Datapane is the easiest way to create data science reports from Python.

Overview

Datapane

Datapane Teams | Documentation | API Docs | Changelog | Twitter | Blog

Pip Downloads Latest release Conda (channel only)

Share interactive plots and data in 3 lines of Python.

Datapane is a Python library for building interactive reports for your end-users in seconds.

Import our library into your existing script/notebook and build reports from pandas Dataframes, plots from Python viz libraries, Markdown, as well as data exploration and layout components.

Export your reports as standalone HTML documents, or share and embed them via our free hosted platform.

Getting Started

Installing Datapane

The best way to install Datapane is through pip or conda.

pip

$ pip3 install -U datapane
$ datapane hello-world

conda

$ conda install -c conda-forge "datapane>=0.12.0"
$ datapane hello-world

Datapane also works well in hosted Jupyter environments such as Colab or Binder, where you can install as follows:

!pip3 install --quiet datapane
!datapane signup

Explainer Video

Datapane.Public.Tutorial.mp4

Hello world

Let's say you wanted to create a report with an interactive plot and table viewer:

import altair as alt
from vega_datasets import data
import datapane as dp

source = data.cars()

plot1 = alt.Chart(source).mark_circle(size=60).encode(
  x='Horsepower',
  y='Miles_per_Gallon',
  color='Origin',
  tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']
).interactive()

dp.Report(
    dp.Text("## Hello world!"),
    dp.Plot(plot1),
    dp.DataTable(source)
).save(path="Hello_world.html")

This will package a standalone HTML document that looks as follows:

Simple Datapane report example with text, plot and table

Your users can scroll & zoom on the chart, filter and download the tabular data.

Advanced Layout Options

Datapane is great for presenting complex data and provides many components for creating advanced interactive layouts. Let's you need to write a technical document:

import altair as alt
from vega_datasets import data
import datapane as dp

source = data.cars()
plot1 = alt.Chart(source).mark_circle(size=60).encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
    tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']
).interactive()

dp.Report(
    dp.Page(title="Charts and analysis",
            blocks=[
                dp.Formula("x^2 + y^2 = z^2"),
                dp.Group(
                    dp.BigNumber(
                        heading="Number of percentage points",
                        value="84%",
                        change="2%",
                        is_upward_change=True
                    ),
                    dp.BigNumber(
                        heading="Simple Statistic",
                        value=100
                    ), columns=2,
                ),
                dp.Select(blocks=[
                    dp.Plot(plot1, label="Plot"),
                    dp.HTML('''<iframe width="560" height="315" src="https://www.youtube.com/embed/dQw4w9WgXcQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>''', label="Video")
                ]),
            ]),
    dp.Page(title="Dataset", blocks=[
            dp.DataTable(source)
    ])
).save(path="Complex_layout.html", open=True)

Layout blocks like dp.Select, dp.Group and dp.Page allow you to highlight key points without sacrificing detail, while content blocks like dp.HTML and dp.Formula (LaTeX) can enrich your report. The final result looks like this:

Complex Datapane report example

Check out the full list of blocks in our documentation.

Sharing Reports

Sign up for a free account

In addition to saving documents locally, you can host, share and embed reports via Datapane Studio.

To get your free API key, run the following command in your terminal to sign up via email/OAuth:

$ datapane signup

If you're using Jupyter, run !datapane signup instead.

Next, in your Python notebook or script simply change the save function to upload on your report:

dp.Report(
 ...
#).save(path="hello_world.html")
).upload(name="Hello world")

Your Studio account comes with the following:

  • Unlimited public reports - great for embedding into places like Medium, Reddit, or your own website (see here)
  • 5 private reports - share these via email within your organization

Featured Examples

Here a few samples of the top reports created by the Datapane community. To see more, check out our gallery section.

Teams

Datapane Teams is our plan for teams, which adds the following features on top of our open-source and Studio plans:

  • Private domain and organizational workspace
  • Multiple projects
  • Client-sharing functionality
  • Unlimited Datapane Apps
  • Custom App packages and environments
  • Secure Warehouse & API Integration
  • File and Dataset APIs
  • Private Slack or Teams support

Datapane Teams is offered as both a managed SaaS service and an on-prem install. For more information, see the documentation. You can find pricing here.

