Fibonacci Sequence
>>> fibs [0, 1]>>> n=input('How many Fibonacci numbers do your what?') How many Fibonacci numbers do your what?10 >>> for n in range(n-2): fibs.append(fibs[-2]+fibs[-1]) >>> fibs [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
Note: The built-in callable function can be used to determine whether the function can be called
def Define function
>>> def hello(name): print "Hello"+name >>> hello('world') Helloworld
Use function to write Fibonacci sequence
>>> def fibs(num): s=[0,1] for i in range(num-2): s.append(s[-2]+s[-1]) >>> fibs(10)
Note: The return statement returns the value from the function
Function description: If you document the function so that others can understand it, you can add comments ( #beginning). Another way is to write the string directly.
>>> def square(x): 'Calculates the square of the number x.' return x*x >>> square.__doc__ 'Calculates the square of the number x.'
The built-in help function can get information about the function, including its documentation string
>>> help(square) Help on function square in module __main__: square(x) Calculates the square of the number x.
Assigning new values to parameters within a function does not change the value of external variables:
>>> def try_to_change(n): n='Mr,Gumby' >>> name='Mrs,Entity' >>> try_to_change(name) >>> name 'Mrs,Entity'
Strings (as well as numbers and tuples) It is immutable, that is, it cannot be modified. If the changeable data structure (list or dictionary) is modified, the parameters will be modified
>>> n=['Bob','Alen'] >>> def change(m): m[0]='Sandy' >>> change(n[:]) >>> n ['Bob', 'Alen'] >>> change(n) >>> n ['Sandy', 'Alen']
Keyword parameters and default values
>>> def hello(name,greeting='Hello',punctuation='!'): print '%s,%s%s' % (greeting,name,punctuation) >>> hello(name='Nsds') Hello,Nsds! >>> hello(name='Nsds',greeting='Hi') Hi,Nsds!
Collect parameters
Return tuple:
>>> def print_params(*params): print params >>> print_params('Testing') #返回元组 ('Testing',) >>> print_params(1,2,3) (1, 2, 3) >>> def print_params_2(title,*params): print title print params >>> print_params_2('Params:',1,2,3) Params: (1, 2, 3)
Return dictionary
>>> def print_params_3(**params): print params >>> print_params_3(x=1,y=2,z=3) {'y': 2, 'x': 1, 'z': 3} >>> def print_params_4(x,y,z=3,*pospar,**keypar): print x,y,z print pospar print keypar >>> print_params_4(1,2,3,5,6,7,foo=1,bar=2) 2 3 (5, 6, 7) {'foo': 1, 'bar': 2} >>> print_params_4(1,2) 2 3 () {}
## Call tuple, dictionary
>>> def add(x,y):return x+y >>> params=(1,2) >>> add(*params) >>> def with_stars(**kwds): print kwds['name'],'is',kwds['age'],'years old'] >>> def without_starts(kwds): print kwds['name'],'is',kwds['age'],'years old' >>> args={'name':'Nsds','age':24} >>> with_stars(**args) Nsds is 24 years old >>> without_starts(args) Nsds is 24 years old >>> add(2,args['age'])The asterisk is only useful when defining a function (allowing an indefinite number of parameters) or calling ("splitting" a dictionary or sequence)
>>> def foo(x,y,z,m=0,n=0): print x,y,z,m,n >>> def call_foo(*args,**kwds): print "Calling foo!" foo(*args,**kwds) >>> d=(1,3,4) >>> f={'m':'Hi','n':'Hello'} >>> foo(*d,**f) 3 4 Hi Hello >>> call_foo(*d,**f) Calling foo! 3 4 Hi HelloA few examples
>>> def story(**kwds): return 'Once upon a time,there was a' \ '%(job)s called %(name)s.' % kwds >>> def power(x,y,*others): if others: print 'Received redundant parameters:',others return pow(x,y) >>> def interval(start,stop=None,step=1): if stop is None: start,stop=0,start #start=0,stop=start result=[] i=start while i<stop: result.append(i) i+=step return result >>> print story(job='king',name='Gumby') Once upon a time,there was aking called Gumby. >>> print story(name='Sir Robin',job='brave knight') Once upon a time,there was abrave knight called Sir Robin. >>> params={'job':'language','name':'Python'} >>> print story(**params) Once upon a time,there was alanguage called Python. >>> del params['job'] >>> print story(job='store of genius',**params) Once upon a time,there was astore of genius called Python. >>> power(2,3) >>> power(y=3,x=2) >>> params=(5,)*2 >>> power(*params) >>> power(3,3,'Helld,world') Received redundant parameters: ('Helld,world',) >>> interval(10) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> interval(1,5) [1, 2, 3, 4] >>> power(*interval(3,7)) Received redundant parameters: (5, 6)
