Install python and pip on windows
- Download python.exe and install, at the same time, add C:\Python27 (python install dir) to system env
- Go to pip download, 解压到随便一个文件夹下,在cmd中找个该解压目录,输入
python setup.py install
- 添加C:\Python27\Scripts到Windows系统环境变量(need to reboot)
- 安装正确会在Python安装目录中多一个Scripts文件夹,此时cmd中找个Scripts文件夹,然后
easy_install.exe pip
打开cmd后,键入 pip install numpy
For certain version:
pip install networkx==1.9.1
enjoy!
Python packaging ecosystem
参考一下链接sholmes,系统是Ubuntu 14.04 Python2.7/3.0.
dist-packages is a Debian-specific convention that is also present in its derivatives, like Ubuntu.
Package via pip are installed from the package manager and put in /usr/local/lib/python2.7/dist-packages
. System packages are located in
/usr/lib/python2.7/dist-packages
on Ubuntu. However, if you manually install Python from source, it uses the site-packages directory.
两个维数相同的bool类型数组做与运算
np.nonzero(ok1 * ok2)[0]
其中ok1和ok2都是一个一维数组bool型的
如果加[0]会使得到的结果仍为一维数组
> ok1*ok2
array([False, True, False, False], dtype=bool)
> np.nonzero(ok1*ok2)[0]
array([1])
> np.nonzero(ok1*ok2)
(array([1]),)
> b1*b2
array([False, True, True, False], dtype=bool)
> np.nonzero(b1*b2)
(array([1, 2]),)
> np.nonzero(b1*b2)[0]
array([1, 2])
文本读取打开
使用例如json从文本中读取到的数字得到的是文本,需要加上int、float强制转换类型
追加文件内容
with open(filename,'a') as filepoint:
filepoint.write(string)
读文件
with open(filename, 'r') as fout:
lines = fout.readlines()
for line in lines:
print line
文件打开方式a append, w empty then write, r read
两个数组合并/某个添加一列
在shape=(432,2)的数组上加上一列使用np.hstack()
e.g.
pixel = np.zeros((439,2), dtype=int)
s = pixel.shape[:-1]+(1,) # s (439, 1)
result = np.hstack((pixel,np.ones(s))) # result (439, 3)
在shape=(432,2)的数组上加上一列使用np.colomn_stack(())
pixel = np.zeros((439,2), dtype=int)
s = np.ones(439, dtype=int)
result = np.column_stack((pixel,s))
In conclusion, hstack needs to make sure two matrixes have same first dimenstion. However, column_stack doesn’t need to do it.
vstack makes sure that the second dimension is the same. For example,
pixel = np.zeros((439, 2), dtype = int)
s = (1,) + pixel.shape[-1:] # s (1, 2)
result = np.vstack((pixel, np.ones(s))) # result(440 * 2)
矢量求范数
bearing_b3 是一个n×3的矩阵
normbearing = np.linalg.norm(bearing_b3, axis=1)[:, np.newaxis]]
e.g.
> bearing_b3
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[2, 5, 8]])
> np.linalg.norm(bearing_b3, axis=1)[:, np.newaxis]
array([[ 3.74165739],
[ 8.77496439],
[ 13.92838828],
[ 9.64365076]])
> np.linalg.norm(bearing_b3, axis=1)
array([ 3.74165739, 8.77496439, 13.92838828, 9.64365076])
cv2.Rodrigues
Python: cv2.Rodrigues(src[, dst[, jacobian]]) → dst, jacobian¶
R’ = cv2.Rodrigues(R)[0]
Note that: [0]
两个大小一致的数组,满足条件的配对
results = np.array([7,8,9,10])
dists = np.array([1,2,3,4])
# bitwise operation: 7 & 1 = 1
good = dists & results
# array([ True, False, False, False], dtype=bool)
matches = zip(results[good],good.nonzero()[0])
# matches is a list
# [(7, array([0]))]
np.array中两个维度相同的数组互相相乘注意
ok1 = np.array([ True, False, False, False], dtype=bool)
ok2 = np.array([ True, False, True, False], dtype=bool)
ok1*ok2
# >>>array([ True, False, False, False], dtype=bool)
np.nonzero(ok1*ok2)
# >>>(array([0]),)
np.nonzero(ok1*ok2)[0]
# >>>array([0])
python运算符优先级
从上到下优先级依次递减
argparse
parser = argparse.ArgumentParser()
parser.add_argument("path", help="Provide ANS_test path for create_link <ANS_test_path>")
args = parser.parse_args()
path = args.path # path is str
# xx.py <path-name> //必须提供此参数,否则程序报错
parser.add_argument("-v","--verbosity", type=int, choices=[0,1,2], help="increase output verbosity")
args = parser.parse_args()
args.verbosity
# ××.py -v 1
parser.add_argument("-v", "--verbosity", action="count", help="increase output verbosity")
args = parser.parse_args()
# ××.py -vv --verbosity --verbosity
parser.add_argument("-v", "--verbosity", action="store_true", help="increase output verbosity")
args = parser.parse_args()
if args.verbosity:
do something
# ××.py -v
**
any() all()
any: If any one of elements is true, then return true.
