https://github.com/rayluo/charts.css.py

`charts.css.py` brings `charts.css` to Python. Online documentation and samples is available at the link below.
https://github.com/rayluo/charts.css.py

chart charts css data-visualization python visualization

Last synced: about 2 months ago
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`charts.css.py` brings `charts.css` to Python. Online documentation and samples is available at the link below.

• Host: GitHub
• URL: https://github.com/rayluo/charts.css.py
• Owner: rayluo
• License: mit
• Created: 2021-05-03T04:27:37.000Z (over 3 years ago)
• Default Branch: dev
• Last Pushed: 2021-07-10T14:56:55.000Z (about 3 years ago)
• Last Synced: 2024-05-27T11:49:10.817Z (4 months ago)
• Topics: chart, charts, css, data-visualization, python, visualization
• Language: Python
• Homepage: https://rayluo.github.io/charts.css.py/
• Size: 89.8 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# charts.css.py

`charts.css.py` provides a python API to convert your 2-dimension data lists into html snippet,

which will be rendered into charts by CSS, when serving inside a browser.

* The output of `charts.css.py` is *not* images. Consequently,

  `charts.css.py` is a pure Python package without any image library dependency.

  You can use `charts.css.py` on any platform.

* The output of charts.css.py is a normal HTML table.

  Search engines and screen readers will be able to consume your data

  even when CSS rendering is unavailable.

* Once the html snippet is delivered into the browser window,

  the rendering is done by CSS, which is typically

  [faster than JS-heavy chart libraries](https://chartscss.org/docs/#alternatives).

* Since the output is normal HTML, you could customize its size and position,

  by defining your own CSS styles.

## Installation

`pip install charts.css.py`

## Usage

Just combine the output of `charts.css.py` functions and the predefined CSS style

``

into your html page.

For example, the following code snippet can convert a 2-dimension list into column chart:

```python

from charts.css import column

STYLESHEET = ''

chart = column(

    [

        ["Continent", "1st year", "2nd year", "3rd year", "4th year", "5th year"],

        ["Asia", 20.0, 30.0, 40.0, 50.0, 75.0],

        ["Euro", 40.0, 60.0, 75.0, 90.0, 100.0],

    ],

    headers_in_first_row=True,

    headers_in_first_column=True,

    )

# Now, variable chart contains html snippet of "...", and

# STYLESHEET is just a constant string of "".

# You can somehow insert them into the proper places of your full html page.

# Here in this sample, we take a shortcut by simply concatenating them.

open("output.html", "w").write(STYLESHEET + chart)

```

The output.html will be rendered in browser like this:

![Sample output](https://raw.githubusercontent.com/rayluo/charts.css.py/dev/sample/sample_chart.png)

## Advanced Usage

There are currently 4 different charts implemented: `bar`, `column`, `line`, `area`.

All those methods support many parameters to further customize the chart appearance.

`bar()` and `column()` also support stacking by value or stacking by percentage.

All those features are demonstrated in the

[different samples in this document](https://rayluo.github.io/charts.css.py/).

Lastly, this package also provides a command-line tool `csv2chart`.

You can use it to convert csv file into an html file.

For example, `csv2chart sample.csv output.html`.

You can also run `csv2chart -h` to know all the parameters it supports.

## Versioning

`charts.css.py` uses [Semantic Versioning](https://semver.org/).