Graph Objects in Python

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Python Fundamentals > Graph Objects

Plotly Python Open Source Graphing Library : Basic Charts
Plotly Python Open Source Graphing Library : Fundamentals

plotly.graph_objects: figures, traces and layout에 대한 low-level interface


Graph Objects 란?

plotly 파이썬 라이브러리가 생성/조작/랜더링한 Figures는 Plotly.js JavaScript 라이브러리가 랜더링을 위해 자동으로 JSON으로 serialized한 tree와 같은 데이터구조를 나타낸다. => Plotly의 python라이브러리나 JavaScript라이브러리나 비슷하다.

이런 tree는 “attributes”라는 명명된 node로 구성되고, 그 구조는 plotly.js 에 의해 정의된다.

import plotly.graph_object as go 는 파이썬 클래스의 자동생성된 계층구조를 가진다..

These trees are composed of named nodes called “attributes”, with their structure defined by the Plotly.js figure schema, which is available in machine-readable form

The plotly.graph_objects module (typically imported as go) contains an automatically-generated hierarchy of Python classes which represent non-leaf nodes in this figure schema. The term “graph objects” refers to instances of these classes.

The primary classes defined in the plotly.graph_objects module are Figure 

and an ipywidgets-compatible variant called FigureWidget, which both represent entire figures.

Instances of these classes have many convenience methods for Pythonically manipulating their attributes (e.g. .update_layout() or .add_trace(), which all accept “magic underscore” notation) as well as rendering them (e.g. .show()) and exporting them to various formats (e.g. .to_json() or .write_image() or .write_html()).

Note: the functions in Plotly Express, which is the recommended entry-point into the plotly library, are all built on top of graph objects, and all return instances of plotly.graph_objects.Figure.

Every non-leaf attribute of a figure is represented by an instance of a class in the plotly.graph_objects hierarchy. For example, a figure fig can have an attribute layout.margin, which contains attributes tlb and r which are leaves of the tree: they have no children. The field at fig.layout is an object of class plotly.graph_objects.Layout and fig.layout.margin is an object of class plotly.graph_objects.layout.Margin which represents the margin node, and it has fields tlb and r, containing the values of the respective leaf-nodes. Note that specifying all of these values can be done without creating intermediate objects using “magic underscore” notationgo.Figure(layout_margin=dict(t=10, b=10, r=10, l=10)).

The objects contained in the list which is the value of the attribute data are called “traces”, and can be of one of more than 40 possible types, each of which has a corresponding class in plotly.graph_objects. For example, traces of type scatter are represented by instances of the class plotly.graph_objects.Scatter. This means that a figure constructed as go.Figure(data=[go.Scatter(x=[1,2], y=[3,4)]) will have the JSON representation {"data": [{"type": "scatter", "x": [1,2], "y": [3,4]}]}.

Graph Objects Compared to Dictionaries

Graph objects는 딕셔너리와 비교해서 여러가지 장점이 있다.

  1. Graph objects provide precise data validation.
    If you provide an invalid property name or an invalid property value as the key to a graph object, an exception will be raised with a helpful error message describing the problem. This is not the case if you use plain Python dictionaries and lists to build your figures.
  2. Graph objects contain descriptions of each valid property as Python docstrings, with a full API reference available.
    You can use these docstrings in the development environment of your choice to learn about the available properties as an alternative to consulting the online Full Reference.
  3. Properties of graph objects can be accessed using both dictionary-style key lookup (e.g. fig["layout"]) or class-style property access (e.g. fig.layout).
  4. Graph objects support higher-level convenience functions for making updates to already constructed figures (.update_layout().add_trace() etc).
  5. Graph object constructors and update methods accept “magic underscores” (e.g. go.Figure(layout_title_text="The Title") rather than dict(layout=dict(title=dict(text="The Title")))) for more compact code.
  6. Graph objects support attached rendering (.show()) and exporting functions (.write_image()) that automatically invoke the appropriate functions from the module.

When to use Graph Objects vs Plotly Express

figure생성은 가능하다면 plotly.express모듈의 function을 사용하는 것이 좋다.

모든 px(Plotly Express) function는  내부적으로 graph objects 사용하고,
 plotly.graph_objects.Figure instance를 return한다.

The recommended way to create figures is using the functions in the module
collectively known as Plotly Express, which all return instances of plotly.graph_objects.Figure, so every figure produced with the plotly library actually uses graph objects under the hood, unless manually constructed out of dictionaries.

That said, certain kinds of figures are not yet possible to create with Plotly Express, such as figures that use certain 3D trace-types like mesh or isosurface. In addition, certain figures are cumbersome to create by starting from a figure created with Plotly Express, for example figures with subplots of different typesdual-axis plots, or faceted plots with multiple different types of traces. To construct such figures, it can be easier to start from an empty plotly.graph_objects.Figure object (or one configured with subplots via the make_subplots() function) and progressively add traces and update attributes as above. Every plotly documentation page lists the Plotly Express option at the top if a Plotly Express function exists to make the kind of chart in question, and then the graph objects version below.

Note that the figures produced by Plotly Express in a single function-call are easy to customize at creation-time, and to manipulate after creation using the update_* and add_* methods.

Comparing Graph Objects and Plotly Express

The figures produced by Plotly Express can always be built from the ground up using graph objects,
but this approach typically takes 5-100 lines of code rather than 1.

Plotly Express로 생성된 figure는 graph object를 사용해서 항상 처음부터 만들어 낼수 있다.
하지만, 꽤 많은 코딩이 필요하다.

Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without.

The data in this example is in “long form” but Plotly Express also accepts data in “wide form” and the line-count savings from Plotly Express over graph objects are comparable.
More complex figures such as sunburstsparallel coordinatesfacet plots or animations require many more lines of figure-specific graph objects code, whereas switching from one representation to another with Plotly Express usually involves changing just a few characters.


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

Everywhere in this page that you see, 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 as px
fig = go.Figure() # or any Plotly Express function e.g.
# 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([

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter
Categories: visualization


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