NumPy Tutorial

Python NumPy Tutorial numpy.empty() in Python numpy.empty_like() in Python numpy.eye() in Python numpy.identity() in Python numpy.ones() in Python numpy.ones_like() in Python numpy.zeros in Python numpy.zeros_like() in Python numpy.full() in Python numpy.full_like() in Python numpy.asarray() in Python numpy.frombuffer() in Python numpy.fromiter() in Python numpy.fromstring () in Python numpy.asanyarray() in Python with Example numpy.ascontiguousarray() in Python with Example Numpy.asmatrix() in Python with Example Numpy.copy() in Python with Example numpy.loadtxt() Python numpy.arrange() in Python numpy.linspace() in Python numpy.logspace() in Python numpy.geomspace() in Python numpy.meshgrid() in Python numpy.diag() in Python numpy.diagflat() in Python numpy.tri() in Python numpy.tril() in Python numpy.copyto() in Python numpy.reshape() in Python numpy.ravel() in Python numpy.ndarray.flat() in Python numpy.ndarray.flatten() in Python numpy.rollaxis() in Python numpy.swapaxes() in Python numpy.ndarray.T in Python numpy.transpose() in Python numpy.atleast_1d() in Python numpy.atleast_2d() in Python numpy.atleast_3d() in Python numpy.broadcast_to() in Python numpy.broadcast_arrays() in Python numpy.expand_dims() in Python numpy.squeeze() in Python numpy.asarray_chkfinite() in Python numpy.asscalar() in Python numpy.concatenate() in Python numpy.stack() in Python numpy.column_stack() in Python numpy.dstack() in Python numpy.hstack() in Python numpy.vstack() in Python numpy.split() in Python numpy.tile() in Python numpy.repeat() in Python numpy.delete() in Python numpy.append() in Python numpy.resize() in Python numpy.trim_zeros() in Python numpy.unique() in Python numpy.flip() in Python NumPy vs SciPy

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Numpy Attributes

numpy.linspace() in Python

numpy.linspace() in Python The linspace() function of Python numpy class returns the number spaces equally over the given interval i.e.  [start, stop]. Syntax

numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
Parameter start: It is an optional parameter which represents the start of the interval range. By default, the start value is 0 stop :This parameter represents the end of the interval range. restep :This parameter represents a Boolean value, and by default, it is False.If passed True, it returns the samples, step. endpoint : It represents a Boolean value. It the value passed is True, stop is the last sample. Otherwise, it is not included. axis: The axis in the result to store the samples. num: It represents the no. of samples to generate. dtype: This parameter depicts the type of output array Return This function returns:·        ndarray:  where the num is equally spaced between the samples in the closed interval [start, stop].·        ‘step’:  The size between the sample(in float) and it is only returned if retstep is True Example 1
# Python Programming to explain
# numpy.linspace() function
importnumpy as np
# restep set to True
print("When retstep is True:", np.linspace(2.0, 3.0, num=5, retstep=True), "\n")
print("When retstep is False:", np.linspace(2.0, 3.0, num=5, retstep=False), "\n")
# To evaluate the cos() in long range 
x = np.linspace(0, 2, 10)
print("Value\n", np.cos(x))
Output
When retstep is True: (array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)
When retstep is False: [ 2.    2.25  2.5   2.75  3.  ]
Value
[ 1.          0.97541009  0.90284967  0.78588726  0.63027505  0.44366602
0.23523757  0.01524018 -0.20550672 -0.41614684]