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count source lines of code (SLOC) using pygments

Project Description


Pygount is a command line tool to scan folders for source code files and count the number of source code lines in it. It is similar to tools like sloccount and cloc but uses the pygments package to analyze the source code and consequently can analyze any programming language supported by pygments.

The name is a combination of pygments and count.

Pygount is open source and distributed under the BSD license. The source code is available from

Download and installation

Pygount is available from and can be installed running:

$ pip install pygount


Simply run and specify the folder to analyze recursively, for example:

$ pygount ~/development/sometool

If you omit the folder, the current folder of your shell is used as starting point. Apart from folders you can also specify single files and shell patterns (using ?, * and ranges like [a-z]).

Certain files and folders are automatically excluded from the analysis:

  • files starting with dot (.) or ending in tilda (~)
  • folders starting with dot (.) or named _svn.

To specify alternative patterns, use --folders-to-skip and --names-to-skip. Both take a comma separated list of patterns, see below on the pattern syntax. To for example also prevent folders starting with two underscores (_) from being analyzed, specify --folders-to-skip=[...],__*.

To limit the analysis on certain file types, you can specify a comma separated list of suffixes to take into account, for example --suffix=py,sql,xml.

By default the result of the analysis are written to the standard output in a format similar to sloccount. To redirect the output to a file, use e.g. --out=counts.txt. To change the format to an XML file similar to cloc, use --format=cloc-xml.


Some command line arguments take patterns as values.

By default, patterns are shell patterns using *, ? and ranges like [a-z] as placeholders. Depending on your platform, the are case sensitive (Unix) or not (Mac OS, Windows).

If a pattern starts with [regex] you can specifiy a comma separated list of regular expressions instead using all the constructs supported by the Python regular expression syntax. Regular expressions are case sensitive unless they include a (?i) flag.

If the first actual pattern is [...] default patterns are included. Without it, defaults are ignored and only the pattern explicitely stated are taken into account.

So for example to specify that generated code can also contain the German word “Generiert” in a case insensivie way use --generated=[regex][...](?i).*generiert.

Source code encoding

When reading source code, pygount automatically detects the encoding. It uses a simple algorithm where it recognizes BOM, XML declaractions such as:

<?xml encoding='cp1252'?>

and “magic” comments such as:

# -*- coding: cp1252 -*-

If the file does not have an appropriate heading, pygount attempts to read it using UTF-8. If this fails, it reads the file using a fallback encoding (by default CP1252) and ignores any encoding errors.

You can change this behavior using the --encoding option:

  • To keep the automatic analysis and use a different fallback encoding specify for example --encoding=automatic;iso-8859-15
  • To use an automatic detection based on heuristic, use --encoding=chardet. For this to work, the chardet package must be installed,
  • To use a specific encoding (for all files analyzed), use for example --encoding=iso-8859-15.

Pseudo languages

If a source code is not counted, the number of lines is 0 and the language shown is a pseudo language indicating the reason:

  • __binary__ - the source code is a binary file; the detection of binary files first ensures that file does not start with a BOM for UTF-8, UTF-16 or UTF-32 (which indicates text files). After that it checks for zero bytes within the initial 8192 bytes of the file.
  • __duplicate__ - the source code is a bytewise identical copy of another file; enable the command line option --duplicates to also count code in duplicates (and gain a minor performance improvement)
  • __empty__ - the source code is an empty file with a size of 0 bytes.
  • __error__ - the source code could not be parsed e.g. due an I/O error.
  • __generated__ - the source code is generated according to the command line option --generated.
  • __unknown__ - pygments does not provide a lexer to parse the source code.

Other information

To get a description of all the available command line options, run:

$ pygount --help

To get the version number, run:

$ pygount --version

Continuous integration

Pygount can produce output that can be processed by the SLOCCount plug-in for the Jenkins continuous integration server.

It’s recommended to run pygount as one of the first steps in your build process before any undesired file like compiler targets or generated source code are built.

An example “Execute shell” build step for Jenkins is:

pygount --format=cloc-xml --out cloc.xml --suffix=py --verbose

Then add a post-build action “Publish SLOCCount analysis results” and set “SLOCCount report” to “cloc.xml”.

How pygount counts code

Pygount basically counts physical lines of source code.

First, it lexes the code using the lexers pygments assigned to it. If pygments cannot find an appropriate lexer, pygount has a few additional internal lexers that can at least distinguish between code and comments:

  • m4, VBScript and WebFOCUS use minimalistic lexers that can distinguish between comments and code.
  • OMG IDL repurposes the existing Java lexer.

Furthermore plain text has a separate lexer that counts all lines as comments.

Lines that only contain comment tokens and white space count as comments. Lines that only contain white space are not taken into account. Everything else counts as code.

If a line contains only “white characters” it is not taken into account presumably because the code is only formatted that way to make it easier to read. Currently white characters are:


Because of that, pygount reports about 10 to 20 percent fewer SLOC for C-like languages than other similar tools.

