor of sort_values. As of version 0.20.0, sort has been completely removed. However, the arguments and results remain unchanged:

df.sort_values(['a', 'b'], ascending=[True, False])

An equivalent way using the deprecated sort method is:

df.sort(['a', 'b'], ascending=[True, False])

For example, consider a dataframe df1 with random integer values in columns 'a' and 'b':

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randint(1, 5, (10, 2)), columns=['a', 'b'])

Sorting this dataframe by 'a' in ascending order and 'b' in descending order gives:

df1.sort(['a', 'b'], ascending=[True, False])

   a  b
2  1  4
7  1  3
1  1  2
3  1  2
4  3  2
6  4  4
0  4  3
9  4  3
5  4  1
8  4  1

Remember that the sort method is not in-place by default. To update df1 with the sorted values, assign the result of the sort method to df1 or use inplace=True in the method call:

df1 = df1.sort(['a', 'b'], ascending=[True, False])

or

df1.sort(['a', 'b'], ascending=[True, False], inplace=True)