Python Pandas - Sorting

There are two kinds of sorting available in Pandas. They are −

By label
By Actual Value

Let us consider an example with an output.

import pandas as pd
import numpy as np

unsorted_df=pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
mns=['col2','col1'])
print unsorted_df

Its output is as follows −

        col2       col1
1  -2.063177   0.537527
4   0.142932  -0.684884
6   0.012667  -0.389340
2  -0.548797   1.848743
3  -1.044160   0.837381
5   0.385605   1.300185
9   1.031425  -1.002967
8  -0.407374  -0.435142
0   2.237453  -1.067139
7  -1.445831  -1.701035

In unsorted_df, the labels and the values are unsorted. Let us see how these can be sorted.

By Label

Using the sort_index() method, by passing the axis arguments and the order of sorting, DataFrame can be sorted. By default, sorting is done on row labels in ascending order.

import pandas as pd
import numpy as np

unsorted_df = pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
   mns = ['col2','col1'])

sorted_df=unsorted_df.sort_index()
print sorted_df

Its output is as follows −

        col2       col1
0   0.208464   0.627037
1   0.641004   0.331352
2  -0.038067  -0.464730
3  -0.638456  -0.021466
4   0.014646  -0.737438
5  -0.290761  -1.669827
6  -0.797303  -0.018737
7   0.525753   1.628921
8  -0.567031   0.775951
9   0.060724  -0.322425

Order of Sorting

By passing the Boolean value to ascending parameter, the order of the sorting can be controlled. Let us consider the following example to understand the same.

import pandas as pd
import numpy as np

unsorted_df = pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
   mns = ['col2','col1'])

sorted_df = unsorted_df.sort_index(ascending=False)
print sorted_df

Its output is as follows −

         col2        col1
9    0.825697    0.374463
8   -1.699509    0.510373
7   -0.581378    0.622958
6   -0.202951    0.954300
5   -1.289321   -1.551250
4    1.302561    0.851385
3   -0.157915   -0.388659
2   -1.222295    0.166609
1    0.584890   -0.291048
0    0.668444   -0.061294

Sort the Columns

By passing the axis argument with a value 0 or 1, the sorting can be done on the column labels. By default, axis=0, sort by row. Let us consider the following example to understand the same.

import pandas as pd
import numpy as np
 
unsorted_df = pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],colu
   mns = ['col2','col1'])
 
sorted_df=unsorted_df.sort_index(axis=1)

print sorted_df

Its output is as follows −

         col1        col2
1   -0.291048    0.584890
4    0.851385    1.302561
6    0.954300   -0.202951
2    0.166609   -1.222295
3   -0.388659   -0.157915
5   -1.551250   -1.289321
9    0.374463    0.825697
8    0.510373   -1.699509
0   -0.061294    0.668444
7    0.622958   -0.581378

By Value

Like index sorting, sort_values() is the method for sorting by values. It accepts a 'by' argument which will use the column name of the DataFrame with which the values are to be sorted.

import pandas as pd
import numpy as np

unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
   sorted_df = unsorted_df.sort_values(by='col1')

print sorted_df

Its output is as follows −

   col1  col2
1    1    3
2    1    2
3    1    4
0    2    1

Observe, col1 values are sorted and the respective col2 value and row index will alter along with col1. Thus, they look unsorted.

'by' argument takes a list of column values.

import pandas as pd
import numpy as np

unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
   sorted_df = unsorted_df.sort_values(by=['col1','col2'])

print sorted_df

Its output is as follows −

Sorting Algorithm

sort_values() provides a provision to choose the algorithm from mergesort, heapsort and quicksort. Mergesort is the only stable algorithm.

Live Demo

import pandas as pd
import numpy as np

unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
sorted_df = unsorted_df.sort_values(by='col1' ,kind='mergesort')

print sorted_df

Its output is as follows −

  col1 col2
1    1    3
2    1    2
3    1    4
0    2    1

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