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What Does Function Mean In Javascript

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Michael Zippo Michael Zippo 04.11.2021

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A python function is a group of code. To run the code in a function, you must call the function. A function can be called from anywhere after the function has been defined. Functions can return a value using a return statement.

Functions are a common feature of all programming languages. They allow developers to write blocks of code that perform specific tasks. A function can run as many times as a developer wants in their code.

Functions allow developers to reduce repetition in code because they can run the same block of code multiple times in a program.

This tutorial will discuss, with examples, the basics of Python functions, how to create and call a function, and how to work with arguments. By the end of reading this tutorial, you will be an expert at writing functions in Python.

What is a Python function?

A function is a block of code that is executed only when it is called. Python functions return a value using a return statement, if specified. A function can be called nowhere after the function has been declared.

By itself, a function does nothing. But, when you need to use a function, you can call it and the code for the function will be executed.

In Python, there are two types of functions:. User defined and integrated. Built-in functions are functions such as:

  • print (), which prints a statement to the console
  • Python len () , which calculates the length of a list
  • Python str () , which converts a value in a string

User- defined functions are reusable blocks of code written by you, the developer. These code blocks allow you to organize your code more efficiently. This is important because the more organized your code is, the easier it will be to maintain

How to define a Python function

Define a function way creating the function . This involves writing a block of code that we can call by referring to the name of our function. A function is indicated by the keyword def, followed by a function name and a series of parentheses

For this example, we’ll create a simple function that prints the declaration It & rsquo ;. Monday at the console. To do this we can use this code:

When we run our code, nothing happens. This is because in order for our function to run, we have to call it. To do this, we can refer to our function name like this:

Our code returns:

break down the main components of our function:

  • the keyword def is used to indicate that we want to create a function.
  • print_monday is the name of our function. It should be unique.
  • () is where our parameters will be stored. We ’ ll talk about this later
  • : .. marks the end of our function header

Now our functions can be as complex as we want them to be. Suppose we want to write a program that tells a user how many letters there are is on their behalf. We could do that using this code:

If we run our code and type the name "Elizabeth", the following response is returned:

we define a function called calculate_name_length () . In the body of the function, we ask the user for their name and then use len () to calculate the length of the user’s name. Finally, we print "The length of your name is [length] letters. "When the duration is the length of the user name, on the console

Parameters and arguments of functions

In our first examples, we used empty parentheses with our functions. this means that our functions do not accept any arguments.

arguments allow you to pass information into a function that the function can read. the arguments of a function are indicated in parentheses after the name of the function.

Let’s move on to a basic example to illustrate how they work arguments.

Example of Python parameters and arguments

Suppose we want to create a program that multiplies two numbers we could do it using this code:.

Our program returns Python:

First , we’ll define a function called multiply_numbers . The names of the parameters in the function accepted by our code are: number1 and number2. We define them in parentheses, which is where the parameter list is defined.

Next, we declare a Python variables called "answer" which multiplies the values ‚Äã‚Äãof number1 and number2. Then we print an instruction to the console with the entire math sum written, followed by the answer to the math problem.

We have specified the required arguments. Indeed, we did not set the default values ‚Äã‚Äãfor each argument. It is necessary to specify a certain number of arguments equal to those of the list of parameters otherwise the Python interpreter returns an error.

Towards the end of our program, we call multiply_numbers function twice.

First , we specify arguments 5 and 10. Our program multiplies these values ‚Äã‚Äãto calculate 50. Next, our program prints "5 x 10 = 50" to the console. Then we specify arguments 15 and 2, which our program multiplies. Then our program prints "15 x 2 = 30" to the console

By default, the order of the arguments passed in function is the order in which they are processed by the program. When we run "multiply_numbers (5, 10)", the value of "number1" becomes 5. The value of "number2" becomes 10. We will talk about how to replace it in the " Keyword Arguments " section.

For more information on arguments see our Python optional arguments tutorial

A note:. Parameters and Arguments

The terms parameter and argument refer to the same thing: passing information to a function. But there is a subtle difference between the two

a parameter is the variable inside the parenthesis in a function. An argument is the value passed to a function when it is called. So, in our last example, "number1" and "number2" are parameters and 5 and 10 are arguments.

keyword arguments Function

As we have, the order you pass the arguments in is the order in which your program will process them. Then the first parameter will be assigned to the first argument and so on . However, there is a way to bypass this rule.

You can use keyword arguments in a function call, which allows you to assign the value of an argument based on the name of the parameter. Using keyword arguments lets you specify the keyword value in the order you want.

words - key arguments work because you are going to use words - key to match the parameter values, rather than relying on the order of arguments to transmit values.

