HI Python

HI Python

1641659400

YAPF: A Current formatters For Python

YAPF

Introduction

Most of the current formatters for Python --- e.g., autopep8, and pep8ify --- are made to remove lint errors from code. This has some obvious limitations. For instance, code that conforms to the PEP 8 guidelines may not be reformatted. But it doesn't mean that the code looks good.

YAPF takes a different approach. It's based off of 'clang-format', developed by Daniel Jasper. In essence, the algorithm takes the code and reformats it to the best formatting that conforms to the style guide, even if the original code didn't violate the style guide. The idea is also similar to the 'gofmt' tool for the Go programming language: end all holy wars about formatting - if the whole codebase of a project is simply piped through YAPF whenever modifications are made, the style remains consistent throughout the project and there's no point arguing about style in every code review.

The ultimate goal is that the code YAPF produces is as good as the code that a programmer would write if they were following the style guide. It takes away some of the drudgery of maintaining your code.

Installation

To install YAPF from PyPI:

$ pip install yapf

(optional) If you are using Python 2.7 and want to enable multiprocessing:

$ pip install futures

YAPF is still considered in "alpha" stage, and the released version may change often; therefore, the best way to keep up-to-date with the latest development is to clone this repository.

Note that if you intend to use YAPF as a command-line tool rather than as a library, installation is not necessary. YAPF supports being run as a directory by the Python interpreter. If you cloned/unzipped YAPF into DIR, it's possible to run:

$ PYTHONPATH=DIR python DIR/yapf [options] ...

Python versions

YAPF supports Python 2.7 and 3.6.4+. (Note that some Python 3 features may fail to parse with Python versions before 3.6.4.)

YAPF requires the code it formats to be valid Python for the version YAPF itself runs under. Therefore, if you format Python 3 code with YAPF, run YAPF itself under Python 3 (and similarly for Python 2).

Usage

Options:

usage: yapf [-h] [-v] [-d | -i] [-r | -l START-END] [-e PATTERN]
            [--style STYLE] [--style-help] [--no-local-style] [-p]
            [-vv]
            [files [files ...]]

Formatter for Python code.

positional arguments:
  files

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show version number and exit
  -d, --diff            print the diff for the fixed source
  -i, --in-place        make changes to files in place
  -r, --recursive       run recursively over directories
  -l START-END, --lines START-END
                        range of lines to reformat, one-based
  -e PATTERN, --exclude PATTERN
                        patterns for files to exclude from formatting
  --style STYLE         specify formatting style: either a style name (for
                        example "pep8" or "google"), or the name of a file
                        with style settings. The default is pep8 unless a
                        .style.yapf or setup.cfg or pyproject.toml file
                        located in the same directory as the source or one of
                        its parent directories (for stdin, the current
                        directory is used).
  --style-help          show style settings and exit; this output can be saved
                        to .style.yapf to make your settings permanent
  --no-local-style      don't search for local style definition
  -p, --parallel        Run yapf in parallel when formatting multiple files.
                        Requires concurrent.futures in Python 2.X
  -vv, --verbose        Print out file names while processing

Return Codes

Normally YAPF returns zero on successful program termination and non-zero otherwise.

If --diff is supplied, YAPF returns zero when no changes were necessary, non-zero otherwise (including program error). You can use this in a CI workflow to test that code has been YAPF-formatted.

Excluding files from formatting (.yapfignore or pyproject.toml)

In addition to exclude patterns provided on commandline, YAPF looks for additional patterns specified in a file named .yapfignore or pyproject.toml located in the working directory from which YAPF is invoked.

.yapfignore's syntax is similar to UNIX's filename pattern matching:

*       matches everything
?       matches any single character
[seq]   matches any character in seq
[!seq]  matches any character not in seq

Note that no entry should begin with ./.

If you use pyproject.toml, exclude patterns are specified by ignore_pattens key in [tool.yapfignore] section. For example:

[tool.yapfignore]
ignore_patterns = [
  "temp/**/*.py",
  "temp2/*.py"
]

Formatting style

The formatting style used by YAPF is configurable and there are many "knobs" that can be used to tune how YAPF does formatting. See the style.py module for the full list.

To control the style, run YAPF with the --style argument. It accepts one of the predefined styles (e.g., pep8 or google), a path to a configuration file that specifies the desired style, or a dictionary of key/value pairs.

