Python | Numpy.dsplit() method

Last Updated : 17 Sep, 2019

With the help of Numpy.dsplit()() method, we can get the splitted dimensions of an array by using Numpy.dsplit()() method.

Syntax : Numpy.dsplit(numpy.array(), split_size)

Return : Return the array having splitted dimensions.

Example #1 :

In this example we can see that using Numpy.expand_dims() method, we are able to get the splitted dimensions using this method.

# import numpy 
import numpy as np 
  
# using Numpy.dsplit() method 
gfg = np.array([[1, 2, 5], 
                [3, 4, 10], 
                [5, 6, 15], 
                [7, 8, 20]]) 
  
gfg = gfg.reshape(1, 2, 6) 
print(gfg) 
  
gfg = np.dsplit(gfg, 2) 
print(gfg) 

Output :

[[[ 1 2 5 3 4 10]
[ 5 6 15 7 8 20]]]

[array([[[ 1, 2, 5],
[ 5, 6, 15]]]),
array([[[ 3, 4, 10],
[ 7, 8, 20]]])]

Example #2 :

# import numpy 
import numpy as np 
  
# using Numpy.expand_dims() method 
gfg = np.array([[1, 2], [7, 8], [5, 10]]) 
gfg = gfg.reshape(1, 2, 3) 
print(gfg) 
  
gfg = np.dsplit(gfg, 3) 
print(gfg) 

Output :

[[[ 1 2 7]
[ 8 5 10]]]

[array([[[1],
[8]]]), array([[[2],
[5]]]), array([[[ 7],
[10]]])]

Python | Numpy.expand_dims() method

J

Jitender_1998

News

Improve

Article Tags :

Practice Tags :

python

Similar Reads

Python | Numpy.dsplit() method

With the help of Numpy.dsplit()() method, we can get the splitted dimensions of an array by using Numpy.dsplit()() method. Syntax : Numpy.dsplit(numpy.array(), split_size) Return : Return the array having splitted dimensions. Example #1 : In this example we can see that using Numpy.expand_dims() met

Python | Numpy.expand_dims() method

With the help of Numpy.expand_dims() method, we can get the expanded dimensions of an array by using Numpy.expand_dims() method. Syntax : Numpy.expand_dims() Return : Return the expanded array. Example #1 : In this example we can see that using Numpy.expand_dims() method, we are able to get the expa

numpy.mod() in Python

numpy.mod() is another function for doing mathematical operations in numpy.It returns element-wise remainder of division between two array arr1 and arr2 i.e. arr1 % arr2 .It returns 0 when arr2 is 0 and both arr1 and arr2 are (arrays of) integers. Syntax : numpy.mod(arr1, arr2, /, out=None, *, where

Python | numpy.fill_diagonal() method

With the help of numpy.fill_diagonal() method, we can get filled the diagonals of numpy array with the value passed as the parameter in numpy.fill_diagonal() method. Syntax : numpy.fill_diagonal(array, value) Return : Return the filled value in the diagonal of an array. Example #1 : In this example

numpy.load() in Python

numpy.load() function return the input array from a disk file with npy extension(.npy). Syntax : numpy.load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII') Parameters: file : : file-like object, string, or pathlib.Path.The file to read. File-like objects must support the

Python | Numpy np.legvander() method

np.legvander() method is used to returns the Vandermonde matrix of degree deg and sample points x. Syntax : np.legvander(x, deg) Parameters: x :[ array_like ] Array of points. The dtype is converted to float64 or complex128 depending on whether any of the elements are complex. If x is scalar it is c

Python | numpy.putmask() method

With the help of numpy.putmask() method, we can change the elements in an array with the help of condition and given value by using numpy.putmask() method. Syntax : numpy.putmask(array, condition, value) Return : Return the array having new elements according to value. Example #1 : In this example w

numpy.insert() in Python

The numpy.insert() function inserts values along the mentioned axis before the given indices. Syntax : numpy.insert(array, object, values, axis = None) Parameters : array : [array_like]Input array. object : [int, array of ints]Sub-array with the index or indices before which values is inserted value

numpy.matrix() in Python

This class returns a matrix from a string of data or array-like object. Matrix obtained is a specialised 2D array. Syntax : numpy.matrix(data, dtype = None) : Parameters : data : data needs to be array-like or string dtype : Data type of returned array. Returns : data interpreted as a matrix # Pytho

numpy.dot() in Python

numpy.dot(vector_a, vector_b, out = None) returns the dot product of vectors a and b. It can handle 2D arrays but considers them as matrix and will perform matrix multiplication. For N dimensions it is a sum-product over the last axis of a and the second-to-last of b : dot(a, b)[i,j,k,m] = sum(a[i,j

Python | Numpy np.hermone() method

With the help of np.hermone() method, we can use hermone instead of np.ones by using np.hermone() method. Syntax : np.hermone() Return : Return array([1]) Example #1 : In this example we can see that by using np.hermone() method, we are able to get the functionality of np.ones as same as this method

numpy.invert() in Python

numpy.invert() function is used to Compute the bit-wise Inversion of an array element-wise. It computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. For signed integer inputs, the two’s complement is returned. In a two’s-complement system negative num

numpy.diag() in Python

numpy.diag(a, k=0) : Extracts and construct a diagonal array Parameters : a : array_like k : [int, optional, 0 by default] Diagonal we require; k>0 means diagonal above main diagonal or vice versa. Returns : ndarray Python Code # Python Programming illustrating # numpy.diag method import numpy as

Python | Numpy MaskedArray.__div__

numpy.ma.MaskedArray class is a subclass of ndarray designed to manipulate numerical arrays with missing data. With the help of Numpy MaskedArray.__div__ we can divide a particular value that is provided as a parameter in the MaskedArray.__div__() method. Syntax: numpy.MaskedArray.__div__ Return: Di

numpy.diff() in Python

numpy.diff(arr[, n[, axis]]) function is used when we calculate the n-th order discrete difference along the given axis. The first order difference is given by out[i] = arr[i+1] - arr[i] along the given axis. If we have to calculate higher differences, we are using diff recursively. Syntax: numpy.di

Python | Numpy np.hermeone() method

With the help of np.hermeone() method, we can use hermeone instead of np.ones() by using np.hermeone() method. Syntax : np.hermeone Return : Return the array of ones. Example #1 : In this example we can see that by using np.hermeone() method, we are able to get the functionality of np.ones as same a

Python | numpy.array_split() method

With the help of numpy.array_split() method, we can get the splitted array of having different dimensions by using numpy.array_split() method. Syntax : numpy.array_split() Return : Return the splitted array of one dimension. Example #1 : In this example we can see that by using numpy.array_split() m

numpy.nanprod() in Python

numpy.nanprod() function is used when we want to compute the product of array elements over a given axis treating NaNs as ones. One is returned for slices that are all-NaN or empty. Syntax : numpy.nanprod(arr, axis=None, dtype=None, out=None, keepdims='class numpy._globals._NoValue'). Parameters : a

numpy.loadtxt() in Python

numpy.load() in Python is used load data from a text file, with aim to be a fast reader for simple text files. Note that each row in the text file must have the same number of values. Syntax: numpy.loadtxt(fname, dtype='float', comments='#', delimiter=None, converters=None, skiprows=0, usecols=None,

numpy.multiply() in Python

numpy.multiply() function is used when we want to compute the multiplication of two array. It returns the product of arr1 and arr2, element-wise. Syntax : numpy.multiply(arr1, arr2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj], ufunc 'multip

翻译：