There are two kinds of sorting available in Pandas. They are ā
- 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 ā
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
InĀ unsorted_df, theĀ labelsĀ and theĀ valuesĀ 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 ā
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 ā
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 ā
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 ā
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 ā
col1 col2 2 1 2 1 1 3 3 1 4 0 2 1
Sorting Algorithm
sort_values()Ā provides a provision to choose the algorithm from mergesort, heapsort and quicksort. Mergesort is the only stable algorithm.
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 ā
col1 col2 1 1 3 2 1 2 3 1 4 0 2 1