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# Min

How to find the minimum value from a NumPy array.

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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


#### Min¶

np.min() is used to compute and output the minimum value from a NumPy array of values.

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

array = np.array([1, 6, 2, 7, 2, 3, 3, 10, 11, 6])
min_value = np.min(array)
x_axis_pts = np.linspace(0, len(array), 100)

trace1 = go.Scatter(
x=[j for j in range(len(array))],
y=array,
mode='markers',
marker = dict(
size=10
),
name='array'
)
trace2 = go.Scatter(
x=np.linspace(0, len(array), 100),
y=[min_value for k in x_axis_pts],
mode='markers',
marker=dict(
size=2
),
name='min value'
)

py.iplot([trace1, trace2], filename='numpy-minimum-example')

Out[2]:
In [3]:
help(np.min)

Help on function amin in module numpy.core.fromnumeric:

amin(a, axis=None, out=None, keepdims=<class numpy._globals._NoValue>)
Return the minimum of an array or minimum along an axis.

Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which to operate.  By default, flattened input is
used.

If this is a tuple of ints, the minimum is selected over multiple axes,
instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result.  Must
be of the same shape and buffer length as the expected output.
See doc.ufuncs (Section "Output arguments") for more details.

keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original arr.

If the default value is passed, then keepdims will not be
passed through to the amin method of sub-classes of
ndarray, however any non-default value will be.  If the
sub-classes sum method does not implement keepdims any
exceptions will be raised.

Returns
-------
amin : ndarray or scalar
Minimum of a. If axis is None, the result is a scalar value.
If axis is given, the result is an array of dimension
a.ndim - 1.

--------
amax :
The maximum value of an array along a given axis, propagating any NaNs.
nanmin :
The minimum value of an array along a given axis, ignoring any NaNs.
minimum :
Element-wise minimum of two arrays, propagating any NaNs.
fmin :
Element-wise minimum of two arrays, ignoring any NaNs.
argmin :
Return the indices of the minimum values.

nanmax, maximum, fmax

Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding min value will be NaN as well. To ignore NaN values

Don't use amin for element-wise comparison of 2 arrays; when
a.shape[0] is 2, minimum(a[0], a[1]) is faster than
amin(a, axis=0).

Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amin(a)           # Minimum of the flattened array
0
>>> np.amin(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> np.amin(a, axis=1)   # Minima along the second axis
array([0, 2])

>>> b = np.arange(5, dtype=np.float)
>>> b[2] = np.NaN
>>> np.amin(b)
nan
>>> np.nanmin(b)
0.0


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