numpy.arange

numpy.arange([start, ]stop, [step, ]dtype=None, *, like=None)

Return evenly spaced values within a given interval.

Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns an ndarray rather than a list.

When using a non-integer 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 round-off 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 array-like 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 N-dimensions.

numpy.mgrid

Grid-shaped arrays of evenly spaced numbers in N-dimensions.

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])