The axis parameter is the index of the new axis in the dimensions of the resultant array(if axis = 0(default), it will be the first dimension, and if axis = -1, it will be the last dimension). The arrays parameters are the input arrays(each array must have the same shape). The stack() function returns a stacked ndarray(all contents of input arrays in one array) that has one more dimension than the input arrays. The NumPy stack() function joins the sequence of arrays along a new axis.

The arrays parameter are arrays, nested list of arrays, and scalars. Instead, leading axes of size 1 are inserted, to make each block’s dimensions the same. Blocks can be of any dimension, but will not be broadcasted using the normal rules. Here, blocks can be array, list of arrays, scalar inputs but not tuples. The NumPy block() returns a ndarray assembled from nested lists of blocks. Print(np.concatenate((a, b), axis = None)) Print(np.concatenate((a, b.T), axis = 1)) The casting parameter controls what kind of data casting may occur and defaults to ‘same_kind’. The dtype parameter is the data type of the resultant array, and the default is None. The out parameter is the destination to place the result. The parameter axis is the axis along which the arrays will be joined(If the axis is None, arrays are flattened before use, Default is set 0). The parameter a1, a2, … are the input arrays(must have the same shape, except in the dimension corresponding to the axis). ), axis=0, out=None, dtype=None, casting='same_kind') The concatenate() function returns a concatenated ndarray. The NumPy library provides several functions for joining arrays.Ĭoncatenation refers to joining, the NumPy concatenate() function is used to join two or more arrays of the same shape along an existing axis. Joining means putting the contents of two or more arrays in a single array. The parameters of squeeze() function is the same as the expand_dims() function except axis is set to None. The squeeze() function returns a ndarray with changed dimensions. The NumPy squeeze() function removes the one-dimensional(removes axes to length one) entry from the given array. The a parameter is the input array and the axis parameter is the position of the new axis (or axes) be placed(input type is integer or tuple of integers). The expand_dims() returns the array with the increased number of dimensions. The expand_dims() function of Numpy is used to expand the array by inserting a new axis at the specified position.

The args parameter is the arrays to broadcast and the subok parameter is the same as the broadcast_to() function. Syntax: broadcast_arrays(*args, subok=False) The broadcast_arrays() returns the views of the list of arrays and are typically not contiguous(not merged). The NumPy broadcast_arrays() function is used to broadcast any number of arrays against each other.

Vstack all input array dimensions