PHP Versus Javascript

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Contents

PHP is a scripting language, while HTML is a markup language. HTML determines the overall structure and content of a web page, while PHP provides dynamic content through a script. PHP is generally a server side language, while HTML is client side.

When you first log in to web design or website development , you may feel overwhelmed by the different languages ‚Äã‚Äãand development approaches that you will come across. PHP and HTML, for example, seem to be everywhere, and people who work with one often work with the other as well. Familiarity with PHP and HTML is pretty much a requirement for website builders of all kinds. Understanding what they are and what they do to keep the modern web world running smoothly will help you contribute to the conversation between PHP code and HTML code.

Our article supports you. With our help, you will gain vital statistics on these two essential web tools and learn more about how developers and designers use them to create websites. You will learn how HTML creates the structure of the Web and how PHP works within that structure. To make connections and perform all kinds of operations. We also show you how PHP and HTML are similar and when to use each in your web development work.


What is HTML ?

Working Pattern Internet Abstract 1089438

Even people with minimal knowledge of how web pages work I have heard of HTML. It won’t surprise most people that HTML is a popular method for creating web pages. But what exactly is this mysterious thing called HTML, and with what magic does it create your fan-art pages, blog posts, and business sites?

HTML (HyperText Markup Language) is what is called a markup language. As a markup language, it is responsible for dictating the fundamentals of the web page. HTML files form the body of a website, including text, images and overall layout. On its own HTML code, it is actually only capable of creating quite ugly static websites. However, the HTML used with CSS allows for colorful and dynamic pages . You use HTML to build the base of your page and influence where everything goes and CSS to change its appearance.


What is PHP?

Blur Business First floor code 270557  Blur Business Close Up code 270557
do you need to create a function for your webpage . Discover PHP

PHP is a programming language used in web design to create dynamic web pages. HTML provides the basis and structure of a web page, while various programming languages ‚Äã‚Äãallow you to create the inner workings of that page. There is no shortage of languages ‚Äã‚Äãthat can do this job for you and PHP is one of the most popular around. PHP scripts are powerful enough to create almost anything you need on your page. It is flexible and easy to learn. With caution, a novice web developer can use PHP to make your pages vibrate.

PHP is also known as a hypertext processor. Like HTML, PHP code is embedded in the web page, although it is sometimes used as a server-side scripting language and executed on the server. Before viewing a page, the web browser processes the necessary PHP information sharing operations, including search functions and other vital elements of the page. Best of all, PHP, like HTML, is open source software and it costs you nothing to use. Together with HTML, PHP is a crucial tool in your web development toolbox.


When to use HTML

Man with brown jacket and gray laptop 874242

HTML is a client side language, so it is used to change the way the web page is viewed by users. As such, HTML is useful for front-end development. Web developers use HTML to change the format and appearance of text. HTML is often used with other languages ‚Äã‚Äãsuch as JavaScript.

when to use PHP

data

PHP is a server side language. It reaches the backend to retrieve data or ask the server to perform a task. As such, PHP is exactly the language you need if you want to build server management applications or functions, both in web pages and on the servers themselves.

Because PHP is such a standard server language, many developers make it their primary language for backend functions. In addition, PHP offers excellent security and supports all types of protocols including IMAP, HTTP and HTTPS, POP3 and NNTP. This means that it isa perfect language for systems and database administrators in many different fields. If you need a web page that checks server function and returns HTML output, PHP is the right choice.

PHP vs HTML: Conclusion

So that’s it, friends. Beginner web developers and designers have to deal with many languages, such as PHP and HTML. Understanding the functions performed by these languages ‚Äã‚Äãwill help you to become a web page builder in no time.

PHP Versus Javascript: StackOverflow Questions

How can I make a time delay in Python?

I would like to know how to put a time delay in a Python script.

Answer #1:

import time
time.sleep(5)   # Delays for 5 seconds. You can also use a float value.

Here is another example where something is run approximately once a minute:

import time
while True:
    print("This prints once a minute.")
    time.sleep(60) # Delay for 1 minute (60 seconds).

Answer #2:

You can use the sleep() function in the time module. It can take a float argument for sub-second resolution.

from time import sleep
sleep(0.1) # Time in seconds

Answer #3:

How can I make a time delay in Python?

In a single thread I suggest the sleep function:

>>> from time import sleep

>>> sleep(4)

This function actually suspends the processing of the thread in which it is called by the operating system, allowing other threads and processes to execute while it sleeps.

Use it for that purpose, or simply to delay a function from executing. For example:

>>> def party_time():
...     print("hooray!")
...
>>> sleep(3); party_time()
hooray!

"hooray!" is printed 3 seconds after I hit Enter.

Example using sleep with multiple threads and processes

Again, sleep suspends your thread - it uses next to zero processing power.

To demonstrate, create a script like this (I first attempted this in an interactive Python 3.5 shell, but sub-processes can"t find the party_later function for some reason):

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed
from time import sleep, time

def party_later(kind="", n=""):
    sleep(3)
    return kind + n + " party time!: " + __name__

def main():
    with ProcessPoolExecutor() as proc_executor:
        with ThreadPoolExecutor() as thread_executor:
            start_time = time()
            proc_future1 = proc_executor.submit(party_later, kind="proc", n="1")
            proc_future2 = proc_executor.submit(party_later, kind="proc", n="2")
            thread_future1 = thread_executor.submit(party_later, kind="thread", n="1")
            thread_future2 = thread_executor.submit(party_later, kind="thread", n="2")
            for f in as_completed([
              proc_future1, proc_future2, thread_future1, thread_future2,]):
                print(f.result())
            end_time = time()
    print("total time to execute four 3-sec functions:", end_time - start_time)

if __name__ == "__main__":
    main()

Example output from this script:

thread1 party time!: __main__
thread2 party time!: __main__
proc1 party time!: __mp_main__
proc2 party time!: __mp_main__
total time to execute four 3-sec functions: 3.4519670009613037

Multithreading

You can trigger a function to be called at a later time in a separate thread with the Timer threading object:

>>> from threading import Timer
>>> t = Timer(3, party_time, args=None, kwargs=None)
>>> t.start()
>>>
>>> hooray!

>>>

The blank line illustrates that the function printed to my standard output, and I had to hit Enter to ensure I was on a prompt.

The upside of this method is that while the Timer thread was waiting, I was able to do other things, in this case, hitting Enter one time - before the function executed (see the first empty prompt).

There isn"t a respective object in the multiprocessing library. You can create one, but it probably doesn"t exist for a reason. A sub-thread makes a lot more sense for a simple timer than a whole new subprocess.

Answer #4:

Delays can be also implemented by using the following methods.

The first method:

import time
time.sleep(5) # Delay for 5 seconds.

The second method to delay would be using the implicit wait method:

 driver.implicitly_wait(5)

The third method is more useful when you have to wait until a particular action is completed or until an element is found:

self.wait.until(EC.presence_of_element_located((By.ID, "UserName"))

How to delete a file or folder in Python?

How do I delete a file or folder in Python?

Answer #1:


Path objects from the Python 3.4+ pathlib module also expose these instance methods:

PHP Versus Javascript: StackOverflow Questions

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

Question 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.)

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

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}

Answer #3:

An alternative:

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

Answer #4:

Another, more concise, option:

z = dict(x, **y)

Note: this has become a popular answer, but it is important to point out that if y has any non-string keys, the fact that this works at all is an abuse of a CPython implementation detail, and it does not work in Python 3, or in PyPy, IronPython, or Jython. Also, Guido is not a fan. So I can"t recommend this technique for forward-compatible or cross-implementation portable code, which really means it should be avoided entirely.

Answer #5:

This probably won"t be a popular answer, but you almost certainly do not want to do this. If you want a copy that"s a merge, then use copy (or deepcopy, depending on what you want) and then update. The two lines of code are much more readable - more Pythonic - than the single line creation with .items() + .items(). Explicit is better than implicit.

In addition, when you use .items() (pre Python 3.0), you"re creating a new list that contains the items from the dict. If your dictionaries are large, then that is quite a lot of overhead (two large lists that will be thrown away as soon as the merged dict is created). update() can work more efficiently, because it can run through the second dict item-by-item.

In terms of time:

>>> timeit.Timer("dict(x, **y)", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
15.52571702003479
>>> timeit.Timer("temp = x.copy()
temp.update(y)", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
15.694622993469238
>>> timeit.Timer("dict(x.items() + y.items())", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
41.484580039978027

IMO the tiny slowdown between the first two is worth it for the readability. In addition, keyword arguments for dictionary creation was only added in Python 2.3, whereas copy() and update() will work in older versions.