Suppose you create a program that prints the name and email address of someone who has joined a mailing list. We could write this program using the following code:

Our feedback from code:

We declare a function that accepts two parameters: name and email. We print "Name:" on the console, followed by the value in the name parameter. Then , we print "_EMAIL:" _ to the console, followed by the value in the email parameter. We use the instructions of print () Python to print these values to the console.

Then call our function and specify two arguments. email The argument is made equal to [email protected] Com and name argument is made equal to Alex Hammond .

In our code, we separate the name of the argument and its value in the way of a sign of equality (=). This meant that we no longer had to specify our arguments in the order our parameters appear (name, email). We could use any order we want.

Default Argument Values

Additionally, you can specify a default argument value for a parameter in a function.

suppose we want the value of email [email protected] by default. We could achieve this using the following code:

Our code returns Python:

We set the value by default parameter email must be [email protected] < / a>. When we run our code and call the print_info () function, we don’t need to specify a value for the email argument. In this example, when we run print_info (), we specify only one argument:. Username

Return Values to Main Program

So far we have discussed how to pass values ‚Äã‚Äãinto a function. But a function can also be used to pass values ‚Äã‚Äã the rest of a program

the return statement closes a function and returns a value to the main program. If you use the return statement with no arguments, the function will return the value None.

Suppose we want to create a program that multiplies two numbers, so when those two numbers have been multiplied, we want to return them to our main program. the make using this code:

Our code returns:

First , we define a function called multiply_numbers. That this function accepts two parameters: number1 and number2 . When this function is called, the values ‚Äã‚Äãof "number1" and "number2" are multiplied. Then we use the return declaration to pass the multiplied number to the main program

We call the multiply_numbers () function and specify two arguments .:5 and 6. Note that we also assign the result of the function to the variable "ans". When this line of code is executed, our function is called and the result is assigned to "ans". So our code prints the value of "ans", which in this case is 30.

Python return Execution of Abandons a function, even if it does not return value. Here is an example of this behavior in action:

Our code does not print anything on the console. Although there is an "impression (" Done ")" statement in our code, it is not executed.

Indeed, when our loop is executed four times (when i is equal to 4), the return instruction is executed. This causes our function to stop executing and prevent our execution loop.

After our function stops working, the code in our main program will continue to run.

Conclusion

Python functions are blocks of code that perform a certain action. Functions can be called as many times as desired in a program. This means that you can run the same block of code multiple times without having to repeat the code.

The functions allow you to reduce repetition in your code, thus making your programs easier for you and for both to read. of other programmers.

For a challenge, write a function that prints each number between 1 and 10 (including 10) on the console. This function must contain a loop. When the function is complete, you should print "Done!" " To the console. Call your function once at the end of your program

The output should be:.

This tutorial has reviewed the basics of python functions, how to write and call a function, and how to work with arguments and parameters. You are now ready to start writing functions in Python like an expert

For recommendations on the best Python courses, books, and learning resources , check out our comprehensive Python Learning Guide .

👻 Read also: what is the best laptop for engineering students?

What Does Function Mean In Javascript exp: Questions

exp

How do I merge two dictionaries in a single expression (taking union of dictionaries)?

5 answers

Carl Meyer By Carl Meyer

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

>>> x = {"a": 1, "b": 2}
>>> y = {"b": 10, "c": 11}
>>> z = x.update(y)
>>> print(z)
None
>>> x
{"a": 1, "b": 10, "c": 11}

How can I get that final merged dictionary in z, not x?

(To be extra-clear, the last-one-wins conflict-handling of dict.update() is what I"m looking for as well.)

5839

Answer #1

How can I merge two Python dictionaries in a single expression?

For dictionaries x and y, z becomes a shallowly-merged dictionary with values from y replacing those from x.

  • In Python 3.9.0 or greater (released 17 October 2020): PEP-584, discussed here, was implemented and provides the simplest method:

    z = x | y          # NOTE: 3.9+ ONLY

  • In Python 3.5 or greater:

    z = {**x, **y}

  • In Python 2, (or 3.4 or lower) write a function:

    def merge_two_dicts(x, y):
    z = x.copy()   # start with keys and values of x
    z.update(y)    # modifies z with keys and values of y
    return z

    and now:

    z = merge_two_dicts(x, y)

Explanation

Say you have two dictionaries and you want to merge them into a new dictionary without altering the original dictionaries:

x = {"a": 1, "b": 2}
y = {"b": 3, "c": 4}

The desired result is to get a new dictionary (z) with the values merged, and the second dictionary"s values overwriting those from the first.

