demos/native_plots/scatter_plot_demo.py

import gradio as gr
import numpy as np
from data import temp_sensor_data, food_rating_data

with gr.Blocks() as scatter_plots:
 with gr.Row():
 start = gr.DateTime("2021-01-01 00:00:00", label="Start")
 end = gr.DateTime("2021-01-05 00:00:00", label="End")
 apply_btn = gr.Button("Apply", scale=0)
 with gr.Row():
 group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
 aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")

 temp_by_time = gr.ScatterPlot(
 temp_sensor_data,
 x="time",
 y="temperature",
 )
 temp_by_time_location = gr.ScatterPlot(
 temp_sensor_data,
 x="time",
 y="temperature",
 color="location",
 )

 time_graphs = [temp_by_time, temp_by_time_location]
 group_by.change(
 lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs), 
 group_by, 
 time_graphs
 )
 aggregate.change(
 lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), 
 aggregate, 
 time_graphs
 )


 # def rescale(select: gr.SelectData):
 # return select.index
 # rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])

 # for trigger in [apply_btn.click, rescale_evt.then]:
 # trigger(
 # lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
 # )

 price_by_cuisine = gr.ScatterPlot(
 food_rating_data,
 x="cuisine",
 y="price",
 )
 with gr.Row():
 price_by_rating = gr.ScatterPlot(
 food_rating_data,
 x="rating",
 y="price",
 color="wait",
 show_actions_button=True,
 )
 price_by_rating_color = gr.ScatterPlot(
 food_rating_data,
 x="rating",
 y="price",
 color="cuisine",
 # color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
 )


if __name__ == "__main__":
 scatter_plots.launch()