Plotly chart studio python

Working With Chart Studio Graphs in Python

How to download Chart Studio users’ public graphs and data into Python.

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Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Get and Change a Public Figure¶

import chart_studio.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("https://plotly.com/~PlotBot/5") fig['layout']['title'] = "Never forget that title!" py.iplot(fig, filename="python-change_plot") 

Get Data and Change Plot¶

import chart_studio.plotly as py import plotly.graph_objects as go # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api data = py.get_figure("https://plotly.com/~PythonPlotBot/3483").data distance = [d['y'][0] for d in data] # check out the data for yourself! fig = go.Figure() fig.add_histogram(y=distance, name="flyby distance", histnorm='probability') xaxis = dict(title="Probability for Flyby at this Distance") yaxis = dict(title="Distance from Earth (Earth Radii)") fig.update_layout(title="data source: https://plotly.com/~AlexHP/68", xaxis=xaxis, yaxis=yaxis) plot_url = py.plot(fig, filename="python-get-data") 

Get and Replot a Public Figure with URL¶

import chart_studio.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("https://plotly.com/~PlotBot/5") plot_url = py.plot(fig, filename="python-replot1") 

Get and Replot a Public Figure with ID¶

import chart_studio.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("PlotBot", 5) plot_url = py.plot(fig, filename="python-replot2") 

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Plotly Python Chart Studio Integration1

Plotly’s Python graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly’s Python API.

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Working With Chart Studio Graphs in Python/v3

How to download Chart Studio users’ public graphs and data with Python.

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Note: this page is part of the documentation for version 3 of Plotly.py, which is not the most recent version.
See our Version 4 Migration Guide for information about how to upgrade.
The version 4 version of this page is here.

Get and Change a Public Figure

import plotly.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("https://plotly.com/~PlotBot/5") fig['layout']['title'] = "Never forget that title!" plot_url = py.plot(fig, filename="python-change_plot") 

Get Data and Change Plot

import plotly.plotly as py import plotly.graph_objs as go # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api data = py.get_figure("https://plotly.com/~AlexHP/68").get_data() distance = [d['y'][0] for d in data] # check out the data for yourself! fig = go.Figure() fig['data'] += [go.Histogram(y=distance, name="flyby distance", histnorm='probability')] xaxis = dict(title="Probability for Flyby at this Distance") yaxis = dict(title="Distance from Earth (Earth Radii)") fig['layout'].update(title="data source: https://plotly.com/~AlexHP/68", xaxis=xaxis, yaxis=yaxis) plot_url = py.plot(fig, filename="python-get-data") 

Get and Replot a Public Figure with URL

import plotly.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("https://plotly.com/~PlotBot/5") plot_url = py.plot(fig, filename="python-replot1") 

Get and Replot a Public Figure with ID

import plotly.plotly as py # Learn about API authentication here: https://plotly.com/python/getting-started # Find your api_key here: https://plotly.com/settings/api fig = py.get_figure("PlotBot", 5) plot_url = py.plot(fig, filename="python-replot2") 

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Getting Started with Plotly in Python

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Overview¶

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 .

Installation¶

plotly may be installed using pip :

This package contains everything you need to write figures to standalone HTML files.

Note: No internet connection, account, or payment is required to use plotly.py. Prior to version 4, this library could operate in either an «online» or «offline» mode. The documentation tended to emphasize the online mode, where graphs get published to the Chart Studio web service. In version 4, all «online» functionality was removed from the plotly package and is now available as the separate, optional, chart-studio package (See below). plotly.py version 4 is «offline» only, and does not include any functionality for uploading figures or data to cloud services.

import plotly.express as px fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2]) fig.write_html('first_figure.html', auto_open=True) 

Plotly charts in Dash¶

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.

JupyterLab Support¶

For use in JupyterLab, install the jupyterlab and ipywidgets packages using pip :

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Create Cloud-hosted Charts with Plotly Chart Studio

Use Pandas and Plotly to create cloud-hosted data visualizations on-demand in Python.

Given the success of Plotly Dash and Plotly Express, it’s easy to forget that Plotly’s rise to success began with a product that was neither of these household names. Dash has cornered the interactive dashboard market, while Plotly Express has become the de facto Python library generating inline charts, particularly for Jupyter notebooks. What we don’t get from either of these tools is a way to create data visualizations for any other scenario, such as creating a single chart to serve publicly as an image or interactive plot.

Plotly Chart Studio shines as a tool to instantly create cloud-hosted data visualizations. The syntax will appear familiar to those who have used Plotly Express (or any other Python data vis library), but Plotly Chart Studio stands alone in how these charts are created: on a publicly accessible cloud. With a Chart Studio account, each chart you plot is saved to a public chart studio profile, like mine:

Each chart in my profile is publicly accessible as your choice of an embedded plot, an image, raw HTML, etc. Here’s an example chart as an iFrame:

Every chart created with Plotly studio automatically generates interactive plots and static image varieties like PNGs. This opens the door to possibilities for us to create charts on demand, like creating dumb Discord bots, for example:

This tutorial will demonstrate plot creation in Chart Studio with a fairly common real-life scenario. We’ll be fetching stock price data from IEX Cloud, transforming the data in a Pandas DataFrame, and outputting a Candlestick chart. This same workflow can be applied to create any of Plotly’s many chart types.

Getting Set Up

The two Plotly-related libraries we need are plotly and chart-studio . We’ll install these along with the usual suspects for Python data analysis:

Organizing Our Work

Throughout this tutorial, you’ve been watching me dump random code blocks. If this confuses the shit out of you, it’ll probably help to know that this is how I chose to structure my project:

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