PHP Versus Javascript: StackOverflow Questions

How do I list all files of a directory?

How can I list all files of a directory in Python and add them to a list?

Answer #1:

os.listdir() will get you everything that"s in a directory - files and directories.

If you want just files, you could either filter this down using os.path:

from os import listdir
from os.path import isfile, join
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]

or you could use os.walk() which will yield two lists for each directory it visits - splitting into files and dirs for you. If you only want the top directory you can break the first time it yields

from os import walk

f = []
for (dirpath, dirnames, filenames) in walk(mypath):
    f.extend(filenames)
    break

or, shorter:

from os import walk

filenames = next(walk(mypath), (None, None, []))[2]  # [] if no file

Answer #2:

I prefer using the glob module, as it does pattern matching and expansion.

import glob
print(glob.glob("/home/adam/*"))

It does pattern matching intuitively

import glob
# All files ending with .txt
print(glob.glob("/home/adam/*.txt")) 
# All files ending with .txt with depth of 2 folder
print(glob.glob("/home/adam/*/*.txt")) 

It will return a list with the queried files:

["/home/adam/file1.txt", "/home/adam/file2.txt", .... ]

Answer #3:

os.listdir() - list in the current directory

With listdir in os module you get the files and the folders in the current dir

 import os
 arr = os.listdir()
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Looking in a directory

arr = os.listdir("c:\files")

glob from glob

with glob you can specify a type of file to list like this

import glob

txtfiles = []
for file in glob.glob("*.txt"):
    txtfiles.append(file)

glob in a list comprehension

mylist = [f for f in glob.glob("*.txt")]

get the full path of only files in the current directory

import os
from os import listdir
from os.path import isfile, join

cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if 
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles) 

["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]

Getting the full path name with os.path.abspath

You get the full path in return

 import os
 files_path = [os.path.abspath(x) for x in os.listdir()]
 print(files_path)
 
 ["F:\documentiapplications.txt", "F:\documenticollections.txt"]

Walk: going through sub directories

os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.

import os

# Getting the current work directory (cwd)
thisdir = os.getcwd()

# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
    for file in f:
        if file.endswith(".docx"):
            print(os.path.join(r, file))

os.listdir(): get files in the current directory (Python 2)

In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.

 import os
 arr = os.listdir(".")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

To go up in the directory tree

# Method 1
x = os.listdir("..")

# Method 2
x= os.listdir("/")

Get files: os.listdir() in a particular directory (Python 2 and 3)

 import os
 arr = os.listdir("F:\python")
 print(arr)
 
 >>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]

Get files of a particular subdirectory with os.listdir()

import os

x = os.listdir("./content")

os.walk(".") - current directory

 import os
 arr = next(os.walk("."))[2]
 print(arr)
 
 >>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]

next(os.walk(".")) and os.path.join("dir", "file")

 import os
 arr = []
 for d,r,f in next(os.walk("F:\_python")):
     for file in f:
         arr.append(os.path.join(r,file))

 for f in arr:
     print(files)

>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt

next(os.walk("F:\") - get the full path - list comprehension

 [os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]
 
 >>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]

os.walk - get full path - all files in sub dirs**

x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)

>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

os.listdir() - get only txt files

 arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
 print(arr_txt)
 
 >>> ["work.txt", "3ebooks.txt"]

Using glob to get the full path of the files

If I should need the absolute path of the files:

from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
    print(f)

>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt

Using os.path.isfile to avoid directories in the list

import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)

>>> ["a simple game.py", "data.txt", "decorator.py"]

Using pathlib from Python 3.4

import pathlib

flist = []
for p in pathlib.Path(".").iterdir():
    if p.is_file():
        print(p)
        flist.append(p)

 >>> error.PNG
 >>> exemaker.bat
 >>> guiprova.mp3
 >>> setup.py
 >>> speak_gui2.py
 >>> thumb.PNG

With list comprehension:

flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]

Alternatively, use pathlib.Path() instead of pathlib.Path(".")

Use glob method in pathlib.Path()

import pathlib

py = pathlib.Path().glob("*.py")
for file in py:
    print(file)

>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py

Get all and only files with os.walk

import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
    for f in t:
        y.append(f)
print(y)

>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]

Get only files with next and walk in a directory

 import os
 x = next(os.walk("F://python"))[2]
 print(x)
 
 >>> ["calculator.bat","calculator.py"]

Get only directories with next and walk in a directory

 import os
 next(os.walk("F://python"))[1] # for the current dir use (".")
 
 >>> ["python3","others"]

Get all the subdir names with walk

for r,d,f in os.walk("F:\_python"):
    for dirs in d:
        print(dirs)

>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints

os.scandir() from Python 3.5 and greater

import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)

>>> ["calculator.bat","calculator.py"]

# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.

import os
with os.scandir() as i:
    for entry in i:
        if entry.is_file():
            print(entry.name)

>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG

Examples:

Ex. 1: How many files are there in the subdirectories?

In this example, we look for the number of files that are included in all the directory and its subdirectories.

import os

def count(dir, counter=0):
    "returns number of files in dir and subdirs"
    for pack in os.walk(dir):
        for f in pack[2]:
            counter += 1
    return dir + " : " + str(counter) + "files"

print(count("F:\python"))

>>> "F:\python" : 12057 files"

Ex.2: How to copy all files from a directory to another?

A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.

import os
import shutil
from path import path

destination = "F:\file_copied"
# os.makedirs(destination)

def copyfile(dir, filetype="pptx", counter=0):
    "Searches for pptx (or other - pptx is the default) files and copies them"
    for pack in os.walk(dir):
        for f in pack[2]:
            if f.endswith(filetype):
                fullpath = pack[0] + "\" + f
                print(fullpath)
                shutil.copy(fullpath, destination)
                counter += 1
    if counter > 0:
        print("-" * 30)
        print("	==> Found in: `" + dir + "` : " + str(counter) + " files
")

for dir in os.listdir():
    "searches for folders that starts with `_`"
    if dir[0] == "_":
        # copyfile(dir, filetype="pdf")
        copyfile(dir, filetype="txt")


>>> _compiti18Compito Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files

Ex. 3: How to get all the files in a txt file

In case you want to create a txt file with all the file names:

import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
    for eachfile in os.listdir():
        mylist += eachfile + "
"
    file.write(mylist)

Example: txt with all the files of an hard drive

"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""

import os

# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
    for root, dirs, files in os.walk("D:\"):
        for file in files:
            listafile.append(file)
            percorso.append(root + "\" + file)
            testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
    for file in listafile:
        testo_ordinato.write(file + "
")

with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
    for file in percorso:
        file_percorso.write(file + "
")

os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")

All the file of C: in one text file

This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.

import os

with open("file.txt", "w", encoding="utf-8") as filewrite:
    for r, d, f in os.walk("C:\"):
        for file in f:
            filewrite.write(f"{r + file}
")

How to write a file with all paths in a folder of a type

With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.

import os

def searchfiles(extension=".ttf", folder="H:\"):
    "Create a txt file with all the file of a type"
    with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
        for r, d, f in os.walk(folder):
            for file in f:
                if file.endswith(extension):
                    filewrite.write(f"{r + file}
")

# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")

>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png

(New) Find all files and open them with tkinter GUI

I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list. enter image description here

import tkinter as tk
import os

def searchfiles(extension=".txt", folder="H:\"):
    "insert all files in the listbox"
    for r, d, f in os.walk(folder):
        for file in f:
            if file.endswith(extension):
                lb.insert(0, r + "\" + file)

def open_file():
    os.startfile(lb.get(lb.curselection()[0]))

root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()

Answer #4:

import os
os.listdir("somedirectory")

will return a list of all files and directories in "somedirectory".

