Scatter Plot for Continuous or Discrete Data
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# x and y given as array_like objects import plotly.express as px fig = px . scatter ( x = [ 0 , 1 , 2 , 3 , 4 ], y = [ 0 , 1 , 4 , 9 , 16 ]) fig . show ()
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# x and y given as DataFrame columns import plotly.express as px df = px . data . iris () # iris is a pandas DataFrame fig = px . scatter ( df , x = "sepal_width" , y = "sepal_length" ) fig . show ()
Setting size and color with column names¶
Scatter plots with variable-sized circular markers are often known as bubble charts. Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter.
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import plotly.express as px df = px . data . iris () fig = px . scatter ( df , x = "sepal_width" , y = "sepal_length" , color = "species" , size = 'petal_length' , hover_data = [ 'petal_width' ]) fig . show ()
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import plotly.express as px df = px . data . iris () fig = px . scatter ( df , x = "sepal_width" , y = "sepal_length" , color = 'petal_length' ) fig . show ()
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import plotly.express as px df = px . data . iris () fig = px . scatter ( df , x = "sepal_width" , y = "sepal_length" , color = "species" , symbol = "species" ) fig . show ()
Scatter plots in Dash¶
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Scatter plots and Categorical Axes¶
Scatter plots can be made using any type of cartesian axis, including linear, logarithmic, categorical or date axes.
Scatter plots where one axis is categorical are often known as dot plots.
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import plotly.express as px df = px . data . medals_long () fig = px . scatter ( df , y = "nation" , x = "count" , color = "medal" , symbol = "medal" ) fig . update_traces ( marker_size = 10 ) fig . show ()
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import plotly.express as px df = px . data . iris () df [ "e" ] = df [ "sepal_width" ] / 100 fig = px . scatter ( df , x = "sepal_width" , y = "sepal_length" , color = "species" , error_x = "e" , error_y = "e" ) fig . show ()
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import plotly.express as px df = px . data . iris () fig = px . scatter ( df , x = "sepal_length" , y = "sepal_width" , marginal_x = "histogram" , marginal_y = "rug" ) fig . show ()
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import plotly.express as px df = px . data . tips () fig = px . scatter ( df , x = "total_bill" , y = "tip" , color = "smoker" , facet_col = "sex" , facet_row = "time" ) fig . show ()
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import plotly.express as px df = px . data . tips () fig = px . scatter ( df , x = "total_bill" , y = "tip" , trendline = "ols" ) fig . show ()
Line plots with Plotly Express¶
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import plotly.express as px import numpy as np t = np . linspace ( 0 , 2 * np . pi , 100 ) fig = px . line ( x = t , y = np . cos ( t ), labels = { 'x' : 't' , 'y' : 'cos(t)' }) fig . show ()
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import plotly.express as px df = px . data . gapminder () . query ( "continent == 'Oceania'" ) fig = px . line ( df , x = 'year' , y = 'lifeExp' , color = 'country' ) fig . show ()
The markers argument can be set to True to show markers on lines.
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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 ()
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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 ()
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import plotly.express as px df = px . data . stocks () fig = px . line ( df , x = 'date' , y = "GOOG" ) fig . show ()
Data Order in Scatter and 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.
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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. Botswana's life expectancy
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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 ()
Scatter and line plots 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 Scatter Plot¶
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import plotly.graph_objects as go import numpy as np N = 1000 t = np . linspace ( 0 , 10 , 100 ) y = np . sin ( t ) fig = go . Figure ( data = go . Scatter ( x = t , y = y , mode = 'markers' )) fig . show ()
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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 fig = go . Figure () # Add traces fig . add_trace ( go . Scatter ( x = random_x , y = random_y0 , mode = 'markers' , name = 'markers' )) 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 = 'lines' , name = 'lines' )) fig . show ()
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import plotly.graph_objects as go fig = go . Figure ( data = go . Scatter ( x = [ 1 , 2 , 3 , 4 ], y = [ 10 , 11 , 12 , 13 ], mode = 'markers' , marker = dict ( size = [ 40 , 60 , 80 , 100 ], color = [ 0 , 1 , 2 , 3 ]) )) fig . show ()
Style Scatter Plots¶
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import plotly.graph_objects as go import numpy as np t = np . linspace ( 0 , 10 , 100 ) fig = go . Figure () fig . add_trace ( go . Scatter ( x = t , y = np . sin ( t ), name = 'sin' , mode = 'markers' , marker_color = 'rgba(152, 0, 0, .8)' )) fig . add_trace ( go . Scatter ( x = t , y = np . cos ( t ), name = 'cos' , marker_color = 'rgba(255, 182, 193, .9)' )) # Set options common to all traces with fig.update_traces fig . update_traces ( mode = 'markers' , marker_line_width = 2 , marker_size = 10 ) fig . update_layout ( title = 'Styled Scatter' , yaxis_zeroline = False , xaxis_zeroline = False ) fig . show ()
Data Labels on Hover¶
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import plotly.graph_objects as go import pandas as pd data = pd . read_csv ( "https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv" ) fig = go . Figure ( data = go . Scatter ( x = data [ 'Postal' ], y = data [ 'Population' ], mode = 'markers' , marker_color = data [ 'Population' ], text = data [ 'State' ])) # hover text goes here fig . update_layout ( title = 'Population of USA States' ) fig . show ()
Scatter with a Color Dimension¶
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import plotly.graph_objects as go import numpy as np fig = go . Figure ( data = go . Scatter ( y = np . random . randn ( 500 ), mode = 'markers' , marker = dict ( size = 16 , color = np . random . randn ( 500 ), #set color equal to a variable colorscale = 'Viridis' , # one of plotly colorscales showscale = True ) )) fig . show ()
Large Data Sets¶
Now in Plotly you can implement WebGL with Scattergl() in place of Scatter()
for increased speed, improved interactivity, and the ability to plot even more data!
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import plotly.graph_objects as go import numpy as np N = 100000 fig = go . Figure ( data = go . Scattergl ( x = np . random . randn ( N ), y = np . random . randn ( N ), mode = 'markers' , marker = dict ( color = np . random . randn ( N ), colorscale = 'Viridis' , line_width = 1 ) )) fig . show ()
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import plotly.graph_objects as go import numpy as np N = 100000 r = np . random . uniform ( 0 , 1 , N ) theta = np . random . uniform ( 0 , 2 * np . pi , N ) fig = go . Figure ( data = go . Scattergl ( x = r * np . cos ( theta ), # non-uniform distribution y = r * np . sin ( theta ), # zoom to see more points at the center mode = 'markers' , marker = dict ( color = np . random . randn ( N ), colorscale = 'Viridis' , line_width = 1 ) )) 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 
Source: https://plotly.com/python/line-and-scatter/
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