Merge pull request #106 from plotly/parallel-category

Parallel category V4

Author: Mahdis-z

python/parallel-categories-diagram.md

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+---
+jupyter:
+  jupytext:
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.1'
+      jupytext_version: 1.2.1
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
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+    name: python
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+    version: 3.6.8
+  plotly:
+    description: How to make parallel categories diagrams in Python with Plotly.
+    display_as: statistical
+    has_thumbnail: true
+    ipynb: ~notebook_demo/258
+    language: python
+    layout: user-guide
+    name: Parallel Categories Diagram
+    order: 10.3
+    page_type: u-guide
+    permalink: python/parallel-categories-diagram/
+    thumbnail: thumbnail/parcats.jpg
+    title: Python Parallel Categories | Plotly
+---
+
+#### Parallel Categories Diagram
+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.
+
+
+#### Basic Parallel Category Diagram with plotly.express
+
+This example visualizes the resturant bills of a sample of 244 people.  Hovering over a category rectangle (sex, smoker, etc) 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 five traits connected by the ribbon.
+
+
+```python
+import plotly.express as px
+
+tips = px.data.tips()
+fig = px.parallel_categories(tips)
+
+fig.show()
+```
+
+#### Style Diagram
+In this example `dimensions` represents a list of stings or the columns of data frame, and `labels` is a dictionary with string keys (column name) and string values ('desired label to be displayed'). See [Plotly express reference page](https://www.plotly.express/plotly_express/#plotly_express.parallel_categories) for more information.
+
+```python
+import plotly.express as px
+
+tips = px.data.tips()
+fig = px.parallel_categories(tips, dimensions=['sex', 'smoker', 'day'], 
+                color="size", color_continuous_scale=px.colors.sequential.Inferno,
+                labels={'sex':'Payer sex', 'smoker':'Smokers at the table', 'day':'Day of week'})
+fig.show()
+```
+
+#### Basic Parallel Categories Diagram
+This example illustartes the hair color, eye color, and sex of a sample of 8 people. 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.
+
+```python
+import plotly.graph_objects as go
+
+fig = go.Figure(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']}]
+))
+
+fig.show()
+```
+
+#### Basic Parallel Categories Diagram with Counts
+If the frequency of occurrence for each combination of attributes is known in advance, this can be specified using the `counts` property
+
+```python
+import plotly.graph_objects as go
+
+fig = go.Figure(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]
+))
+
+
+fig.show()
+```
+
+#### Mutli-Color Parallel Categories Diagram
+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.
+
+```python
+import plotly.graph_objects as go
+import pandas as pd
+
+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']];
+
+fig = go.Figure(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')])
+
+fig.show()
+```
+
+#### Parallel Categories Linked Brushing
+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. 
+
+```python
+import plotly.graph_objects as go
+from ipywidgets import widgets
+import pandas as pd
+import numpy as np
+
+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'})
+    ])
+
+fig.update_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)
+
+fig
+```
+
+![Single-color brushing](https://user-images.githubusercontent.com/15064365/48087050-4a3eae80-e1cc-11e8-94c9-c0ffcedd3aa9.gif)
+
+
+#### Parallel Categories with Multi-Color Linked Brushing
+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.
+
+```python
+import plotly.graph_objects as go
+import ipywidgets as widgets
+import pandas as pd
+import numpy as np
+
+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'})]
+)
+
+fig.update_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])
+```
+
+![Multi-color brushing](https://user-images.githubusercontent.com/15064365/48087055-4e6acc00-e1cc-11e8-8f0c-c8316eb4def2.gif)
+
+
+#### Reference
+See [reference page](https://plot.ly/python/reference/#parcats) for more information and chart attribute options!