The config file is a simple listing of (case-insensitive) key = value pairs with a [style] heading. For example:

[style]
based_on_style = pep8
spaces_before_comment = 4
split_before_logical_operator = true

The based_on_style setting determines which of the predefined styles this custom style is based on (think of it like subclassing). Four styles are predefined:

See _STYLE_NAME_TO_FACTORY in style.py for details.

It's also possible to do the same on the command line with a dictionary. For example:

--style='{based_on_style: pep8, indent_width: 2}'

This will take the pep8 base style and modify it to have two space indentations.

YAPF will search for the formatting style in the following manner:

  1. Specified on the command line
  2. In the [style] section of a .style.yapf file in either the current directory or one of its parent directories.
  3. In the [yapf] section of a setup.cfg file in either the current directory or one of its parent directories.
  4. In the [tool.yapf] section of a pyproject.toml file in either the current directory or one of its parent directories.
  5. In the [style] section of a ~/.config/yapf/style file in your home directory.

If none of those files are found, the default style is used (PEP8).

Example

An example of the type of formatting that YAPF can do, it will take this ugly code:

x = {  'a':37,'b':42,

'c':927}

y = 'hello ''world'
z = 'hello '+'world'
a = 'hello {}'.format('world')
class foo  (     object  ):
  def f    (self   ):
    return       37*-+2
  def g(self, x,y=42):
      return y
def f  (   a ) :
  return      37+-+a[42-x :  y**3]

and reformat it into:

x = {'a': 37, 'b': 42, 'c': 927}

y = 'hello ' 'world'
z = 'hello ' + 'world'
a = 'hello {}'.format('world')


class foo(object):
    def f(self):
        return 37 * -+2

    def g(self, x, y=42):
        return y


def f(a):
    return 37 + -+a[42 - x:y**3]

Example as a module

The two main APIs for calling yapf are FormatCode and FormatFile, these share several arguments which are described below:

>>> from yapf.yapflib.yapf_api import FormatCode  # reformat a string of code

>>> formatted_code, changed = FormatCode("f ( a = 1, b = 2 )")
>>> formatted_code
'f(a=1, b=2)\n'
>>> changed
True

A style_config argument: Either a style name or a path to a file that contains formatting style settings. If None is specified, use the default style as set in style.DEFAULT_STYLE_FACTORY.

>>> FormatCode("def g():\n  return True", style_config='pep8')[0]
'def g():\n    return True\n'

A lines argument: A list of tuples of lines (ints), [start, end], that we want to format. The lines are 1-based indexed. It can be used by third-party code (e.g., IDEs) when reformatting a snippet of code rather than a whole file.

>>> FormatCode("def g( ):\n    a=1\n    b = 2\n    return a==b", lines=[(1, 1), (2, 3)])[0]
'def g():\n    a = 1\n    b = 2\n    return a==b\n'

A print_diff (bool): Instead of returning the reformatted source, return a diff that turns the formatted source into reformatted source.

>>> print(FormatCode("a==b", filename="foo.py", print_diff=True)[0])
--- foo.py (original)
+++ foo.py (reformatted)
@@ -1 +1 @@
-a==b
+a == b

Note: the filename argument for FormatCode is what is inserted into the diff, the default is <unknown>.

FormatFile returns reformatted code from the passed file along with its encoding:

>>> from yapf.yapflib.yapf_api import FormatFile  # reformat a file

>>> print(open("foo.py").read())  # contents of file
a==b

>>> reformatted_code, encoding, changed = FormatFile("foo.py")
>>> formatted_code
'a == b\n'
>>> encoding
'utf-8'
>>> changed
True

The in_place argument saves the reformatted code back to the file:

>>> FormatFile("foo.py", in_place=True)[:2]
(None, 'utf-8')

>>> print(open("foo.py").read())  # contents of file (now fixed)
a == b

Formatting diffs

Options:

usage: yapf-diff [-h] [-i] [-p NUM] [--regex PATTERN] [--iregex PATTERN][-v]
                 [--style STYLE] [--binary BINARY]

This script reads input from a unified diff and reformats all the changed
lines. This is useful to reformat all the lines touched by a specific patch.
Example usage for git/svn users:

  git diff -U0 --no-color --relative HEAD^ | yapf-diff -i
  svn diff --diff-cmd=diff -x-U0 | yapf-diff -p0 -i

It should be noted that the filename contained in the diff is used
unmodified to determine the source file to update. Users calling this script
directly should be careful to ensure that the path in the diff is correct
relative to the current working directory.

