vprof is a Python package providing rich and interactive visualizations for various Python program characteristics such as running time and memory usage. It supports Python 2.7, Python 3.4, Python 3.5 and distributed under BSD license.
vprof alternatives and similar packages
Based on the "Code Analysis" category.
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coala8.1 0.0 L4 vprof VS coalacoala provides a unified command-line interface for linting and fixing all your code, regardless of the programming languages you use.
code2flow7.0 3.1 L1 vprof VS code2flowPretty good call graphs for dynamic languages
pycallgraph6.1 0.0 L4 vprof VS pycallgraphA library that visualises the flow (call graph) of your Python application.
pysonar26.0 0.0 L1 vprof VS pysonar2PySonar2: a semantic indexer for Python with interprocedual type inference
Undebt5.6 0.0 L5 vprof VS UndebtA fast, straightforward, reliable tool for performing massive, automated code refactoring
pydeps4.9 7.7 vprof VS pydepsPython Module Dependency graphs
Write Clean Python Code. Always.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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vprof is a Python package providing rich and interactive visualizations for various Python program characteristics such as running time and memory usage. It supports Python 3.4+ and distributed under BSD license.
The project is in active development and some of its features might not work as expected.
All contributions are highly encouraged! You can add new features, report and fix existing bugs and write docs and tutorials. Feel free to open an issue or send a pull request!
Dependencies to build
vprof from source code:
- Python 3.4+
npm is required to build
vprof from sources only.
All Python and
npm module dependencies are listed in
vprof can be installed from PyPI
pip install vprof
vprof from sources, clone this repository and execute
python3 setup.py deps_install && python3 setup.py build_ui && python3 setup.py install
To install just
vprof dependencies, run
python3 setup.py deps_install
vprof -c <config> <src>
<config> is a combination of supported modes:
c- CPU flame graph ⚠️ Not available for windows #62
Shows CPU flame graph for
Runs built-in Python profiler on
<src> and displays results.
m- memory graph
Shows objects that are tracked by CPython GC and left in memory after code
execution. Also shows process memory usage after execution of each line of
h- code heatmap
Displays all executed code of
<src> with line run times and execution counts.
<src> can be Python source file (e.g.
testscript.py) or path to package
To run scripts with arguments use double quotes
vprof -c cmh "testscript.py --foo --bar"
Modes can be combined
vprof -c cm testscript.py
vprof can also profile functions. In order to do this,
vprof in remote mode:
vprof will open new tab in default web browser and then wait for stats.
To profile a function run
from vprof import runner def foo(arg1, arg2): ... runner.run(foo, 'cmhp', args=(arg1, arg2), host='localhost', port=8000)
cmhp is profiling mode,
port are hostname and port of
vprof server launched in remote mode. Obtained stats will be rendered in new
tab of default web browser, opened by
vprof -r command.
vprof can save profile stats to file and render visualizations from
previously saved file.
vprof -c cmh src.py --output-file profile.json
writes profile to file and
vprof --input-file profile.json
renders visualizations from previously saved file.
vprof -h for full list of supported parameters.
To show UI help, press
h when visualizations are displayed.
Also you can check
examples directory for more profiling examples.
*Note that all licence references and agreements mentioned in the vprof README section above are relevant to that project's source code only.