Answer #5:

A one-line solution to get only list of files (no subdirectories):

filenames = next(os.walk(path))[2]

or absolute pathnames:

paths = [os.path.join(path, fn) for fn in next(os.walk(path))[2]]

PHP Versus Javascript: StackOverflow Questions

Removing white space around a saved image in matplotlib

I need to take an image and save it after some process. The figure looks fine when I display it, but after saving the figure, I got some white space around the saved image. I have tried the "tight" option for savefig method, did not work either. The code:

  import matplotlib.image as mpimg
  import matplotlib.pyplot as plt

  fig = plt.figure(1)
  img = mpimg.imread(path)
  plt.imshow(img)
  ax=fig.add_subplot(1,1,1)

  extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
  plt.savefig("1.png", bbox_inches=extent)

  plt.axis("off") 
  plt.show()

I am trying to draw a basic graph by using NetworkX on a figure and save it. I realized that without a graph it works, but when added a graph I get white space around the saved image;

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import networkx as nx

G = nx.Graph()
G.add_node(1)
G.add_node(2)
G.add_node(3)
G.add_edge(1,3)
G.add_edge(1,2)
pos = {1:[100,120], 2:[200,300], 3:[50,75]}

fig = plt.figure(1)
img = mpimg.imread("image.jpg")
plt.imshow(img)
ax=fig.add_subplot(1,1,1)

nx.draw(G, pos=pos)

extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig("1.png", bbox_inches = extent)

plt.axis("off") 
plt.show()

Answer #1:

You can remove the white space padding by setting bbox_inches="tight" in savefig:

plt.savefig("test.png",bbox_inches="tight")

You"ll have to put the argument to bbox_inches as a string, perhaps this is why it didn"t work earlier for you.


Possible duplicates:

Matplotlib plots: removing axis, legends and white spaces

How to set the margins for a matplotlib figure?

Reduce left and right margins in matplotlib plot

Answer #2:

I cannot claim I know exactly why or how my “solution” works, but this is what I had to do when I wanted to plot the outline of a couple of aerofoil sections — without white margins — to a PDF file. (Note that I used matplotlib inside an IPython notebook, with the -pylab flag.)

plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.margins(0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.savefig("filename.pdf", bbox_inches = "tight",
    pad_inches = 0)

I have tried to deactivate different parts of this, but this always lead to a white margin somewhere. You may even have modify this to keep fat lines near the limits of the figure from being shaved by the lack of margins.

PHP Versus Javascript: StackOverflow Questions

How do I install pip on macOS or OS X?

I spent most of the day yesterday searching for a clear answer for installing pip (package manager for Python). I can"t find a good solution.

How do I install it?

Answer #1:

UPDATE (Jan 2019):

easy_install has been deprecated. Please use get-pip.py instead.


Old answer:

easy_install pip

If you need admin privileges to run this, try:

sudo easy_install pip

Answer #2:

⚡️ TL;DR — One line solution.

All you have to do is:

sudo easy_install pip

2019: ⚠️easy_install has been deprecated. Check Method #2 below for preferred installation!

Details:

⚡️ OK, I read the solutions given above, but here"s an EASY solution to install pip.

MacOS comes with Python installed. But to make sure that you have Python installed open the terminal and run the following command.

python --version

If this command returns a version number that means Python exists. Which also means that you already have access to easy_install considering you are using macOS/OSX.

ℹ️ Now, all you have to do is run the following command.

sudo easy_install pip

After that, pip will be installed and you"ll be able to use it for installing other packages.

Let me know if you have any problems installing pip this way.

Cheers!

P.S. I ended up blogging a post about it. QuickTip: How Do I Install pip on macOS or OS X?


✅ UPDATE (Jan 2019): METHOD #2: Two line solution —

easy_install has been deprecated. Please use get-pip.py instead.

First of all download the get-pip file

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

Now run this file to install pip

python get-pip.py

That should do it.

Another gif you said? Here ya go!

Answer #3:

You can install it through Homebrew on OS X. Why would you install Python with Homebrew?

The version of Python that ships with OS X is great for learning but it’s not good for development. The version shipped with OS X may be out of date from the official current Python release, which is considered the stable production version. (source)

Homebrew is something of a package manager for OS X. Find more details on the Homebrew page. Once Homebrew is installed, run the following to install the latest Python, Pip & Setuptools:

brew install python

Answer #4:

I"m surprised no-one has mentioned this - since 2013, python itself is capable of installing pip, no external commands (and no internet connection) required.

sudo -H python -m ensurepip

This will create a similar install to what easy_install would.

Answer #5:

On Mac:

  1. Install easy_install

    curl https://bootstrap.pypa.io/ez_setup.py -o - | sudo python
    
  2. Install pip

    sudo easy_install pip
    
  3. Now, you could install external modules. For example

    pip install regex   # This is only an example for installing other modules
    

PHP Versus Javascript: StackOverflow Questions

JSON datetime between Python and JavaScript

Question 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?

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 equivalent of Python"s zip function

Is there a javascript equivalent of Python"s zip function? That is, given multiple arrays of equal lengths create an array of pairs.

For instance, if I have three arrays that look like this:

var array1 = [1, 2, 3];
var array2 = ["a","b","c"];
var array3 = [4, 5, 6];

The output array should be:

var output array:[[1,"a",4], [2,"b",5], [3,"c",6]]

Answer #1:

2016 update:

Here"s a snazzier Ecmascript 6 version:

zip= rows=>rows[0].map((_,c)=>rows.map(row=>row[c]))

Illustration equiv. to Python{zip(*args)}:

> zip([["row0col0", "row0col1", "row0col2"],
       ["row1col0", "row1col1", "row1col2"]]);
[["row0col0","row1col0"],
 ["row0col1","row1col1"],
 ["row0col2","row1col2"]]

(and FizzyTea points out that ES6 has variadic argument syntax, so the following function definition will act like python, but see below for disclaimer... this will not be its own inverse so zip(zip(x)) will not equal x; though as Matt Kramer points out zip(...zip(...x))==x (like in regular python zip(*zip(*x))==x))

Alternative definition equiv. to Python{zip}:

> zip = (...rows) => [...rows[0]].map((_,c) => rows.map(row => row[c]))
> zip( ["row0col0", "row0col1", "row0col2"] ,
       ["row1col0", "row1col1", "row1col2"] );
             // note zip(row0,row1), not zip(matrix)
same answer as above

(Do note that the ... syntax may have performance issues at this time, and possibly in the future, so if you use the second answer with variadic arguments, you may want to perf test it. That said it"s been quite a while since it"s been in the standard.)

Make sure to note the addendum if you wish to use this on strings (perhaps there"s a better way to do it now with es6 iterables).


Here"s a oneliner:

function zip(arrays) {
    return arrays[0].map(function(_,i){
        return arrays.map(function(array){return array[i]})
    });
}

// > zip([[1,2],[11,22],[111,222]])
// [[1,11,111],[2,22,222]]]

// If you believe the following is a valid return value:
//   > zip([])
//   []
// then you can special-case it, or just do
//  return arrays.length==0 ? [] : arrays[0].map(...)

The above assumes that the arrays are of equal size, as they should be. It also assumes you pass in a single list of lists argument, unlike Python"s version where the argument list is variadic. If you want all of these "features", see below. It takes just about 2 extra lines of code.

The following will mimic Python"s zip behavior on edge cases where the arrays are not of equal size, silently pretending the longer parts of arrays don"t exist:

function zip() {
    var args = [].slice.call(arguments);
    var shortest = args.length==0 ? [] : args.reduce(function(a,b){
        return a.length<b.length ? a : b
    });

    return shortest.map(function(_,i){
        return args.map(function(array){return array[i]})
    });
}

// > zip([1,2],[11,22],[111,222,333])
// [[1,11,111],[2,22,222]]]

// > zip()
// []

This will mimic Python"s itertools.zip_longest behavior, inserting undefined where arrays are not defined:

function zip() {
    var args = [].slice.call(arguments);
    var longest = args.reduce(function(a,b){
        return a.length>b.length ? a : b
    }, []);

    return longest.map(function(_,i){
        return args.map(function(array){return array[i]})
    });
}

// > zip([1,2],[11,22],[111,222,333])
// [[1,11,111],[2,22,222],[null,null,333]]

// > zip()
// []

If you use these last two version (variadic aka. multiple-argument versions), then zip is no longer its own inverse. To mimic the zip(*[...]) idiom from Python, you will need to do zip.apply(this, [...]) when you want to invert the zip function or if you want to similarly have a variable number of lists as input.


addendum:

To make this handle any iterable (e.g. in Python you can use zip on strings, ranges, map objects, etc.), you could define the following:

function iterView(iterable) {
    // returns an array equivalent to the iterable
}

However if you write zip in the following way, even that won"t be necessary:

function zip(arrays) {
    return Array.apply(null,Array(arrays[0].length)).map(function(_,i){
        return arrays.map(function(array){return array[i]})
    });
}

Demo:

> JSON.stringify( zip(["abcde",[1,2,3,4,5]]) )
[["a",1],["b",2],["c",3],["d",4],["e",5]]

(Or you could use a range(...) Python-style function if you"ve written one already. Eventually you will be able to use ECMAScript array comprehensions or generators.)