all: if all of elements is ture, then return ture.
def any(iterable):
for element in iterable:
if element:
return True
return False
def all(iterable):
for element in iterable:
if not element:
return False
return True
ord() chr()
ord('a') -> 97 # 返回ascII
chr(97) --> 'a' # return 字符
list <==> str
string to list
str1='123456789'
str2='1.2.3.65'
str3 = 'user1 user2 user3'
list(str1) # ['1', '2', '3', '4', '5', '6', '7', '8', '9']
str2.split('.') # ['1', '2', '3', '65']
str3.split() # 移除string空格 ['user1', 'user2', 'user3']
str1.strip() # 移除空格和回车
list to str
#将list中元素组成一个string
''.join(list)
list to array
a = np.array([[1,2],[4,5],[2,3]])
# 此时才能用切片功能,a不能是list,执行下列操作:
plt.plot(a[:,0],a[:,1])
# numpy to list
numpy.ndarry.tolist
整数之间的进制转换:
10进制转16进制: hex(16) ==> 0x10
16进制转10进制: int('0x10', 16) ==> 16
字符串转整数:
10进制字符串: int('10') ==> 10
16进制字符串: int('10', 16) ==> 16
16进制字符串: int('0x10', 16) ==> 16
字节串转整数:
转义为short型整数: struct.unpack('<hh', bytes(b'\x01\x00\x00\x00')) ==> (1, 0)
转义为long型整数: struct.unpack('<L', bytes(b'\x01\x00\x00\x00')) ==> (1,)
整数转字节串:
转为两个字节: struct.pack('<HH', 1,2) ==> b'\x01\x00\x02\x00'
转为四个字节: struct.pack('<LL', 1,2) ==> b'\x01\x00\x00\x00\x02\x00\x00\x00'
字符串转字节串:
字符串编码为字节码: '12abc'.encode('ascii') ==> b'12abc'
数字或字符数组: bytes([1,2, ord('1'),ord('2')]) ==> b'\x01\x0212'
16进制字符串: bytes().fromhex('010210') ==> b'\x01\x02\x10'
16进制字符串: bytes(map(ord, '\x01\x02\x31\x32')) ==> b'\x01\x0212'
16进制数组: bytes([0x01,0x02,0x31,0x32]) ==> b'\x01\x0212'
字节串转字符串:
字节码解码为字符串: bytes(b'\x31\x32\x61\x62').decode('ascii') ==> 12ab
字节串转16进制表示,夹带ascii: str(bytes(b'\x01\x0212'))[2:-1] ==> \x01\x0212
字节串转16进制表示,固定两个字符表示: str(binascii.b2a_hex(b'\x01\x0212'))[2:-1] ==> 01023132
字节串转16进制数组: [hex(x) for x in bytes(b'\x01\x0212')] ==> ['0x1', '0x2', '0x31', '0x32']
list.pop()使用
l=['a','b','c','d','e','f','g','h','i']
for i in xrange(len(l))
for j in xrange(i+1, len(l))
L = l[:]
L = [v for i, v in enumerate(l) if i not in frozenset((i,j))]
删除了元素i,j在list L中
小数的舍入
round() 四舍五入
floor() 11.9=>11
ceil() 11.1=>11
int() 直接截去小数部分
二维数组按照size排序方法
已知一个二维数组,现在使用每行的第三列数据大小作为依据,将二维数组排序
size = p_unsorted[:, 2]
order = np.argsort(size)
p_sorted = p_unsorted[order, :]
At this time, I will introduce you argsort(axis=, kind=, order=).
