Sorting rows in pandas DataFrame (original) (raw)

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 pandas as pd

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' , 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 pandas as pd

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)

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

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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