np.apply_along_axis[lambda y: [str[i] for i in y], 0, x]
Example
>>> import numpy as np
>>> x = np.array[[-1]*10+[0]*10+[1]*10]
array[[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
>>> np.apply_along_axis[lambda y: [str[i] for i in y], 0, x].tolist[]
['-1', '-1', '-1', '-1', '-1', '-1', '-1', '-1', '-1', '-1', '0', '0',
'0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1', '1', '1', '1',
'1', '1', '1', '1']
Used functions: map: Return an iterator that applies function to every item of iterable,
yielding the results. If additional iterable arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. With multiple iterables, the iterator stops when the shortest iterable is exhausted. int: Return an integer object constructed from a number or string x, or return filter: Construct an
iterator from those elements of iterable for which function returns true. iterable may be either a sequence, a container which supports iteration, or an iterator. If function is Python Code:
#this line create a string variable. stringOfInteger=["1","5","10","15",None,"","20","25","30"] #split string and create an integer data type
array. intArray=map[int,filter[None,stringOfInteger]] print["elements of int array............"] forainintArray: print[a,type[a]]
0 if no arguments are given.None, the identity function is assumed, that is, all elements of iterable that are false are removed.
Output:
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Sometimes in a competitive coding environment, we get input in some other datatypes and we need to convert them into other forms this problem is the same as that we have an input in the form of string and we need to convert it into floats. Let’s discuss a few ways to convert an array of strings to an array of floats.
Example:
initial array: ['1.1' '1.5' '2.7' '8.9'] final array: [ 1.1 1.5 2.7 8.9]
Convert array of strings to array of floats using astype
Pandas astype[] is one of the most important methods. It is used to change the datatype of a series. if a column could be imported as a string but to do operations we have to convert it into a float, astype[] is used to do such data type conversions.
Python3
import numpy as np
ini_array = np.array[["1.1", "1.5", "2.7", "8.9"]]
print ["initial array", str[ini_array]]
res = ini_array.astype[np.float]
print ["final array", str[res]]
Output:
initial array ['1.1' '1.5' '2.7' '8.9'] final array [ 1.1 1.5 2.7 8.9]
Convert array of strings to array of floats using np.fromstring
The numpy.fromstring[] function creates a new one-dimensional array initialized from text data in a string.
Python3
import numpy as np
ini_array = np.array[["1.1", "1.5", "2.7", "8.9"]]
print ["initial array", str[ini_array]]
ini_array =
', '.join[ini_array]
ini_array = np.fromstring[ini_array, dtype = np.float,
sep =', ' ]
print ["final array", str[ini_array]]
Output:
initial array ['1.1' '1.5' '2.7' '8.9'] final array [ 1.1 1.5 2.7 8.9]
Convert array of strings to array of floats using np.asarray[] and type
The numpy.asarray[]function is used when we want to convert the input to an array. Input can be lists, lists of tuples, tuples, tuples of tuples, tuples of lists and arrays.
Python3
import numpy as np
ini_array = np.array[["1.1", "1.5", "2.7", "8.9"]]
print ["initial array", str[ini_array]]
final_array = b = np.asarray[ini_array,
dtype =
np.float64, order ='C']
print ["final array", str[final_array]]
Output:
initial array ['1.1' '1.5' '2.7' '8.9'] final array [ 1.1 1.5 2.7 8.9]
Convert array of strings to array of floats using np.asfarray
The numpy.asfarray[] function is used when we want to convert input to a float type array. Input includes scalar, lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.
Python3
import numpy as np
ini_array = np.array[["1.1", "1.5", "2.7", "8.9"]]
print ["initial array", str[ini_array]]
final_array = b = np.asfarray[ini_array,dtype = float]
print ["final array", str[final_array]]
Output:
initial array ['1.1' '1.5' '2.7' '8.9'] final array [1.1 1.5 2.7 8.9]