معرفی شرکت ها
chartify-4.0.3
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Python library to make plotting simpler for data scientists
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | chartify-4.0.3 |
| نام | chartify |
| نسخه کتابخانه | 4.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | chalpert@spotify.com |
| آدرس صفحه اصلی | https://github.com/spotify/chartify |
| آدرس اینترنتی | https://pypi.org/project/chartify/ |
| مجوز | Apache 2 |
Chartify
========
|status| |release| |python| |CI|
.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg
.. |release| image:: https://img.shields.io/badge/Release-4.0.3-blue.svg
.. |python| image:: https://img.shields.io/badge/Python-3.7-blue.svg
.. |CI| image:: https://github.com/spotify/chartify/workflows/Tox/badge.svg
:target: https://github.com/spotify/chartify/actions
Chartify is a Python library that makes it easy for data scientists to create charts.
Why use Chartify?
-----------------
- Consistent input data format: Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format.
- Smart default styles: Create pretty charts with very little customization required.
- Simple API: We've attempted to make the API as intuitive and easy to learn as possible.
- Flexibility: Chartify is built on top of `Bokeh <http://bokeh.pydata.org/en/latest/>`_, so if you do need more control you can always fall back on Bokeh's API.
Examples
--------
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify1.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify2.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify3.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify4.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify5.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify6.png
`See this notebook for more examples! </examples/Examples.ipynb>`_.
Installation
------------
1. Chartify can be installed via pip:
``pip3 install chartify``
2. Install chromedriver requirement (Optional. Needed for PNG output):
- Install google chrome.
- Download the appropriate version of chromedriver for your OS `here <https://sites.google.com/chromium.org/driver/>`_.
- Copy the executable file to a directory within your PATH.
- View directorys in your PATH variable: ``echo $PATH``
- Copy chromedriver to the appropriate directory, e.g.: ``cp chromedriver /usr/local/bin``
Getting started
---------------
This `tutorial notebook <https://github.com/spotify/chartify/blob/master/examples/Chartify%20Tutorial.ipynb>`_ is the best place to get started with a guided tour of the core concepts of Chartify.
From there, check out the `example notebook <https://github.com/spotify/chartify/blob/master/examples/Examples.ipynb>`_ for a list of all the available plots.
Docs
---------------
Documentation available on `chartify.readthedocs.io <https://chartify.readthedocs.io/en/latest/>`_
Getting support
---------------
Join #chartify on spotify-foss.slack.com (`Get an invite <https://slackin.spotify.com/>`_)
Use the `chartify tag on StackOverflow <https://stackoverflow.com/questions/tagged/chartify>`_.
Resources
---------------
- Data Visualization with `Chartify <https://www.section.io/engineering-education/data-viz-chartify/>`_
Code of Conduct
---------------
This project adheres to the `Open Code of Conduct <https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md>`_. By participating, you are expected to honor this code.
Contributing
------------
`See the contributing docs <CONTRIBUTING.rst>`_.
=======
History
=======
4.0.3 (2023-04-21)
------------------
* Require jupyter_bokeh to enable html output
4.0.2 (2023-03-30)
------------------
* Fix categorical_order_by check for scatter plot
* Fix categorical_order_by check for _construct_source
* Refactor category sorting in _construct_source
* Add tests for categorical_order_by
* Fix scatter plot tests that used line plots
4.0.1 (2023-03-24)
------------------
* Updated version requirement of pillow to avoid bug
4.0.0 (2023-03-23)
------------------
* Dropped support for python 3.6 and 3.7
3.1.0 (2023-03-22)
------------------
* Added Boxplot Chart including example in examples notebook
3.0.5 (2022-12-13)
------------------
* Fixed a few errors in example and tutorial notebooks
* Fixed a typo in requirements.txt
3.0.4 (2022-10-18)
------------------
* Updated package requirements
* Got rid of future deprecation warnings
* Bugfix related to legend for graphs with multiple groups and colors
3.0.2 (2020-10-21)
------------------
* Support pyyaml 5.2+
3.0.1 (2020-06-02)
------------------
* Reduce dependencies by switching from Jupyter to IPython.
