Numpy quick start

Before You Begin

Note
Make sure you installed NumPy in your system, if not, please take a look at Install scipy

The Basics

NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes.

For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3.

[[ 1., 2., 0.],
 [ 0., 5., 3.]]

Array in NumPy is called ndarray which have some important objects:

ndarray.ndim the number of axes (dimensions) of the array.
ndarray.shape the dimensions of the array.
ndarray.size the total number of elements of the array.
ndarray.dtype an object describing the type of the elements in the array.
ndarray.itemsize the size in bytes of each element of the array.
ndarray.data the buffer containing the actual elements of the array.

Example

>>> import numpy as np
>>> a = np.arange(20).reshape(4,5)
>>> a
array([[ 0,  1,  2,  3,  4],
      [ 5,  6,  7,  8,  9],
      [10, 11, 12, 13, 14],
      [15, 16, 17, 18, 19]])
>>> a.shape
(4, 5)
>>> a.ndim
2
>>> a.size
20
>>> a.dtype
dtype('int32')
>>> a.itemsize
4
>>> a.data
<memory at 0x000001BEC6B93CF0>
>>>

Array creatation

>>> import numpy as np
>>> a = np.array([3,4,5,6])
>>> a
array([3, 4, 5, 6])
>>> b = np.array([(1,2,3), (4,5,6)])
>>> b
array([[1, 2, 3],
       [4, 5, 6]])
>>> c = np.array( [ [3,4], [5,6] ], dtype=complex )
>>> c
array([[3.+0.j, 4.+0.j],
       [5.+0.j, 6.+0.j]])
>>> d = np.zeros(5,6)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: data type not understood
>>> d = np.zeros((5,6))
>>> d
array([[0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.]])
>>>

Basic Operations

Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result.

>>> a = np.array( [50,60,70,80] )
>>> b  = np.array( [20,30,40,50] )
>>> c = a + b
>>> c
array([ 70,  90, 110, 130])
>>> d = a - b
>>> d
array([30, 30, 30, 30])
>>>

Subarrays as no-copy views

>>> import numpy as np
>>> x = np.random.randint(10, size=(3, 4))
>>> x
array([[7, 7, 8, 0],
       [3, 3, 9, 2],
       [7, 2, 2, 0]])
>>> x_sub = x[:2, :2]
>>>
>>> x_sub
array([[7, 7],
       [3, 3]])
>>> x_sub[0, 0] = 1000
>>> x
array([[1000,    7,    8,    0],
       [   3,    3,    9,    2],
       [   7,    2,    2,    0]])
>>>

To create copy of a array, call .copy() method

>>> x_sub_copy = x[:2, :2].copy()
>>> x_sub_copy[0, 0] = 123

>>> x_sub_copy
array([[123,   7],
       [  3,   3]])
>>> x
array([[1000,    7,    8,    0],
       [   3,    3,    9,    2],
       [   7,    2,    2,    0]])
>>>

Concatenation of arrays

>>> x = np.array([4,5,6])
>>> y = np.array([3,6,2,3,4,5])
>>> z = np.concatenate([x,y])
>>> z
array([4, 5, 6, 3, 6, 2, 3, 4, 5])
>>>

Splitting of arrays

>>> x = np.array([1,2,3,4,2,3,12,3,1,2,2,5,3,5,3,6,2,2,2])
>>> x
array([ 1,  2,  3,  4,  2,  3, 12,  3,  1,  2,  2,  5,  3,  5,  3,  6,  2,
        2,  2])
>>> x1, x2, x3 = np.split(x, [5,6])
>>> x1, x2, x3
(array([1, 2, 3, 4, 2]), array([3]), array([12,  3,  1,  2,  2,  5,  3,  5,  3,  6,  2,  2,
2]))
>>>

Last modified October 4, 2020