Anonymous functions, iterators, exception handling

What is an anonymous function?

• Function without name

Syntax format

• lambda parameter: return value
  Anonymous functions are generally not used alone, but are often used together with other functions

res = lambda x: x ** 2 print(res(2))

# 1.map() mapping format Format: map(function,iterable) # Iteratable: iteratable object The first parameter is the function name, and the second parameter is the iteratable object process Loop through each element in the iteratable object and pass it to function Function to save return value and return a new iteratable object with theout modifying principle. l = [1, 2, 3, 4, 5, 6, 7, 8, 9] # def index(n): # return n ** 2 print(list(map(lambda x: x ** 2, l))) # 2. Zip (zipper) format filter(function , iterable) function Is the function name, iterable Is an iteratable object l = [11, 22, 33] name_list = ['jason', 'kevin', 'tony', 'jerry'] # The elements in the two lists correspond one to one in the form of small tuples and are placed in a new list new_list = [] for i in range(len(l)): new_list.append((l[i],name_list[i])) print(new_list) res = list(zip(l, name_list)) print(res) # 3.max and min max are the maximum and min is the minimum format max(*args, key=None) min(*args, key=None) parameter key It is used to specify a function. The function of this function is to formulate sorting rules. It is generally used lambda function d = { 'jason':3000, 'Bevin':1000000, 'Ascar':10000000000, 'aerry':88888 } # Compare values and return key def index(key): return d[key] print(max(d,key=lambda key:d[key])) # Ascar print(min(d,key=lambda key:d[key])) # jason # 4.filter filtering Format: filter(function , iterable) function Is the function name, iterable Is an iteratable object l = [11, 22, 33, 44, 55] res = filter(lambda x:x>30,l) # 5.reduce summary ps:stay python 3.0.0.0 in the future, reduce Not anymore built-in function Inside, need from functools import reduce. format reduce(function，sequence [ ,initial] ) function Is the function name, function Must be two parameters in, sequence Is a sequence, initial Having a specified value is equivalent to appending usage data, or when sequence Null as the return value. List summation from functools import reduce d = [11, 22, 33, 44, 55, 66, 77, 88, 99] res = reduce(lambda x, y: x + y, d) Factorial of realization 6 from functools import reduce res = reduce(lambda x, y: x * y,[x for x in range(1,7)])

List of built-in functions

• Iteration means updating. Each update must rely on the results of the previous iteration

ps: iteration provides us with a way that does not depend on index values

• Iteratable object

Generally speaking, the built-in methods include__ iter__ Methods can be called iteratable objects
Contains strings, lists, tuples, dictionaries, collections, and file objects

__ iter__ There is also a simple way to write iter() when calling the method
Generally, all double drop methods will have a corresponding simplified version method name ()

d = {'username':'alan','pwd':123} print(d.__iter__()) # Get is the key of the dictionary

• Iterator objects

In short, it contains__ iter__ The method also contains__ next__ method
The file object does not need to be converted, and is itself an iterator object
Other types of iteratable objects require__ iter__ Transform it

Why is it necessary to create an iterator object?

One of the most important factors is to find a function that can achieve value without relying on index

d = {'username':'alan','pwd':123} res = iter(d) print(next(res)) # username print(next(res)) # pwd print(next(res)) # When the element is retrieved, an error will be reported

l1 = [1,2,3,4,5,6,7,8,9,11,22,33,44,55] # It is required to print every element in l1 in a loop, but the for loop cannot be used__ next__() next() res = l1.__iter__() while True: print(res.__next__()) # It is obvious that in this step, the loop continues all the time. After all elements are fetched, an error is reported directly. Therefore, an exception capture operation is added in this step Method 1: n = 0 while n < len(l1): print(res.__next__()) n += 1 Method 2: d = {'name':'alan','pwd':123,'hobby':'read'} res = d.__iter__() while True: try: print(res.__next__()) except StopIteration as e: break

• What is exception capture

An error in code operation will lead to an exception. If there is no corresponding solution after the exception, the whole program will end directly

• Three important components of anomalies

1.traceback

Error location, turn to the bottom of the prompt information, click the link, and directly challenge the corresponding location of the error
2.XXXError
Error type
3. Type after error type
This is the detailed reason for the error report (very important, many problems can be solved according to this)

• Types of errors

syntax error

This situation is not allowed. Once it occurs, it should be corrected immediately
Logical error
It can be allowed, but try to avoid it

• Exception capture basic syntax

try: Error prone code except Error type as e: Corresponding processing mechanism after error(e Refers to the details of the error) except Error type as e1: Corresponding processing mechanism after error(e Refers to the details of the error) except Error type as e2: Corresponding processing mechanism after error(e Refers to the details of the error)

Universal exception handling Exception try: # int('abc') print(name) # l = [11] # l[100] except Exception: print('Whatever you come, it doesn't matter') Exception capture sentence patterns and universal exceptions 1.Code that may have errors needs to be monitored 2.The less code to be monitored, the better 3.The lower the frequency of exception capture, the better

• Exception capture full version

try: Detected code except Error type as e: ... else: The command executed when the detected code does not report an error finally: Whether an error is reported or not, it will be executed # assert name = 'alan' assert isinstance(name,str) # Active error reporting raise Error type

Iterative value advantage: 1.A general value taking method independent of index shortcoming: 1.The order of values is always fixed and cannot be obtained repeatedly from left to right Index value shortcoming: 1.You need to provide an ordered container type to get the value(Not a universal way) advantage: 1.Values can be repeated

• What is a generator

The generator is essentially a custom iterator with the named keyword yield

# The definition stage is an ordinary function def my_ge(): print('first') yield 123, 152 print('second') yield 'alan' res = my_ge() # Call the function for the first time and turn the function into a generator ret = res.__next__() # first, each time you execute next, the code runs to yield to stop and return the following parameters print(ret) # (123, 152) ret = res.__next__() # second print(ret) # alan

• The essence of range function

def my_range(start, stop=None, step=1): if not stop: stop = start start = 0 while start < stop: yield start start += step for i in my_range(5): print(i)

• yield value

def eat(name): print('%s Ready to cook!!!' %name) while True: food = yield print('%s I am eating%s'% (name,food)) res = eat('alan') res.__next__() res.send('Rice')

• yield vs return

yield 1.Can return value(Multiple are supported and organized into meta groups) 2.Function body code encountered yield Not over, but"Stop" 3.yield You can turn functions into generators and also support external value transfer return 1.Can return value(Multiple are supported and organized into meta groups) 2.Function body code encountered return Direct end

• Generator Expressions

l = [11, 22, 33, 44, 55, 66, 77, 88, 99] res1 = (i+1 for i in l if i!=44) print(res1.__next__)

1 December 2021, 21:37 | Views: 5270