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How to sample numbers from a range of integers uniformly.

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This tutorial imports Plotly and Numpy.

In [1]:
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np


np.random.randint() allows users to pick uniformly from a set of integers [low, low + 1, ..., high].

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

num_of_points = 200
random_numbers = np.random.randint(0, 10, num_of_points)

trace1 = go.Scatter(
    x=[j for j in range(num_of_points)],
    marker = dict(
    name='Numbers sampled from 0 to 9'

py.iplot([trace1], filename='numpy-randint')
In [3]:
Help on built-in function randint:

    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`).
    low : int
        Lowest (signed) integer to be drawn from the distribution (unless
        ``high=None``, in which case this parameter is the *highest* such
    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 ''.
        .. versionadded:: 1.11.0
    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.
    >>> 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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