Line (original) (raw)
In [1]:
import plotly.express as px
df = px.data.gapminder().query("country=='Canada'") fig = px.line(df, x="year", y="lifeExp", title='Life expectancy in Canada') fig.show()
Line Plots with column encoding color¶
In [2]:
import plotly.express as px
df = px.data.gapminder().query("continent=='Oceania'") fig = px.line(df, x="year", y="lifeExp", color='country') fig.show()
Line charts 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.
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Data Order in Line Charts¶
Plotly line charts are implemented as connected scatterplots (see below), meaning that the points are plotted and connected with lines in the order they are provided, with no automatic reordering.
This makes it possible to make charts like the one below, but also means that it may be required to explicitly sort data before passing it to Plotly to avoid lines moving "backwards" across the chart.
In [4]:
import plotly.express as px import pandas as pd
df = pd.DataFrame(dict( x = [1, 3, 2, 4], y = [1, 2, 3, 4] )) fig = px.line(df, x="x", y="y", title="Unsorted Input") fig.show()
df = df.sort_values(by="x") fig = px.line(df, x="x", y="y", title="Sorted Input") fig.show()
Connected Scatterplots¶
In a connected scatterplot, two continuous variables are plotted against each other, with a line connecting them in some meaningful order, usually a time variable. In the plot below, we show the "trajectory" of a pair of countries through a space defined by GDP per Capita and Life Expectancy.
In [5]:
import plotly.express as px
df = px.data.gapminder().query("country in ['Canada', 'Botswana']")
fig = px.line(df, x="lifeExp", y="gdpPercap", color="country", text="year") fig.update_traces(textposition="bottom right") fig.show()
Line charts with markers¶
The markers argument can be set to True to show markers on lines.
In [6]:
import plotly.express as px df = px.data.gapminder().query("continent == 'Oceania'") fig = px.line(df, x='year', y='lifeExp', color='country', markers=True) fig.show()
In [7]:
import plotly.express as px df = px.data.gapminder().query("continent == 'Oceania'") fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country") fig.show()
In [8]:
import plotly.express as px
df = px.data.stocks() fig = px.line(df, x='date', y="GOOG") fig.show()
Sparklines with Plotly Express¶
Sparklines are scatter plots inside subplots, with gridlines, axis lines, and ticks removed.
In [9]:
import plotly.express as px df = px.data.stocks(indexed=True) fig = px.line(df, facet_row="company", facet_row_spacing=0.01, height=200, width=200)
hide and lock down axes
fig.update_xaxes(visible=False, fixedrange=True) fig.update_yaxes(visible=False, fixedrange=True)
remove facet/subplot labels
fig.update_layout(annotations=[], overwrite=True)
strip down the rest of the plot
fig.update_layout( showlegend=False, plot_bgcolor="white", margin=dict(t=10,l=10,b=10,r=10) )
disable the modebar for such a small plot
fig.show(config=dict(displayModeBar=False))
Line Plot with go.Scatter¶
If Plotly Express does not provide a good starting point, it is possible to use the more generic go.Scatter class from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page.
Simple Line Plot¶
In [10]:
import plotly.graph_objects as go import numpy as np
x = np.arange(10)
fig = go.Figure(data=go.Scatter(x=x, y=x**2)) fig.show()
Line Plot Modes¶
In [11]:
import plotly.graph_objects as go
Create random data with numpy
import numpy as np np.random.seed(1)
N = 100 random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N) + 5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N) - 5
Create traces
fig = go.Figure() fig.add_trace(go.Scatter(x=random_x, y=random_y0, mode='lines', name='lines')) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='markers', name='markers'))
fig.show()
Style Line Plots¶
This example styles the color and dash of the traces, adds trace names, modifies line width, and adds plot and axes titles.
