Python | Numpy ndarray.item() (original) (raw)
Last Updated : 27 Mar, 2019
With the help of **numpy.ndarray.item()**
method, we can fetch the data elements that is found at the given index on numpy array. Remember we can give index as one dimensional parameter or can be two dimensional.
Parameters:
*args : Arguments (variable number and type)
-> none: This argument only works when size of an array is 1.
-> int_type: This argument is interpreted as a flat index into the array, specifying which element to return.
-> tuple of int_types: This argument is interpreted as a two dimensional array, by specifying which element to return.Returns: Copy of an Item
Example #1 :
In this example we can see that by specifying the argument in ndarray.item()
method, we can have the element if it existed on this index.
import
numpy as np
gfg
=
np.array([
1
,
2
,
3
,
4
,
5
])
print
(gfg.item(
2
))
Example #2 :
import
numpy as np
gfg
=
np.array([[
1
,
2
,
3
,
4
,
5
],
`` [
6
,
5
,
4
,
3
,
2
]])
print
(gfg.item((
1
,
2
)))
Similar Reads
- Python | Numpy ndarray.__imod__() With the help of Numpy ndarray.__imod__(), every element in an array is operated on binary operator i.e mod(%). Remember we can use any type of values in an array and value for mod is applied as the parameter in ndarray.__imod__(). Syntax: ndarray.__imod__($self, value, /) Return: self%=value Exampl 1 min read
- Python | Numpy ndarray.__ior__() With the help of Numpy ndarray.__ior__() method, we can get the elements that is OR by the value that is provided as a parameter in numpy.ndarray.__ior__() method. Syntax: ndarray.__ior__($self, value, /) Return: self|=value Example #1 : In this example we can see that every element is or by the val 1 min read
- Python | Numpy ndarray.__itruediv__() With the help of Numpy ndarray.__itruediv__(), we can divide a particular value that is provided as a parameter in the ndarray.__itruediv__() method. Value will be divided to each and every element in a numpy array. Syntax: ndarray.__itruediv__($self, value, /) Return: self/=value Example #1 : In th 1 min read
- Python | Numpy ndarray.__iand__() With the help of Numpy ndarray.__iand__() method, we can get the elements that is anded by the value that is provided as a parameter in numpy.ndarray.__iand__() method. Syntax: ndarray.__iand__($self, value, /) Return: self&=value Example #1 : In this example we can see that every element is and 1 min read
- Python | Numpy ndarray.__ipow__() With the help of Numpy ndarray.__ipow__() method, we will get all the elements powered with the value that is provided as a parameter in numpy.ndarray.__ipow__() method. Syntax: ndarray.__ipow__($self, value, /) Return: self**=value Example #1 : In this example we can see that every element get powe 1 min read
- Python | Numpy ndarray.__ixor__() With the help of Numpy ndarray.__ixor__() method, we can get the elements that is XOR by the value that is provided as a parameter in numpy.ndarray.__ixor__() method. Syntax: ndarray.__ixor__($self, value, /) Return: self^=value Example #1 : In this example we can see that every element is xor by th 1 min read
- Python | Numpy ndarray.__copy__() With the help of Numpy ndarray.__copy__() method, we can make a copy of all the data elements that is present in numpy array. If you change any data element in the copy, it will not affect the original numpy array. Syntax : numpy.__copy__() Return : Copy of all the data elements Example #1 : In this 1 min read
- numpy.ndarray.fill() in Python numpy.ndarray.fill() method is used to fill the numpy array with a scalar value. If we have to initialize a numpy array with an identical value then we use numpy.ndarray.fill(). Suppose we have to create a NumPy array a of length n, each element of which is v. Then we use this function as a.fill(v). 2 min read
- numpy.ndarray.flat() in Python The numpy.ndarray.flat() function is used as a 1_D iterator over N-dimensional arrays. It is not a subclass of, Python’s built-in iterator object, otherwise it a numpy.flatiter instance. Syntax : numpy.ndarray.flat() Parameters : index : [tuple(int)] index of the values to iterate Return : 1-D itera 3 min read
- numpy.ndarray.view() in Python numpy.ndarray.view() helps to get a new view of array with the same data. Syntax: ndarray.view(dtype=None, type=None)Parameters: dtype : Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. type : Python type, optio 3 min read