optional arguments:
  -h, --help            show this help message and exit
  -i, --in-place        apply edits to files instead of displaying a diff
  -p NUM, --prefix NUM  strip the smallest prefix containing P slashes
  --regex PATTERN       custom pattern selecting file paths to reformat
                        (case sensitive, overrides -iregex)
  --iregex PATTERN      custom pattern selecting file paths to reformat
                        (case insensitive, overridden by -regex)
  -v, --verbose         be more verbose, ineffective without -i
  --style STYLE         specify formatting style: either a style name (for
                        example "pep8" or "google"), or the name of a file
                        with style settings. The default is pep8 unless a
                        .style.yapf or setup.cfg or pyproject.toml file
                        located in the same directory as the source or one of
                        its parent directories (for stdin, the current
                        directory is used).
  --binary BINARY       location of binary to use for yapf

Knobs

ALIGN_CLOSING_BRACKET_WITH_VISUAL_INDENT

Align closing bracket with visual indentation.

ALLOW_MULTILINE_LAMBDAS

Allow lambdas to be formatted on more than one line.

ALLOW_MULTILINE_DICTIONARY_KEYS

Allow dictionary keys to exist on multiple lines. For example:

x = {
    ('this is the first element of a tuple',
     'this is the second element of a tuple'):
         value,
}

ALLOW_SPLIT_BEFORE_DEFAULT_OR_NAMED_ASSIGNS

Allow splitting before a default / named assignment in an argument list.

ALLOW_SPLIT_BEFORE_DICT_VALUE

Allow splits before the dictionary value.

ARITHMETIC_PRECEDENCE_INDICATION

Let spacing indicate operator precedence. For example:

a = 1 * 2 + 3 / 4
b = 1 / 2 - 3 * 4
c = (1 + 2) * (3 - 4)
d = (1 - 2) / (3 + 4)
e = 1 * 2 - 3
f = 1 + 2 + 3 + 4

will be formatted as follows to indicate precedence:

a = 1*2 + 3/4
b = 1/2 - 3*4
c = (1+2) * (3-4)
d = (1-2) / (3+4)
e = 1*2 - 3
f = 1 + 2 + 3 + 4

BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF

Insert a blank line before a def or class immediately nested within another def or class. For example:

class Foo:
                   # <------ this blank line
    def method():
        pass

BLANK_LINE_BEFORE_MODULE_DOCSTRING

Insert a blank line before a module docstring.

BLANK_LINE_BEFORE_CLASS_DOCSTRING

Insert a blank line before a class-level docstring.

BLANK_LINES_AROUND_TOP_LEVEL_DEFINITION

Sets the number of desired blank lines surrounding top-level function and class definitions. For example:

class Foo:
    pass
                   # <------ having two blank lines here
                   # <------ is the default setting
class Bar:
    pass

BLANK_LINES_BETWEEN_TOP_LEVEL_IMPORTS_AND_VARIABLES

Sets the number of desired blank lines between top-level imports and variable definitions. Useful for compatibility with tools like isort.

COALESCE_BRACKETS

Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS or INDENT_CLOSING_BRACKETS is set. For example:

call_func_that_takes_a_dict(
    {
        'key1': 'value1',
        'key2': 'value2',
    }
)

would reformat to:

call_func_that_takes_a_dict({
    'key1': 'value1',
    'key2': 'value2',
})

COLUMN_LIMIT

The column limit (or max line-length)

CONTINUATION_ALIGN_STYLE

The style for continuation alignment. Possible values are:

  • SPACE: Use spaces for continuation alignment. This is default behavior.
  • FIXED: Use fixed number (CONTINUATION_INDENT_WIDTH) of columns (ie: CONTINUATION_INDENT_WIDTH/INDENT_WIDTH tabs or CONTINUATION_INDENT_WIDTH spaces) for continuation alignment.
  • VALIGN-RIGHT: Vertically align continuation lines to multiple of INDENT_WIDTH columns. Slightly right (one tab or a few spaces) if cannot vertically align continuation lines with indent characters.

CONTINUATION_INDENT_WIDTH

Indent width used for line continuations.

DEDENT_CLOSING_BRACKETS

Put closing brackets on a separate line, dedented, if the bracketed expression can't fit in a single line. Applies to all kinds of brackets, including function definitions and calls. For example:

config = {
    'key1': 'value1',
    'key2': 'value2',
}  # <--- this bracket is dedented and on a separate line

time_series = self.remote_client.query_entity_counters(
    entity='dev3246.region1',
    key='dns.query_latency_tcp',
    transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
    start_ts=now()-timedelta(days=3),
    end_ts=now(),
)  # <--- this bracket is dedented and on a separate line

DISABLE_ENDING_COMMA_HEURISTIC

Disable the heuristic which places each list element on a separate line if the list is comma-terminated.

