該教程來自於 Justin Johnson
我們將會使用Python編程語言來完成本課程(斯坦福大學cs231n)的所有作業。Python是一個偉大的通用編程語言,在一些流行庫(numpy,scipy,matplotlib)的幫助下,它可以提供一個科學計算的強大環境。
我們希望你們之中的大多數人已經有了使用Python和numpy的經驗;其餘的人,這個部分將作爲一個速成課程,幫助你們掌握Python編程語言,並且使用Python來做科學計算。
也許有些人有過matlab的使用經驗,所以我們也推薦numpyfor matlab user。
你還可以找到 IPythonnotebook version of this tutorial here。
Python
Python是一個高級、動態類型多範性編程語言。Python與僞代碼很相似,它允許你使用非常少的代碼來表達強大的思想。舉個例子,下面是一個經典的快速排序算法的Python實現:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) / 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print quicksort([3,6,8,10,1,2,1])
# Prints "[1, 1, 2, 3, 6, 8, 10]"
Python版本
目前有兩種不同的Python支持版本——Python 2.7和Python 3.4。Python 3.0引入了很多向後不兼容的變化,所以使用2.7編寫的代碼在3.4下可能無法正常工作,反之亦然。這門課程使用的是Python 2.7。
你可以通過在命令行運行 python --version 來查看Python版本。
Basic data types
與其他語言類似,Python有很多基本的數據類型,包括整型、浮點型、布爾型、字符串型。這些類型的表現與在其他編程語言中類似。
Numbers:整數和浮點數與其他語言中類似:
x = 3
print type(x) # Prints "<type 'int'>"
print x # Prints "3"
print x + 1 # Addition; prints "4"
print x - 1 # Subtraction; prints "2"
print x * 2 # Multiplication; prints "6"
print x ** 2 # Exponentiation; prints "9"
x += 1
print x # Prints "4"
x *= 2
print x # Prints "8"
y = 2.5
print type(y) # Prints "<type 'float'>"
print y, y + 1, y * 2, y ** 2 # Prints "2.5 3.5 5.0 6.25"
與許多其他語言不同的是,Python沒有一元增加(x++)和減少(x–)操作。
Python也有內置的長整型和複雜數類型,你可以在相關文檔中找到。
Booleans:Python實現所有的布爾邏輯通用操作,但是它不使用符號(&&,II,etc),而是使用英文單詞:
True
f = False
print type(t) # Prints "<type 'bool'>"
print t and f # Logical AND; prints "False"
print t or f # Logical OR; prints "True"
print not t # Logical NOT; prints "False"
print t != f # Logical XOR; prints "True"
Strings:Python對字符串支持很好:
hello = 'hello' # String literals can use single quotes
world = "world" # or double quotes; it does not matter.
print hello # Prints "hello"
print len(hello) # String length; prints "5"
hw = hello + ' ' + world # String concatenation
print hw # prints "hello world"
hw12 = '%s %s %d' % (hello, world, 12) # sprintf style string formatting
print hw12 # prints "hello world 12"
字符串對象有很多有用的方法;例如:
s = "hello"
print s.capitalize() # Capitalize a string; prints "Hello"
print s.upper() # Convert a string to uppercase; prints "HELLO"
print s.rjust(7) # Right-justify a string, padding with spaces; prints " hello"
print s.center(7) # Center a string, padding with spaces; prints " hello "
print s.replace('l', '(ell)') # Replace all instances of one substring with another;
# prints "he(ell)(ell)o"
print ' world '.strip() # Strip leading and trailing whitespace; prints "world"
你可以在相關文檔中找到string方法的列表。
Containers
Python包含一些內置的容器類型:lists(列表),dictionaries(字典), sets(集合), and tuples(元組)。
Lists
list是數組在Python中的等價物,但是它是可變大小的,且可以包含不同類型的元素:
xs = [3, 1, 2] # Create a list
print xs, xs[2] # Prints "[3, 1, 2] 2"
print xs[-1] # Negative indices count from the end of the list; prints "2"
xs[2] = 'foo' # Lists can contain elements of different types
print xs # Prints "[3, 1, 'foo']"
xs.append('bar') # Add a new element to the end of the list
print xs # Prints "[3, 1, 'foo', 'bar']"
x = xs.pop() # Remove and return the last element of the list
print x, xs # Prints "bar [3, 1, 'foo']"
實際上,你可以在官網文檔中找到更多的關於lists的細節。
Slicing:除了可以每次訪問列表的一個元素,Python提供了簡潔的語法來訪問子列表;這就叫做slicing:
nums = 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]"
我們還會在numpy arrays上下文中看到slicing。
Loops:你可以像這樣循環遍歷列表中的元素:
animals = ['cat', 'dog', 'monkey']
for animal in animals:
