Table of Contents

• Overview
• Reference
  • argmax
  • zeros
• Terminology
• Topics
  • Dimensions
  • Slicing and Indexing
• How-to
• Links

Overview

Reference

argmax

>>> a = np.arange(6).reshape(2,3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])

zeros

numpy.zeros(shape, dtype=float, order='C')

Terminology

Topics

Dimensions

>>> a = np.ones((2, 3))
>>> a
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])
>>> np.sum(a, axis=0)
array([ 2.,  2.,  2.])
>>> np.sum(a, axis=1)
array([ 3.,  3.])
>>> np.sum(a, axis=1, keepdims=True)
array([[ 3.],
       [ 3.]])
>>> np.sum(a, axis=1).shape
(2,)
>>> np.sum(a, axis=1, keepdims=True).shape
(2, 1)

Slicing and Indexing

• i:j:k where i is the starting index, j is the stopping index, and k is the step (k != 0).

>>> x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> x[1:7:2]
array([1, 3, 5])

• if the number of objects in the selection tuple is less than N , then : is assumed for any subsequent dimensions.
• Ellipsis expand to the number of : objects needed to make a selection tuple of the same length as x.ndim. There may only be a single ellipsis present.

>>> x = np.array([[[1],[2],[3]], [[4],[5],[6]]])
>>> x.shape
(2, 3, 1)
>>> x[1:2]
array([[[4],
        [5],
        [6]]])

>>> x[...,0]
array([[1, 2, 3],
       [4, 5, 6]])

• Be careful that x[1:2][3:4] is not equivalent to x[1:2, 3:4]
  • For the first one, both slicing(1:2, 3:4) operates over the first dim, whereas for the second one, both slicing works on the first and second dim respectively.

How-to

Links