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# Randint

How to sample numbers from a range of integers uniformly.

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### Imports¶

This tutorial imports Plotly and Numpy.

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

import numpy as np


#### Randint¶

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)],
y=random_numbers,
mode='markers',
marker = dict(
size=7,
color=random_numbers,
colorscale='Jet',
symbol='diamond'
),
name='Numbers sampled from 0 to 9'
)

py.iplot([trace1], filename='numpy-randint')

Out[2]:
In [3]:
help(np.random.randint)

Help on built-in function randint:

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).

Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution (unless
high=None, in which case this parameter is 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'.

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.

--------
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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