print animal
# Prints "cat", "dog","monkey", each on its own line.
如果你想在循環體中訪問每個元素的索引,可使用內置的 enumerate 函數:
animals = ['cat', 'dog', 'monkey']
for idx, animal in enumerate(animals):
print '#%d: %s' % (idx + 1, animal)
# Prints "#1: cat", "#2: dog", "#3: monkey", each on its own line
List comprehensions:編程的時候,經常會涉及到把數據從一個類型轉換到另一個類型。舉個簡單的例子,考慮下面計算平方數的代碼:
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
squares.append(x ** 2)
print squares # Prints [0, 1, 4, 9, 16]
list comprehensions 也可以包含條件:
nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x %2 == 0]
print even_squares # Prints "[0, 4, 16]"
Dictionaries
一個字典存儲了(key,value)對,這與Java中的Map或者Javascript中的object都很相似。你可以這樣使用字典:
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"
d['fish'] = 'wet' # Set an entry in a dictionary
print d['fish'] # Prints "wet"
# print d['monkey'] # KeyError: 'monkey' not a key of d
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"
del d['fish'] # Remove an element from a dictionary
print d.get('fish', 'N/A') # "fish" is no longer a key; prints "N/A"
在官方文檔中可以找到所有關於字典的知識。
Loops:很容易對字典中的keys進行迭代:
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal in d:
legs = d[animal]
print 'A %s has %d legs' % (animal, legs)
# Prints "A person has 2 legs", "A spider has 8 legs", "A cat has 4 legs"
如果想要訪問keys和對應的values,可以使用iteritems 方法:
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.iteritems():
print 'A %s has %d legs' % (animal, legs)
# Prints "A person has 2 legs", "A spider has 8 legs", "A cat has 4 legs"
Dictionary comprehensions:這與list comprehensions是相似的,但是允許你方便地構建字典。例如:
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 # Prints "{0: 0, 2: 4, 4: 16}"
Sets
Set是不同元素的無序集合。下面是一個簡單的例子:
animals = {'cat', 'dog'}
print 'cat' in animals # Check if an element is in a set; prints "True"
print 'fish' in animals # prints "False"
animals.add('fish') # Add an element to a set
print 'fish' in animals # Prints "True"
print len(animals) # Number of elements in a set; prints "3"
animals.add('cat') # Adding an element that is already in the set does nothing
print len(animals) # Prints "3"
animals.remove('cat') # Remove an element from a set
print len(animals) # Prints "2"
通常,你想要知道的所有關於sets的東西可以在官方文檔中找到。
Loops:set中的迭代與list中具有相同的語法;然而,由於sets是無序的,你不能對訪問set中元素的順序做出假設:
animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
print '#%d: %s' % (idx + 1, animal)
# Prints "#1: fish", "#2: dog", "#3: cat"
Set comprehensions:與dictionaries和lists類似,我們可以很容易地使用set comprehensions來構建sets:
from math import sqrt
nums = {int(sqrt(x)) for x in range(30)}
print nums # Prints "set([0, 1, 2, 3, 4, 5])"
Tuples
一個tuple是一個(不可改變)有序值列表。Tuple在很多方面和list相似;最大的不同是tuples可以被用作字典的keys和sets的元素,但是lists卻不能。這裏是一個簡單的例子:
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) # Prints "<type 'tuple'>"
print d[t] # Prints "5"
print d[(1, 2)] # Prints "1"
官方文檔中有更多的關於tuple的例子。
Functions
Python函數使用def關鍵字來定義。例如:
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)
# Prints "negative", "zero", "positive"
我們經常會將函數定義爲可選參數的,像這樣:
def hello(name, loud=False):
if loud:
print 'HELLO, %s!' % name.upper()
else:
print 'Hello, %s' % name
hello('Bob') # Prints "Hello, Bob"
hello('Fred', loud=True) # Prints "HELLO, FRED!"
更多的關於Python函數的內容請參考官方文檔。
Classes
Python中定義類的語法是簡潔明瞭的:
class Greeter(object):
# Constructor
def __init__(self, name):
self.name = name # Create an instance variable
# Instance method
def greet(self, loud=False):
if loud:
print 'HELLO, %s!' % self.name.upper()
else:
print 'Hello, %s' % 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!"
同樣可以在官方文檔中找到更多的內容。
======================================
關於Numpy和其他庫的使用將會在下一篇中介紹。