What blocks Ruby, Python to get Javascript V8 speed?

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.

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.

Django Template Variables and Javascript

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.

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.

Web-scraping JavaScript page with Python

I"m trying to develop a simple web scraper. I want to extract text without the HTML code. In fact, I achieve this goal, but I have seen that in some pages where JavaScript is loaded I didn"t obtain good results.

For example, if some JavaScript code adds some text, I can"t see it, because when I call

response = urllib2.urlopen(request)

I get the original text without the added one (because JavaScript is executed in the client).

So, I"m looking for some ideas to solve this problem.

Answer #1:

EDIT 30/Dec/2017: This answer appears in top results of Google searches, so I decided to update it. The old answer is still at the end.

dryscape isn"t maintained anymore and the library dryscape developers recommend is Python 2 only. I have found using Selenium"s python library with Phantom JS as a web driver fast enough and easy to get the work done.

Once you have installed Phantom JS, make sure the phantomjs binary is available in the current path:

phantomjs --version
# result:
2.1.1

Example

To give an example, I created a sample page with following HTML code. (link):

<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>Javascript scraping test</title>
</head>
<body>
  <p id="intro-text">No javascript support</p>
  <script>
     document.getElementById("intro-text").innerHTML = "Yay! Supports javascript";
  </script> 
</body>
</html>

without javascript it says: No javascript support and with javascript: Yay! Supports javascript

Scraping without JS support:

import requests
from bs4 import BeautifulSoup
response = requests.get(my_url)
soup = BeautifulSoup(response.text)
soup.find(id="intro-text")
# Result:
<p id="intro-text">No javascript support</p>

Scraping with JS support:

from selenium import webdriver
driver = webdriver.PhantomJS()
driver.get(my_url)
p_element = driver.find_element_by_id(id_="intro-text")
print(p_element.text)
# result:
"Yay! Supports javascript"

You can also use Python library dryscrape to scrape javascript driven websites.

Scraping with JS support:

import dryscrape
from bs4 import BeautifulSoup
session = dryscrape.Session()
session.visit(my_url)
response = session.body()
soup = BeautifulSoup(response)
soup.find(id="intro-text")
# Result:
<p id="intro-text">Yay! Supports javascript</p>

PHP Versus Javascript: StackOverflow Questions

Python"s equivalent of && (logical-and) in an if-statement

Question by delete

Here"s my code:

def front_back(a, b):
  # +++your code here+++
  if len(a) % 2 == 0 && len(b) % 2 == 0:
    return a[:(len(a)/2)] + b[:(len(b)/2)] + a[(len(a)/2):] + b[(len(b)/2):] 
  else:
    #todo! Not yet done. :P
  return

I"m getting an error in the IF conditional.
What am I doing wrong?

Answer #1:

You would want and instead of &&.

Answer #2:

Python uses and and or conditionals.

i.e.

if foo == "abc" and bar == "bac" or zoo == "123":
  # do something

Answer #3:

I"m getting an error in the IF conditional. What am I doing wrong?

There reason that you get a SyntaxError is that there is no && operator in Python. Likewise || and ! are not valid Python operators.

Some of the operators you may know from other languages have a different name in Python. The logical operators && and || are actually called and and or. Likewise the logical negation operator ! is called not.

So you could just write:

if len(a) % 2 == 0 and len(b) % 2 == 0:

or even:

if not (len(a) % 2 or len(b) % 2):

Some additional information (that might come in handy):

I summarized the operator "equivalents" in this table:

+------------------------------+---------------------+
|  Operator (other languages)  |  Operator (Python)  |
+==============================+=====================+
|              &&              |         and         |
+------------------------------+---------------------+
|              ||              |         or          |
+------------------------------+---------------------+
|              !               |         not         |
+------------------------------+---------------------+

See also Python documentation: 6.11. Boolean operations.

Besides the logical operators Python also has bitwise/binary operators:

+--------------------+--------------------+
|  Logical operator  |  Bitwise operator  |
+====================+====================+
|        and         |         &          |
+--------------------+--------------------+
|         or         |         |          |
+--------------------+--------------------+

There is no bitwise negation in Python (just the bitwise inverse operator ~ - but that is not equivalent to not).

See also 6.6. Unary arithmetic and bitwise/binary operations and 6.7. Binary arithmetic operations.

The logical operators (like in many other languages) have the advantage that these are short-circuited. That means if the first operand already defines the result, then the second operator isn"t evaluated at all.

To show this I use a function that simply takes a value, prints it and returns it again. This is handy to see what is actually evaluated because of the print statements:

>>> def print_and_return(value):
...     print(value)
...     return value

>>> res = print_and_return(False) and print_and_return(True)
False

As you can see only one print statement is executed, so Python really didn"t even look at the right operand.

This is not the case for the binary operators. Those always evaluate both operands:

>>> res = print_and_return(False) & print_and_return(True);
False
True

But if the first operand isn"t enough then, of course, the second operator is evaluated:

>>> res = print_and_return(True) and print_and_return(False);
True
False

To summarize this here is another Table:

+-----------------+-------------------------+
|   Expression    |  Right side evaluated?  |
+=================+=========================+
| `True` and ...  |           Yes           |
+-----------------+-------------------------+
| `False` and ... |           No            |
+-----------------+-------------------------+
|  `True` or ...  |           No            |
+-----------------+-------------------------+
| `False` or ...  |           Yes           |
+-----------------+-------------------------+

The True and False represent what bool(left-hand-side) returns, they don"t have to be True or False, they just need to return True or False when bool is called on them (1).

So in Pseudo-Code(!) the and and or functions work like these:

def and(expr1, expr2):
    left = evaluate(expr1)
    if bool(left):
        return evaluate(expr2)
    else:
        return left

def or(expr1, expr2):
    left = evaluate(expr1)
    if bool(left):
        return left
    else:
        return evaluate(expr2)

Note that this is pseudo-code not Python code. In Python you cannot create functions called and or or because these are keywords. Also you should never use "evaluate" or if bool(...).

Customizing the behavior of your own classes

This implicit bool call can be used to customize how your classes behave with and, or and not.

To show how this can be customized I use this class which again prints something to track what is happening:

class Test(object):
    def __init__(self, value):
        self.value = value

    def __bool__(self):
        print("__bool__ called on {!r}".format(self))
        return bool(self.value)

    __nonzero__ = __bool__  # Python 2 compatibility

    def __repr__(self):
        return "{self.__class__.__name__}({self.value})".format(self=self)

So let"s see what happens with that class in combination with these operators:

>>> if Test(True) and Test(False):
...     pass
__bool__ called on Test(True)
__bool__ called on Test(False)

>>> if Test(False) or Test(False):
...     pass
__bool__ called on Test(False)
__bool__ called on Test(False)

>>> if not Test(True):
...     pass
__bool__ called on Test(True)

If you don"t have a __bool__ method then Python also checks if the object has a __len__ method and if it returns a value greater than zero. That might be useful to know in case you create a sequence container.

See also 4.1. Truth Value Testing.

NumPy arrays and subclasses

Probably a bit beyond the scope of the original question but in case you"re dealing with NumPy arrays or subclasses (like Pandas Series or DataFrames) then the implicit bool call will raise the dreaded ValueError:

>>> import numpy as np
>>> arr = np.array([1,2,3])
>>> bool(arr)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> arr and arr
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

>>> import pandas as pd
>>> s = pd.Series([1,2,3])
>>> bool(s)
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
>>> s and s
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

In these cases you can use the logical and function from NumPy which performs an element-wise and (or or):

>>> np.logical_and(np.array([False,False,True,True]), np.array([True, False, True, False]))
array([False, False,  True, False])
>>> np.logical_or(np.array([False,False,True,True]), np.array([True, False, True, False]))
array([ True, False,  True,  True])

If you"re dealing just with boolean arrays you could also use the binary operators with NumPy, these do perform element-wise (but also binary) comparisons:

>>> np.array([False,False,True,True]) & np.array([True, False, True, False])
array([False, False,  True, False])
>>> np.array([False,False,True,True]) | np.array([True, False, True, False])
array([ True, False,  True,  True])

(1)

That the bool call on the operands has to return True or False isn"t completely correct. It"s just the first operand that needs to return a boolean in it"s __bool__ method:

class Test(object):
    def __init__(self, value):
        self.value = value

    def __bool__(self):
        return self.value

    __nonzero__ = __bool__  # Python 2 compatibility

    def __repr__(self):
        return "{self.__class__.__name__}({self.value})".format(self=self)

>>> x = Test(10) and Test(10)
TypeError: __bool__ should return bool, returned int
>>> x1 = Test(True) and Test(10)
>>> x2 = Test(False) and Test(10)

That"s because and actually returns the first operand if the first operand evaluates to False and if it evaluates to True then it returns the second operand:

>>> x1
Test(10)
>>> x2
Test(False)

Similarly for or but just the other way around:

>>> Test(True) or Test(10)
Test(True)
>>> Test(False) or Test(10)
Test(10)

However if you use them in an if statement the if will also implicitly call bool on the result. So these finer points may not be relevant for you.