EACH_DICT_ENTRY_ON_SEPARATE_LINE

Place each dictionary entry onto its own line.

FORCE_MULTILINE_DICT

Respect EACH_DICT_ENTRY_ON_SEPARATE_LINE even if the line is shorter than COLUMN_LIMIT.

I18N_COMMENT

The regex for an internationalization comment. The presence of this comment stops reformatting of that line, because the comments are required to be next to the string they translate.

I18N_FUNCTION_CALL

The internationalization function call names. The presence of this function stops reformatting on that line, because the string it has cannot be moved away from the i18n comment.

INDENT_DICTIONARY_VALUE

Indent the dictionary value if it cannot fit on the same line as the dictionary key. For example:

config = {
    'key1':
        'value1',
    'key2': value1 +
            value2,
}

INDENT_WIDTH

The number of columns to use for indentation.

INDENT_BLANK_LINES

Set to True to prefer indented blank lines rather than empty

INDENT_CLOSING_BRACKETS

Put closing brackets on a separate line, indented, if the bracketed expression can't fit in a single line. Applies to all kinds of brackets, including function definitions and calls. For example:

config = {
    'key1': 'value1',
    'key2': 'value2',
    }  # <--- this bracket is indented and on a separate line

time_series = self.remote_client.query_entity_counters(
    entity='dev3246.region1',
    key='dns.query_latency_tcp',
    transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
    start_ts=now()-timedelta(days=3),
    end_ts=now(),
    )  # <--- this bracket is indented and on a separate line

JOIN_MULTIPLE_LINES

Join short lines into one line. E.g., single line if statements.

NO_SPACES_AROUND_SELECTED_BINARY_OPERATORS

Do not include spaces around selected binary operators. For example:

1 + 2 * 3 - 4 / 5

will be formatted as follows when configured with *, /:

1 + 2*3 - 4/5

SPACES_AROUND_POWER_OPERATOR

Set to True to prefer using spaces around **.

SPACES_AROUND_DEFAULT_OR_NAMED_ASSIGN

Set to True to prefer spaces around the assignment operator for default or keyword arguments.

SPACES_AROUND_DICT_DELIMITERS

Adds a space after the opening '{' and before the ending '}' dict delimiters.

{1: 2}

will be formatted as:

{ 1: 2 }

SPACES_AROUND_LIST_DELIMITERS

Adds a space after the opening '[' and before the ending ']' list delimiters.

[1, 2]

will be formatted as:

[ 1, 2 ]

SPACES_AROUND_SUBSCRIPT_COLON

Use spaces around the subscript / slice operator. For example:

my_list[1 : 10 : 2]

SPACES_AROUND_TUPLE_DELIMITERS

Adds a space after the opening '(' and before the ending ')' tuple delimiters.

(1, 2, 3)

will be formatted as:

( 1, 2, 3 )

SPACES_BEFORE_COMMENT

The number of spaces required before a trailing comment. This can be a single value (representing the number of spaces before each trailing comment) or list of of values (representing alignment column values; trailing comments within a block will be aligned to the first column value that is greater than the maximum line length within the block). For example:

With spaces_before_comment=5:

1 + 1 # Adding values

will be formatted as:

1 + 1     # Adding values <-- 5 spaces between the end of the statement and comment

With spaces_before_comment=15, 20:

1 + 1 # Adding values
two + two # More adding

longer_statement # This is a longer statement
short # This is a shorter statement

a_very_long_statement_that_extends_beyond_the_final_column # Comment
short # This is a shorter statement

will be formatted as:

1 + 1          # Adding values <-- end of line comments in block aligned to col 15
two + two      # More adding

longer_statement    # This is a longer statement <-- end of line comments in block aligned to col 20
short               # This is a shorter statement

a_very_long_statement_that_extends_beyond_the_final_column  # Comment <-- the end of line comments are aligned based on the line length
short                                                       # This is a shorter statement

SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET

Insert a space between the ending comma and closing bracket of a list, etc.

SPACE_INSIDE_BRACKETS

Use spaces inside brackets, braces, and parentheses. For example:

method_call( 1 )
my_dict[ 3 ][ 1 ][ get_index( *args, **kwargs ) ]
my_set = { 1, 2, 3 }

SPLIT_ARGUMENTS_WHEN_COMMA_TERMINATED

Split before arguments if the argument list is terminated by a comma.

