Getting started with NumPy |  |
Getting started with NumPy | |
Use the np.arange() function to build a vector
containing an arithmetic progression such as [0.0 0.1
0.2 0.3].
>>> print np.arange(0.0, 0.4, 0.1) [ 0. 0.1 0.2 0.3] >>> print np.arange(5.0, -0.5, -0.5) [ 5. 4.5 4. 3.5 3. 2.5 2. 1.5 1. 0.5 0. ]
Here is the general form:
np.arange(start, stop=None, step=1, dtype=None)
start
The first value in the sequence.
stop
The limiting value: the last element of the sequence
will never be greater than or equal
to this value (assuming that the step value is positive; for negative step values, the last element of the sequence
will always be greater than the stop
value).
>>> print np.arange(1.0, 4.0) [ 1. 2. 3.]
If you omit the stop value, you will get
a sequence starting at zero and using start as the limiting value.
>>> print np.arange(4) [0 1 2 3]
step
The common difference between successive values of the array. The default value is one.
dtype
Use this argument to force representation using a specific type.
>>> print np.arange(10) [0 1 2 3 4 5 6 7 8 9] >>> print np.arange(10, dtype=np.float_) [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]