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Sorting rows in pandas DataFrame
  • Last Updated : 06 Jan, 2019

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.

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