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Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
+
You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
+
We also have a quick-reference cheatsheet (new!) to help you get started!
Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.
import plotly
+plotly.__version__
+from plotly.offline import iplot, init_notebook_mode
+import plotly.graph_objs as go
+
+import pandas as pd
+import numpy as np
+import ipywidgets as widgets
+We'll configure the notebook for use in offline mode
+ +init_notebook_mode(connected=True)
+The parallel categories diagram is a visualization of multi-dimensional categorical data sets. Each variable in the data set is represented by a column of rectangles, where each rectangle corresponds to a discrete value taken on by that variable. The relative heights of the rectangles reflect the relative frequency of occurrence of the corresponding value.
+Combinations of category rectangles across dimensions are connected by ribbons, where the height of the ribbon corresponds to the relative frequency of occurrence of the combination of categories in the data set.
+In this first example, we visualize the hair color, eye color, and sex of a sample of 8 people. Hovering over a category rectangle displays a tooltip with the number of people with that single trait. Hovering over a ribbon in the diagram displays a tooltip with the number of people with a particular combination of the three traits connected by the ribbon.
+The dimension labels can be dragged horizontally to reorder the dimensions and the category rectangles can be dragged vertically to reorder the categories within a dimension.
+ +parcats = go.Parcats(
+ dimensions=[
+ {'label': 'Hair',
+ 'values': ['Black', 'Black', 'Black', 'Brown',
+ 'Brown', 'Brown', 'Red', 'Brown']},
+ {'label': 'Eye',
+ 'values': ['Brown', 'Brown', 'Brown', 'Brown',
+ 'Brown', 'Blue', 'Blue', 'Blue']},
+ {'label': 'Sex',
+ 'values': ['Female', 'Female', 'Female', 'Male',
+ 'Female', 'Male', 'Male', 'Male']}]
+)
+
+iplot([parcats])
+If the frequency of occurrence for each combination of attributes is known in advance, this can be specified using the counts property
parcats = go.Parcats(
+ dimensions=[
+ {'label': 'Hair',
+ 'values': ['Black', 'Brown', 'Brown', 'Brown', 'Red']},
+ {'label': 'Eye',
+ 'values': ['Brown', 'Brown', 'Brown', 'Blue', 'Blue']},
+ {'label': 'Sex',
+ 'values': ['Female', 'Male', 'Female', 'Male', 'Male']}],
+ counts=[6, 10, 40, 23, 7]
+)
+
+iplot([parcats])
+The color of the ribbons can be specified with the line.color property. Similar to other trace types, this property may be set to an array of numbers, which are then mapped to colors according to the the colorscale specified in the line.colorscale property.
Here is an example of visualizing the survival rate of passengers in the titanic dataset, where the ribbons are colored based on survival outcome.
+By setting the hoveron property to 'color' and the hoverinfo property to 'count+probability' the tooltips now display count and probability information for each color (survival outcome) per category.
By setting the arrangement property to 'freeform' it is now possible to drag categories horizontally to reorder dimensions as well as vertically to reorder categories within the dimension.
titanic_df = pd.read_csv(
+ "https://raw.githubusercontent.com/plotly/datasets/master/titanic.csv")
+
+# Create dimensions
+class_dim = go.parcats.Dimension(
+ values=titanic_df.Pclass,
+ categoryorder='category ascending',
+ label="Class"
+)
+
+gender_dim = go.parcats.Dimension(
+ values=titanic_df.Sex,
+ label="Gender"
+)
+
+survival_dim = go.parcats.Dimension(
+ values=titanic_df.Survived,
+ label="Outcome",
+ categoryarray=[0, 1],
+ ticktext=['perished', 'survived'],
+)
+
+# Create parcats trace
+color = titanic_df.Survived;
+colorscale = [[0, 'lightsteelblue'], [1, 'mediumseagreen']];
+
+data = [
+ go.Parcats(
+ dimensions=[class_dim, gender_dim, survival_dim],
+ line={'color': color,
+ 'colorscale': colorscale},
+ hoveron='color',
+ hoverinfo='count+probability',
+ labelfont={'size': 18, 'family': 'Times'},
+ tickfont={'size': 16, 'family': 'Times'},
+ arrangement='freeform'
+ )
+]
+
+# Display figure
+iplot(data)
+This example demonstrates how the on_selection and on_click callbacks can be used to implement linked brushing between 3 categorical dimensions displayed with a parcats trace and 2 continuous dimensions displayed with a scatter trace.
This example also sets the line.shape property to hspline to cause the ribbons to curve between categories.
