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Grouping strings in pandas

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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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