How can pd.cut return a number as group?





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Example



pd.cut(df['a'],[0,2,4,10,np.inf],right=False)


It returns [0,2),[2,4),[4,10),[10,np.inf) .



But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)?






pandas







share|improve this question























  • Can you please share a sample of df?

    – Mayank Porwal
    Nov 25 '18 at 9:31


















0















Example



pd.cut(df['a'],[0,2,4,10,np.inf],right=False)


It returns [0,2),[2,4),[4,10),[10,np.inf) .



But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)?






pandas







share|improve this question























  • Can you please share a sample of df?

    – Mayank Porwal
    Nov 25 '18 at 9:31














0












0








0








Example



pd.cut(df['a'],[0,2,4,10,np.inf],right=False)


It returns [0,2),[2,4),[4,10),[10,np.inf) .



But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)?






pandas







share|improve this question














Example



pd.cut(df['a'],[0,2,4,10,np.inf],right=False)


It returns [0,2),[2,4),[4,10),[10,np.inf) .



But how can I get [0],(0,2),[2,4),[4,10),[10,np.inf)?






pandas




pandas






share|improve this question













share|improve this question











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asked Nov 25 '18 at 9:07









JackJack

3091417




3091417













  • Can you please share a sample of df?

    – Mayank Porwal
    Nov 25 '18 at 9:31



















  • Can you please share a sample of df?

    – Mayank Porwal
    Nov 25 '18 at 9:31

















Can you please share a sample of df?

– Mayank Porwal
Nov 25 '18 at 9:31





Can you please share a sample of df?

– Mayank Porwal
Nov 25 '18 at 9:31












1 Answer
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If all values are integers and greater than zero, this could work:



import numpy as np

import pandas as pd



df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})

pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)





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    1 Answer
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    active

    oldest

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    1 Answer
    1






    active

    oldest

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    active

    oldest

    votes






    active

    oldest

    votes









    1














    If all values are integers and greater than zero, this could work:



    import numpy as np
    
import pandas as pd
    

    
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
    
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)





    share|improve this answer




























      1














      If all values are integers and greater than zero, this could work:



      import numpy as np
      
import pandas as pd
      

      
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
      
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)





      share|improve this answer


























        1












        1








        1







        If all values are integers and greater than zero, this could work:



        import numpy as np
        
import pandas as pd
        

        
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
        
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)





        share|improve this answer













        If all values are integers and greater than zero, this could work:



        import numpy as np
        
import pandas as pd
        

        
df = pd.DataFrame({'a': [1, 3, 5, 7, 9, 11, 13]})
        
pd.cut(df['a'], [-np.inf, 1, 2, 4, 10, np.inf], right=False)






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 25 '18 at 11:39









        E. ZeytinciE. Zeytinci

        10317




        10317
































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