Grouping strings in pandas

Python functions | String Variables

Michael Zippo 18.07.2021

Let’s see how to group rows in Pandas Dataframe with some examples.

Example 1:

To group rows in Pandas, we’ll start by creating pandas dataframe first.

# pandas import

import pandas as pd

# sample dataframe

example = { ’Team’ : [ ’ Arsenal’ , ’ Manchester United’ , ’Arsenal’ ,

’Arse nal’ , ’Chelsea’ , ’Manchester United’ ,

’Manchester United’ , ’ Chelsea’ , ’Chelsea’ , ’ Chelsea’ ],

’Player’ : [ ’ Ozil’ , ’ Pogba’ , ’Lucas’ , ’Aubameyang’ ,

’Hazard’ , ’Mata’ , ’Lukaku’ , ’ Morata’ ,

   ’Giroud’  ,   ’Kante’  ], 
    
   ’Goals’  : [  5  ,   3  ,   6  ,   4  ,   9  ,   2  ,   0  ,   5  ,   2  ,   3  ]} 
   
   df   =   pd.DataFrame (example) 
   
   print   (df)

Now , create grouping object means an object that represents that particular group.

total_goals = df [ ’Goals’ ]. groupby (df [ ’Team’ ])

# print tool value

print (total_goals.mean ())

Output:

Example 2 :

import pandas as pd

# example dataframe

example = { ’Team’ : [ ’Australia’ , ’ England’ , ’South Africa’ ,

   ’Australia’  ,   ’England’  ,  ’ India’  ,   ’India’  ,  
   ’South Africa’  ,   ’England’  ,  ’ India’  ],  
   
   ’Player’  : [ ’ Ricky Ponting’  ,   ’Joe Root’  ,  ’ Hashim Amla’  , 
     ’David Warner’  ,  ’ Jos Buttler’  ,   ’Virat Kohli’  , 
   ’Rohit Sharma’  ,  ’ David Miller’  ,   ’Eoin Morgan’  ,  
   ’Dinesh Karthik’  ],  
   
   ’Runs’  : [  345  ,   336  ,   689  ,   490  ,   989  ,   672  ,   560  ,   455  ,   342  ,   376  ],  
   
   ’Salary’  : [  34500  ,   33600  ,   68900  ,   49000  ,   98899  ,  
   67562  ,   56760  ,   45675  ,   34542  ,   31176  ]} 
   
   df   =   pd.DataFrame (example) 
    
   total_salary   =   df [  ’Salary’  ]. groupby (df [ ’ Team’  ]) 
   
  # print tool value  
   print   (total_salary.mean ())

Output:

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Grouping strings in pandas

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