numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0, *, device = None) [source] # Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

Use numpy.linspace if you want the endpoint to be included in the result, or if you are using a non-integer step size. numpy.linspace can include the endpoint and determines step size from the num argument, which specifies the number of elements in the returned array.

numpy. linspace (start, stop, num = 50, endpoint = True, retstep = False, dtype = None, axis = 0) [source] ¶ Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [ start , stop ].

>>> y = np. linspace (start, stop, num = num, endpoint = endpoint)... >>> power (base, y). astype (dtype)...

In such cases, the use of numpy.linspace should be preferred. The built-in range generates Python built-in integers that have arbitrary size , while numpy.arange produces numpy.int32 or numpy.int64 numbers.

meshgrid is very useful to evaluate functions on a grid. If the function depends on all coordinates, both dense and sparse outputs can be used.

You can also use np.linspace() to create an array with values that are spaced linearly in a specified interval: >>> np . linspace ( 0 , 10 , num = 5 ) array([ 0. , 2.5, 5. , 7.5, 10. Specifying your data type

Plot the sine function: >>> import matplotlib.pylab as plt >>> x = np . linspace ( - np . pi , np . pi , 201 ) >>> plt . plot ( x , np . sin ( x )) >>> plt . xlabel ( 'Angle [rad]' ) >>> plt . ylabel ( 'sin(x)' ) >>> plt . axis ( 'tight' ) >>> plt . show ()