图像识别相关Python与Numpy学习备忘
Colab真是学编程的好东西，不过需要科学上网，为了方便查询我把CS231n Python Tutorial With Google Colab上的东西搬来了
基础数据类型
Numbers
x = 3
print(x, type(x))
3 <class 'int'>
print(x + 1) # Addition
print(x - 1) # Subtraction
print(x * 2) # Multiplication
print(x ** 2) # Exponentiation
4
2
6
9
x += 1
print(x)
x *= 2
print(x)
9
18
y = 2.5
print(type(y))
print(y, y + 1, y * 2, y ** 2)
<class 'float'>
2.5 3.5 5.0 6.25
⚠️ Python不支持递增(x++)或递减(x--)
Booleans
t, f = True, False
print(type(t))
<class 'bool'>
Python用英文单词来表示布尔运算符（and 表示&&, or 表示||）
print(t and f) # Logical AND;
print(t or f) # Logical OR;
print(not t) # Logical NOT;
print(t != f) # Logical XOR;
False
True
False
True
Strings
hello = 'hello' # String literals can use single quotes
world = "world" # or double quotes; it does not matter
print(hello, len(hello))
hello 5
hw = hello + ' ' + world # String concatenation
print(hw)
hello world
hw12 = '{} {} {}'.format(hello, world, 12) # string formatting
print(hw12)
hello world 12
s = "hello"
print(s.capitalize()) # Capitalize a string
print(s.upper()) # Convert a string to uppercase; prints "HELLO"
print(s.rjust(7)) # Right-justify a string, padding with spaces
print(s.center(7)) # Center a string, padding with spaces
print(s.replace('l', '(ell)')) # Replace all instances of one substring with another
print(' world '.strip()) # Strip leading and trailing whitespace
Hello
HELLO
hello
hello
he(ell)(ell)o
world
更多的方法在这儿 https://docs.python.org/3.7/library/stdtypes.html#string-methods
Containers
lists, dictionaries, sets. tuples
Lists
list等同于array，长度可变，可包含不同数据类型
xs = [3, 1, 2] # Create a list
print(xs, xs[2])
print(xs[-1]) # Negative indices count from the end of the list; prints "2"
[3, 1, 2] 2
2
xs[2] = 'foo' # Lists can contain elements of different types
print(xs)
[3, 1, 'foo']
xs.append('bar') # Add a new element to the end of the list
print(xs)
[3, 1, 'foo', 'bar']
x = xs.pop() # Remove and return the last element of the list
print(x, xs)
bar [3, 1, 'foo']
list更多操作在这儿：https://docs.python.org/3.7/tutorial/datastructures.html#more-on-lists
slicing
nums = list(range(5)) # range is a built-in function that creates a list of integers
print(nums) # Prints "[0, 1, 2, 3, 4]"
print(nums[2:4]) # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]"
print(nums[2:]) # Get a slice from index 2 to the end; prints "[2, 3, 4]"
print(nums[:2]) # Get a slice from the start to index 2 (exclusive); prints "[0, 1]"
print(nums[:]) # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]"
print(nums[:-1]) # Slice indices can be negative; prints ["0, 1, 2, 3]"
nums[2:4] = [8, 9] # Assign a new sublist to a slice
print(nums) # Prints "[0, 1, 8, 9, 4]"
[0, 1, 2, 3, 4]
[2, 3]
[2, 3, 4]
[0, 1]
[0, 1, 2, 3, 4]
[0, 1, 2, 3]
[0, 1, 8, 9, 4]
Loops
animals = ['cat', 'dog', 'monkey']
for animal in animals:
print(animal)
cat
dog
monkey
animals = ['cat', 'dog', 'monkey']
for idx, animal in enumerate(animals):
print('#{}: {}'.format(idx + 1, animal))
#1: cat
#2: dog
#3: monkey
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
squares.append(x ** 2)
print(squares)
List comprehensions
[0, 1, 4, 9, 16]
等同于
nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]
print(squares)
[0, 1, 4, 9, 16]
也可以加条件
nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print(even_squares)
[0, 4, 16]
Dictionaries
d = {'cat': 'cute', 'dog': 'furry'} # Create a new dictionary with some data
print(d['cat']) # Get an entry from a dictionary; prints "cute"
print('cat' in d) # Check if a dictionary has a given key; prints "True"
cute
True
d['fish'] = 'wet' # Set an entry in a dictionary
print(d['fish']) # Prints "wet"
wet
print(d['monkey']) # KeyError: 'monkey' not a key of d
monkey不是d的key，所以会报错
print(d.get('monkey', 'N/A')) # Get an element with a default; prints "N/A"
print(d.get('fish', 'N/A')) # Get an element with a default; prints "wet"
N/A
wet
del d['fish'] # Remove an element from a dictionary
print(d.get('fish', 'N/A')) # "fish" is no longer a key; prints "N/A"
N/A
更多关于dictionary：https://docs.python.org/2/library/stdtypes.html#dict
字典遍历
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.items():
print('A {} has {} legs'.format(animal, legs))
A person has 2 legs
A cat has 4 legs
A spider has 8 legs
Dictionary comprehension
nums = [0, 1, 2, 3, 4]
even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}
print(even_num_to_square)
{0: 0, 2: 4, 4: 16}
Sets
animals = {'cat', 'dog'}
print('cat' in animals) # Check if an element is in a set; prints "True"
print('fish' in animals) # prints "False"
True
False
animals.add('fish') # Add an element to a set
print('fish' in animals)
print(len(animals)) # Number of elements in a set;
True
3
animals.add('cat') # Adding an element that is already in the set does nothing
print(len(animals))
animals.remove('cat') # Remove an element from a set
print(len(animals))
3
2
set与list类似，可以遍历，但由于set是无序的，所以不能指望每次打印的顺序一样
animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
print('#{}: {}'.format(idx + 1, animal))
#1: dog
#2: cat
#3: fish
Set comprehensions
from math import sqrt
print({int(sqrt(x)) for x in range(30)})
{0, 1, 2, 3, 4, 5}
Tuples
tuple与list类似，不同的地方在于
- tuple可以被用作dictionary的key，而list不能
- tuple不能用下标取值，list可以
d = {(x, x + 1): x for x in range(10)} # Create a dictionary with tuple keys
t = (5, 6) # Create a tuple
print(type(t))
print(d[t])
print(d[(1, 2)])
<class 'tuple'>
5
1
t[0] = 1
会报错
Functions
Python用def定义函数
def sign(x):
if x > 0:
return 'positive'
elif x < 0:
return 'negative'
else:
return 'zero'
for x in [-1, 0, 1]:
print(sign(x))
negative
zero
positive
常常定义函数来接收可空参数
def hello(name, loud=False):
if loud:
print('HELLO, {}'.format(name.upper()))
else:
print('Hello, {}!'.format(name))
hello('Bob')
hello('Fred', loud=True)
Hello, Bob!