>>> z
{"a": 1, "b": 3, "c": 4}

A new syntax for this, proposed in PEP 448 and available as of Python 3.5, is

z = {**x, **y}

And it is indeed a single expression.

Note that we can merge in with literal notation as well:

z = {**x, "foo": 1, "bar": 2, **y}

and now:

>>> z
{"a": 1, "b": 3, "foo": 1, "bar": 2, "c": 4}

It is now showing as implemented in the release schedule for 3.5, PEP 478, and it has now made its way into the What"s New in Python 3.5 document.

However, since many organizations are still on Python 2, you may wish to do this in a backward-compatible way. The classically Pythonic way, available in Python 2 and Python 3.0-3.4, is to do this as a two-step process:

z = x.copy()
z.update(y) # which returns None since it mutates z

In both approaches, y will come second and its values will replace x"s values, thus b will point to 3 in our final result.

Not yet on Python 3.5, but want a single expression

If you are not yet on Python 3.5 or need to write backward-compatible code, and you want this in a single expression, the most performant while the correct approach is to put it in a function:

def merge_two_dicts(x, y):
    """Given two dictionaries, merge them into a new dict as a shallow copy."""
    z = x.copy()
    z.update(y)
    return z

and then you have a single expression:

z = merge_two_dicts(x, y)

You can also make a function to merge an arbitrary number of dictionaries, from zero to a very large number:

def merge_dicts(*dict_args):
    """
    Given any number of dictionaries, shallow copy and merge into a new dict,
    precedence goes to key-value pairs in latter dictionaries.
    """
    result = {}
    for dictionary in dict_args:
        result.update(dictionary)
    return result

This function will work in Python 2 and 3 for all dictionaries. e.g. given dictionaries a to g:

z = merge_dicts(a, b, c, d, e, f, g)

and key-value pairs in g will take precedence over dictionaries a to f, and so on.

Critiques of Other Answers

Don"t use what you see in the formerly accepted answer:

z = dict(x.items() + y.items())

In Python 2, you create two lists in memory for each dict, create a third list in memory with length equal to the length of the first two put together, and then discard all three lists to create the dict. In Python 3, this will fail because you"re adding two dict_items objects together, not two lists -

>>> c = dict(a.items() + b.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: "dict_items" and "dict_items"

and you would have to explicitly create them as lists, e.g. z = dict(list(x.items()) + list(y.items())). This is a waste of resources and computation power.

Similarly, taking the union of items() in Python 3 (viewitems() in Python 2.7) will also fail when values are unhashable objects (like lists, for example). Even if your values are hashable, since sets are semantically unordered, the behavior is undefined in regards to precedence. So don"t do this:

>>> c = dict(a.items() | b.items())

This example demonstrates what happens when values are unhashable:

>>> x = {"a": []}
>>> y = {"b": []}
>>> dict(x.items() | y.items())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: "list"

Here"s an example where y should have precedence, but instead the value from x is retained due to the arbitrary order of sets:

>>> x = {"a": 2}
>>> y = {"a": 1}
>>> dict(x.items() | y.items())
{"a": 2}

Another hack you should not use:

z = dict(x, **y)

This uses the dict constructor and is very fast and memory-efficient (even slightly more so than our two-step process) but unless you know precisely what is happening here (that is, the second dict is being passed as keyword arguments to the dict constructor), it"s difficult to read, it"s not the intended usage, and so it is not Pythonic.

Here"s an example of the usage being remediated in django.

Dictionaries are intended to take hashable keys (e.g. frozensets or tuples), but this method fails in Python 3 when keys are not strings.

>>> c = dict(a, **b)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: keyword arguments must be strings

From the mailing list, Guido van Rossum, the creator of the language, wrote:

I am fine with declaring dict({}, **{1:3}) illegal, since after all it is abuse of the ** mechanism.

and

Apparently dict(x, **y) is going around as "cool hack" for "call x.update(y) and return x". Personally, I find it more despicable than cool.

It is my understanding (as well as the understanding of the creator of the language) that the intended usage for dict(**y) is for creating dictionaries for readability purposes, e.g.:

dict(a=1, b=10, c=11)

instead of

{"a": 1, "b": 10, "c": 11}

Response to comments

Despite what Guido says, dict(x, **y) is in line with the dict specification, which btw. works for both Python 2 and 3. The fact that this only works for string keys is a direct consequence of how keyword parameters work and not a short-coming of dict. Nor is using the ** operator in this place an abuse of the mechanism, in fact, ** was designed precisely to pass dictionaries as keywords.

