DriViz
Data Visualization Library containing the Dribia Theme.
Documentation: https://dribia.github.io/driviz
Source Code: https://github.com/dribia/driviz
Key features
- Consistent look: A theme for Altair and Matplotlib that provides a consistent look across projects.
- Easy to use: Just import the theme and enable it.
- Customizable: The theme can be customized to fit particular needs.
Installation
driviz is available on PyPI, so you can install it with pip:
Example
import altair as alt
import pandas as pd
from driviz import theme
theme.enable()
source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})
base = alt.Chart(source).encode(
alt.Theta("values:Q").stack(True), # noqa: PD013
alt.Radius("values").scale(type="sqrt", zero=True, rangeMin=20),
color="values:N",
)
c1 = base.mark_arc(innerRadius=20, stroke="#fff")
c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")
chart = c1 + c2
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.20.1.json",
"config": {
"axis": {
"domain": false,
"grid": true,
"gridColor": "#dfe6ea",
"gridOpacity": 1,
"labelColor": "#00004e",
"labelFont": "Roboto",
"labelFontSize": 18,
"labelFontWeight": "normal",
"tickColor": "#00004e",
"titleColor": "#00004e",
"titleFont": "Roboto",
"titleFontSize": 16.2,
"titleFontWeight": "normal"
},
"background": "white",
"circle": {
"fill": "#0043af",
"stroke": null
},
"header": {
"labelColor": "#00004e",
"labelFont": "Roboto",
"labelFontSize": 18,
"labelFontWeight": "normal",
"titleColor": "#00004e",
"titleFont": "Roboto",
"titleFontSize": 18,
"titleFontWeight": "normal"
},
"legend": {
"labelColor": "#00004e",
"labelFont": "Roboto",
"labelFontSize": 16.2,
"labelFontWeight": "normal",
"titleColor": "#00004e",
"titleFont": "Roboto",
"titleFontSize": 18,
"titleFontWeight": "normal"
},
"line": {
"stroke": "#0043af"
},
"point": {
"fill": "#73baff",
"stroke": "#0043af"
},
"range": {
"category": [
"#00004e",
"#0043af",
"#3d97f2",
"#73baff",
"#a8c2aa",
"#cbc771",
"#ffcd1b",
"#ff9424",
"#ff6d2a",
"#f33",
"#a0213e",
"#601445"
],
"diverging": [
"#0043af",
"#fff",
"#f33"
],
"heatmap": [
"#00004e",
"#0043af",
"#3d97f2",
"#73baff",
"#a8c2aa",
"#cbc771",
"#ffcd1b",
"#ff9424",
"#ff6d2a",
"#f33"
],
"ramp": [
"#00004e",
"#0043af",
"#3d97f2",
"#73baff",
"#a8c2aa",
"#cbc771",
"#ffcd1b",
"#ff9424",
"#ff6d2a",
"#f33",
"#a0213e",
"#601445"
]
},
"rect": {
"fill": "#0043af"
},
"title": {
"anchor": "start",
"color": "#00004e",
"font": "Roboto",
"fontSize": 27.0,
"fontWeight": "normal",
"offset": 15,
"subtitleFont": "Roboto",
"subtitleFontSize": 16.2,
"subtitleFontStyle": "normal",
"subtitleFontWeight": "normal"
},
"view": {
"continuousHeight": 400,
"continuousWidth": 711
}
},
"data": {
"name": "data-9c366822ded4e1935da444f2425f2c26"
},
"datasets": {
"data-9c366822ded4e1935da444f2425f2c26": [
{
"values": 12
},
{
"values": 23
},
{
"values": 47
},
{
"values": 6
},
{
"values": 52
},
{
"values": 19
}
]
},
"layer": [
{
"encoding": {
"color": {
"field": "values",
"type": "nominal"
},
"radius": {
"field": "values",
"scale": {
"rangeMin": 20,
"type": "sqrt",
"zero": true
},
"type": "quantitative"
},
"theta": {
"field": "values",
"stack": true,
"type": "quantitative"
}
},
"mark": {
"innerRadius": 20,
"stroke": "#fff",
"type": "arc"
}
},
{
"encoding": {
"color": {
"field": "values",
"type": "nominal"
},
"radius": {
"field": "values",
"scale": {
"rangeMin": 20,
"type": "sqrt",
"zero": true
},
"type": "quantitative"
},
"text": {
"field": "values",
"type": "quantitative"
},
"theta": {
"field": "values",
"stack": true,
"type": "quantitative"
}
},
"mark": {
"radiusOffset": 10,
"type": "text"
}
}
]
}