SPLIT_ALL_COMMA_SEPARATED_VALUES

If a comma separated list (dict, list, tuple, or function def) is on a line that is too long, split such that each element is on a separate line.

SPLIT_ALL_TOP_LEVEL_COMMA_SEPARATED_VALUES

Variation on SPLIT_ALL_COMMA_SEPARATED_VALUES in which, if a subexpression with a comma fits in its starting line, then the subexpression is not split. This avoids splits like the one for b in this code:

abcdef(
    aReallyLongThing: int,
    b: [Int,
        Int])

With the new knob this is split as:

abcdef(
    aReallyLongThing: int,
    b: [Int, Int])

SPLIT_BEFORE_BITWISE_OPERATOR

Set to True to prefer splitting before &, | or ^ rather than after.

SPLIT_BEFORE_ARITHMETIC_OPERATOR

Set to True to prefer splitting before +, -, *, /, //, or @ rather than after.

SPLIT_BEFORE_CLOSING_BRACKET

Split before the closing bracket if a list or dict literal doesn't fit on a single line.

SPLIT_BEFORE_DICT_SET_GENERATOR

Split before a dictionary or set generator (comp_for). For example, note the split before the for:

foo = {
    variable: 'Hello world, have a nice day!'
    for variable in bar if variable != 42
}

SPLIT_BEFORE_DOT

Split before the . if we need to split a longer expression:

foo = ('This is a really long string: {}, {}, {}, {}'.format(a, b, c, d))

would reformat to something like:

foo = ('This is a really long string: {}, {}, {}, {}'
       .format(a, b, c, d))

SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN

Split after the opening paren which surrounds an expression if it doesn't fit on a single line.

SPLIT_BEFORE_FIRST_ARGUMENT

If an argument / parameter list is going to be split, then split before the first argument.

SPLIT_BEFORE_LOGICAL_OPERATOR

Set to True to prefer splitting before and or or rather than after.

SPLIT_BEFORE_NAMED_ASSIGNS

Split named assignments onto individual lines.

SPLIT_COMPLEX_COMPREHENSION

For list comprehensions and generator expressions with multiple clauses (e.g multiple for calls, if filter expressions) and which need to be reflowed, split each clause onto its own line. For example:

result = [
    a_var + b_var for a_var in xrange(1000) for b_var in xrange(1000)
    if a_var % b_var]

would reformat to something like:

result = [
    a_var + b_var
    for a_var in xrange(1000)
    for b_var in xrange(1000)
    if a_var % b_var]

SPLIT_PENALTY_AFTER_OPENING_BRACKET

The penalty for splitting right after the opening bracket.

SPLIT_PENALTY_AFTER_UNARY_OPERATOR

The penalty for splitting the line after a unary operator.

SPLIT_PENALTY_ARITHMETIC_OPERATOR

The penalty of splitting the line around the +, -, *, /, //, %, and @ operators.

SPLIT_PENALTY_BEFORE_IF_EXPR

The penalty for splitting right before an if expression.

SPLIT_PENALTY_BITWISE_OPERATOR

The penalty of splitting the line around the &, |, and ^ operators.

SPLIT_PENALTY_COMPREHENSION

The penalty for splitting a list comprehension or generator expression.

SPLIT_PENALTY_EXCESS_CHARACTER

The penalty for characters over the column limit.

SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT

The penalty incurred by adding a line split to the logical line. The more line splits added the higher the penalty.

SPLIT_PENALTY_IMPORT_NAMES

The penalty of splitting a list of import as names. For example:

from a_very_long_or_indented_module_name_yada_yad import (long_argument_1,
                                                          long_argument_2,
                                                          long_argument_3)

would reformat to something like:

from a_very_long_or_indented_module_name_yada_yad import (
    long_argument_1, long_argument_2, long_argument_3)

SPLIT_PENALTY_LOGICAL_OPERATOR

The penalty of splitting the line around the and and or operators.

USE_TABS

Use the Tab character for indentation.

(Potentially) Frequently Asked Questions

Why does YAPF destroy my awesome formatting?

YAPF tries very hard to get the formatting correct. But for some code, it won't be as good as hand-formatting. In particular, large data literals may become horribly disfigured under YAPF.

The reasons for this are manyfold. In short, YAPF is simply a tool to help with development. It will format things to coincide with the style guide, but that may not equate with readability.

