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79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
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import pandas as pd
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from random import randint, random
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import gradio as gr
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temp_sensor_data = pd.DataFrame(
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{
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"time": pd.date_range("2021-01-01", end="2021-01-05", periods=200),
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"temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
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"humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
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"location": ["indoor", "outdoor"] * 100,
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}
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)
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food_rating_data = pd.DataFrame(
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{
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"cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)],
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"rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)],
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"price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
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"wait": [random() for i in range(100)],
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}
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)
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with gr.Blocks() as scatter_plots:
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with gr.Row():
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start = gr.DateTime("2021-01-01 00:00:00", label="Start")
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end = gr.DateTime("2021-01-05 00:00:00", label="End")
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apply_btn = gr.Button("Apply", scale=0)
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with gr.Row():
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group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
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aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
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temp_by_time = gr.ScatterPlot(
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temp_sensor_data,
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x="time",
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y="temperature",
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)
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temp_by_time_location = gr.ScatterPlot(
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temp_sensor_data,
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x="time",
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y="temperature",
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color="location",
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)
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time_graphs = [temp_by_time, temp_by_time_location]
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group_by.change(
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lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs),
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group_by,
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time_graphs
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)
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aggregate.change(
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lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),
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aggregate,
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time_graphs
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)
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price_by_cuisine = gr.ScatterPlot(
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food_rating_data,
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x="cuisine",
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y="price",
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)
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with gr.Row():
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price_by_rating = gr.ScatterPlot(
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food_rating_data,
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x="rating",
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y="price",
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color="wait",
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show_actions_button=True,
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)
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price_by_rating_color = gr.ScatterPlot(
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food_rating_data,
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x="rating",
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y="price",
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color="cuisine",
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)
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if __name__ == "__main__":
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scatter_plots.launch()
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