numpy string operations | split() function

numpy.core.defchararray.split(arr, sep=None, maxsplit=None) is another function for doing string operations in numpy.It returns a list of the words in the string, using sep as the delimiter string for each element in arr.

Parameters:
arr : array_like of str or unicode.Input array.
sep : [ str or unicode, optional] specifies the separator to use when splitting the string.
maxsplit : how many maximum splits to do.

Returns : [ndarray] Output Array containing of list objects.



Code #1 :

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# Python program explaining
# numpy.char.split() method 
  
# importing numpy 
import numpy as geek
  
# input array  
in_arr = geek.array(['geeks for geeks'])
print ("Input array : ", in_arr) 
  
# output array 
out_arr = geek.char.split(in_arr)
print ("Output splitted array: ", out_arr) 

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Output:


Input array :  ['geeks for geeks']
Output splitted array:  [['geeks', 'for', 'geeks']]





  


Code #2 :  











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# Python program explaining 


# numpy.char.split() method  


  


# importing numpy  


import numpy as geek 


  


# input array  


in_arr = geek.array(['Num-py', 'Py-th-on', 'Pan-das']) 


print ("Input array : ", in_arr)  


  


  


# output array  


out_arr = geek.char.split(in_arr, sep ='-') 


print ("Output splitted array: ", out_arr)  



















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Output:


Input array :  ['Num-py' 'Py-th-on' 'Pan-das']
Output splitted array:  [['Num', 'py'] ['Py', 'th', 'on'] ['Pan', 'das']]





  


Code #3 :  











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# Python program explaining 


# numpy.char.split() method  


  


# importing numpy  


import numpy as geek 


  


# input array  


in_arr = geek.array(['Num-py', 'Py-th-on', 'Pan-das']) 


print ("Input array : ", in_arr)  


  


  


# output array when maximum spliting  


# of every array element is 1 


out_arr = geek.char.split(in_arr, sep ='-', maxsplit = 1) 


print ("Output splitted array: ", out_arr)  



















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Output:


Input array :  ['Num-py' 'Py-th-on' 'Pan-das']
Output splitted array:  [['Num', 'py'] ['Py', 'th-on'] ['Pan', 'das']]






          
          
          
          

            

    

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