Hướng dẫn keyword parameter in python

• 4.1. if Statements¶
• 4.2. for Statements¶
• 4.3. The range() Function¶
• 4.4. break and continue Statements, and else Clauses on Loops¶
• 4.5. pass Statements¶
• 4.6. match Statements¶
• 4.7. Defining Functions¶
• 4.8. More on Defining Functions¶
  • 4.8.1. Default Argument Values¶
  • 4.8.2. Keyword Arguments¶
  • 4.8.3. Special parameters¶
    • 4.8.3.1. Positional-or-Keyword Arguments¶
    • 4.8.3.2. Positional-Only Parameters¶
    • 4.8.3.3. Keyword-Only Arguments¶
    • 4.8.3.4. Function Examples¶
    • 4.8.3.5. Recap¶
  • 4.8.4. Arbitrary Argument Lists¶
  • 4.8.5. Unpacking Argument Lists¶
  • 4.8.6. Lambda Expressions¶
  • 4.8.7. Documentation Strings¶
  • 4.8.8. Function Annotations¶
• 4.9. Intermezzo: Coding Style¶

4.1. if Statements¶

Perhaps the most well-known statement type is the if statement. For example:

>>> x=int(input("Please enter an integer: "))Please enter an integer: 42>>> ifx<0:... x=0... print('Negative changed to zero')... elifx==0:... print('Zero')... elifx==1:... print('Single')... else:... print('More')...More

There can be zero or more elif parts, and the else part is optional. The keyword ‘elif’ is short for ‘else if’, and is useful to avoid excessive indentation. An if …
elif … elif … sequence is a substitute for the switch or case statements found in other languages.

If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements.

4.2. for Statements¶

The for statement in Python differs a bit from what you
may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

>>> # Measure some strings:... words=['cat','window','defenestrate']>>> forwinwords:... print(w,len(w))...cat 3window 6defenestrate 12

Code that modifies a collection while iterating over that same collection can be tricky to get right.
Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:

# Create a sample collectionusers={'Hans':'active','Éléonore':'inactive','景太郎':'active'}# Strategy:  Iterate over a copyforuser,statusinusers.copy().items():ifstatus=='inactive':delusers[user]# Strategy:  Create a new collectionactive_users={}foruser,statusinusers.items():ifstatus=='active':active_users[user]=status

4.3. The range() Function¶

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

>>> foriinrange(5):... print(i)...01234

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a
sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):

>>> list(range(5,10))[5, 6, 7, 8, 9]>>> list(range(0,10,3))[0, 3, 6, 9]>>> list(range(-10,-100,-30))[-10, -40, -70]

To iterate over the indices of a sequence, you can combine range() and len() as follows:

>>> a=['Mary','had','a','little','lamb']>>> foriinrange(len(a)):... print(i,a[i])...0 Mary1 had2 a3 little4 lamb

In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.

A strange thing happens if you just print a range:

>>> range(10)range(0, 10)

In many ways the object
returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.

We say such an object is iterable, that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct,
while an example of a function that takes an iterable is sum():

>>> sum(range(4))# 0 + 1 + 2 + 36

Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures, we will discuss in more detail about list().

4.4. break and continue Statements, and else Clauses on Loops¶

The break statement, like in C, breaks out of the innermost
enclosing for or while loop.

Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the iterable (with for) or when the condition becomes false (with while), but not when the loop is terminated by a break statement. This is exemplified by the following loop, which searches for prime numbers:

>>> forninrange(2,10):... forxinrange(2,n):... ifn%x==0:... print(n,'equals',x,'*',n//x)... break... else:... # loop fell through without finding a factor... print(n,'is a prime number')...2 is a prime number3 is a prime number4 equals 2 * 25 is a prime number6 equals 2 * 37 is a prime number8 equals 2 * 49 equals 3 * 3

(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not
the if statement.)

When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions.

The continue statement, also borrowed from C, continues with the next iteration of the
loop:

>>> fornuminrange(2,10):... ifnum%2==0:... print("Found an even number",num)... continue... print("Found an odd number",num)...Found an even number 2Found an odd number 3Found an even number 4Found an odd number 5Found an even number 6Found an odd number 7Found an even number 8Found an odd number 9

4.5. pass Statements¶

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

>>> whileTrue:... pass# Busy-wait for keyboard interrupt (Ctrl+C)...

This is commonly used for creating minimal classes:

>>> classMyEmptyClass:... pass...

Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more
abstract level. The pass is silently ignored:

>>> definitlog(*args):... pass# Remember to implement this!...

4.6. match Statements¶

A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets
executed and it can also extract components (sequence elements or object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

defhttp_error(status):matchstatus:case400:return"Bad request"case404:return"Not found"case418:return"I'm a teapot"case_:return"Something's wrong with the internet"

Note the last block: the “variable name” _ acts as a wildcard and never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

case401|403|404:return"Not allowed"

Patterns can look like unpacking assignments,
and can be used to bind variables:

# point is an (x, y) tuplematchpoint:case(0,0):print("Origin")case(0,y):print(f"Y={y}")case(x,0):print(f"X={x}")case(x,y):print(f"X={x}, Y={y}")case_:raiseValueError("Not a point")

Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject (point). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x,y)=point.

