Javascript Pause For Loop

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Contents

Python’s break statement breaks the loop in which the statement is inserted. A continue Python statement skips a single iteration in a loop. The break and continue statements can be used in a for or while loop.

You may want to skip a particular iteration of a loop or end a loop completely. This is where the break and continue statements come in. These statements allow you to control how a loop unfolds.

The break statement built into Python allows you to exit a loop when a condition is met. The continue statement allows you to skip part of a loop when a condition is met. In this guide, we’ll see how to use the break and continue Python statements.


Updating loops

Programmers use loops to automate and repeat similar tasks. One of the most commonly used loops is an for loop. An for loop repeats a block of code until a certain condition is met. Here is the syntax of a for loop in Python:

The following for loop will iterate through a list of numbers from 0 to 2 and print them:

Our code returns the following:

Our code example printed the value i three times. This is a basic example of a cycle. Shows how a programmer can use loops to perform repetitive tasks on a block of code.


Python Break statement

The Python break statement breaks the loop in which the statement entered. When a break statement is executed, the statements after the contents of the loop are executed.

A break statement can be placed inside a nested loop. If a break statement is displayed in a nested loop, only the inner loop will interrupt execution. The outer loop will continue to run until all iterations have occurred or until the outer loop is aborted with a break statement.

You can use break statements to break out of a loop when a specific condition is met. You declare an break statement in your loop, usually under an if statement.

Break Python Example

For example, you may have a list of student names to print. to stop after printing the middle name. This will allow you to verify that the program is working. Here is an example of a program that uses a break statement to do this:

First , we declared a Python list . This list contains the names of the students in the class. We then created a for loop. This loop displays the name of each student in the Python shell.

In our for loop, we’ve added a break statement. This instruction will be executed if a student has the value of index 2 in our list. When the break statement is executed, the loop breaks.

Our code returns the following:

Our program prints the names of the first two students (who have the index values ‚Äã‚Äã and 1 in our table) . When the program has reached the student with the index value 2, the cycle is terminated. The Python print at the end of our executed program.

We have used an else clause to tell our program what to do if our condition is not met. If our condition is not met, the name of the student we are iterating on is displayed on the Python console.

break statements cause a program to terminate a loop. The program continues to execute the following instructions in a main program after the loop has stopped.


Python continue statement

The continue statement indicates a loop to continue the next iteration. Any code following the continue statement is not executed. Unlike a break statement, a la L ’continue statement does not completely terminate a loop.

You can use a continue statement in Python to skip part of a loop when a condition is met. loop will continue to run. You use continue statements inside loops, usually after an if statement.

Continue Python example

Let & rsquo; s use an example to illustrate how the continue statement works in Python. In the example below, we use a continue statement to ignore the printing of the middle name in our table, then count nua the iteration:

Our code returns the following:

Our continue statement is executed when a condition external is triggered. In our program, this condition is "student == 2". When student equals 2 our program stops executing the loop iteration .

Our program continued to cycle through the items in the following list after continuing education was run. If we had used a break statement, our loop would have stopped completely.

The continue statement has several use cases. For example, let’s say you validate the data. You may want your loop to iterate over if a value is empty. This is because an empty value could interrupt the flow of validation code.

Conclusion

When working with loops in Python, you might want to skip an iteration or break your loop entirely. This is where the continue and break statements come in handy, respectively.

In this tutorial, we have explained how to use the break and continue statements in Python to use loops in your code more efficiently. You are ready to work with it. the break and continue statements like a Python expert!

To learn more about programming in Python, read our full guide on How to learn Python .

Javascript Pause For Loop: StackOverflow Questions

DatabaseError: current transaction is aborted, commands ignored until end of transaction block?

I got a lot of errors with the message :

"DatabaseError: current transaction is aborted, commands ignored until end of transaction block"

after changed from python-psycopg to python-psycopg2 as Django project"s database engine.

The code remains the same, just don"t know where those errors are from.

Answer #1:

This is what postgres does when a query produces an error and you try to run another query without first rolling back the transaction. (You might think of it as a safety feature, to keep you from corrupting your data.)

To fix this, you"ll want to figure out where in the code that bad query is being executed. It might be helpful to use the log_statement and log_min_error_statement options in your postgresql server.

Answer #2:

To get rid of the error, roll back the last (erroneous) transaction after you"ve fixed your code:

from django.db import transaction
transaction.rollback()

You can use try-except to prevent the error from occurring:

from django.db import transaction, DatabaseError
try:
    a.save()
except DatabaseError:
    transaction.rollback()

Refer : Django documentation

How do I abort the execution of a Python script?