3.0.0 (2020-05-29)
------------------
* Updated Python to 3.6+ and Pandas to 1.0+ (Thanks @tomasaschan!)
* Updated Bokeh to 2.0+
* Removed colour dependency to fix setup errors.
2.7.0 (2019-11-27)
------------------
Bugfixes:
* Updated default yaml loader to move off of
deprecated method (Thanks @vh920!)
* Updated legend handling to adjust for deprecated methods
in recent versions of Bokeh (Thanks for reporting @jpkoc)
* Updated license in setup.py (Thanks for reporting @jsignell)
* Bump base Pillow dependency to avoid insecure version.
* Update MANIFEST to include missing files (Thanks @toddrme2178!)
2.6.1 (2019-08-15)
------------------
Bugfixes:
* Moved package requirements and fixed bug that occured with
latest version of Bokeh (Thanks @emschuch & @mollymzhu!)
* Fixed bug in README while generating docs (Thanks @Bharat123rox!)
2.6.0 (2019-03-08)
------------------
Improvements:
* Allows users to plot colors on bar charts that aren't contained in the
categorical axis.
Bugfixes:
* Fixed bug that caused float types to break when plotted with categorical
text plots (Thanks for finding @danela!)
* Fixed broken readme links.
2.5.0 (2019-02-17)
------------------
Improvements:
* Added Radar Chart
2.4.0 (2019-02-16)
------------------
Improvements:
* Added second Y axis plotting.
* Removed Bokeh loading notification on import (Thanks @canavandl!)
* Added support for custom Bokeh resource loading (Thanks @canavandl!)
* Added example for Chart.save() method (Thanks @david30907d!)
Bugfixes:
* Updated documentation for saving and showing svgs.
* Fixed bug that broke plots with no difference between min and max
points. (Thanks for finding @fabioconcina!)
2.3.5 (2018-11-21)
------------------
Improvements:
* Updated docstrings (Thanks @gregorybchris @ItsPugle!)
* Added SVG output options to Chart.show() and Chart.save()
(Thanks for the suggestion @jdmendoza!)
Bugfixes:
* Fixed bug that caused source label to overlap with xaxis labels.
* Fixed bug that prevented x axis orientation changes
with datetime axes (Thanks for finding @simonwongwong!)
* Fixed bug that caused subtitle to disappear
with `outside_top` legend location (Thanks for finding @simonwongwong!)
* Line segment callout properties will work
correctly. (Thanks @gregorybchris!)
2.3.4 (2018-11-13)
------------------
* Updated Bokeh version requirements to support 1.0
2.3.3 (2018-10-24)
------------------
* Removed upper bound of Pillow dependency.
2.3.2 (2018-10-18)
------------------
* Stacked bar and area order now matches default vertical legend order.
* Added method for shifting color palettes.
* Added scatter plots with a single categorical axis.
* Fixed bug with text_stacked that occurred with multiple categorical levels.
2.3.1 (2018-09-27)
------------------
* Fix scatter plot bug that can occur due to nested data types.
2.3.0 (2018-09-26)
------------------
* Added hexbin plot type.
* More control over grouped axis label orientation.
* Added alpha control to scatter, line, and parallel plots.
* Added control over marker style to scatter plot.
* Added ability to create custom color palettes.
* Changed default accent color.
* Visual tweaks to lollipop plot.
* Bar plots with a few number of series will have better widths.
2.2.0 (2018-09-17)
------------------
* First release on PyPI.
نیازمندی
| مقدار | نام |
|---|---|
| <2.0.0,>=1.2.0 | pandas |
| >=9.1.0 | Pillow |
| >=4.0.0 | selenium |
| >=3.0.0 | bokeh |
| <2.0.0,>=1.6.0 | scipy |
| >=6.0 | ipykernel |
| >=7.17.0 | ipython |
| >=6.0.0 | pyyaml |
| >=3.1.0 | Jinja2 |
| >=3.0.7 | jupyter-bokeh |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<4 | Python |
نحوه نصب
نصب پکیج whl chartify-4.0.3:
pip install chartify-4.0.3.whl
نصب پکیج tar.gz chartify-4.0.3:
pip install chartify-4.0.3.tar.gz