- kind : {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with data type. The ‘mergesort’ option is retained for backwards compatibility.
Changed in version 1.15.0.: The ‘stable’ option was added.
Python内置函数
map(func, seq1[, seq2,…])
第一个参数接受一个函数名,后面的参数接受一个或多个可迭代的序列,返回的是一个list。如果func为None,作用同zip()。map相当于
def map(f, iterable)
return [f(x) for x in iterable]
print map(lambda x , y : x ** y, [2,4,6],[3,2,1])
[8, 16, 6]
print map(None, [2,4,6],[3,2,1])
[(2, 3), (4, 2), (6, 1)]
map(int, (1,2,3))
[1, 2, 3]
map(int, '1234')
[1, 2, 3, 4]
reduce(func, seq1[,seu2,…])
reduce相当于
def reduce(f, list)
product = 1
for num in list:
product = f(product,num)
return product
multiprocessing Module
Python中一种多进程方法(another way: Threading.Thread()) apply_async(func,args=(),kwds={}, callback=None) 非阻塞式,异步
divmod(a,b)
相当于(math.floor(a/b), a%b) if a or b is float 和 (a//b, a % b) if a or b is int
Numpy使用(import numpy as np)
# Create array from 0 to 9
np.arange(10)
# >>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
# Create a 3×3 numpy array of all True’s
np.full((3, 3), True, dtype=bool)
np.ones((3,3), dtype=bool)
# >> array([[ True, True, True],
# >> [ True, True, True],
# >> [ True, True, True]], dtype=bool)
# Extract all odd numbers from arr
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
arr % 2 == 1
# >>array([False, True, False, True, False, True, False, True, False,True])
arr[arr % 2 == 1]
# >>array([1, 3, 5, 7, 9])
# Replace all odd numbers in arr with -1
arr[arr % 2 == 1] = -1
# >>array([ 0, -1, 2, -1, 4, -1, 6, -1, 8, -1])
# Stack arrays a and b vertically
a = np.arange(10).reshape(2,-1)
b = np.repeat(1, 10).reshape(2,-1)
np.vstack([a, b])
np.r_[a, b]
np.concatenate([a, b], axis=0)
# >> array([[0, 1, 2, 3, 4],
# >> [5, 6, 7, 8, 9],
# >> [1, 1, 1, 1, 1],
# >> [1, 1, 1, 1, 1]])
# Stack the arrays a and b horizontally.
a = np.arange(10).reshape(2,-1)
b = np.repeat(1, 10).reshape(2,-1)
np.hstack([a, b])
np.c_[a, b]
np.concatenate([a, b], axis=1)
# >> array([[0, 1, 2, 3, 4, 1, 1, 1, 1, 1],
# >> [5, 6, 7, 8, 9, 1, 1, 1, 1, 1]])
# Create the following pattern without hardcoding.
a = np.array([1,2,3])
np.r_[np.repeat(a, 3), np.tile(a, 3)]
# >> array([1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])
以上是Numpy小技巧前10个整理,请一定掌握!
Python file path not related to input file path in terminal
#!/usr/bin/env python
import os
print("Path at terminal when executing this file")
# 运行python脚步的路径
print(os.getcwd() + "\n")
print("This file path, relative to os.getcwd()")
print(__file__ + "\n")
print("This file full path (following symlinks)")
full_path = os.path.realpath(__file__)
# python real path
print(full_path + "\n")
print("This file directory and name")
path, filename = os.path.split(full_path)
print(path + ' --> ' + filename + "\n")
print("This file directory only")
print(os.path.dirname(full_path))
How do I write a function with output parameters (call by reference)?
Remember that arguments are passed by assignment in Python. Since assignment just creates references to objects, there’s no alias between an argument name in the caller and callee, and so no call-by-reference. However, there are a number of ways which achieve desired effect.
- By returning the tuple of the results:
def f(a, b): a = a + 1 b = b + 2 return a, b
- By using global variables
- By passing the mutable (changable in-place) object
# a is list, and list is mutable def f(a, b): a[0] = a[0] + b
Then let me show mutable and immutable objects in python:
Everything in Python is an object. And every variable holds an object instance. All objects in Python can be either mutable or immutable.
In short, objects of built-in types like (int, float, bool, str, tuple, unicode) are immutable. Objects of built-in types like (list, set, dict) are mutable. Custom classes are generally mutable.
