Sorting Data Frame objects in Python
By Sachin VermaIn this tutorial, we will be going to discuss sorting data frames in the pandas library in Python. So basically what is a data frame?
A data frame is a two-dimensional representation of data organized in the form of rows and columns. A data frame can be created by using the pandas.DataFrame() method of the pandas package. For example,
import pandas
my_data = {'Name':['Sachin', 'Sourabh', 'Subhojeet', 'Anirudh',
'Vedant', 'Abhishek', 'Shivam']}
df = pandas.DataFrame(my_data)
print(df)
print(type(df))Output:
Name 0 Sachin 1 Sourabh 2 Subhojeet 3 Anirudh 4 Vedant 5 Abhishek 6 Shivam
<class 'pandas.core.frame.DataFrame'>
Here we have created a data frame object of the data of a group of people. You can see the type of data frame object created.
Sorting DataFrame object in Python
Now let’s take a look at how to sort the data frame object. For sorting the data frame we use pandas.DataFrame.sort() method. Pandas sort_values() function sorts values in required order(either ascending or descending).
Syntax: DataFrame.sort_values(by, axis , ascending , inplace , kind , na_position)
- by -> name of the column/columns to be sorted.
- axis -> determines the axis to be sorted. Default: 0
- ascending -> boolean value. If true sorts the given data frame in ascending order otherwise in descending order. Default: True
- inplace -> boolean value. If true sorts the given data frame in place otherwise not in place. Default: False.
- kind -> It determines the type of sorting technique used. It can take quicksort, heapsort, mergesort as the argument. Default: quicksort
- na_position -> If first It puts all NaN’s in first. If last puts all the NaN’s in last.
Let’s first import our dataset into the program.
import pandas
my_data = pandas.read_excel("Cricket World Cup Winners.xlsx")
my_data| Year | Host | Venue for Final | Team-1 | Team-2 | Winner | Margin | |
|---|---|---|---|---|---|---|---|
| 0 | 1975 | England | Lord’s | WI | Aus | WI | 17 runs |
| 1 | 1979 | England | Lord’s | WI | Eng | WI | 92 runs |
| 2 | 1983 | England | Lord’s | Ind | WI | Ind | 43 runs |
| 3 | 1987 | India | Kolkata | Aus | Eng | Aus | 7 runs |
| 4 | 1992 | Australia, New Zealand | Melbourne | Pak | Eng | Pak | 22 runs |
| 5 | 1996 | India, Pakistan, Sri Lanka | Lahore (Gdffi) | Aus | SL | SL | 7 wickets |
| 6 | 1999 | England | Lord’s | Pak | Aus | Aus | 8 wickets |
| 7 | 2003 | South Africa | Wanderers | Aus | Ind | Aus | 125 runs |
| 8 | 2007 | West Indies | Bridgetown | Aus | SL | Aus | 53 runs |
| 9 | 2011 | India, Pakistan, Sri Lanka, Bangladesh | Wankhede | SL | Ind | Ind | 6 wickets |
| 10 | 2015 | Australia, New Zealand | Melbourne | NZ | Aus | Aus | 7 wickets |
Here is our data set which consists of all the world cup winners of cricket. Download the excel file here cricket
Now we can use the Dataframe.sort_values method to sort a particular column. For instance, here I have sorted the hostname columns in ascending order.
import pandas
my_data = pandas.read_excel("Cricket World Cup Winners.xlsx")
my_data.sort_values("Host", axis = 0, ascending = True,inplace = True, na_position ='last')
print(my_data)Year Host Venue for Final Team-1 Team-2 Winner Margin 4 1992 Australia, New Zealand Melbourne Pak Eng Pak 22 runs 10 2015 Australia, New Zealand Melbourne NZ Aus Aus 7 wickets 0 1975 England Lord's WI Aus WI 17 runs 1 1979 England Lord's WI Eng WI 92 runs 2 1983 England Lord's Ind WI Ind 43 runs 6 1999 England Lord's Pak Aus Aus 8 wickets 3 1987 India Kolkata Aus Ind Aus 7 runs 5 1996 India, Pakistan, Sri Lanka Lahore (Gdffi) Aus SL SL 7 wickets 9 2011 India, Pakistan, Sri Lanka, Bangladesh Wankhede SL Ind Ind 6 wickets 7 2003 South Africa Wanderers Aus Ind Aus 125 runs 8 2007 West Indies Bridgetown Aus SL Aus 53 runs
Here you can see the Host column is sorted in ascending order.
You can also sort two multiple columns simultaneously.
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