What can be done to alleviate this situation is to indicate regions YAPF should ignore when reformatting something:

# yapf: disable
FOO = {
    # ... some very large, complex data literal.
}

BAR = [
    # ... another large data literal.
]
# yapf: enable

You can also disable formatting for a single literal like this:

BAZ = {
    (1, 2, 3, 4),
    (5, 6, 7, 8),
    (9, 10, 11, 12),
}  # yapf: disable

To preserve the nice dedented closing brackets, use the dedent_closing_brackets in your style. Note that in this case all brackets, including function definitions and calls, are going to use that style. This provides consistency across the formatted codebase.

Why Not Improve Existing Tools?

We wanted to use clang-format's reformatting algorithm. It's very powerful and designed to come up with the best formatting possible. Existing tools were created with different goals in mind, and would require extensive modifications to convert to using clang-format's algorithm.

Can I Use YAPF In My Program?

Please do! YAPF was designed to be used as a library as well as a command line tool. This means that a tool or IDE plugin is free to use YAPF.

I still get non Pep8 compliant code! Why?

YAPF tries very hard to be fully PEP 8 compliant. However, it is paramount to not risk altering the semantics of your code. Thus, YAPF tries to be as safe as possible and does not change the token stream (e.g., by adding parentheses). All these cases however, can be easily fixed manually. For instance,

from my_package import my_function_1, my_function_2, my_function_3, my_function_4, my_function_5

FOO = my_variable_1 + my_variable_2 + my_variable_3 + my_variable_4 + my_variable_5 + my_variable_6 + my_variable_7 + my_variable_8

won't be split, but you can easily get it right by just adding parentheses:

from my_package import (my_function_1, my_function_2, my_function_3,
                        my_function_4, my_function_5)

FOO = (my_variable_1 + my_variable_2 + my_variable_3 + my_variable_4 +
       my_variable_5 + my_variable_6 + my_variable_7 + my_variable_8)

Gory Details

Algorithm Design

The main data structure in YAPF is the LogicalLine object. It holds a list of FormatTokens, that we would want to place on a single line if there were no column limit. An exception being a comment in the middle of an expression statement will force the line to be formatted on more than one line. The formatter works on one LogicalLine object at a time.

An LogicalLine typically won't affect the formatting of lines before or after it. There is a part of the algorithm that may join two or more LogicalLines into one line. For instance, an if-then statement with a short body can be placed on a single line:

if a == 42: continue

YAPF's formatting algorithm creates a weighted tree that acts as the solution space for the algorithm. Each node in the tree represents the result of a formatting decision --- i.e., whether to split or not to split before a token. Each formatting decision has a cost associated with it. Therefore, the cost is realized on the edge between two nodes. (In reality, the weighted tree doesn't have separate edge objects, so the cost resides on the nodes themselves.)

For example, take the following Python code snippet. For the sake of this example, assume that line (1) violates the column limit restriction and needs to be reformatted.

def xxxxxxxxxxx(aaaaaaaaaaaa, bbbbbbbbb, cccccccc, dddddddd, eeeeee):  # 1
    pass                                                               # 2

For line (1), the algorithm will build a tree where each node (a FormattingDecisionState object) is the state of the line at that token given the decision to split before the token or not. Note: the FormatDecisionState objects are copied by value so each node in the graph is unique and a change in one doesn't affect other nodes.

Heuristics are used to determine the costs of splitting or not splitting. Because a node holds the state of the tree up to a token's insertion, it can easily determine if a splitting decision will violate one of the style requirements. For instance, the heuristic is able to apply an extra penalty to the edge when not splitting between the previous token and the one being added.

There are some instances where we will never want to split the line, because doing so will always be detrimental (i.e., it will require a backslash-newline, which is very rarely desirable). For line (1), we will never want to split the first three tokens: def, xxxxxxxxxxx, and (. Nor will we want to split between the ) and the : at the end. These regions are said to be "unbreakable." This is reflected in the tree by there not being a "split" decision (left hand branch) within the unbreakable region.

Now that we have the tree, we determine what the "best" formatting is by finding the path through the tree with the lowest cost.

And that's it!


YAPF is not an official Google product (experimental or otherwise), it is just code that happens to be owned by Google.

Download Details:
Author: google
Source Code: https://github.com/google/yapf
License: Apache-2.0 License

#python #google 

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YAPF: A Current formatters For Python
Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind

1602968400

Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind

1602666000

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python