廖雪峰博客速刷
默认参数
def add_end(L=[]):
L.append('END')
return L
Python函数在定义的时候,默认参数L 的值就被计算出来了,即[],因为默认参数L也是一个变量,它指向对象[],每次调用该函数,如果改变了L的内容,则下次调用时,默认参数的内容就变了,不再是函数定义时的[]了。
定义默认参数要牢记一点:默认参数必须指向不变对象!
上面使用None这个不变对象来实现:def add_end(L=None):
可变参数
可变参数允许你传入0个或任意个参数,这些可变参数在函数调用时自动组装为一个tuple。
def calc(*numbers):
sum = 0
for n in numbers:
sum = sum + n * n
return sum
调用上式可以采用calc(1,2,3)
的方式,但是如果现在已经有了num=[1,2,3]
,可以采用这样的方法调用上函数:calc(*num)
。*nums
表示把nums
这个list的所有元素作为可变参数传进去。
关键字参数
关键字参数允许你传入0个或任意个含参数名的参数,这些关键字参数在函数内部自动组装为一个dict。
def person(name, age, **kw):
print('name:', name, 'age:', age, 'other:', kw)
person('Bob', 35, city='Beijing')
person('Adam', 45, gender='M', job='Engineer')
和可变参数一样,如果已经有了extra = {'city': 'Beijing', 'job': 'Engineer'}
,可以采用这样的方法调用函数:person('Jack', 24, **extra)
。**extra
表示把extra这个dict的所有key-value用关键字参数传入到函数的**kw
参数,kw将获得一个dict,注意kw获得的dict是extra的一份拷贝
,对kw的改动不会影响到函数外的extra。
命名关键字参数
调用者可以传入不受限制的关键字参数,如果只想接受city和job相关的dict,则person函数的定义改为:
def person(name, age, *, city, job):
print(name, age, city, job)
调用方式如下:
>>> person('Jack', 24, city='Beijing', job='Engineer')
Jack 24 Beijing Engineer
参数组合
- 在Python中定义函数,可以用必选参数、默认参数、可变参数、关键字参数和命名关键字参数,这5种参数都可以组合使用。参数定义的顺序必须是:必选参数、默认参数、可变参数、命名关键字参数和关键字参数。
- 对于任意函数,都可以通过类似
func(*args, **kw)
的形式调用它,无论它的参数是如何定义的。
高级特性
切片
L[:3]
L[-2:]
L[:] #复制一个list
迭代
dict迭代的是key,for key in d
。如果要迭代value,可以用for value in d.values()
,如果要同时迭代key和value,可以用for k, v in d.items()
。
Python内置的enumerate函数可以把一个list变成索引-元素对:
for i, value in enumerate(['A', 'B', 'C']):
print(i, value)
for x, y in [(1, 1), (2, 4), (3, 9)]:
print(x, y)
列表生成式
[x * x for x in range(1, 11)]
[m + n for m in 'ABC' for n in 'XYZ']
[s.lower() for s in L]
生成器
要创建一个generator。第一种方法很简单,只要把一个列表生成式的[]改成():
L = [x * x for x in range(10)]
g = (x * x for x in range(10))
next(g)
第二种方法,如果一个函数定义中包含yield关键字,那么这个函数就不再是一个普通函数,而是一个generator:每次调用next()的时候执行,遇到yield语句返回,再次执行时从上次返回的yield语句处继续执行。
def odd():
print('step 1')
yield 1
print('step 2')
yield(3)
print('step 3')
yield(5)
# >>> o = odd()
# >>> next(o)
# step 1
# 1
# >>> next(o)
# step 2
# 3
# >>> next(o)
# step 3
# 5
# >>> next(o)
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# StopIteration
以上函数可以增加except StopIteration as e:
作为退出条件。
迭代器
可以直接作用于for循环的对象统称为可迭代对象:Iterable。
一类是集合数据类型,如list、tuple、dict、set、str等。另一类是generator,包括生成器和带yield的generator function。
可以使用isinstance()判断一个对象是否是Iterable对象:
>>> from collections import Iterable
>>> isinstance([], Iterable)
True
>>> isinstance({}, Iterable)
True
>>> isinstance('abc', Iterable)
True
可以被next()函数调用并不断返回下一个值的对象称为迭代器:Iterator。可以使用isinstance()判断一个对象是否是Iterator对象:
>>> from collections import Iterator
>>> isinstance((x for x in range(10)), Iterator)
True