How do you get the logical xor of two variables in Python?

Question by Zach Hirsch

How do you get the logical xor of two variables in Python?

For example, I have two variables that I expect to be strings. I want to test that only one of them contains a True value (is not None or the empty string):

str1 = raw_input("Enter string one:")
str2 = raw_input("Enter string two:")
if logical_xor(str1, str2):
    print "ok"
else:
    print "bad"

The ^ operator seems to be bitwise, and not defined on all objects:

>>> 1 ^ 1
0
>>> 2 ^ 1
3
>>> "abc" ^ ""
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for ^: "str" and "str"

Answer #1:

If you"re already normalizing the inputs to booleans, then != is xor.

bool(a) != bool(b)

Answer #2:

You can always use the definition of xor to compute it from other logical operations:

(a and not b) or (not a and b)

But this is a little too verbose for me, and isn"t particularly clear at first glance. Another way to do it is:

bool(a) ^ bool(b)

The xor operator on two booleans is logical xor (unlike on ints, where it"s bitwise). Which makes sense, since bool is just a subclass of int, but is implemented to only have the values 0 and 1. And logical xor is equivalent to bitwise xor when the domain is restricted to 0 and 1.

So the logical_xor function would be implemented like:

def logical_xor(str1, str2):
    return bool(str1) ^ bool(str2)

Credit to Nick Coghlan on the Python-3000 mailing list.

PHP Versus Javascript: StackOverflow Questions

Meaning of @classmethod and @staticmethod for beginner?

Question by user1632861

Could someone explain to me the meaning of @classmethod and @staticmethod in python? I need to know the difference and the meaning.

As far as I understand, @classmethod tells a class that it"s a method which should be inherited into subclasses, or... something. However, what"s the point of that? Why not just define the class method without adding @classmethod or @staticmethod or any @ definitions?

tl;dr: when should I use them, why should I use them, and how should I use them?

Answer #1:

Though classmethod and staticmethod are quite similar, there"s a slight difference in usage for both entities: classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.

Example

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        date1 = cls(day, month, year)
        return date1

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        return day <= 31 and month <= 12 and year <= 3999

date2 = Date.from_string("11-09-2012")
is_date = Date.is_date_valid("11-09-2012")

Explanation

Let"s assume an example of a class, dealing with date information (this will be our boilerplate):

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

This class obviously could be used to store information about certain dates (without timezone information; let"s assume all dates are presented in UTC).

Here we have __init__, a typical initializer of Python class instances, which receives arguments as a typical instancemethod, having the first non-optional argument (self) that holds a reference to a newly created instance.

Class Method

We have some tasks that can be nicely done using classmethods.

Let"s assume that we want to create a lot of Date class instances having date information coming from an outer source encoded as a string with format "dd-mm-yyyy". Suppose we have to do this in different places in the source code of our project.

So what we must do here is:

  1. Parse a string to receive day, month and year as three integer variables or a 3-item tuple consisting of that variable.
  2. Instantiate Date by passing those values to the initialization call.

This will look like:

day, month, year = map(int, string_date.split("-"))
date1 = Date(day, month, year)

For this purpose, C++ can implement such a feature with overloading, but Python lacks this overloading. Instead, we can use classmethod. Let"s create another "constructor".

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        date1 = cls(day, month, year)
        return date1

date2 = Date.from_string("11-09-2012")

Let"s look more carefully at the above implementation, and review what advantages we have here:

  1. We"ve implemented date string parsing in one place and it"s reusable now.
  2. Encapsulation works fine here (if you think that you could implement string parsing as a single function elsewhere, this solution fits the OOP paradigm far better).
  3. cls is an object that holds the class itself, not an instance of the class. It"s pretty cool because if we inherit our Date class, all children will have from_string defined also.

Static method

What about staticmethod? It"s pretty similar to classmethod but doesn"t take any obligatory parameters (like a class method or instance method does).

Let"s look at the next use case.

We have a date string that we want to validate somehow. This task is also logically bound to the Date class we"ve used so far, but doesn"t require instantiation of it.

Here is where staticmethod can be useful. Let"s look at the next piece of code:

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        return day <= 31 and month <= 12 and year <= 3999

    # usage:
    is_date = Date.is_date_valid("11-09-2012")

So, as we can see from usage of staticmethod, we don"t have any access to what the class is---it"s basically just a function, called syntactically like a method, but without access to the object and its internals (fields and another methods), while classmethod does.

Answer #2:

Rostyslav Dzinko"s answer is very appropriate. I thought I could highlight one other reason you should choose @classmethod over @staticmethod when you are creating an additional constructor.

In the example above, Rostyslav used the @classmethod from_string as a Factory to create Date objects from otherwise unacceptable parameters. The same can be done with @staticmethod as is shown in the code below:

class Date:
  def __init__(self, month, day, year):
    self.month = month
    self.day   = day
    self.year  = year


  def display(self):
    return "{0}-{1}-{2}".format(self.month, self.day, self.year)


  @staticmethod
  def millenium(month, day):
    return Date(month, day, 2000)

new_year = Date(1, 1, 2013)               # Creates a new Date object
millenium_new_year = Date.millenium(1, 1) # also creates a Date object. 

# Proof:
new_year.display()           # "1-1-2013"
millenium_new_year.display() # "1-1-2000"

isinstance(new_year, Date) # True
isinstance(millenium_new_year, Date) # True

Thus both new_year and millenium_new_year are instances of the Date class.

But, if you observe closely, the Factory process is hard-coded to create Date objects no matter what. What this means is that even if the Date class is subclassed, the subclasses will still create plain Date objects (without any properties of the subclass). See that in the example below:

class DateTime(Date):
  def display(self):
      return "{0}-{1}-{2} - 00:00:00PM".format(self.month, self.day, self.year)


datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # False

datetime1.display() # returns "10-10-1990 - 00:00:00PM"
datetime2.display() # returns "10-10-2000" because it"s not a DateTime object but a Date object. Check the implementation of the millenium method on the Date class for more details.

datetime2 is not an instance of DateTime? WTF? Well, that"s because of the @staticmethod decorator used.

In most cases, this is undesired. If what you want is a Factory method that is aware of the class that called it, then @classmethod is what you need.

Rewriting Date.millenium as (that"s the only part of the above code that changes):

@classmethod
def millenium(cls, month, day):
    return cls(month, day, 2000)

ensures that the class is not hard-coded but rather learnt. cls can be any subclass. The resulting object will rightly be an instance of cls.
Let"s test that out:

datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # True


datetime1.display() # "10-10-1990 - 00:00:00PM"
datetime2.display() # "10-10-2000 - 00:00:00PM"

The reason is, as you know by now, that @classmethod was used instead of @staticmethod

Answer #3:

@classmethod means: when this method is called, we pass the class as the first argument instead of the instance of that class (as we normally do with methods). This means you can use the class and its properties inside that method rather than a particular instance.

@staticmethod means: when this method is called, we don"t pass an instance of the class to it (as we normally do with methods). This means you can put a function inside a class but you can"t access the instance of that class (this is useful when your method does not use the instance).

What is the meaning of single and double underscore before an object name?

Can someone please explain the exact meaning of having single and double leading underscores before an object"s name in Python, and the difference between both?

Also, does that meaning stay the same regardless of whether the object in question is a variable, a function, a method, etc.?

Answer #1:

Single Underscore

Names, in a class, with a leading underscore are simply to indicate to other programmers that the attribute or method is intended to be private. However, nothing special is done with the name itself.

To quote PEP-8:

_single_leading_underscore: weak "internal use" indicator. E.g. from M import * does not import objects whose name starts with an underscore.

Double Underscore (Name Mangling)

From the Python docs:

Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, so it can be used to define class-private instance and class variables, methods, variables stored in globals, and even variables stored in instances. private to this class on instances of other classes.