HELLO, FRED
Classes
class Greeter:
# Constructor
def __init__(self, name):
self.name = name # Create an instance variable
# Instance method
def greet(self, loud=False):
if loud:
print('HELLO, {}'.format(self.name.upper()))
else:
print('Hello, {}!'.format(self.name))
g = Greeter('Fred') # Construct an instance of the Greeter class
g.greet() # Call an instance method; prints "Hello, Fred"
g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"
Hello, Fred!
HELLO, FRED
Numpy
（Numpy的部分有点抽象，自己上手去试会好理解很多。）
Numpy是Python中科学计算的核心库，它提供了一个高性能的多维度数组对象，和处理这些对象的方法。
要是用Numpy，首先得引入Numpy包：
import numpy as np
Arrays
一个Numpy数组是一个相同类型的值的网格，并通过自然数（非负整数）构成的tuple索引。数据的维度（dimension）即为数组的（rank）；数组的形状（shape）是一个tuple，此tuple的每个值是数据每个维度的大小。
a = np.array([1, 2, 3]) # Create a rank 1 array
print(type(a), a.shape, a[0], a[1], a[2])
a[0] = 5 # Change an element of the array
print(a)
<class 'numpy.ndarray'> (3,) 1 2 3
[5 2 3]
b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array
print(b)
[[1 2 3]
[4 5 6]]
print(b.shape)
print(b[0, 0], b[0, 1], b[1, 0])
(2, 3)
1 2 4
Numpy也提供生成array的方法
a = np.zeros((2,2)) # Create an array of all zeros
print(a)
[[0. 0.]
[0. 0.]]
b = np.ones((1,2)) # Create an array of all ones
print(b)
[[1. 1.]]
c = np.full((4,6), 7) # Create a constant array
print(c)
创建一个shape为(4, 6)，rank为2的array
[[7 7 7 7 7 7]
[7 7 7 7 7 7]
[7 7 7 7 7 7]
[7 7 7 7 7 7]]
c = np.full((2,5,7), 9) # Create a constant array
print(c)
print(type(c))
print(c.ndim)
print(c.shape)
[[[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]]
[[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]
[9 9 9 9 9 9 9]]]
<class 'numpy.ndarray'>
3
(2, 5, 7)
创建一个rank为3，shape为(2,5,7)的array
d = np.eye(2) # Create a 2x2 identity matrix
print(d)
[[1. 0.]
[0. 1.]]
e = np.random.random((2,2)) # Create an array filled with random values
print(e)
[[0.8690054 0.57244319]
[0.29647245 0.81464494]]
Array indexing
与Python的list类似，array可以被slice，由于array是多维度的，你必须指定每个维度的slice
import numpy as np
# 创建一个shape (3, 4)的2维array
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
# 从a中切出前两行中间两列的一个shape为(2, 2)的subarray，a[0:2, 1:3]中的0可以省略
# [[2 3]
# [6 7]]
b = a[0:2, 1:3]
print(b)
[[2 3]
[6 7]]
slice切出的数据类似于指针，所以修改slice的数据会修改原array
print(a[0, 1])
b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]
print(a[0, 1])
2
77
你也可以混合integer indexing和slice indexing，然而，这样会生成一个比原array维度低的新的array。
# 创建一个rank为2，shape为 (3, 4)的array
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print(a)
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
获取中间一行数据的2种方法：
- 混合integer indexing和slice并生成新的低维array
- 只用slice生成一个与原array相同维度的array
row_r1 = a[1, :] # Rank 1 view of the second row of a
row_r2 = a[1:2, :] # Rank 2 view of the second row of a
row_r3 = a[[1], :] # Rank 2 view of the second row of a
print(row_r1, row_r1.shape)
print(row_r2, row_r2.shape)
print(row_r3, row_r3.shape)
[5 6 7 8] (4,)
[[5 6 7 8]] (1, 4)
[[5 6 7 8]] (1, 4)
# 取array的某列数据时也会有类似的区别:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print(col_r1, col_r1.shape)
print()
print(col_r2, col_r2.shape)
[ 2 6 10] (3,)
[[ 2]
[ 6]
[10]] (3, 1)
