Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). We often need to do certain operations on both rows and column while handling the data.
Let’s see how to sort rows in pandas DataFrame.
Code #1: Sorting rows by Science
# import modules import pandas as pd # create dataframe data = {'name': ['Simon', 'Marsh', 'Gaurav', 'Alex', 'Selena'], 'Maths': [8, 5, 6, 9, 7], 'Science': [7, 9, 5, 4, 7], 'English': [7, 4, 7, 6, 8]} df = pd.DataFrame(data) # Sort the dataframe’s rows by Science, # in descending order a = df.sort_values(by ='Science', ascending = 0) print("Sorting rows by Science:\n \n", a) |
Output:
Sorting rows by Science:
English Maths Science name
1 4 5 9 Marsh
0 7 8 7 Simon
4 8 7 7 Selena
2 7 6 5 Gaurav
3 6 9 4 Alex
Code #2: Sort rows by Maths and then by English.
# import modules import pandas as pd # create dataframe data = {'name': ['Simon', 'Marsh', 'Gaurav', 'Alex', 'Selena'], 'Maths': [8, 5, 6, 9, 7], 'Science': [7, 9, 5, 4, 7], 'English': [7, 4, 7, 6, 8]} df = pd.DataFrame(data) # Sort the dataframe’s rows by Maths # and then by English, in ascending order b = df.sort_values(by =['Maths', 'English']) print("Sort rows by Maths and then by English: \n\n", b) |
Output:
Sort rows by Maths and then by English:
English Maths Science name
1 4 5 9 Marsh
2 7 6 5 Gaurav
4 8 7 7 Selena
0 7 8 7 Simon
3 6 9 4 Alex
Code #3: If you want missing values first.
import pandas as pd # create dataframe data = {'name': ['Simon', 'Marsh', 'Gaurav', 'Alex', 'Selena'], 'Maths': [8, 5, 6, 9, 7], 'Science': [7, 9, 5, 4, 7], 'English': [7, 4, 7, 6, 8]} df = pd.DataFrame(data) a = df.sort_values(by ='Science', na_position ='first' ) print(a) |
Output:
English Maths Science name 3 6 9 4 Alex 2 7 6 5 Gaurav 0 7 8 7 Simon 4 8 7 7 Selena 1 4 5 9 Marsh
As there are no missing values in this example this will produce same output as the above one, but sorted in ascending order.
