Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://plotly.com/python/getting-started below:

Getting started with plotly in Python

>

Getting Started with Plotly for Python.

Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Sign up for early access now.

The plotly Python library is an interactive, open-source plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.

Built on top of the Plotly JavaScript library (plotly.js), plotly enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash. The plotly Python library is sometimes referred to as "plotly.py" to differentiate it from the JavaScript library.

Thanks to deep integration with our Kaleido image export utility, plotly also provides great support for non-web contexts including desktop editors (e.g. QtConsole, Spyder, PyCharm) and static document publishing (e.g. exporting notebooks to PDF with high-quality vector images).

This Getting Started guide explains how to install plotly and related optional pages. Once you've installed, you can use our documentation in three main ways:

1. You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
2. If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
3. You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

plotly may be installed using pip:

$ pip install plotly

or conda:

$ conda install -c conda-forge plotly

If you want to use Plotly Express, install its required dependencies with:

pip install plotly[express]

You'll also need to install a supported dataframe library.

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.

To use plotly in JupyterLab, install the jupyterlab and anywidget packages in the same environment as you installed plotly, using pip:

$ pip install jupyterlab anywidget

or conda:

$ conda install jupyterlab anywidget

Launch JupyterLab with:

$ jupyter lab

and display plotly figures inline:

In [2]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.show()

or using FigureWidget objects.

In [3]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])

import plotly.graph_objects as go
fig_widget = go.FigureWidget(fig)
fig_widget

For use in the classic Jupyter Notebook, install the notebook and ipywidgets packages using pip:

pip install "notebook>=7.0" "anywidget>=0.9.13"

or conda:

conda install "notebook>=7.0" "anywidget>=0.9.13"

These packages contain everything you need to run a Jupyter notebook...

$ jupyter notebook

and display plotly figures inline using the notebook renderer...

In [4]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.show()

or using FigureWidget objects.

In [5]:

import plotly.express as px
fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])

import plotly.graph_objects as go
fig_widget = go.FigureWidget(fig)
fig_widget

plotly.py supports static image export, using the kaleido package. (Support for the legacy orca image export utility is deprecated and will be removed after September 2025.)

The kaleido package has no dependencies and can be installed using pip...

$ pip install --upgrade kaleido

or conda.

$ conda install -c plotly python-kaleido

Some plotly.py features rely on fairly large geographic shape files. The county choropleth figure factory is one such example. These shape files are distributed as a separate plotly-geo package. This package can be installed using pip...

$ pip install plotly-geo==1.0.0

or conda.

$ conda install -c plotly plotly-geo=1.0.0

See USA County Choropleth Maps in Python for more information on the county choropleth figure factory.

Once you've installed, you can use our documentation in three main ways:

1. You jump right in to examples of how to make basic charts, statistical charts, scientific charts, financial charts, maps, and 3-dimensional charts.
2. If you prefer to learn about the fundamentals of the library first, you can read about the structure of figures, how to create and update figures, how to display figures, how to theme figures with templates, how to export figures to various formats and about Plotly Express, the high-level API for doing all of the above.
3. You can check out our exhaustive reference guides: the Python API reference or the Figure Reference

For information on using Python to build web applications containing plotly figures, see the Dash User Guide.

We also encourage you to join the Plotly Community Forum if you want help with anything related to plotly.

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( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4