Hướng dẫn proportional area chart python

This example shows how to fill the area enclosed by traces.

Filled area plot with plotly.express¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

px.area creates a stacked area plot. Each filled area corresponds to one value of the column given by the line_group parameter.

In [1]:

import plotly.express as px
df = px.data.gapminder()
fig = px.area(df, x="year", y="pop", color="continent", line_group="country")
fig.show()

Filled area plot in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

In [3]:

import plotly.express as px
df = px.data.medals_long()

fig = px.area(df, x="medal", y="count", color="nation",
             pattern_shape="nation", pattern_shape_sequence=[".", "x", "+"])
fig.show()

Filled area chart with plotly.graph_objects¶

Basic Overlaid Area Chart¶

In [4]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy')) # fill down to xaxis
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty')) # fill to trace0 y

fig.show()

Overlaid Area Chart Without Boundary Lines¶

In [5]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[0, 2, 3, 5], fill='tozeroy',
                    mode='none' # override default markers+lines
                    ))
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 5, 1, 7], fill='tonexty',
                    mode= 'none'))

fig.show()

Interior Filling for Area Chart¶

In [6]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[3, 4, 8, 3],
    fill=None,
    mode='lines',
    line_color='indigo',
    ))
fig.add_trace(go.Scatter(
    x=[1, 2, 3, 4],
    y=[1, 6, 2, 6],
    fill='tonexty', # fill area between trace0 and trace1
    mode='lines', line_color='indigo'))

fig.show()

Stacked Area Chart¶

The stackgroup parameter is used to add the y values of the different traces in the same group. Traces in the same group fill up to the next trace of the group.

In [7]:

import plotly.graph_objects as go

x=['Winter', 'Spring', 'Summer', 'Fall']

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=x, y=[40, 60, 40, 10],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(131, 90, 241)'),
    stackgroup='one' # define stack group
))
fig.add_trace(go.Scatter(
    x=x, y=[20, 10, 10, 60],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(111, 231, 219)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[40, 30, 50, 30],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(184, 247, 212)'),
    stackgroup='one'
))

fig.update_layout(yaxis_range=(0, 100))
fig.show()

Stacked Area Chart with Normalized Values¶

In [8]:

import plotly.graph_objects as go

x=['Winter', 'Spring', 'Summer', 'Fall']
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=x, y=[40, 20, 30, 40],
    mode='lines',
    line=dict(width=0.5, color='rgb(184, 247, 212)'),
    stackgroup='one',
    groupnorm='percent' # sets the normalization for the sum of the stackgroup
))
fig.add_trace(go.Scatter(
    x=x, y=[50, 70, 40, 60],
    mode='lines',
    line=dict(width=0.5, color='rgb(111, 231, 219)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[70, 80, 60, 70],
    mode='lines',
    line=dict(width=0.5, color='rgb(127, 166, 238)'),
    stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[100, 100, 100, 100],
    mode='lines',
    line=dict(width=0.5, color='rgb(131, 90, 241)'),
    stackgroup='one'
))

fig.update_layout(
    showlegend=True,
    xaxis_type='category',
    yaxis=dict(
        type='linear',
        range=[1, 100],
        ticksuffix='%'))

fig.show()

Select Hover Points¶

In [9]:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[0,0.5,1,1.5,2], y=[0,1,2,1,0],
                    fill='toself', fillcolor='darkviolet',
                    hoveron = 'points+fills', # select where hover is active
                    line_color='darkviolet',
                    text="Points + Fills",
                    hoverinfo = 'text+x+y'))

fig.add_trace(go.Scatter(x=[3,3.5,4,4.5,5], y=[0,1,2,1,0],
                    fill='toself', fillcolor = 'violet',
                    hoveron='points',
                    line_color='violet',
                    text="Points only",
                    hoverinfo='text+x+y'))

fig.update_layout(
    title = "hover on points or fill",
    xaxis_range = [0,5.2],
    yaxis_range = [0,3]
)

fig.show()

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter