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How to find the minimum value from a NumPy array.

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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.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))],
    marker = dict(
trace2 = go.Scatter(
    x=np.linspace(0, len(array), 100),
    y=[min_value for k in x_axis_pts],
    name='min value'

py.iplot([trace1, trace2], filename='numpy-minimum-example')
In [3]:
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.
    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
        .. versionadded: 1.7.0
        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.
    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``.
    See Also
    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
    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
    (MATLAB behavior), please use nanmin.
    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)``.
    >>> a = np.arange(4).reshape((2,2))
    >>> a
    array([[0, 1],
           [2, 3]])
    >>> np.amin(a)           # Minimum of the flattened array
    >>> 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)
    >>> np.nanmin(b)

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