And a warning from the same page:

Name mangling is intended to give classes an easy way to define “private” instance variables and methods, without having to worry about instance variables defined by derived classes, or mucking with instance variables by code outside the class. Note that the mangling rules are designed mostly to avoid accidents; it still is possible for a determined soul to access or modify a variable that is considered private.

Example

>>> class MyClass():
...     def __init__(self):
...             self.__superprivate = "Hello"
...             self._semiprivate = ", world!"
...
>>> mc = MyClass()
>>> print mc.__superprivate
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: myClass instance has no attribute "__superprivate"
>>> print mc._semiprivate
, world!
>>> print mc.__dict__
{"_MyClass__superprivate": "Hello", "_semiprivate": ", world!"}

Answer #2:

__foo__: this is just a convention, a way for the Python system to use names that won"t conflict with user names.

_foo: this is just a convention, a way for the programmer to indicate that the variable is private (whatever that means in Python).

__foo: this has real meaning: the interpreter replaces this name with _classname__foo as a way to ensure that the name will not overlap with a similar name in another class.

No other form of underscores have meaning in the Python world.

There"s no difference between class, variable, global, etc in these conventions.

PHP Versus Javascript: StackOverflow Questions

How can I open multiple files using "with open" in Python?

I want to change a couple of files at one time, iff I can write to all of them. I"m wondering if I somehow can combine the multiple open calls with the with statement:

try:
  with open("a", "w") as a and open("b", "w") as b:
    do_something()
except IOError as e:
  print "Operation failed: %s" % e.strerror

If that"s not possible, what would an elegant solution to this problem look like?

Answer #1:

As of Python 2.7 (or 3.1 respectively) you can write

with open("a", "w") as a, open("b", "w") as b:
    do_something()

In earlier versions of Python, you can sometimes use contextlib.nested() to nest context managers. This won"t work as expected for opening multiples files, though -- see the linked documentation for details.


In the rare case that you want to open a variable number of files all at the same time, you can use contextlib.ExitStack, starting from Python version 3.3:

with ExitStack() as stack:
    files = [stack.enter_context(open(fname)) for fname in filenames]
    # Do something with "files"

Most of the time you have a variable set of files, you likely want to open them one after the other, though.

Answer #2:

For opening many files at once or for long file paths, it may be useful to break things up over multiple lines. From the Python Style Guide as suggested by @Sven Marnach in comments to another answer:

with open("/path/to/InFile.ext", "r") as file_1, 
     open("/path/to/OutFile.ext", "w") as file_2:
    file_2.write(file_1.read())

open() in Python does not create a file if it doesn"t exist

What is the best way to open a file as read/write if it exists, or if it does not, then create it and open it as read/write? From what I read, file = open("myfile.dat", "rw") should do this, right?

It is not working for me (Python 2.6.2) and I"m wondering if it is a version problem, or not supposed to work like that or what.

The bottom line is, I just need a solution for the problem. I am curious about the other stuff, but all I need is a nice way to do the opening part.

The enclosing directory was writeable by user and group, not other (I"m on a Linux system... so permissions 775 in other words), and the exact error was:

IOError: no such file or directory.

Answer #1:

You should use open with the w+ mode:

file = open("myfile.dat", "w+")

Answer #2:

The advantage of the following approach is that the file is properly closed at the block"s end, even if an exception is raised on the way. It"s equivalent to try-finally, but much shorter.

with open("file.dat";"a+") as f:
    f.write(...)
    ...

a+ Opens a file for both appending and reading. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing. -Python file modes

seek() method sets the file"s current position.

f.seek(pos [, (0|1|2)])
pos .. position of the r/w pointer
[] .. optionally
() .. one of ->
  0 .. absolute position
  1 .. relative position to current
  2 .. relative position from end

Only "rwab+" characters are allowed; there must be exactly one of "rwa" - see Stack Overflow question Python file modes detail.

Difference between modes a, a+, w, w+, and r+ in built-in open function?

In the python built-in open function, what is the exact difference between the modes w, a, w+, a+, and r+?

In particular, the documentation implies that all of these will allow writing to the file, and says that it opens the files for "appending", "writing", and "updating" specifically, but does not define what these terms mean.

Answer #1:

The opening modes are exactly the same as those for the C standard library function fopen().

The BSD fopen manpage defines them as follows:

 The argument mode points to a string beginning with one of the following
 sequences (Additional characters may follow these sequences.):

 ``r""   Open text file for reading.  The stream is positioned at the
         beginning of the file.

 ``r+""  Open for reading and writing.  The stream is positioned at the
         beginning of the file.

 ``w""   Truncate file to zero length or create text file for writing.
         The stream is positioned at the beginning of the file.

 ``w+""  Open for reading and writing.  The file is created if it does not
         exist, otherwise it is truncated.  The stream is positioned at
         the beginning of the file.

 ``a""   Open for writing.  The file is created if it does not exist.  The
         stream is positioned at the end of the file.  Subsequent writes
         to the file will always end up at the then current end of file,
         irrespective of any intervening fseek(3) or similar.

 ``a+""  Open for reading and writing.  The file is created if it does not
         exist.  The stream is positioned at the end of the file.  Subse-
         quent writes to the file will always end up at the then current
         end of file, irrespective of any intervening fseek(3) or similar.

PHP Versus Javascript: StackOverflow Questions

How to get all subsets of a set? (powerset)

Given a set

{0, 1, 2, 3}

How can I produce the subsets:

[set(),
 {0},
 {1},
 {2},
 {3},
 {0, 1},
 {0, 2},
 {0, 3},
 {1, 2},
 {1, 3},
 {2, 3},
 {0, 1, 2},
 {0, 1, 3},
 {0, 2, 3},
 {1, 2, 3},
 {0, 1, 2, 3}]

Answer #1:

The Python itertools page has exactly a powerset recipe for this:

from itertools import chain, combinations

def powerset(iterable):
    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
    s = list(iterable)
    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))

Output:

>>> list(powerset("abcd"))
[(), ("a",), ("b",), ("c",), ("d",), ("a", "b"), ("a", "c"), ("a", "d"), ("b", "c"), ("b", "d"), ("c", "d"), ("a", "b", "c"), ("a", "b", "d"), ("a", "c", "d"), ("b", "c", "d"), ("a", "b", "c", "d")]

If you don"t like that empty tuple at the beginning, you can just change the range statement to range(1, len(s)+1) to avoid a 0-length combination.

PHP Versus Javascript: StackOverflow Questions

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

Question 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.)

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

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}

Answer #3:

An alternative:

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

Answer #4:

Another, more concise, option:

z = dict(x, **y)

Note: this has become a popular answer, but it is important to point out that if y has any non-string keys, the fact that this works at all is an abuse of a CPython implementation detail, and it does not work in Python 3, or in PyPy, IronPython, or Jython. Also, Guido is not a fan. So I can"t recommend this technique for forward-compatible or cross-implementation portable code, which really means it should be avoided entirely.

Answer #5:

This probably won"t be a popular answer, but you almost certainly do not want to do this. If you want a copy that"s a merge, then use copy (or deepcopy, depending on what you want) and then update. The two lines of code are much more readable - more Pythonic - than the single line creation with .items() + .items(). Explicit is better than implicit.

In addition, when you use .items() (pre Python 3.0), you"re creating a new list that contains the items from the dict. If your dictionaries are large, then that is quite a lot of overhead (two large lists that will be thrown away as soon as the merged dict is created). update() can work more efficiently, because it can run through the second dict item-by-item.

In terms of time:

>>> timeit.Timer("dict(x, **y)", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
15.52571702003479
>>> timeit.Timer("temp = x.copy()
temp.update(y)", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
15.694622993469238
>>> timeit.Timer("dict(x.items() + y.items())", "x = dict(zip(range(1000), range(1000)))
y=dict(zip(range(1000,2000), range(1000,2000)))").timeit(100000)
41.484580039978027

IMO the tiny slowdown between the first two is worth it for the readability. In addition, keyword arguments for dictionary creation was only added in Python 2.3, whereas copy() and update() will work in older versions.

PHP Versus Javascript: StackOverflow Questions

Separation of business logic and data access in django

I am writing a project in Django and I see that 80% of the code is in the file models.py. This code is confusing and, after a certain time, I cease to understand what is really happening.

Here is what bothers me:

  1. I find it ugly that my model level (which was supposed to be responsible only for the work with data from a database) is also sending email, walking on API to other services, etc.
  2. Also, I find it unacceptable to place business logic in the view, because this way it becomes difficult to control. For example, in my application there are at least three ways to create new instances of User, but technically it should create them uniformly.
  3. I do not always notice when the methods and properties of my models become non-deterministic and when they develop side effects.

