Python | Numpy.dsplit() method Last Updated : 17 Sep, 2019 Comments Improve Suggest changes Like Article Like Report 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. Python3 1=1 # 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 : Python3 1=1 # 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]]])] Comment More infoAdvertise with us Next Article numpy.load() in Python J jitender_1998 Follow Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Similar Reads Numpy dstack() method-Python numpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b) 2 min read Numpy dstack() method-Python numpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b) 2 min read Numpy dstack() method-Python numpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b) 2 min read 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 2 min read 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 1 min read numpy.diff() in Python numpy.diff() calculate the n-th discrete difference along the specified axis. It is commonly used to find differences between consecutive elements in a NumPy array, such as in time series or signal data. Example:Pythonimport numpy as np a = np.array([1, 2, 4, 7, 0]) res = np.diff(a) print(res)Output 3 min read Like