In [12]:
import plotly.graph_objects as go
Add data
month = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] high_2000 = [32.5, 37.6, 49.9, 53.0, 69.1, 75.4, 76.5, 76.6, 70.7, 60.6, 45.1, 29.3] low_2000 = [13.8, 22.3, 32.5, 37.2, 49.9, 56.1, 57.7, 58.3, 51.2, 42.8, 31.6, 15.9] high_2007 = [36.5, 26.6, 43.6, 52.3, 71.5, 81.4, 80.5, 82.2, 76.0, 67.3, 46.1, 35.0] low_2007 = [23.6, 14.0, 27.0, 36.8, 47.6, 57.7, 58.9, 61.2, 53.3, 48.5, 31.0, 23.6] high_2014 = [28.8, 28.5, 37.0, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9] low_2014 = [12.7, 14.3, 18.6, 35.5, 49.9, 58.0, 60.0, 58.6, 51.7, 45.2, 32.2, 29.1]
fig = go.Figure()
Create and style traces
fig.add_trace(go.Scatter(x=month, y=high_2014, name='High 2014', line=dict(color='firebrick', width=4))) fig.add_trace(go.Scatter(x=month, y=low_2014, name = 'Low 2014', line=dict(color='royalblue', width=4))) fig.add_trace(go.Scatter(x=month, y=high_2007, name='High 2007', line=dict(color='firebrick', width=4, dash='dash') # dash options include 'dash', 'dot', and 'dashdot' )) fig.add_trace(go.Scatter(x=month, y=low_2007, name='Low 2007', line = dict(color='royalblue', width=4, dash='dash'))) fig.add_trace(go.Scatter(x=month, y=high_2000, name='High 2000', line = dict(color='firebrick', width=4, dash='dot'))) fig.add_trace(go.Scatter(x=month, y=low_2000, name='Low 2000', line=dict(color='royalblue', width=4, dash='dot')))
Edit the layout
fig.update_layout( title=dict( text='Average High and Low Temperatures in New York' ), xaxis=dict( title=dict( text='Month' ) ), yaxis=dict( title=dict( text='Temperature (degrees F)' ) ), )
fig.show()
Connect Data Gaps¶
connectgaps determines if missing values in the provided data are shown as a gap in the graph or not. In this tutorial, we showed how to take benefit of this feature and illustrate multiple areas on a tile map.
In [13]:
import plotly.graph_objects as go
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
fig = go.Figure()
fig.add_trace(go.Scatter( x=x, y=[10, 20, None, 15, 10, 5, 15, None, 20, 10, 10, 15, 25, 20, 10], name = 'No Gaps', # Style name/legend entry with html tags connectgaps=True # override default to connect the gaps )) fig.add_trace(go.Scatter( x=x, y=[5, 15, None, 10, 5, 0, 10, None, 15, 5, 5, 10, 20, 15, 5], name='Gaps', ))
fig.show()
Interpolation with Line Plots¶
In [14]:
import plotly.graph_objects as go import numpy as np
x = np.array([1, 2, 3, 4, 5]) y = np.array([1, 3, 2, 3, 1])
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name="linear",
line_shape='linear'))
fig.add_trace(go.Scatter(x=x, y=y + 5, name="spline",
text=["tweak line smoothness
with 'smoothing' in line object"],
hoverinfo='text+name',
line_shape='spline'))
fig.add_trace(go.Scatter(x=x, y=y + 10, name="vhv",
line_shape='vhv'))
fig.add_trace(go.Scatter(x=x, y=y + 15, name="hvh",
line_shape='hvh'))
fig.add_trace(go.Scatter(x=x, y=y + 20, name="vh",
line_shape='vh'))
fig.add_trace(go.Scatter(x=x, y=y + 25, name="hv",
line_shape='hv'))
fig.update_traces(hoverinfo='text+name', mode='lines+markers') fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=16))
fig.show()
Label Lines with Annotations¶
In [15]:
import plotly.graph_objects as go import numpy as np
title = 'Main Source for News' labels = ['Television', 'Newspaper', 'Internet', 'Radio'] colors = ['rgb(67,67,67)', 'rgb(115,115,115)', 'rgb(49,130,189)', 'rgb(189,189,189)']
mode_size = [8, 8, 12, 8] line_size = [2, 2, 4, 2]
x_data = np.vstack((np.arange(2001, 2014),)*4)