Modify global variables
>>> def f(): global x x=x+1 >>> f() >>> x >>> f() >>> x
Nesting
>>> def multiplier(factor): def multiplyByFactor(number): return number*factor return multiplyByFactor >>> double=multiplier(2) >>> double(5) >>> multiplier(2*5) <function multiplyByFactor at 0x0000000002F8C6D8> >>> multiplier(2)(5)
Recursive (call)
Factorial and power>>> def factorial(n): if n==1: return 1 else: return n*factorial(n-1) >>> factorial(5) >>> range(3) [0, 1, 2] >>> def power(x,n): result=1 for i in range(n): result *= x return result >>> power(5,3)
>>> def power(x,n): if n==0: return 1 else: return x*power(x,n-1) >>> power(2,3)
>>> def search(s,n,min=0,max=0):
if max==0:
max=len(s)-1
if min==max:
assert n==s[max]
return max
else:
middle=(min+max)/2
if n>s[middle]:
return search(s,n,middle+1,max)
else:
return search(s,n,min,middle)
>>> search(seq,100)
It receives a function and a list, and uses the function to act on each element of the list in turn to get a new list and returns
>>> map(str,range(10)) ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] >>> def f(x): return x*x >>> print map(f,[1,2,3,4,5,6,7]) [1, 4, 9, 16, 25, 36, 49]
>>> def format_name(s): s1=s[0].upper()+s[1:].lower() return s1 >>> print map(format_name,['ASDF','jskk']) ['Asdf', 'Jskk']filter function
it Receives a function and a list (list). This function judges each element in turn and returns True or False. filter() automatically filters out elements that do not meet the conditions based on the judgment results and returns a new list composed of elements that meet the conditions.
>>> def is_not_empty(s): return s and len(s.strip())>0 >>> filter(is_not_empty,[None,'dshk',' ','sd']) ['dshk', 'sd'] >>> def pfg(x): s=math.sqrt(x) if s%1==0: return x >>> import math >>> pfg(100) >>> pfg(5) >>> filter(pfg,range(100)) [1, 4, 9, 16, 25, 36, 49, 64, 81] >>> def is_sqr(x): return math.sqrt(x)%1==0 >>> is_sqr(100) True >>> filter(is_sqr,range(100)) [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]lambda function
is also called an anonymous function, that is, the function has no specific name and is created with def The method is named
>>> def foo():return 'Begin' >>> lambda:'begin' <function <lambda> at 0x0000000002ECC2E8> >>> s=lambda:'begin' >>> print s() begin >>> s= lambda x,y:x+y >>> print s(1,2) >>> def sum(x,y=6):return x+y >>> sum2=lambda x,y=6:x+y >>> sum2(4)
>>> filter(lambda x:x*x,range(1,5)) [1, 2, 3, 4]>>> map(lambda x:x*x,range(1,5)) [1, 4, 9, 16]>>> filter(lambda x:x.isalnum(),['8ui','&j','lhg',')j']) ['8ui', 'lhg']
reduce function
It receives a function and A list (list), the function must receive two parameters. This function calls each element of the list in turn and returns a new list composed of the result values
>>> reduce(lambda x,y:x*y,range(1,5)) 24 >>> reduce(lambda x,y:x+y,[23,9,5,6],100) #初始值为100,依次相加列表中的值 143
For more detailed descriptions of functions in python, please pay attention to the PHP Chinese website!

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.

Python is suitable for rapid development and data processing, while C is suitable for high performance and underlying control. 1) Python is easy to use, with concise syntax, and is suitable for data science and web development. 2) C has high performance and accurate control, and is often used in gaming and system programming.

The time required to learn Python varies from person to person, mainly influenced by previous programming experience, learning motivation, learning resources and methods, and learning rhythm. Set realistic learning goals and learn best through practical projects.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.


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