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import numpy as np

print(np.random.randint(1,100));

SIGNATURE
low,
high=None,
size=None,
dtype='l'
RETURNSint | ndarray
HOW OTHERS USED THIS
randint(​low, high​)
randint(​low​)
randint(​low, high, size​)
randint(​low, size​)
randint(​low, high, size​)
DOCUMENTATION
randint(low, high=None, size=None, dtype='l')

Return random integers from `low` (inclusive) to `high` (exclusive).

Return random integers from the "discrete uniform" distribution of
the specified dtype in the "half-open" interval [`low`, `high`). If
`high` is None (the default), then results are from [0, `low`).

Parameters
----------
low : int
    Lowest (signed) integer to be drawn from the distribution (unless
    ``high=None``, in which case this parameter is one above the
    *highest* such integer).
high : int, optional
    If provided, one above the largest (signed) integer to be drawn
    from the distribution (see above for behavior if ``high=None``).
size : int or tuple of ints, optional
    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
    ``m * n * k`` samples are drawn.  Default is None, in which case a
    single value is returned.
dtype : dtype, optional
    Desired dtype of the result. All dtypes are determined by their
    name, i.e., 'int64', 'int', etc, so byteorder is not available
    and a specific precision may have different C types depending
    on the platform. The default value is 'np.int'.

    .. versionadded:: 1.11.0

Returns
-------
out : int or ndarray of ints
    `size`-shaped array of random integers from the appropriate
    distribution, or a single such random int if `size` not provided.

See Also
--------
random.random_integers : similar to `randint`, only for the closed
    interval [`low`, `high`], and 1 is the lowest value if `high` is
    omitted. In particular, this other one is the one to use to generate
    uniformly distributed discrete non-integers.

Examples
--------
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Generate a 2 x 4 array of ints between 0 and 4, inclusive:

>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
       [3, 2, 2, 0]])
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