>>> isinstance([], Iterator)
False
>>> isinstance({}, Iterator)
False
>>> isinstance('abc', Iterator)
False
生成器都是Iterator对象,但list、dict、str虽然是Iterable,却不是Iterator。把list、dict、str等Iterable变成Iterator可以使用iter()函数。因为Python的Iterator对象表示的是一个数据流,Iterator对象可以被next()函数调用并不断返回下一个数据,直到没有数据时抛出StopIteration错误。可以把这个数据流看做是一个有序序列,但我们却不能提前知道序列的长度,只能不断通过next()函数实现按需计算下一个数据,所以Iterator的计算是惰性的,只有在需要返回下一个数据时它才会计算。
Iterator甚至可以表示一个无限大的数据流,例如全体自然数。而使用list是永远不可能存储全体自然数的。
函数式编程
返回函数
def lazy_sum(*args):
def sum():
ax = 0
for n in args:
ax = ax + n
return ax
return sum
>>> f = lazy_sum(1, 3, 5, 7, 9)
>>> f
<function lazy_sum.<locals>.sum at 0x101c6ed90>
>>> f() #返回的函数并没有立刻执行,而是直到调用了f()才执行
25
我们在函数lazy_sum中又定义了函数sum,并且,内部函数sum可以引用外部函数lazy_sum的参数和局部变量,当lazy_sum返回函数sum时,相关参数和变量都保存在返回的函数中,这种称为“闭包(Closure)”的程序结构拥有极大的威力。我们调用lazy_sum()时,每次调用都会返回一个新的函数,即使传入相同的参数。
匿名函数
lambda x: x * x
实际上就是
def f(x):
return x*x
关键字lambda表示匿名函数,冒号前面的x表示函数参数。匿名函数有个限制,就是只能有一个表达式,不用写return,返回值就是该表达式的结果。
装饰器
在代码运行期间动态增加功能的方式,称之为“装饰器”(Decorator).装饰器的参数是函数,同时返回一个函数。
import functools
def log(func):
@functools.wraps(func)
def wrapper(*args, **kw):
print('call %s():' % func.__name__)
return func(*args, **kw)
return wrapper
以上是一个decorator,所以接受一个函数作为参数,并返回一个函数。我们要借助Python的@语法,把decorator置于函数的定义处:
@log
def now():
print('2015-3-25')
这样得到的结果是:
call now();
2015-3-25
The above is simplified to now = log(now)
. But now本身而言,now.__name__
becomes wrapper
. So the above is changed and add @functools.wraps(func)
If I need to add param to log, then the function will modified to:
import functools
def log(text): # text is "I " here
def decorator(func): # param is now
@functools.wraps(func)
def wrapper(*args, **kw): #now param
print('%s cal %s' %(text, func.__name__))
return func(*args, **kw) #let now() to run
return wrapper
return decorator
@log("I ")
def now():
print('2015-3-25')
The above decorator is simplified to now = log('execute')(now)
偏函数
functools.partial的作用就是,把一个函数的某些参数给固定住(也就是设置默认值),返回一个新的函数,调用这个新函数会更简单。
int('1234', base = 10) # return 1234
int2 = functools.partial(int, base = 2)
int2('101') #return 5
int2仅仅固定base参数,所以int2('101', base = 10)
同样会返回101.
类的私有对象和访问限制
如果要让内部属性不被外部访问,可以把属性的名称前加上两个下划线__,在Python中,实例的变量名如果以__开头,就变成了一个私有变量(private),只有内部可以访问,外部不能访问。
class Student(object):
def __init__(self, name, score):
self.__name = name
self.__score = score
def print_score(self):
print('%s: %s' % (self.__name, self.__score))
- 在Python中,变量名类似__xxx__的,也就是以双下划线开头,并且以双下划线结尾的,是特殊变量,特殊变量是可以直接访问的,不是private变量,所以,不能用__name__、__score__这样的变量名。
- 以一个下划线开头的实例变量名,比如_name,这样的实例变量外部是可以访问的,但是,按照约定俗成的规定,当你看到这样的变量时,意思就是,“虽然我可以被访问,但是,请把我视为私有变量,不要随意访问”。
类的继承与多态
class Animal(object):
def run(self):
print('Animal is running...')