Here is a simple example. At first, the User model was like this:

class User(db.Models):

    def get_present_name(self):
        return self.name or "Anonymous"

    def activate(self):
        self.status = "activated"
        self.save()

Over time, it turned into this:

class User(db.Models):

    def get_present_name(self): 
        # property became non-deterministic in terms of database
        # data is taken from another service by api
        return remote_api.request_user_name(self.uid) or "Anonymous" 

    def activate(self):
        # method now has a side effect (send message to user)
        self.status = "activated"
        self.save()
        send_mail("Your account is activated!", "…", [self.email])

What I want is to separate entities in my code:

  1. Entities of my database, persistence level: What data does my application keep?
  2. Entities of my application, business logic level: What does my application do?

What are the good practices to implement such an approach that can be applied in Django?

Answer #1:

It seems like you are asking about the difference between the data model and the domain model – the latter is where you can find the business logic and entities as perceived by your end user, the former is where you actually store your data.

Furthermore, I"ve interpreted the 3rd part of your question as: how to notice failure to keep these models separate.

These are two very different concepts and it"s always hard to keep them separate. However, there are some common patterns and tools that can be used for this purpose.

About the Domain Model

The first thing you need to recognize is that your domain model is not really about data; it is about actions and questions such as "activate this user", "deactivate this user", "which users are currently activated?", and "what is this user"s name?". In classical terms: it"s about queries and commands.

Thinking in Commands

Let"s start by looking at the commands in your example: "activate this user" and "deactivate this user". The nice thing about commands is that they can easily be expressed by small given-when-then scenario"s:

given an inactive user
when the admin activates this user
then the user becomes active
and a confirmation e-mail is sent to the user
and an entry is added to the system log
(etc. etc.)

Such scenario"s are useful to see how different parts of your infrastructure can be affected by a single command – in this case your database (some kind of "active" flag), your mail server, your system log, etc.

Such scenario"s also really help you in setting up a Test Driven Development environment.

And finally, thinking in commands really helps you create a task-oriented application. Your users will appreciate this :-)

Expressing Commands

Django provides two easy ways of expressing commands; they are both valid options and it is not unusual to mix the two approaches.

The service layer

The service module has already been described by @Hedde. Here you define a separate module and each command is represented as a function.

services.py

def activate_user(user_id):
    user = User.objects.get(pk=user_id)

    # set active flag
    user.active = True
    user.save()

    # mail user
    send_mail(...)

    # etc etc

Using forms

The other way is to use a Django Form for each command. I prefer this approach, because it combines multiple closely related aspects:

  • execution of the command (what does it do?)
  • validation of the command parameters (can it do this?)
  • presentation of the command (how can I do this?)

forms.py

class ActivateUserForm(forms.Form):

    user_id = IntegerField(widget = UsernameSelectWidget, verbose_name="Select a user to activate")
    # the username select widget is not a standard Django widget, I just made it up

    def clean_user_id(self):
        user_id = self.cleaned_data["user_id"]
        if User.objects.get(pk=user_id).active:
            raise ValidationError("This user cannot be activated")
        # you can also check authorizations etc. 
        return user_id

    def execute(self):
        """
        This is not a standard method in the forms API; it is intended to replace the 
        "extract-data-from-form-in-view-and-do-stuff" pattern by a more testable pattern. 
        """
        user_id = self.cleaned_data["user_id"]

        user = User.objects.get(pk=user_id)

        # set active flag
        user.active = True
        user.save()

        # mail user
        send_mail(...)

        # etc etc

Thinking in Queries

You example did not contain any queries, so I took the liberty of making up a few useful queries. I prefer to use the term "question", but queries is the classical terminology. Interesting queries are: "What is the name of this user?", "Can this user log in?", "Show me a list of deactivated users", and "What is the geographical distribution of deactivated users?"

Before embarking on answering these queries, you should always ask yourself this question, is this:

  • a presentational query just for my templates, and/or
  • a business logic query tied to executing my commands, and/or
  • a reporting query.

Presentational queries are merely made to improve the user interface. The answers to business logic queries directly affect the execution of your commands. Reporting queries are merely for analytical purposes and have looser time constraints. These categories are not mutually exclusive.

The other question is: "do I have complete control over the answers?" For example, when querying the user"s name (in this context) we do not have any control over the outcome, because we rely on an external API.

Making Queries

The most basic query in Django is the use of the Manager object:

User.objects.filter(active=True)

Of course, this only works if the data is actually represented in your data model. This is not always the case. In those cases, you can consider the options below.

Custom tags and filters

The first alternative is useful for queries that are merely presentational: custom tags and template filters.

template.html

<h1>Welcome, {{ user|friendly_name }}</h1>

template_tags.py

@register.filter
def friendly_name(user):
    return remote_api.get_cached_name(user.id)

Query methods

If your query is not merely presentational, you could add queries to your services.py (if you are using that), or introduce a queries.py module:

queries.py

def inactive_users():
    return User.objects.filter(active=False)


def users_called_publysher():
    for user in User.objects.all():
        if remote_api.get_cached_name(user.id) == "publysher":
            yield user 

Proxy models

Proxy models are very useful in the context of business logic and reporting. You basically define an enhanced subset of your model. You can override a Manager’s base QuerySet by overriding the Manager.get_queryset() method.

models.py

class InactiveUserManager(models.Manager):
    def get_queryset(self):
        query_set = super(InactiveUserManager, self).get_queryset()
        return query_set.filter(active=False)

class InactiveUser(User):
    """
    >>> for user in InactiveUser.objects.all():
    …        assert user.active is False 
    """

    objects = InactiveUserManager()
    class Meta:
        proxy = True

Query models

For queries that are inherently complex, but are executed quite often, there is the possibility of query models. A query model is a form of denormalization where relevant data for a single query is stored in a separate model. The trick of course is to keep the denormalized model in sync with the primary model. Query models can only be used if changes are entirely under your control.

models.py

class InactiveUserDistribution(models.Model):
    country = CharField(max_length=200)
    inactive_user_count = IntegerField(default=0)

The first option is to update these models in your commands. This is very useful if these models are only changed by one or two commands.

forms.py

class ActivateUserForm(forms.Form):
    # see above
   
    def execute(self):
        # see above
        query_model = InactiveUserDistribution.objects.get_or_create(country=user.country)
        query_model.inactive_user_count -= 1
        query_model.save()

A better option would be to use custom signals. These signals are of course emitted by your commands. Signals have the advantage that you can keep multiple query models in sync with your original model. Furthermore, signal processing can be offloaded to background tasks, using Celery or similar frameworks.

signals.py

user_activated = Signal(providing_args = ["user"])
user_deactivated = Signal(providing_args = ["user"])

forms.py

class ActivateUserForm(forms.Form):
    # see above
   
    def execute(self):
        # see above
        user_activated.send_robust(sender=self, user=user)

models.py

class InactiveUserDistribution(models.Model):
    # see above

@receiver(user_activated)
def on_user_activated(sender, **kwargs):
        user = kwargs["user"]
        query_model = InactiveUserDistribution.objects.get_or_create(country=user.country)
        query_model.inactive_user_count -= 1
        query_model.save()
    

Keeping it clean

When using this approach, it becomes ridiculously easy to determine if your code stays clean. Just follow these guidelines:

  • Does my model contain methods that do more than managing database state? You should extract a command.
  • Does my model contain properties that do not map to database fields? You should extract a query.
  • Does my model reference infrastructure that is not my database (such as mail)? You should extract a command.

The same goes for views (because views often suffer from the same problem).

  • Does my view actively manage database models? You should extract a command.

Some References

Django documentation: proxy models

Django documentation: signals

Architecture: Domain Driven Design

Answer #2:

I usually implement a service layer in between views and models. This acts like your project"s API and gives you a good helicopter view of what is going on. I inherited this practice from a colleague of mine that uses this layering technique a lot with Java projects (JSF), e.g:

models.py

class Book:
   author = models.ForeignKey(User)
   title = models.CharField(max_length=125)

   class Meta:
       app_label = "library"

services.py

from library.models import Book

def get_books(limit=None, **filters):
    """ simple service function for retrieving books can be widely extended """
    return Book.objects.filter(**filters)[:limit]  # list[:None] will return the entire list

views.py

from library.services import get_books

class BookListView(ListView):
    """ simple view, e.g. implement a _build and _apply filters function """
    queryset = get_books()

Mind you, I usually take models, views and services to module level and separate even further depending on the project"s size

Cosine Similarity between 2 Number Lists

I want to calculate the cosine similarity between two lists, let"s say for example list 1 which is dataSetI and list 2 which is dataSetII.

