Migration to Version 6

See https://plotly.com/python/v6-migration/

Migration to Version 5

See https://community.plotly.com/t/introducing-plotly-py-5-0-0-a-new-federated-jupyter-extension-icicle-charts-and-bar-chart-patterns/54039

Migration to Version 4

See https://plotly.com/python/v4-migration/

Migration to Version 3

There are many new and great features in plotly.py 3.0 including deeper Jupyter integration, deeper figure validation, improved performance, and more. This guide contains a summary of the breaking changes that you need to be aware of when migrating code from version 2 to version 3.

For a high level overview, read our announcement post.

Simple FigureWidget Example

We now have seamless integration with the Jupyter widget ecosystem. We've introduced a new graph object called go.FigureWidget that acts like a regular plotly go.Figure that can be directly displayed in the notebook.

Simple Example: Make a Scatter Plot

import plotly
import plotly.graph_objs as go

f = go.FigureWidget()
f  # printing the widget will display it

This means that plotly.offline.iplot and plotly.offline.init_notebook_mode() are no longer required (although still supported).

Tab Completion

Entering f.add_<tab> displays add methods for all of the supported trace types. Try it!

f.add_

Entering f.add_scatter(<tab>) displays the names of all of the top-level properties for the scatter trace type

Entering f.add_scatter(<shift+tab>) displays the signature pop-up. Expanding this pop-up reveals the method doc string which contains the descriptions of all of the top level properties. Let's finish add a scatter trace to f:

f.add_scatter(x=[1,2,3], y=[3,4,2])
f

New Plotly Object Representation

Plotly figures and graph objects have an updated __repr__ method that displays objects in a pretty printed form that can be copied, pasted, and evaluated to recreate the object.

Eg. print(f) returns

FigureWidget({
    'data': [{'type': 'scatter', 'uid': '07968b11-7b0a-11e8-ba67-c869cda04ed6', 'x': [1, 2, 3], 'y': [4, 3, 2]}],
    'layout': {}
})

New add trace methods that handle subplots

The legacy append_trace method for adding traces to subplots has been deprecated in favor of the new add_trace, add_traces, and add_* methods. Each of these new methods accepts optional row/column information that may be used to add traces to subplots for figures initialized by the plotly.tools.make_subplots function.

Let's create a subplot then turn it into a FigureWidget to display in the notebook.

import plotly
import plotly.graph_objs as go
import plotly.tools as tls

import pandas as pd
dataset = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')

subplot = tls.make_subplots(2, 2, print_grid=False)
f2 = go.FigureWidget(subplot)

# Use add_trace method with optional row/col parameters
f2.add_trace(go.Scatter(x=dataset['Age'], y=dataset['Pregnancies'], mode='markers'), row=1, col=1)

# Use add_traces with optional rows/cols parameters
f2.add_traces([
    go.Scatter(x=dataset['Age'], y=dataset['BMI'], mode='markers'),
    go.Scatter(x=dataset['Age'], y=dataset['SkinThickness'], mode='markers')],
    rows=[1, 2], cols=[2, 1]
)

# Use add_scatter with optional row/col parameters
f2.add_scatter(x=dataset['Age'], y=dataset['BloodPressure'], mode='markers', row=2, col=2)

f2.layout.title = 'Age and Diabetes Factors'
f2

Breaking Changes

Graph Objects Superclass

Graph objects are no longer dict subclasses, though they still provide many dict-like magic methods.

Graph Objects Hierarchy

All graph objects are now placed in a package hierarchy that matches their position in the object hierarchy. For example, go.Marker is now accessible as go.scatter.Marker or go.bar.Marker or whatever trace it is nested within. By providing unique objects under the parent-trace namespace, we can provide better validation (the properties for a marker object within a scatter trace may be different than the properties of a marker object within a bar trace). Although deprecated, the previous objects are still supported, they just won’t provide the same level of validation as our new objects.

For example, the following approach to creating a Marker object for a Scatter trace is now deprecated.

import plotly.graph_objs as go
go.Scatter(
    x=[0],
    y=[0],
    marker=go.Marker(
        color='rgb(255,45,15)'
    )
)

Instead, use the Marker object in the go.scatter package.

import plotly.graph_objs as go
go.Scatter(
    x=[0],
    y=[0],
    marker=go.scatter.Marker(
        color='rgb(255,45,15)'
    )
)

You can still use dict as well. The previous figure is equivalent to:

import plotly.graph_objs as go
dict(
    type='scatter',
    x=[0],
    y=[0],
    marker=go.scatter.Marker(
        color='rgb(255,45,15)'
    )
)

which is also equivalent to

import plotly.graph_objs as go
dict(
    type='scatter',
    x=[0],
    y=[0],
    marker=dict(
        color='rgb(255,45,15)'
    )
)

Property Immutability

In order to support the automatic synchronization a FigureWidget object and the front-end view in a notebook, it is necessary for the FigureWidget to be aware of all changes to its properties. This is accomplished by presenting the individual properties to the user as immutable objects. For example, the layout.xaxis.range property may be assigned using a list, but it will be returned as a tuple. Similarly, object arrays (Figure.data, Layout.images, Parcoords.dimensions, etc.) are now represented as tuples of graph objects, not lists.

Object Array Classes Deprecated

Since graph object arrays are now represented as tuple of graph objects, the following object array classes are deprecated: go.Data, go.Annotations, and go.Frames. Instead, just use lists.

That is, previously we used:

layout = go.Layout(
    annotations=go.Annotations([
        go.layout.Annotations(text='annotation')
    ])
)

Now, we should write:

layout = go.Layout(
    annotations=[
        go.layout.Annotations(text='annotation')
    ]
)

New Figure.data Assignment

There are new restriction on the assignment of traces to the data property of a figure. The assigned value must be a list or a tuple of a subset of the traces already present in the figure. Assignment to data may be used to reorder and remove existing traces, but it may not currently be used to add new traces. New traces must be added using the add_trace, add_traces, or add_* methods.

For example, suppose a figure, fig, has 3 traces. The following command is valid and it will move the third trace to be the first, the first trace to be the second, and it will remove the second trace.

fig.data = [fig.data[2], fig.data[0]]

However this is not valid:

fig.data = [fig.data[0], go.Scatter(y=[2, 3, 1])]

It's not valid because it's introducing a new trace during assignment. This trace would need to be added using add_trace instead.

Data array properties may not be specified as scalars

For example, the following is now invalid:

import plotly.graph_objs as go
go.Bar(x=1)

This should be replaced by:

import plotly.graph_objs as go
go.Bar(x=[1])

Removal of undocumented methods and properties

Several undocumented Figure methods have been removed. These include: .to_string, .strip_style, .get_data, .validate and .to_dataframe.
Graph objects no longer support the undocumented _raise parameter. They are always validated and always raise an exception on validation failures. It is still possible to pass a dict to plot/iplot with validate=False to bypass validation.

Uh oh!