y_data = np.array([ [74, 82, 80, 74, 73, 72, 74, 70, 70, 66, 66, 69], [45, 42, 50, 46, 36, 36, 34, 35, 32, 31, 31, 28], [13, 14, 20, 24, 20, 24, 24, 40, 35, 41, 43, 50], [18, 21, 18, 21, 16, 14, 13, 18, 17, 16, 19, 23], ])
fig = go.Figure()
for i in range(0, 4): fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', name=labels[i], line=dict(color=colors[i], width=line_size[i]), connectgaps=True, ))
# endpoints
fig.add_trace(go.Scatter(
x=[x_data[i][0], x_data[i][-1]],
y=[y_data[i][0], y_data[i][-1]],
mode='markers',
marker=dict(color=colors[i], size=mode_size[i])
))fig.update_layout( xaxis=dict( showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict( family='Arial', size=12, color='rgb(82, 82, 82)', ), ), yaxis=dict( showgrid=False, zeroline=False, showline=False, showticklabels=False, ), autosize=False, margin=dict( autoexpand=False, l=100, r=20, t=110, ), showlegend=False, plot_bgcolor='white' )
annotations = []
Adding labels
for y_trace, label, color in zip(y_data, labels, colors): # labeling the left_side of the plot annotations.append(dict(xref='paper', x=0.05, y=y_trace[0], xanchor='right', yanchor='middle', text=label + ' {}%'.format(y_trace[0]), font=dict(family='Arial', size=16), showarrow=False)) # labeling the right_side of the plot annotations.append(dict(xref='paper', x=0.95, y=y_trace[11], xanchor='left', yanchor='middle', text='{}%'.format(y_trace[11]), font=dict(family='Arial', size=16), showarrow=False))
Title
annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05, xanchor='left', yanchor='bottom', text='Main Source for News', font=dict(family='Arial', size=30, color='rgb(37,37,37)'), showarrow=False))
Source
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1, xanchor='center', yanchor='top', text='Source: PewResearch Center & ' + 'Storytelling with data', font=dict(family='Arial', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
fig.show()
Filled Lines¶
In [16]:
import plotly.graph_objects as go import numpy as np
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] x_rev = x[::-1]
Line 1
y1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] y1_upper = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11] y1_lower = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] y1_lower = y1_lower[::-1]
Line 2
y2 = [5, 2.5, 5, 7.5, 5, 2.5, 7.5, 4.5, 5.5, 5] y2_upper = [5.5, 3, 5.5, 8, 6, 3, 8, 5, 6, 5.5] y2_lower = [4.5, 2, 4.4, 7, 4, 2, 7, 4, 5, 4.75] y2_lower = y2_lower[::-1]
Line 3
y3 = [10, 8, 6, 4, 2, 0, 2, 4, 2, 0] y3_upper = [11, 9, 7, 5, 3, 1, 3, 5, 3, 1] y3_lower = [9, 7, 5, 3, 1, -.5, 1, 3, 1, -1] y3_lower = y3_lower[::-1]
fig = go.Figure()
fig.add_trace(go.Scatter( x=x+x_rev, y=y1_upper+y1_lower, fill='toself', fillcolor='rgba(0,100,80,0.2)', line_color='rgba(255,255,255,0)', showlegend=False, name='Fair', )) fig.add_trace(go.Scatter( x=x+x_rev, y=y2_upper+y2_lower, fill='toself', fillcolor='rgba(0,176,246,0.2)', line_color='rgba(255,255,255,0)', name='Premium', showlegend=False, )) fig.add_trace(go.Scatter( x=x+x_rev, y=y3_upper+y3_lower, fill='toself', fillcolor='rgba(231,107,243,0.2)', line_color='rgba(255,255,255,0)', showlegend=False, name='Ideal', )) fig.add_trace(go.Scatter( x=x, y=y1, line_color='rgb(0,100,80)', name='Fair', )) fig.add_trace(go.Scatter( x=x, y=y2, line_color='rgb(0,176,246)', name='Premium', )) fig.add_trace(go.Scatter( x=x, y=y3, line_color='rgb(231,107,243)', name='Ideal', ))
fig.update_traces(mode='lines') 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( ... )
from dash import Dash, dcc, html
app = Dash() app.layout = html.Div([ dcc.Graph(figure=fig) ])
app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
