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python numpy速成手册

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Numpy Handbook  
By Patrick Loeber - Available at python-engineer.com
 
Learn NumPy with this Handbook! It covers code examples for all essential functions and some 
tricks and useful methods. NumPy is the core library for scientific computing in Python. It is 
essential for any  data science  or machine learning algorithms. 
 
Outline  
 
 NumPy Introduction
 Installation and Basics
 Array vs List
 Dot Product
 Speed Test Array vs List
 Multidimensional (nd) Arrays
 Indexing, Slicing, And Boolean Indexing
 Reshaping
 Concatenation
 Broadcasting
 Functions and Axis
 Datatypes
 Copying
 Generating Arrays
 Random Numbers
 Linear Algebra (Eigenvalues / Solving Linear Systems)
 Loading CSV Files
 
 

1. NumPy Introduction

NumPy is the core library for scientific computing in Python. The central object in the NumPy

library is the NumPy array. The NumPy array is a high-performance multidimensional array

object, which is designed specifically to perform math operations, linear algebra, and probability

calculations. Using a NumPy array is usually a lot faster and needs less code than using a Python

list. A huge part of the NumPy library consists of C code with the Python API serving as a wrapper

around these C functions. This is one of the reasons why NumPy is so fast.

Most of the popular Machine Learning, Deep Learning, and Data Science libraries use NumPy

under the hood:

Scikit-learn

Matplotlib

Pandas

Different use cases and operations that can be achieved easily with NumPy:

Dot product/inner product

Matrix multiplication

Element wise matrix product

Solving linear systems

Inverse

Determinant

Choose random numbers (e.g. Gaussian/Uniform)

Working with images represented as array

... and many more

2. Installation and Array Basics

Installation with pip or Anaconda:

Import numpy:

Central object is the array:

Essential methods:

$ pip install numpy

$ conda install numpy

import numpy as np

# check verion

np.__version__

# --> 1.19.1

a = np.array([1,2,3,4,5])

a # [1 2 3 4 5]

a.shape # shape of the array: (5,)

a.dtype # type of the elements: int32

a.ndim # number of dimensions: 1

a.size # total number of elements: 5

a.itemsize # the size in bytes of each element: 4

a = np.array([1,2,3])

# access and change elements

print(a[0]) # 1

a[0] = 5

print(a) # [5 2 3]

# elementwise math operations

b = a * np.array([2,0,2])

print(b) # [10 0 6]

print(a.sum()) # 10

3. Array vs List

l = [1,2,3]

a = np.array([1,2,3]) # create an array from a list

print(l) # [1, 2, 3]

print(a) # [1 2 3]

# adding new item

l.append(4)

#a.append(4) error: size of array is fixed

# there are ways to add items, but this essentially creates new arrays

l2 = l + [5]

print(l2) # [1, 2, 3, 4, 5]

a2 = a + np.array([4])

print(a2) # this is called broadcasting, adds 4 to each element

# -> [5 6 7]

# vector addidion (this is technically correct compared to broadcasting)

a3 = a + np.array([4,4,4])

print(a3) # [5 6 7]

#a3 = a + np.array([4,5]) # error, can't add vectors of different sizes

# multiplication

l2 = 2 * l # list l repeated 2 times, same a l+l

print(l2)

# -> [1, 2, 3, 4, 1, 2, 3, 4]

a3 = 2 * a # multiplication for each element

print(a3)

# -> [2 4 6]

# modify each item in the list

l2 = []

for i in l:

l2.append(i**2)

print(l2) # [1, 4, 9, 16]

# or list comprehension

l2 = [i**2 for i in l]

print(l2) # [1, 4, 9, 16]

a2 = a**2 # -> squares each element!

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