# inherit from Animal
class Cat(Animal):
def run(self):
print('Cat is running...')
def eat(self):
print('Cat eats fish.')
class Dog(Animal):
def run(self):
print('Dog is running...')
def eat(self):
print('Dog eats meat')
Polymorphism can be accomplished by this way:
def run_twice(animal):
animal.run()
We could have this result:
>>> run_twice(Animal())
Animal is running...
Animal is running...
>>> run_twice(Dog())
Dog is running...
Dog is running...
>>> run_twice(Cat())
Cat is running...
Cat is running...
对于一个变量,我们只需要知道它是Animal
类型,无需确切地知道它的子类型,就可以放心地调用run()
方法,而具体调用的run()
方法是作用在Animal、Dog、Cat还是其他Animal子类对象上,由运行时该对象的确切类型决定,这就是多态真正的威力:调用方只管调用,不管细节,而当我们新增一种Animal的子类时,只要确保run()方法编写正确,不用管原来的代码是如何调用的。这就是著名的“开闭”原则:
对扩展开放:允许新增Animal子类;对修改封闭:不需要修改依赖Animal类型的run_twice()等函数。
静态语言 vs 动态语言
对于静态语言(例如Java, C++)来说,如果需要传入Animal类型,则传入的对象必须是Animal类型或者它的子类,否则,将无法调用run()方法。
对于Python这样的动态语言来说,则不一定需要传入Animal类型。我们只需要保证传入的对象有一个run()方法就可以了:
class Timer(object):
def run(self):
print('Start...')
这就是动态语言的“鸭子类型”,它并不要求严格的继承体系,一个对象只要“看起来像鸭子,走起路来像鸭子”,那它就可以被看做是鸭子。
Python的“file-like object“就是一种鸭子类型。对真正的文件对象,它有一个read()方法,返回其内容。但是,许多对象,只要有read()方法,都被视为“file-like object“。许多函数接收的参数就是“file-like object“,你不一定要传入真正的文件对象,完全可以传入任何实现了read()方法的对象。
def readImage(fp):
if hasattr(fp, 'read'):
return readData(fp)
return None
从文件流fp中读取图像,我们首先要判断该fp对象是否存在read方法,如果存在,则该对象是一个流。但有read()方法,不代表该fp对象就是一个文件流,它也可能是网络流,也可能是内存中的一个字节流,但只要read()方法返回的是有效的图像数据,就不影响读取图像的功能。
获取对象信息
>>> type(123)
<class 'int'>
>>> type('str')
<class 'str'>
>>> type(None)
<type(None) 'NoneType'>
>>> type(abs)
<class 'builtin_function_or_method'>
>>> a = Animal()
>>> type(a)
<class '__main__.Animal'>
对于class的继承关系来说,使用type()就很不方便。我们要判断class的类型,可以使用isinstance()函数:
# object -> Animal -> Dog -> Husky
>>> a = Animal()
>>> d = Dog()
>>> h = Husky()
>>> isinstance(h, Husky)
True
>>> isinstance(h, Dog)
True
>>> isinstance(b'a', bytes)
True
>>> isinstance(123, int)
True
>>> isinstance('a', str)
True
## list or tuple, either is true.
>>> isinstance([1, 2, 3], (list, tuple))
True
>>> isinstance((1, 2, 3), (list, tuple))
True
使用dir()
如果要获得一个对象的所有属性和方法,可以使用dir()函数,它返回一个包含字符串的list,比如,获得一个str对象的所有属性和方法:
>>> dir('ABC')
['__add__', '__class__',..., '__subclasshook__', 'capitalize', 'casefold',..., 'zfill']
其中__×××__有特殊用途,比如__len__方法返回长度,调用len()
函数试图获取一个对象的长度,实际上,在len()函数内部,它自动去调用该对象的__len__()
方法。
>>> len('ABC')
3
>>> 'ABC'.__len__()
3
- 我们自己写的类,如果也想用len(myObj)的话,就自己写一个
__len__()
方法:>>> class MyDog(object): ... def __len__(self): ... return 100 >>> dog = MyDog() >>> len(dog) 100
Python动态绑定属性和方法
给类的对象绑定属性和方法:
## Bind attributes
class Student(object):
pass
>>> s = Student()
>>> s.name = 'Michael' # 动态给实例绑定一个属性
>>> print(s.name)
Michael
## Bind function
>>> def set_age(self, age): # 定义一个函数作为实例方法
... self.age = age
...