Again, it doesn"t work for 3 when keys are not strings. The implicit calling contract is that namespaces take ordinary dictionaries, while users must only pass keyword arguments that are strings. All other callables enforced it. dict broke this consistency in Python 2:

>>> foo(**{("a", "b"): None})
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: foo() keywords must be strings
>>> dict(**{("a", "b"): None})
{("a", "b"): None}

This inconsistency was bad given other implementations of Python (PyPy, Jython, IronPython). Thus it was fixed in Python 3, as this usage could be a breaking change.

I submit to you that it is malicious incompetence to intentionally write code that only works in one version of a language or that only works given certain arbitrary constraints.

More comments:

dict(x.items() + y.items()) is still the most readable solution for Python 2. Readability counts.

My response: merge_two_dicts(x, y) actually seems much clearer to me, if we"re actually concerned about readability. And it is not forward compatible, as Python 2 is increasingly deprecated.

{**x, **y} does not seem to handle nested dictionaries. the contents of nested keys are simply overwritten, not merged [...] I ended up being burnt by these answers that do not merge recursively and I was surprised no one mentioned it. In my interpretation of the word "merging" these answers describe "updating one dict with another", and not merging.

Yes. I must refer you back to the question, which is asking for a shallow merge of two dictionaries, with the first"s values being overwritten by the second"s - in a single expression.

Assuming two dictionaries of dictionaries, one might recursively merge them in a single function, but you should be careful not to modify the dictionaries from either source, and the surest way to avoid that is to make a copy when assigning values. As keys must be hashable and are usually therefore immutable, it is pointless to copy them:

from copy import deepcopy

def dict_of_dicts_merge(x, y):
    z = {}
    overlapping_keys = x.keys() & y.keys()
    for key in overlapping_keys:
        z[key] = dict_of_dicts_merge(x[key], y[key])
    for key in x.keys() - overlapping_keys:
        z[key] = deepcopy(x[key])
    for key in y.keys() - overlapping_keys:
        z[key] = deepcopy(y[key])
    return z

Usage:

>>> x = {"a":{1:{}}, "b": {2:{}}}
>>> y = {"b":{10:{}}, "c": {11:{}}}
>>> dict_of_dicts_merge(x, y)
{"b": {2: {}, 10: {}}, "a": {1: {}}, "c": {11: {}}}

Coming up with contingencies for other value types is far beyond the scope of this question, so I will point you at my answer to the canonical question on a "Dictionaries of dictionaries merge".

Less Performant But Correct Ad-hocs

These approaches are less performant, but they will provide correct behavior. They will be much less performant than copy and update or the new unpacking because they iterate through each key-value pair at a higher level of abstraction, but they do respect the order of precedence (latter dictionaries have precedence)

You can also chain the dictionaries manually inside a dict comprehension:

{k: v for d in dicts for k, v in d.items()} # iteritems in Python 2.7

or in Python 2.6 (and perhaps as early as 2.4 when generator expressions were introduced):

dict((k, v) for d in dicts for k, v in d.items()) # iteritems in Python 2

itertools.chain will chain the iterators over the key-value pairs in the correct order:

from itertools import chain
z = dict(chain(x.items(), y.items())) # iteritems in Python 2

Performance Analysis

I"m only going to do the performance analysis of the usages known to behave correctly. (Self-contained so you can copy and paste yourself.)

from timeit import repeat
from itertools import chain

x = dict.fromkeys("abcdefg")
y = dict.fromkeys("efghijk")

def merge_two_dicts(x, y):
    z = x.copy()
    z.update(y)
    return z

min(repeat(lambda: {**x, **y}))
min(repeat(lambda: merge_two_dicts(x, y)))
min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
min(repeat(lambda: dict(chain(x.items(), y.items()))))
min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))

In Python 3.8.1, NixOS:

>>> min(repeat(lambda: {**x, **y}))
1.0804965235292912
>>> min(repeat(lambda: merge_two_dicts(x, y)))
1.636518670246005
>>> min(repeat(lambda: {k: v for d in (x, y) for k, v in d.items()}))
3.1779992282390594
>>> min(repeat(lambda: dict(chain(x.items(), y.items()))))
2.740647904574871
>>> min(repeat(lambda: dict(item for d in (x, y) for item in d.items())))
4.266070580109954

$ uname -a
Linux nixos 4.19.113 #1-NixOS SMP Wed Mar 25 07:06:15 UTC 2020 x86_64 GNU/Linux

Resources on Dictionaries

5839

Answer #2

In your case, what you can do is:

z = dict(list(x.items()) + list(y.items()))

This will, as you want it, put the final dict in z, and make the value for key b be properly overridden by the second (y) dict"s value:

