Creating Plots

Gradio is a great way to create extremely customizable dashboards. Gradio comes with three native Plot components: gr.LinePlot, gr.ScatterPlot and gr.BarPlot. All these plots have the same API. Let's take a look how to set them up.

Creating a Plot with a pd.Dataframe

Plots accept a pandas Dataframe as their value. The plot also takes x and y which represent the names of the columns that represent the x and y axes respectively. Here's a simple example:

import gradio as gr
import pandas as pd
import numpy as np
import random

df = pd.DataFrame({
    'height': np.random.randint(50, 70, 25),
    'weight': np.random.randint(120, 320, 25),
    'age': np.random.randint(18, 65, 25),
    'ethnicity': [random.choice(["white", "black", "asian"]) for _ in range(25)]
})

with gr.Blocks() as demo:
    gr.LinePlot(df, x="weight", y="height")

demo.launch()

All plots have the same API, so you could swap this out with a gr.ScatterPlot:

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.ScatterPlot(df, x="weight", y="height")

demo.launch()

The y axis column in the dataframe should have a numeric type, but the x axis column can be anything from strings, numbers, categories, or datetimes.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.ScatterPlot(df, x="ethnicity", y="height")

demo.launch()

Breaking out Series by Color

You can break out your plot into series using the color argument.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.ScatterPlot(df, x="weight", y="height", color="ethnicity")

demo.launch()

If you wish to assign series specific colors, use the color_map arg, e.g. gr.ScatterPlot(..., color_map={'white': '#FF9988', 'asian': '#88EEAA', 'black': '#333388'})

The color column can be numeric type as well.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.ScatterPlot(df, x="weight", y="height", color="age")

demo.launch()

Aggregating Values

You can aggregate values into groups using the x_bin and y_aggregate arguments. If your x-axis is numeric, providing an x_bin will create a histogram-style binning:

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.BarPlot(df, x="weight", y="height", x_bin=10, y_aggregate="sum")

demo.launch()

If your x-axis is a string type instead, they will act as the category bins automatically:

import gradio as gr
from data import df

with gr.Blocks() as demo:
    gr.BarPlot(df, x="ethnicity", y="height", y_aggregate="mean")

demo.launch()

Selecting Regions

You can use the .select listener to select regions of a plot. Click and drag on the plot below to select part of the plot.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    plt = gr.LinePlot(df, x="weight", y="height")
    selection_total = gr.Number(label="Total Weight of Selection")

    def select_region(selection: gr.SelectData):
        min_w, max_w = selection.index
        return df[(df["weight"] >= min_w) & (df["weight"] <= max_w)]["weight"].sum()

    plt.select(select_region, None, selection_total)

demo.launch()

You can combine this and the .double_click listener to create some zoom in/out effects by changing x_lim which sets the bounds of the x-axis:

import gradio as gr
from data import df

with gr.Blocks() as demo:
    plt = gr.LinePlot(df, x="weight", y="height")

    def select_region(selection: gr.SelectData):
        min_w, max_w = selection.index
        return gr.LinePlot(x_lim=(min_w, max_w)) 

    plt.select(select_region, None, plt)
    plt.double_click(lambda: gr.LinePlot(x_lim=None), None, plt)

demo.launch()

If you had multiple plots with the same x column, your event listeners could target the x limits of all other plots so that the x-axes stay in sync.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    plt1 = gr.LinePlot(df, x="weight", y="height")
    plt2 = gr.BarPlot(df, x="weight", y="age", x_bin=10)
    plots = [plt1, plt2]

    def select_region(selection: gr.SelectData):
        min_w, max_w = selection.index
        return [gr.LinePlot(x_lim=(min_w, max_w))] * len(plots) 

    for plt in plots:
        plt.select(select_region, None, plots)
        plt.double_click(lambda: [gr.LinePlot(x_lim=None)] * len(plots), None, plots)

demo.launch()

Making an Interactive Dashboard

Take a look how you can have an interactive dashboard where the plots are functions of other Components.

import gradio as gr
from data import df

with gr.Blocks() as demo:
    with gr.Row():
        ethnicity = gr.Dropdown(["all", "white", "black", "asian"], value="all")
        max_age = gr.Slider(18, 65, value=65)

    def filtered_df(ethnic, age):
        _df = df if ethnic == "all" else df[df["ethnicity"] == ethnic]
        _df = _df[_df["age"] < age]
        return _df

    gr.ScatterPlot(filtered_df, inputs=[ethnicity, max_age], x="weight", y="height", title="Weight x Height")
    gr.LinePlot(filtered_df, inputs=[ethnicity, max_age], x="age", y="height", title="Age x Height")

demo.launch()

It's that simple to filter and control the data presented in your visualization!