Question by Ray Vega

I have a simple Python script that I want to stop executing if a condition is met.

For example:

done = True
if done:
    # quit/stop/exit
else:
    # do other stuff

Essentially, I am looking for something that behaves equivalently to the "return" keyword in the body of a function which allows the flow of the code to exit the function and not execute the remaining code.

Answer #1:

To exit a script you can use,

import sys
sys.exit()

You can also provide an exit status value, usually an integer.

import sys
sys.exit(0)

Exits with zero, which is generally interpreted as success. Non-zero codes are usually treated as errors. The default is to exit with zero.

import sys
sys.exit("aa! errors!")

Prints "aa! errors!" and exits with a status code of 1.

There is also an _exit() function in the os module. The sys.exit() function raises a SystemExit exception to exit the program, so try statements and cleanup code can execute. The os._exit() version doesn"t do this. It just ends the program without doing any cleanup or flushing output buffers, so it shouldn"t normally be used.

The Python docs indicate that os._exit() is the normal way to end a child process created with a call to os.fork(), so it does have a use in certain circumstances.

Javascript Pause For Loop: 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.

Javascript Pause For Loop: StackOverflow Questions

How to insert newlines on argparse help text?

I"m using argparse in Python 2.7 for parsing input options. One of my options is a multiple choice. I want to make a list in its help text, e.g.

from argparse import ArgumentParser

parser = ArgumentParser(description="test")

parser.add_argument("-g", choices=["a", "b", "g", "d", "e"], default="a",
    help="Some option, where
"
         " a = alpha
"
         " b = beta
"
         " g = gamma
"
         " d = delta
"
         " e = epsilon")

parser.parse_args()

However, argparse strips all newlines and consecutive spaces. The result looks like

~/Downloads:52$ python2.7 x.py -h
usage: x.py [-h] [-g {a,b,g,d,e}]

test

optional arguments:
  -h, --help      show this help message and exit
  -g {a,b,g,d,e}  Some option, where a = alpha b = beta g = gamma d = delta e
                  = epsilon

How to insert newlines in the help text?

Answer #1:

Try using RawTextHelpFormatter:

from argparse import RawTextHelpFormatter
parser = ArgumentParser(description="test", formatter_class=RawTextHelpFormatter)

Is a Python list guaranteed to have its elements stay in the order they are inserted in?

If I have the following Python code

>>> x = []
>>> x = x + [1]
>>> x = x + [2]
>>> x = x + [3]
>>> x
[1, 2, 3]

Will x be guaranteed to always be [1,2,3], or are other orderings of the interim elements possible?

Answer #1:

Yes, the order of elements in a python list is persistent.

Inserting image into IPython notebook markdown

I am starting to depend heavily on the IPython notebook app to develop and document algorithms. It is awesome; but there is something that seems like it should be possible, but I can"t figure out how to do it:

I would like to insert a local image into my (local) IPython notebook markdown to aid in documenting an algorithm. I know enough to add something like <img src="image.png"> to the markdown, but that is about as far as my knowledge goes. I assume I could put the image in the directory represented by 127.0.0.1:8888 (or some subdirectory) to be able to access it, but I can"t figure out where that directory is. (I"m working on a mac.) So, is it possible to do what I"m trying to do without too much trouble?

Answer #1:

Most of the answers given so far go in the wrong direction, suggesting to load additional libraries and use the code instead of markup. In Ipython/Jupyter Notebooks it is very simple. Make sure the cell is indeed in markup and to display a image use:

![alt text](imagename.png "Title")

Further advantage compared to the other methods proposed is that you can display all common file formats including jpg, png, and gif (animations).

Answer #2:

Files inside the notebook dir are available under a "files/" url. So if it"s in the base path, it would be <img src="files/image.png">, and subdirs etc. are also available: <img src="files/subdir/image.png">, etc.

Update: starting with IPython 2.0, the files/ prefix is no longer needed (cf. release notes). So now the solution <img src="image.png"> simply works as expected.

how do I insert a column at a specific column index in pandas?

Can I insert a column at a specific column index in pandas?

import pandas as pd
df = pd.DataFrame({"l":["a","b","c","d"], "v":[1,2,1,2]})
df["n"] = 0

This will put column n as the last column of df, but isn"t there a way to tell df to put n at the beginning?