For some languages “no operations” are detected and treated as white space. For example Python’s pass or Transact-SQL’s begin and end .

As example consider this Python code:

class SomeError(Exception):
    Some error caused by some issue.

This counts as 1 line of code and 3 lines of comments. The line with pass is considered a “no operation” and thus not taken into account.

Comparison with other tools

Pygount can analyze more languages than other common tools such as sloccount or cloc because it builds on pygments, which provides lexers for hundreds of languages. This also makes it easy to support another language: simply write your own lexer.

For certain corner cases pygount give more accurate results because it actually lexes the code unlike other tools that mostly look for comment markers and can get confused when they show up inside strings. In practice though this should not make much of a difference.

Pygount is slower than most other tools. Partially this is due to actually lexing instead of just scanning the code. Partially other tools can use statically compiled languages such as Java or C, which are generally faster than dynamic languages. For many applications though pygount should be “fast enough”, especially when called during a nightly build.


Pygount provides a simple API to integrate it in other tools. This however is currently still a work in progress and subject to change.

Here’s an example on how to analyze one of pygount’s own source codes:

>>> import pygount
>>> analysis = pygount.source_analysis('pygount/', 'pygount')
>>> analysis
SourceAnalysis(path='pygount/', language='Python', group='pygount', code=302, documentation=66, empty=62, string=23, state='analyzed', state_info=None)

Version history

Version 0.9, 2017-05-04

  • Fixed AssertionError when option --encoding=chardet was specified.
  • Changed warning message “no fallback encoding specified, using <encoding>” to a debug message because it did not add any interesting information as the encoding actually used is visible in the info message for each file.
  • Added detection of binary files and excluded them from the analysis. In particular Django model objects (*.mo) are not considered Modelica source code anymore (issue #11).
  • Added detection of DocBook XML by DTD (issue #10).
  • Added support for suffices to indicate PL/SQL files according to Oracle FAQ entry on file extensions (issue #12).
  • Added possibility to specify a fallback encoding for encoding ‘chardet’. Use e.g. --encoding=chardet;cp1252.

Version 0.8, 2016-10-07

  • Fixed option --verbose. Now each analyzed source code results in at least one informational message in the log.
  • Added detection of duplicates using size and then MD5 code as criteria (issue #2). Use the option --duplicates to still count duplicate source code.
  • Improved detetion of programming language, which is now more consistent and yields the same language between Python invocations.

Version 0.7, 2016-09-28

  • Fixed that option --generated was ignored.
  • Added support for a couple of languages not supported by pygments yet:
    • m4, VBScript and WebFOCUS use minimalistic lexers that can distinguish between comments and code.
    • OMG IDL repurposes the existing Java lexer.
  • Added detection of certain XML dialects as separate language (issue #8).

Version 0.6, 2016-09-26

  • Fixed that source files could end up as __error__ if the first non ASCII characters showed up only after 16 kilobyte and the encoding was not UTF-8. Now pygount attempts to read the whole file as UTF-8 before assuming it actually is UTF-8.
  • Changed lines in plain text files to count as comments (issue #9). Before pygments treated them as \(ResourceBundle\).
  • Changed that empty files have __empty__ as language (issue #7).
  • Extended workaround for pygments issue #1284 to replace any lexer *+Evoque by *.

Version 0.5, 2016-09-22

  • Added that generated source code is excluded from analysis (issue #1). Use option --generated to specify patterns that indicate generated code.
  • Added workaround for pygments sometimes detecting the same XML file as XML and other times as XML+Evoque (probably depending on the hash seed). Now XML+Evoque is always changed to XML.
  • Added __pycache__ as default --folder-to-skip.
  • Added notes on pseudo languages for source code that cannot be analyzed.

Version 0.4, 2016-09-11

  • Fixed LookupError on broken encoding in magic comment (issue #4).
  • Added options --folders-to-skip and --names-to-skip to specify which files should be excluded from analysis.
  • Added comma (,) and colon (:) to list of “white characters” that do not count as code if there is nothing else in the line.
  • Improved pattern matching: for all options that according to --help take PATTERNS you can now specify that the patterns are regular expressions instead of shell patterns (using [regex]) and that they should extend the default patterns (using [...]).
  • Improved documentation: added notes on how code is counted and how pygount compares to other similar tools.

Version 0.3, 2016-08-20

  • Fixed @rem comments in DOS batch files (issue #3).
  • Cleaned up code.

Version 0.2, 2016-07-10

  • Fixed that files starting with underscore (e.g. were excluded from analysis.
  • Changed chardet package to be optional.
  • Added possibility to specify single files and glob patterns to analyze.
  • Added that lines containing only certain characters are treated as white space instead of code. Currently this concerns brackets (()[]{}) and semicolon (;).
  • Added that Python’s pass statement is treated as white space instead of code.
  • Cleaned up and (slightly) optimized code.

Version 0.1, 2016-07-05

  • Initial public release.
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