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 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)
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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)
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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)
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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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