https://github.com/avrabyt/streamlit-ipyvizzu
Create animated charts easily! A ipyvizzu wrapper for intuitive usage of ipyvizzu functions and Streamlit embed support.
https://github.com/avrabyt/streamlit-ipyvizzu
animation chart ipyvizzu plots-in-python python streamlit
Last synced: 5 months ago
JSON representation
Create animated charts easily! A ipyvizzu wrapper for intuitive usage of ipyvizzu functions and Streamlit embed support.
- Host: GitHub
- URL: https://github.com/avrabyt/streamlit-ipyvizzu
- Owner: avrabyt
- License: mit
- Created: 2022-10-04T00:39:21.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-03T09:18:39.000Z (almost 2 years ago)
- Last Synced: 2024-10-11T04:42:04.803Z (9 months ago)
- Topics: animation, chart, ipyvizzu, plots-in-python, python, streamlit
- Language: Python
- Homepage: https://hellostvizzu.streamlitapp.com
- Size: 29.5 MB
- Stars: 59
- Watchers: 2
- Forks: 12
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Streamlit-ipyvizzu (st-vizzu)
**Create animated charts easily! A [ipyvizzu](https://github.com/vizzuhq/ipyvizzu) wrapper for intuitive usage of ipyvizzu functions and Streamlit embed support.**
[](https://pypi.org/project/st-vizzu/)
[](https://pepy.tech/project/st-vizzu)
[](https://pepy.tech/project/st-vizzu)
[](https://hellostvizzu.streamlit.app/)
## Installation
```console
pip install st-vizzu
```
### ⭐️ Support me to keep this development going ☕️
[](https://www.buymeacoffee.com/AvraCodes)
## Workflow simplification
1. **Create** ipyvizzu object using,
`create_vizzu_obj()`
or define preset ipyvizzu charts for example,bar charts using, `bar_chart()`
2. **Animate** the charts using, _generalized_ function, `vizzu_animate()` or _argument specific_ function, `beta_vizzu_animate()`
3. **Embed** the charts within Streamlit front-end using, `vizzu_plot()`
### Quickstart
```python
from st_vizzu import *
import pandas as pd
import streamlit as st
# Load Data
df = pd.read_csv("Data/music_data.csv", index_col=0)
# Create ipyvizzu Object with the DataFrame
obj = create_vizzu_obj(df)
# Preset plot usage. Preset plots works directly with DataFrames.
bar_obj = bar_chart(df,
x = "Kinds",
y = "Popularity",
title= "1.Using preset plot function `bar_chart()`"
)
# Animate with defined arguments
anim_obj = beta_vizzu_animate( bar_obj,
x = "Genres",
y = ["Popularity", "Kinds"],
title = "Animate with beta_vizzu_animate () function",
label= "Popularity",
color="Genres",
legend="color",
sort="byValue",
reverse=True,
align="center",
split=False,
)
# Animate with general dict based arguments
_dict = {"size": {"set": "Popularity"},
"geometry": "circle",
"coordSystem": "polar",
"title": "Animate with vizzu_animate () function",
}
anim_obj2 = vizzu_animate(anim_obj,_dict)
# Visualize within Streamlit
with st.container(): # Maintaining the aspect ratio
st.button("Animate")
vizzu_plot(anim_obj2)
```
## Example
[](https://hellostvizzu.streamlitapp.com/)
### Animated charts ✨
### Story-presentation ⌛️
### ipyvizzu-notebook📒
## Video Tutorials
#### [Part 1](https://youtu.be/jUh923Z4fuk)
[](https://youtu.be/jUh923Z4fuk)
## Resources
- [streamlit-extras](https://github.com/arnaudmiribel/streamlit-extras)
- [ipyvizzu](https://github.com/vizzuhq/ipyvizzu)
- [Streamlit-Pages](https://github.com/blackary/st_pages)
- [Streamlit](https://discuss.streamlit.io/)