Sort a DataFrame by rows and columns in Pandas

Output

   score_1  score_2
5       15       11
3       19        2
2       25        9
0       30        3
1       40        7
4       60        5

We are using the sort_values() function of DataFrame to sort row values in ascending and descending order. By default, the function sort the values in ascending order. if you want to sort the values in descending order, you can pass asending=False to the sort_values() function.

Sort by multiple columns

To sort a Dataframe by multiple columns values, we can pass the list of column names that you want to sort in the "by" argument of sort_values() function.

import pandas as pd

data = {
  'score_1': [30, 40, 25, 19, 60, 15],
  'score_2': [3, 7, 9, 2, 5, 11]
}

# create dataframe
df = pd.DataFrame(data)

# sort DataFrame by multiple columns
result = df.sort_values(by=['score_1', 'score_2'])

print(result)

Output

   score_1  score_2
5       15       11
3       19        2
2       25        9
0       30        3
1       40        7
4       60        5

Sort DataFrame in Descending order

When we use the sort_values() function of DataFrame to sort the column values, it will sort them in ascending order by default. To sort the values in descending order, set ascending=False. Check the below example for reference

Code Example

import pandas as pd

data = {
  'company': ['Devsheet', 'Tesla', 'Microsoft', 'Amazon', 'SpaceX'],
  'value': [20, 50, 30, 90, 10]
}

# create dataframe
df = pd.DataFrame(data)

# sort DataFrame in descending order
result = df.sort_values(by='value', ascending=False)

print(result)

Output

     company  value
3     Amazon     90
1      Tesla     50
2  Microsoft     30
0   Devsheet     20
4     SpaceX     10