numpy.arange¶

numpy.
arange
([start, ]stop, [step, ]dtype=None, *, like=None)¶ Return evenly spaced values within a given interval.
Values are generated within the halfopen interval
[start, stop)
(in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python builtin range function, but returns an ndarray rather than a list.When using a noninteger step, such as 0.1, the results will often not be consistent. It is better to use
numpy.linspace
for these cases. Parameters
 startinteger or real, optional
Start of interval. The interval includes this value. The default start value is 0.
 stopinteger or real
End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point roundoff affects the length of out.
 stepinteger or real, optional
Spacing between values. For any output out, this is the distance between two adjacent values,
out[i+1]  out[i]
. The default step size is 1. If step is specified as a position argument, start must also be given. dtypedtype
The type of the output array. If
dtype
is not given, infer the data type from the other input arguments. likearray_like
Reference object to allow the creation of arrays which are not NumPy arrays. If an arraylike passed in as
like
supports the__array_function__
protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.Note
The
like
keyword is an experimental feature pending on acceptance of NEP 35.New in version 1.20.0.
 Returns
 arangendarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
ceil((stop  start)/step)
. Because of floating point overflow, this rule may result in the last element of out being greater than stop.
See also
numpy.linspace
Evenly spaced numbers with careful handling of endpoints.
numpy.ogrid
Arrays of evenly spaced numbers in Ndimensions.
numpy.mgrid
Gridshaped arrays of evenly spaced numbers in Ndimensions.
Examples
>>> np.arange(3) array([0, 1, 2]) >>> np.arange(3.0) array([ 0., 1., 2.]) >>> np.arange(3,7) array([3, 4, 5, 6]) >>> np.arange(3,7,2) array([3, 5])