Answer #1:

see docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.insert.html

using loc = 0 will insert at the beginning

df.insert(loc, column, value)

df = pd.DataFrame({"B": [1, 2, 3], "C": [4, 5, 6]})

df
Out: 
   B  C
0  1  4
1  2  5
2  3  6

idx = 0
new_col = [7, 8, 9]  # can be a list, a Series, an array or a scalar   
df.insert(loc=idx, column="A", value=new_col)

df
Out: 
   A  B  C
0  7  1  4
1  8  2  5
2  9  3  6

Javascript Pause For Loop: 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>

Javascript Pause For Loop: StackOverflow Questions

Create list of single item repeated N times

I want to create a series of lists, all of varying lengths. Each list will contain the same element e, repeated n times (where n = length of the list).

How do I create the lists, without using a list comprehension [e for number in xrange(n)] for each list?

Answer #1:

You can also write:

[e] * n

You should note that if e is for example an empty list you get a list with n references to the same list, not n independent empty lists.

Performance testing

At first glance it seems that repeat is the fastest way to create a list with n identical elements:

>>> timeit.timeit("itertools.repeat(0, 10)", "import itertools", number = 1000000)
0.37095273281943264
>>> timeit.timeit("[0] * 10", "import itertools", number = 1000000)
0.5577236771712819

But wait - it"s not a fair test...

>>> itertools.repeat(0, 10)
repeat(0, 10)  # Not a list!!!

The function itertools.repeat doesn"t actually create the list, it just creates an object that can be used to create a list if you wish! Let"s try that again, but converting to a list:

>>> timeit.timeit("list(itertools.repeat(0, 10))", "import itertools", number = 1000000)
1.7508119747063233

So if you want a list, use [e] * n. If you want to generate the elements lazily, use repeat.

Answer #2:

>>> [5] * 4
[5, 5, 5, 5]

Be careful when the item being repeated is a list. The list will not be cloned: all the elements will refer to the same list!

>>> x=[5]
>>> y=[x] * 4
>>> y
[[5], [5], [5], [5]]
>>> y[0][0] = 6
>>> y
[[6], [6], [6], [6]]

What is the best way to repeatedly execute a function every x seconds?

Question by DavidM

I want to repeatedly execute a function in Python every 60 seconds forever (just like an NSTimer in Objective C). This code will run as a daemon and is effectively like calling the python script every minute using a cron, but without requiring that to be set up by the user.

In this question about a cron implemented in Python, the solution appears to effectively just sleep() for x seconds. I don"t need such advanced functionality so perhaps something like this would work

while True:
    # Code executed here
    time.sleep(60)

Are there any foreseeable problems with this code?

Answer #1:

If your program doesn"t have a event loop already, use the sched module, which implements a general purpose event scheduler.

import sched, time
s = sched.scheduler(time.time, time.sleep)
def do_something(sc): 
    print("Doing stuff...")
    # do your stuff
    s.enter(60, 1, do_something, (sc,))

s.enter(60, 1, do_something, (s,))
s.run()

If you"re already using an event loop library like asyncio, trio, tkinter, PyQt5, gobject, kivy, and many others - just schedule the task using your existing event loop library"s methods, instead.

Answer #2:

Lock your time loop to the system clock like this:

import time
starttime = time.time()
while True:
    print "tick"
    time.sleep(60.0 - ((time.time() - starttime) % 60.0))

Answer #3:

If you want a non-blocking way to execute your function periodically, instead of a blocking infinite loop I"d use a threaded timer. This way your code can keep running and perform other tasks and still have your function called every n seconds. I use this technique a lot for printing progress info on long, CPU/Disk/Network intensive tasks.

Here"s the code I"ve posted in a similar question, with start() and stop() control:

from threading import Timer

class RepeatedTimer(object):
    def __init__(self, interval, function, *args, **kwargs):
        self._timer     = None
        self.interval   = interval
        self.function   = function
        self.args       = args
        self.kwargs     = kwargs
        self.is_running = False
        self.start()

    def _run(self):
        self.is_running = False
        self.start()
        self.function(*self.args, **self.kwargs)

    def start(self):
        if not self.is_running:
            self._timer = Timer(self.interval, self._run)
            self._timer.start()
            self.is_running = True

    def stop(self):
        self._timer.cancel()
        self.is_running = False

Usage:

from time import sleep

def hello(name):
    print "Hello %s!" % name

print "starting..."
rt = RepeatedTimer(1, hello, "World") # it auto-starts, no need of rt.start()
try:
    sleep(5) # your long-running job goes here...
finally:
    rt.stop() # better in a try/finally block to make sure the program ends!

Features:

  • Standard library only, no external dependencies
  • start() and stop() are safe to call multiple times even if the timer has already started/stopped
  • function to be called can have positional and named arguments
  • You can change interval anytime, it will be effective after next run. Same for args, kwargs and even function!

Javascript Pause For Loop: 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.

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