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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[%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#creating-a-plot-with-a-pd-dataframe)
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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()
Textbox
[gradio/plot\_guide\_line](https://huggingface.co/spaces/gradio/plot_guide_line) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Textbox
[gradio/plot\_guide\_scatter](https://huggingface.co/spaces/gradio/plot_guide_scatter) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Loading...
[gradio/plot\_guide\_scatter\_nominal](https://huggingface.co/spaces/gradio/plot_guide_scatter_nominal) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
Breaking out Series by Color[%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#breaking-out-series-by-color)
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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()
Loading...
[gradio/plot\_guide\_series\_nominal](https://huggingface.co/spaces/gradio/plot_guide_series_nominal) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Loading...
[gradio/plot\_guide\_series\_quantitative](https://huggingface.co/spaces/gradio/plot_guide_series_quantitative) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
Aggregating Values[%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#aggregating-values)
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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()
Loading...
[gradio/plot\_guide\_aggregate\_quantitative](https://huggingface.co/spaces/gradio/plot_guide_aggregate_quantitative) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Loading...
[gradio/plot\_guide\_aggregate\_nominal](https://huggingface.co/spaces/gradio/plot_guide_aggregate_nominal) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
Selecting Regions[%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#selecting-regions)
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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()
Loading...
[gradio/plot\_guide\_selection](https://huggingface.co/spaces/gradio/plot_guide_selection) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Loading...
[gradio/plot\_guide\_zoom](https://huggingface.co/spaces/gradio/plot_guide_zoom) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
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()
Loading...
[gradio/plot\_guide\_zoom\_sync](https://huggingface.co/spaces/gradio/plot_guide_zoom_sync) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
Making an Interactive Dashboard[%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)](#making-an-interactive-dashboard)
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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()
Loading...
[gradio/plot\_guide\_interactive](https://huggingface.co/spaces/gradio/plot_guide_interactive) built with [Gradio](https://gradio.app). Hosted on [ Spaces](https://huggingface.co/spaces)
It's that simple to filter and control the data presented in your visualization! |