Note: In order for the callback functions to be executed the figure must be a FigureWidget, and the figure should display itself. In particular the plot and iplot functions should not be used.
cars_df = pd.read_csv(
+ 'https://raw.githubusercontent.com/plotly/datasets/master/imports-85.csv')
+
+# Build parcats dimensions
+categorical_dimensions = [
+ 'body-style',
+ 'drive-wheels',
+ 'fuel-type'
+];
+
+dimensions = [
+ dict(values=cars_df[label], label=label)
+ for label in categorical_dimensions
+]
+
+# Build colorscale
+color = np.zeros(len(cars_df), dtype='uint8')
+colorscale = [[0, 'gray'], [1, 'firebrick']]
+
+# Build figure as FigureWidget
+fig = go.FigureWidget(
+ data=[
+ go.Scatter(
+ x=cars_df.horsepower,
+ y=cars_df['highway-mpg'],
+ marker={'color': 'gray'},
+ mode='markers',
+ selected={'marker': {'color': 'firebrick'}},
+ unselected={'marker': {'opacity': 0.3}}),
+
+ go.Parcats(
+ domain={'y': [0, 0.4]},
+ dimensions=dimensions,
+ line={
+ 'colorscale': colorscale,
+ 'cmin': 0,
+ 'cmax': 1,
+ 'color': color,
+ 'shape': 'hspline'})
+ ],
+ layout=go.Layout(
+ height=800,
+ xaxis={'title': 'Horsepower'},
+ yaxis={'title': 'MPG',
+ 'domain': [0.6, 1]},
+ dragmode='lasso',
+ hovermode='closest')
+)
+
+# Update color callback
+def update_color(trace, points, state):
+ # Update scatter selection
+ fig.data[0].selectedpoints = points.point_inds
+
+ # Update parcats colors
+ new_color = np.zeros(len(cars_df), dtype='uint8')
+ new_color[points.point_inds] = 1
+ fig.data[1].line.color = new_color
+
+# Register callback on scatter selection...
+fig.data[0].on_selection(update_color)
+# and parcats click
+fig.data[1].on_click(update_color)
+
+# Display figure
+fig
+
This example extends the previous example to support brushing with multiple colors. The toggle buttons above may be used to select the active color, and this color will be applied when points are selected in the scatter trace and when categories or ribbons are clicked in the parcats trace.
cars_df = pd.read_csv(
+ 'https://raw.githubusercontent.com/plotly/datasets/master/imports-85.csv')
+
+# Build parcats dimensions
+categorical_dimensions = [
+ 'body-style',
+ 'drive-wheels',
+ 'fuel-type'
+];
+
+dimensions = [
+ dict(values=cars_df[label], label=label)
+ for label in categorical_dimensions
+]
+
+# Build colorscale
+color = np.zeros(len(cars_df), dtype='uint8')
+colorscale = [[0, 'gray'], [0.33, 'gray'],
+ [0.33, 'firebrick'], [0.66, 'firebrick'],
+ [0.66, 'blue'], [1.0, 'blue']];
+cmin = -0.5
+cmax = 2.5
+
+# Build figure as FigureWidget
+fig = go.FigureWidget(
+ data=[
+ go.Scatter(
+ x=cars_df.horsepower,
+ y=cars_df['highway-mpg'],
+ marker={'color': color,
+ 'cmin': cmin,
+ 'cmax': cmax,
+ 'colorscale': colorscale,
+ 'showscale': True,
+ 'colorbar': {'tickvals': [0, 1, 2],
+ 'ticktext': ['None', 'Red', 'Blue']}
+ },
+ mode='markers'),
+
+ go.Parcats(
+ domain={'y': [0, 0.4]},
+ dimensions=dimensions,
+ line={
+ 'colorscale': colorscale,
+ 'cmin': cmin,
+ 'cmax': cmax,
+ 'color': color,
+ 'shape': 'hspline'})
+ ],
+ layout=go.Layout(
+ height=800,
+ xaxis={'title': 'Horsepower'},
+ yaxis={'title': 'MPG',
+ 'domain': [0.6, 1]},
+ dragmode='lasso',
+ hovermode='closest')
+)
+
+# Build color selection widget
+color_toggle = widgets.ToggleButtons(
+ options=['None', 'Red', 'Blue'],
+ index=1,
+ description='Brush Color:',
+ disabled=False,
+)
+
+# Update color callback
+def update_color(trace, points, state):
+ # Compute new color array
+ new_color = np.array(fig.data[0].marker.color)
+ new_color[points.point_inds] = color_toggle.index
+
+ with fig.batch_update():
+ # Update scatter color
+ fig.data[0].marker.color = new_color
+
+ # Update parcats colors
+ fig.data[1].line.color = new_color
+
+# Register callback on scatter selection...
+fig.data[0].on_selection(update_color)
+# and parcats click
+fig.data[1].on_click(update_color)
+
+# Display figure
+widgets.VBox([color_toggle, fig])
+
See https://plot.ly/python/reference/#parcats for more information and chart attribute options!
+ +