NumPy

NumPy is a Python package with well-optimized C core. 
NumPy provides powerful N-dimensional array computing through the using of concepts of vectorization, indexing, and broadcasting. Besides, Numpy also offers comprehensive mathematical functions for numerical computing.
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

Installation

The only prerequisite for installing NumPy is Python itself.

Conda

# Best practice, use an environment rather than install in the base env
conda create -n my-env
conda activate my-env
# If you want to install from conda-forge
conda config --env --add channels conda-forge
# The actual install command
conda install numpy

PIP

pip install numpy

Anaconda

The installation of Anaconda will also installs Numpy and some other useful tools and packages.

Numpy Objects

Working data of NumPy is stored in an N-dimensional array object.

Array Objects

ndarray Object

The N-dimensional array, ndarray, is a multidimensional container of items of the same type and size. Different ndarrays can share th same data as usual. That is an ndarray can be a view to another ndarray. The typical characteristics of a ndarray are dtype and shape

Examples

Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 23:11:46) [MSC v.1916 64 bit (AM
D64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> x=[[1,2,3],[4,5,6],[7,8,9]]
>>> x
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> type(x)
<class 'list'>
>>> import numpy as np
>>> x=np.array(x)
>>> type(x)
<class 'numpy.ndarray'>
>>> x.dtype
dtype('int32')
>>> x.shape
(3, 3)
>>>

Array Scalars

A array scalar is the basic array unit element of a ndarray. Array scalars have the same attributes and methods as ndarrays.

Examples

Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 23:11:46) [MSC v.1916 64 bit (AM
D64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> x=np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> x.shape
(3, 3)
>>> x.dtype
dtype('int32')
>>> x[0,0]
1
>>> x[0,0].shape
()
>>> x[0,0].dtype
dtype('int32')
>>>

Data type objects

A data type object is used to describe how the bytes in the fixed-size block of memory corresponding to an array item of a ndarray should be interpreted.

Examples

Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 23:11:46) [MSC v.1916 64 bit (AM
D64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> x=np.array([3])
>>> x.dtype
dtype('int32')
>>>
>>> np.int32
<class 'numpy.int32'>
>>>
>>> dt=np.dtype('int32')
>>> dt.name
'int32'
>>> dt.itemsize
4
>>> dt.byteorder
'='
>>> dt.kind
'i'
>>>

Examples

Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 23:11:46) [MSC v.1916 64 bit (AM
D64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> dt = np.dtype([('string', np.unicode_, 16), ('numberpair', np.float64, (2,))
])
>>> dt
dtype([('string', '<U16'), ('numberpair', '<f8', (2,))])
>>> dt['string']
dtype('<U16')
>>> dt['numberpair']
dtype(('<f8', (2,)))
>>>
>>> x = np.array([('asdf', (1,2)), ('hjkl', (3,4))], dtype=dt)
>>> x.shape
(2,)
>>> x.dtype
dtype([('string', '<U16'), ('numberpair', '<f8', (2,))])
>>> type(x)
<class 'numpy.ndarray'>
>>> x[0]
('asdf', [1., 2.])
>>> x[0].shape
()
>>> x[0].dtype
dtype([('string', '<U16'), ('numberpair', '<f8', (2,))])
>>> type(x[0])
<class 'numpy.void'>
>>> x[0]['string']
'asdf'
>>> x[0]['string'].shape
()
>>> x[0]['string'].dtype
dtype('<U4')
>>> type(x[0]['string'])
<class 'numpy.str_'>
>>>

Constants

NumPy includes some common constants, such as infinity, e, pi, for floating-point arithmetic

Source and Reference

• https://numpy.org/
• https://numpy.org/install/
• https://numpy.org/doc/stable/reference/index.html
• https://numpy.org/doc/stable/index.html
• https://en.wikipedia.org/wiki/NumPy