| Name | pynimate JSON |
| Version |
1.2.0
JSON |
| download |
| home_page | |
| Summary | Python package for statistical data animations |
| upload_time | 2023-04-15 21:11:04 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License (MIT) |
| keywords |
animations
framework
data
plots
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Pynimate
[](https://pypi.org/project/pynimate/)
[](https://pepy.tech/project/pynimate)
[](https://github.com/julkaar9/pynimate/blob/main/LICENSE)
[](https://github.com/psf/black)
Python package for statistical data animations.
## Installation
### with pip
Pynimate is avaialbe at [pypi](https://pypi.org/project/pynimate/)
``` sh
pip install pynimate
```
## How to use
Pynimate expects pandas dataframe formatted in this manner:
Where the time column is set to index.
```python
time, col1, col2, col3
2012 1 2 1
2013 1 1 2
2014 2 1.5 3
2015 2.5 2 3.5
```
## Bar Chart Example
```python
import pandas as pd
from matplotlib import pyplot as plt
import pynimate as nim
df = pd.DataFrame(
{
"time": ["1960-01-01", "1961-01-01", "1962-01-01"],
"Afghanistan": [1, 2, 3],
"Angola": [2, 3, 4],
"Albania": [1, 2, 5],
"USA": [5, 3, 4],
"Argentina": [1, 4, 5],
}
).set_index("time")
cnv = nim.Canvas()
bar = nim.Barhplot.from_df(df, "%Y-%m-%d", "2d")
bar.set_time(callback=lambda i, datafier: datafier.data.index[i].year)
cnv.add_plot(bar)
cnv.animate()
plt.show()
```
## Documentation
The official documentation : https://julkaar9.github.io/pynimate/
## License
[MIT License (MIT)](LICENSE)
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