>>> x = {"a":1, "b": 2}
>>> y = {"b":10, "c": 11}
>>> z = dict(list(x.items()) + list(y.items()))
>>> z
{"a": 1, "c": 11, "b": 10}

If you use Python 2, you can even remove the list() calls. To create z:

>>> z = dict(x.items() + y.items())
>>> z
{"a": 1, "c": 11, "b": 10}

If you use Python version 3.9.0a4 or greater, then you can directly use:

x = {"a":1, "b": 2}
y = {"b":10, "c": 11}
z = x | y
print(z)

{"a": 1, "c": 11, "b": 10}

5839

Answer #3

An alternative:

z = x.copy()
z.update(y)

JavaScript

JSON datetime between Python and JavaScript

4 answers

kevin By kevin

I want to send a datetime.datetime object in serialized form from Python using JSON and de-serialize in JavaScript using JSON. What is the best way to do this?

403

Answer #1

You can add the "default" parameter to json.dumps to handle this:

date_handler = lambda obj: (
    obj.isoformat()
    if isinstance(obj, (datetime.datetime, datetime.date))
    else None
)
json.dumps(datetime.datetime.now(), default=date_handler)
""2010-04-20T20:08:21.634121""

Which is ISO 8601 format.

A more comprehensive default handler function:

def handler(obj):
    if hasattr(obj, "isoformat"):
        return obj.isoformat()
    elif isinstance(obj, ...):
        return ...
    else:
        raise TypeError, "Object of type %s with value of %s is not JSON serializable" % (type(obj), repr(obj))

Update: Added output of type as well as value.
Update: Also handle date

JavaScript

What blocks Ruby, Python to get Javascript V8 speed?

4 answers

Are there any Ruby / Python features that are blocking implementation of optimizations (e.g. inline caching) V8 engine has?

Python is co-developed by Google guys so it shouldn"t be blocked by software patents.

Or this is rather matter of resources put into the V8 project by Google.

260

Answer #1

What blocks Ruby, Python to get Javascript V8 speed?

Nothing.

Well, okay: money. (And time, people, resources, but if you have money, you can buy those.)

V8 has a team of brilliant, highly-specialized, highly-experienced (and thus highly-paid) engineers working on it, that have decades of experience (I"m talking individually – collectively it"s more like centuries) in creating high-performance execution engines for dynamic OO languages. They are basically the same people who also created the Sun HotSpot JVM (among many others).

Lars Bak, the lead developer, has been literally working on VMs for 25 years (and all of those VMs have lead up to V8), which is basically his entire (professional) life. Some of the people writing Ruby VMs aren"t even 25 years old.

Are there any Ruby / Python features that are blocking implementation of optimizations (e.g. inline caching) V8 engine has?

Given that at least IronRuby, JRuby, MagLev, MacRuby and Rubinius have either monomorphic (IronRuby) or polymorphic inline caching, the answer is obviously no.

Modern Ruby implementations already do a great deal of optimizations. For example, for certain operations, Rubinius"s Hash class is faster than YARV"s. Now, this doesn"t sound terribly exciting until you realize that Rubinius"s Hash class is implemented in 100% pure Ruby, while YARV"s is implemented in 100% hand-optimized C.

So, at least in some cases, Rubinius can generate better code than GCC!

Or this is rather matter of resources put into the V8 project by Google.

Yes. Not just Google. The lineage of V8"s source code is 25 years old now. The people who are working on V8 also created the Self VM (to this day one of the fastest dynamic OO language execution engines ever created), the Animorphic Smalltalk VM (to this day one of the fastest Smalltalk execution engines ever created), the HotSpot JVM (the fastest JVM ever created, probably the fastest VM period) and OOVM (one of the most efficient Smalltalk VMs ever created).

In fact, Lars Bak, the lead developer of V8, worked on every single one of those, plus a few others.

JavaScript

Django Template Variables and Javascript

4 answers

When I render a page using the Django template renderer, I can pass in a dictionary variable containing various values to manipulate them in the page using {{ myVar }}.

Is there a way to access the same variable in Javascript (perhaps using the DOM, I don"t know how Django makes the variables accessible)? I want to be able to lookup details using an AJAX lookup based on the values contained in the variables passed in.

256

Answer #1

The {{variable}} is substituted directly into the HTML. Do a view source; it isn"t a "variable" or anything like it. It"s just rendered text.

Having said that, you can put this kind of substitution into your JavaScript.

<script type="text/javascript"> 
   var a = "{{someDjangoVariable}}";
</script>

This gives you "dynamic" javascript.

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