Let"s say dataSetI is [3, 45, 7, 2] and dataSetII is [2, 54, 13, 15]. The length of the lists are always equal. I want to report cosine similarity as a number between 0 and 1.

dataSetI = [3, 45, 7, 2]
dataSetII = [2, 54, 13, 15]

def cosine_similarity(list1, list2):
  # How to?
  pass

print(cosine_similarity(dataSetI, dataSetII))

Answer #1:

You should try SciPy. It has a bunch of useful scientific routines for example, "routines for computing integrals numerically, solving differential equations, optimization, and sparse matrices." It uses the superfast optimized NumPy for its number crunching. See here for installing.

Note that spatial.distance.cosine computes the distance, and not the similarity. So, you must subtract the value from 1 to get the similarity.

from scipy import spatial

dataSetI = [3, 45, 7, 2]
dataSetII = [2, 54, 13, 15]
result = 1 - spatial.distance.cosine(dataSetI, dataSetII)

Answer #2:

another version based on numpy only

from numpy import dot
from numpy.linalg import norm

cos_sim = dot(a, b)/(norm(a)*norm(b))

Haversine Formula in Python (Bearing and Distance between two GPS points)

Problem

I would like to know how to get the distance and bearing between 2 GPS points. I have researched on the haversine formula. Someone told me that I could also find the bearing using the same data.

Edit

Everything is working fine but the bearing doesn"t quite work right yet. The bearing outputs negative but should be between 0 - 360 degrees. The set data should make the horizontal bearing 96.02166666666666 and is:

Start point: 53.32055555555556 , -1.7297222222222221   
Bearing:  96.02166666666666  
Distance: 2 km  
Destination point: 53.31861111111111, -1.6997222222222223  
Final bearing: 96.04555555555555

Here is my new code:

from math import *

Aaltitude = 2000
Oppsite  = 20000

lat1 = 53.32055555555556
lat2 = 53.31861111111111
lon1 = -1.7297222222222221
lon2 = -1.6997222222222223

lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * atan2(sqrt(a), sqrt(1-a))
Base = 6371 * c


Bearing =atan2(cos(lat1)*sin(lat2)-sin(lat1)*cos(lat2)*cos(lon2-lon1), sin(lon2-lon1)*cos(lat2)) 

Bearing = degrees(Bearing)
print ""
print ""
print "--------------------"
print "Horizontal Distance:"
print Base
print "--------------------"
print "Bearing:"
print Bearing
print "--------------------"


Base2 = Base * 1000
distance = Base * 2 + Oppsite * 2 / 2
Caltitude = Oppsite - Aaltitude

a = Oppsite/Base
b = atan(a)
c = degrees(b)

distance = distance / 1000

print "The degree of vertical angle is:"
print c
print "--------------------"
print "The distance between the Balloon GPS and the Antenna GPS is:"
print distance
print "--------------------"

Answer #1:

Here"s a Python version:

from math import radians, cos, sin, asin, sqrt

def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance in kilometers between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles. Determines return value units.
    return c * r

PHP Versus Javascript: StackOverflow Questions

How to execute a program or call a system command?

Question by alan lai

How do you call an external command (as if I"d typed it at the Unix shell or Windows command prompt) from within a Python script?

Answer #1:

Use the subprocess module in the standard library:

import subprocess
subprocess.run(["ls", "-l"])

The advantage of subprocess.run over os.system is that it is more flexible (you can get the stdout, stderr, the "real" status code, better error handling, etc...).

Even the documentation for os.system recommends using subprocess instead:

The subprocess module provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using this function. See the Replacing Older Functions with the subprocess Module section in the subprocess documentation for some helpful recipes.

On Python 3.4 and earlier, use subprocess.call instead of .run:

subprocess.call(["ls", "-l"])

Answer #2:

Here"s a summary of the ways to call external programs and the advantages and disadvantages of each:

  1. os.system("some_command with args") passes the command and arguments to your system"s shell. This is nice because you can actually run multiple commands at once in this manner and set up pipes and input/output redirection. For example:

    os.system("some_command < input_file | another_command > output_file")  
    

    However, while this is convenient, you have to manually handle the escaping of shell characters such as spaces, et cetera. On the other hand, this also lets you run commands which are simply shell commands and not actually external programs. See the documentation.

  2. stream = os.popen("some_command with args") will do the same thing as os.system except that it gives you a file-like object that you can use to access standard input/output for that process. There are 3 other variants of popen that all handle the i/o slightly differently. If you pass everything as a string, then your command is passed to the shell; if you pass them as a list then you don"t need to worry about escaping anything. See the documentation.

  3. The Popen class of the subprocess module. This is intended as a replacement for os.popen, but has the downside of being slightly more complicated by virtue of being so comprehensive. For example, you"d say:

    print subprocess.Popen("echo Hello World", shell=True, stdout=subprocess.PIPE).stdout.read()
    

    instead of

    print os.popen("echo Hello World").read()
    

    but it is nice to have all of the options there in one unified class instead of 4 different popen functions. See the documentation.

  4. The call function from the subprocess module. This is basically just like the Popen class and takes all of the same arguments, but it simply waits until the command completes and gives you the return code. For example:

    return_code = subprocess.call("echo Hello World", shell=True)
    

    See the documentation.

  5. If you"re on Python 3.5 or later, you can use the new subprocess.run function, which is a lot like the above but even more flexible and returns a CompletedProcess object when the command finishes executing.

  6. The os module also has all of the fork/exec/spawn functions that you"d have in a C program, but I don"t recommend using them directly.

The subprocess module should probably be what you use.

Finally, please be aware that for all methods where you pass the final command to be executed by the shell as a string and you are responsible for escaping it. There are serious security implications if any part of the string that you pass can not be fully trusted. For example, if a user is entering some/any part of the string. If you are unsure, only use these methods with constants. To give you a hint of the implications consider this code:

print subprocess.Popen("echo %s " % user_input, stdout=PIPE).stdout.read()

and imagine that the user enters something "my mama didnt love me && rm -rf /" which could erase the whole filesystem.

Answer #3:

Typical implementation:

import subprocess

p = subprocess.Popen("ls", shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
for line in p.stdout.readlines():
    print line,
retval = p.wait()

You are free to do what you want with the stdout data in the pipe. In fact, you can simply omit those parameters (stdout= and stderr=) and it"ll behave like os.system().

Answer #4:

Some hints on detaching the child process from the calling one (starting the child process in background).

Suppose you want to start a long task from a CGI script. That is, the child process should live longer than the CGI script execution process.

The classical example from the subprocess module documentation is:

import subprocess
import sys

# Some code here

pid = subprocess.Popen([sys.executable, "longtask.py"]) # Call subprocess

# Some more code here

The idea here is that you do not want to wait in the line "call subprocess" until the longtask.py is finished. But it is not clear what happens after the line "some more code here" from the example.

My target platform was FreeBSD, but the development was on Windows, so I faced the problem on Windows first.

On Windows (Windows XP), the parent process will not finish until the longtask.py has finished its work. It is not what you want in a CGI script. The problem is not specific to Python; in the PHP community the problems are the same.

The solution is to pass DETACHED_PROCESS Process Creation Flag to the underlying CreateProcess function in Windows API. If you happen to have installed pywin32, you can import the flag from the win32process module, otherwise you should define it yourself:

DETACHED_PROCESS = 0x00000008

pid = subprocess.Popen([sys.executable, "longtask.py"],
                       creationflags=DETACHED_PROCESS).pid

/* UPD 2015.10.27 @eryksun in a comment below notes, that the semantically correct flag is CREATE_NEW_CONSOLE (0x00000010) */

On FreeBSD we have another problem: when the parent process is finished, it finishes the child processes as well. And that is not what you want in a CGI script either. Some experiments showed that the problem seemed to be in sharing sys.stdout. And the working solution was the following:

pid = subprocess.Popen([sys.executable, "longtask.py"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)

I have not checked the code on other platforms and do not know the reasons of the behaviour on FreeBSD. If anyone knows, please share your ideas. Googling on starting background processes in Python does not shed any light yet.

Answer #5:

import os
os.system("your command")

Note that this is dangerous, since the command isn"t cleaned. I leave it up to you to google for the relevant documentation on the "os" and "sys" modules. There are a bunch of functions (exec* and spawn*) that will do similar things.

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