If you are using classes to structure your data you can use the class name
followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:

classPoint:x:inty:intdefwhere_is(point):matchpoint:casePoint(x=0,y=0):print("Origin")casePoint(x=0,y=y):print(f"Y={y}")casePoint(x=x,y=0):print(f"X={x}")casePoint():print("Somewhere else")case_:print("Not a point")

You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var
variable):

Point(1,var)Point(1,y=var)Point(x=1,y=var)Point(y=var,x=1)

A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.

Patterns can be arbitrarily
nested. For example, if we have a short list of points, we could match it like this:

matchpoints:case[]:print("No points")case[Point(0,0)]:print("The origin")case[Point(x,y)]:print(f"Single point {x}, {y}")case[Point(0,y1),Point(0,y2)]:print(f"Two on the Y axis at {y1}, {y2}")case_:print("Something else")

We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:

matchpoint:casePoint(x,y)ifx==y:print(f"Y=X at {x}")casePoint(x,y):print(f"Not on the diagonal")

Several other key features of this statement:

• Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important
  exception is that they don’t match iterators or strings.

• Sequence patterns support extended unpacking: [x,y,*rest] and (x,y,*rest) work similar to unpacking assignments. The name after * may also be _, so (x,y,*_) matches a sequence of at least two items without binding the remaining items.

• Mapping patterns: {"bandwidth":b,"latency":l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is
  also supported. (But **_ would be redundant, so it is not allowed.)

• Subpatterns may be captured using the as keyword:

  case(Point(x1,y1),Point(x2,y2)asp2):...

  will capture the second element of the input as p2 (as long as the input is a sequence of two points)

• Most literals are compared by equality, however the singletons True, False and None are compared by identity.

• Patterns may use named constants. These must be dotted names to prevent them from
  being interpreted as capture variable:

  fromenumimportEnumclassColor(Enum):RED='red'GREEN='green'BLUE='blue'color=Color(input("Enter your choice of 'red', 'blue' or 'green': "))matchcolor:caseColor.RED:print("I see red!")caseColor.GREEN:print("Grass is green")caseColor.BLUE:print("I'm feeling the blues :(")

For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format.

4.7. Defining Functions¶

We can create a function that writes the Fibonacci series to an arbitrary boundary:

>>> deffib(n):# write Fibonacci series up to n... """Print a Fibonacci series up to n."""... a,b=0,1... whilea<n:... print(a,end=' ')... a,b=b,a+b... print()...>>> # Now call the function we just defined:... fib(2000)0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

The keyword def introduces a function definition. It must be followed by the
function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.

The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce online or printed documentation,
or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol
table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are
passed using call by value (where the value is always an object reference, not the value of the object). 1 When a function calls another function, or calls itself recursively, a new local symbol table is created for that call.

A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be
used to access the function:

>>> fib>>> f=fib>>> f(100)0 1 1 2 3 5 8 13 21 34 55 89

Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print():

>>> fib(0)>>> print(fib(0))None

It
is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

>>> deffib2(n):# return Fibonacci series up to n... """Return a list containing the Fibonacci series up to n."""... result=[]... a,b=0,1... whilea<n:... result.append(a)# see below... a,b=b,a+b... returnresult...>>> f100=fib2(100)# call it>>> f100# write the result[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]

This example, as usual, demonstrates some new Python features:

• The return statement returns with a value from a function. return without an expression argument returns None. Falling off the end of a function also returns None.

• The statement result.append(a) calls a method of the list object result. A method is a function that ‘belongs’
  to an object and is named obj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) The method append() shown in the example is defined for list objects; it adds a new element at the end of the
  list. In this example it is equivalent to result=result+[a], but more efficient.

4.8. More on Defining Functions¶

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

defask_ok(prompt,retries=4,reminder='Please try again!'):whileTrue:ok=input(prompt)ifokin('y','ye','yes'):returnTrueifokin('n','no','nop','nope'):returnFalseretries=retries-1ifretries<0:raiseValueError('invalid user response')print(reminder)

i=5deff(arg=i):print(arg)i=6f()

deff(a,L=[]):L.append(a)returnLprint(f(1))print(f(2))print(f(3))

deff(a,L=None):ifLisNone:L=[]L.append(a)returnL

defparrot(voltage,state='a stiff',action='voom',type='Norwegian Blue'):print("-- This parrot wouldn't",action,end=' ')print("if you put",voltage,"volts through it.")print("-- Lovely plumage, the",type)print("-- It's",state,"!")

parrot(1000)# 1 positional argumentparrot(voltage=1000)# 1 keyword argumentparrot(voltage=1000000,action='VOOOOOM')# 2 keyword argumentsparrot(action='VOOOOOM',voltage=1000000)# 2 keyword argumentsparrot('a million','bereft of life','jump')# 3 positional argumentsparrot('a thousand',state='pushing up the daisies')# 1 positional, 1 keyword

parrot()# required argument missingparrot(voltage=5.0,'dead')# non-keyword argument after a keyword argumentparrot(110,voltage=220)# duplicate value for the same argumentparrot(actor='John Cleese')# unknown keyword argument