>>> from types import MethodType
>>> s.set_age = MethodType(set_age, s) # 给实例绑定一个方法
>>> s.set_age(25) # 调用实例方法
>>> s.age # 测试结果
25
绑定对象不会影响该类其他对象的属性和方法,如果想给所有该类的对象都绑定属性和方法:
# Bind attributes
Student.name = 'Mike'
## Bind function to class
>>> def set_score(self, score):
... self.score = score
...
>>> Student.set_score = set_score
使用__slot__
限制class可以绑定(添加)的属性,__slots__
定义的属性仅对当前类实例起作用,对继承的子类是不起作用的。
class Student(object):
__slots__ = ('name', 'age') # 用tuple定义允许绑定的属性名称
使用property
Python内置的@property装饰器就是负责把一个方法变成属性调用的:
class Student(object):
@property
def score(self):
return self._score
@score.setter
def score(self, value):
if not isinstance(value, int):
raise ValueError('score must be an integer!')
if value < 0 or value > 100:
raise ValueError('score must between 0 ~ 100!')
self._score = value
>>> s = Student()
>>> s.score = 60 # OK,实际转化为s.set_score(60)
>>> s.score # OK,实际转化为s.get_score()
60
>>> s.score = 9999
Traceback (most recent call last):
...
ValueError: score must between 0 ~ 100!
Dict Operations
new a dict and initialize it
def main():
'''
Creating empty Dictionary
'''
# Creating an empty dict using empty brackets
wordFrequency = {}
# Creating an empty dict using dict()
wordFrequency = dict()
print(wordFrequency)
'''
Creating Dictionaries with literals
'''
wordFrequency = {
"Hello" : 7,
"hi" : 10,
"there" : 45,
"at" : 23,
"this" : 77
}
print(wordFrequency)
'''
Creating Dictionaries by passing parametrs in dict constructor
'''
wordFrequency = dict(Hello = 7,
hi = 10,
there = 45,
at = 23,
this = 77
)
print(wordFrequency)
'''
Creating Dictionaries by a list of tuples
'''
# List of tuples
listofTuples = [("Hello" , 7), ("hi" , 10), ("there" , 45),("at" , 23),("this" , 77)]
# Creating and initializing a dict by tuple
wordFrequency = dict(listofTuples)
print(wordFrequency)
'''
Creating Dictionary by a list of keys and initialzing all with same value
'''
listofStrings = ["Hello", "hi", "there", "at", "this"]
# create and Initialize a dictionary by this list elements as keys and with same value 0
wordFrequency = dict.fromkeys(listofStrings,0 )
print(wordFrequency)
'''
Creating a Dictionary by a two lists
'''
# List of strings
listofStrings = ["Hello", "hi", "there", "at", "this"]
# List of ints
listofInts = [7, 10, 45, 23, 77]
# Merge the two lists to create a dictionary
wordFrequency = dict( zip(listofStrings,listofInts ))
print(wordFrequency)
if __name__ == "__main__":
main()
find the max value for some key in a dict
## return a list and l[0] is key and l[1] is value
l = max(stat.items(), key=operator.itemgetter(1))
## dict remove a pair: stat.pop(key, default)
stat.pop(l[0])
sort a 2D list
sort a 2D list according to the second element, and consider second if the first element of two elements is equal
# first method:
courses = sorted(courses, key=lambda sl: (-sl[0],sl[1]))
# second method:
li.sort(key=itemgetter(1))
li.sort(key=itemgetter(0), reverse = True)
new a 2-D array/list full of zeros
L = [[0 for _ in range(n)] for _ in range(n)]
L = np.zeros((n, n), dtype=int)
python mutable variable by reference or by value
The following code is an example of leetcode called subset:
class Solution(object):
def subsets(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
solutions = []
self._get_subset(nums, 0, [], solutions)
return solutions
@staticmethod
def _get_subset(nums, curr, path, solutions):
if curr>= len(nums):
# path[:] is a copy of path
# solutions.push_back(path[:])
# pass by reference: element of solution changes with path changing
solutions.append(path)
return
path.append(nums[curr])
Solution._get_subset(nums, curr+1, path, solutions)
path.pop()
Solution._get_subset(nums, curr+1, path, solutions)
Note that: All mutable varible is transformed by reference.