>>> deffunction(a):... pass...>>> function(0,a=0)Traceback (most recent call last):
  File "", line 1, in TypeError: function() got multiple values for argument 'a'

defcheeseshop(kind,*arguments,**keywords):print("-- Do you have any",kind,"?")print("-- I'm sorry, we're all out of",kind)forarginarguments:print(arg)print("-"*40)forkwinkeywords:print(kw,":",keywords[kw])

cheeseshop("Limburger","It's very runny, sir.","It's really very, VERY runny, sir.",shopkeeper="Michael Palin",client="John Cleese",sketch="Cheese Shop Sketch")

-- Do you have any Limburger ?
-- I'm sorry, we're all out of Limburger
It's very runny, sir.
It's really very, VERY runny, sir.
----------------------------------------
shopkeeper : Michael Palin
client : John Cleese
sketch : Cheese Shop Sketch

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
      -----------    ----------     ----------
        |             |                  |
        |        Positional or keyword   |
        |                                - Keyword only
         -- Positional only

>>> defstandard_arg(arg):... print(arg)...>>> defpos_only_arg(arg,/):... print(arg)...>>> defkwd_only_arg(*,arg):... print(arg)...>>> defcombined_example(pos_only,/,standard,*,kwd_only):... print(pos_only,standard,kwd_only)

>>> standard_arg(2)2>>> standard_arg(arg=2)2

>>> pos_only_arg(1)1>>> pos_only_arg(arg=1)Traceback (most recent call last):
  File "", line 1, in TypeError: pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg'

>>> kwd_only_arg(3)Traceback (most recent call last):
  File "", line 1, in TypeError: kwd_only_arg() takes 0 positional arguments but 1 was given>>> kwd_only_arg(arg=3)3

>>> combined_example(1,2,3)Traceback (most recent call last):
  File "", line 1, in TypeError: combined_example() takes 2 positional arguments but 3 were given>>> combined_example(1,2,kwd_only=3)1 2 3>>> combined_example(1,standard=2,kwd_only=3)1 2 3>>> combined_example(pos_only=1,standard=2,kwd_only=3)Traceback (most recent call last):
  File "", line 1, in TypeError: combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only'

deffoo(name,**kwds):return'name'inkwds

>>> foo(1,**{'name':2})Traceback (most recent call last):
  File "", line 1, in TypeError: foo() got multiple values for argument 'name'>>>

deffoo(name,/,**kwds):return'name'inkwds>>>foo(1,**{'name':2})True

deff(pos1,pos2,/,pos_or_kwd,*,kwd1,kwd2):

defwrite_multiple_items(file,separator,*args):file.write(separator.join(args))

>>> defconcat(*args,sep="/"):... returnsep.join(args)...>>> concat("earth","mars","venus")'earth/mars/venus'>>> concat("earth","mars","venus",sep=".")'earth.mars.venus'

>>> list(range(3,6))# normal call with separate arguments[3, 4, 5]>>> args=[3,6]>>> list(range(*args))# call with arguments unpacked from a list[3, 4, 5]

>>> defparrot(voltage,state='a stiff',action='voom'):... print("-- This parrot wouldn't",action,end=' ')... print("if you put",voltage,"volts through it.",end=' ')... print("E's",state,"!")...>>> d={"voltage":"four million","state":"bleedin' demised","action":"VOOM"}>>> parrot(**d)-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !

>>> defmake_incrementor(n):... returnlambdax:x+n...>>> f=make_incrementor(42)>>> f(0)42>>> f(1)43

>>> pairs=[(1,'one'),(2,'two'),(3,'three'),(4,'four')]>>> pairs.sort(key=lambdapair:pair[1])>>> pairs[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]

>>> defmy_function():... """Do nothing, but document it.......     No, really, it doesn't do anything....     """... pass...>>> print(my_function.__doc__)Do nothing, but document it.    No, really, it doesn't do anything.

>>> deff(ham:str,eggs:str='eggs')->str:... print("Annotations:",f.__annotations__)... print("Arguments:",ham,eggs)... returnham+' and '+eggs...>>> f('spam')Annotations: {'ham': , 'return': , 'eggs': }Arguments: spam eggs'spam and eggs'

4.9. Intermezzo: Coding Style¶

Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.

For Python,
PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:

• Use 4-space indentation, and no tabs.

  4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.

• Wrap lines so that they don’t exceed 79 characters.

  This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.

• Use blank lines to separate functions and classes, and larger blocks of code inside functions.

• When possible, put comments on a line of their own.

• Use docstrings.

• Use spaces around operators and after commas, but not directly inside bracketing
  constructs: a=f(1,2)+g(3,4).

• Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).

• Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.

• Likewise,
  don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.

Footnotes

1

Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).