Next Steps

Analytics

By default, the Datapane Python library collects error reports and usage telemetry. This is used by us to help make the product better and to fix bugs. If you would like to disable this, simply create a file called no_analytics in your datapane config directory, e.g.

Linux

$ mkdir -p ~/.config/datapane && touch ~/.config/datapane/no_analytics

macOS

$ mkdir -p ~/Library/Application\ Data/datapane && touch ~/Library/Application\ Data/no_analytics

Windows (PowerShell)

PS> mkdir ~/AppData/Roaming/datapane -ea 0
PS> ni ~/AppData/Roaming/datapane/no_analytics -ea 0

You may need to try ~/AppData/Local instead of ~/AppData/Roaming on certain Windows configurations depending on the type of your user-account.

Joining the community

Looking to get answers to questions or engage with us and the wider community? Check out our GitHub Discussions board.

Submit feature requests, issues, and bug reports on this GitHub repo.

Open-source, not open-contribution

Datapane is currently closed to external code contributions. However, we are tremendously grateful to the community for any feature requests, ideas, discussions, and bug reports.

Owner
Datapane
Create and share interactive reports from Python
Datapane
LinkedIn connections analyzer

LinkedIn Connections Analyzer 🔗 https://linkedin-analzyer.herokuapp.com Hey hey 👋 , welcome to my LinkedIn connections analyzer. I recently found ou

Okkar Min 5 Sep 13, 2022
Data aggregated from the reports found at the MCPS COVID Dashboard into a set of visualizations.

Montgomery County Public Schools COVID-19 Visualizer Contents About this project Data Support this project About this project Data All data we use can

James 3 Jan 19, 2022
Drug design and development team HackBio internship is a virtual bioinformatics program that introduces students and professional to advanced practical bioinformatics and its applications globally.

-Nyokong. Drug design and development team HackBio internship is a virtual bioinformatics program that introduces students and professional to advance

4 Aug 04, 2022
A Python toolbox for gaining geometric insights into high-dimensional data

"To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say 'fourteen' very loudly. Everyone does it." - Geoff Hinton Overview

Contextual Dynamics Laboratory 1.8k Dec 29, 2022
Data visualization electromagnetic spectrum

Datenvisualisierung-Elektromagnetischen-Spektrum Anhand des Moduls matplotlib sollen die Daten des elektromagnetischen Spektrums dargestellt werden. D

Pulsar 1 Sep 01, 2022
Visualization Library

CamViz Overview // Installation // Demos // License Overview CamViz is a visualization library developed by the TRI-ML team with the goal of providing

Toyota Research Institute - Machine Learning 67 Nov 24, 2022
Visualizations for machine learning datasets

Introduction The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive

PAIR code 7.1k Jan 07, 2023
An interactive GUI for WhiteboxTools in a Jupyter-based environment

whiteboxgui An interactive GUI for WhiteboxTools in a Jupyter-based environment GitHub repo: https://github.com/giswqs/whiteboxgui Documentation: http

Qiusheng Wu 105 Dec 15, 2022
Create charts with Python in a very similar way to creating charts using Chart.js

Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive and modular and are displayed directly in the output of the t

Nicolas H 68 Dec 08, 2022
An interactive UMAP visualization of the MNIST data set.

Code for an interactive UMAP visualization of the MNIST data set. Demo at https://grantcuster.github.io/umap-explorer/. You can read more about the de

grant 70 Dec 27, 2022
This is a Cross-Platform Plot Manager for Chia Plotting that is simple, easy-to-use, and reliable.

Swar's Chia Plot Manager A plot manager for Chia plotting: https://www.chia.net/ Development Version: v0.0.1 This is a cross-platform Chia Plot Manage

Swar Patel 1.3k Dec 13, 2022
Python scripts to manage Chia plots and drive space, providing full reports. Also monitors the number of chia coins you have.

Chia Plot, Drive Manager & Coin Monitor (V0.5 - April 20th, 2021) Multi Server Chia Plot and Drive Management Solution Be sure to ⭐ my repo so you can

338 Nov 25, 2022
Flow-based visual scripting for Python

A simple visual node editor for Python Ryven combines flow-based visual scripting with Python. It gives you absolute freedom for your nodes and a simp

Leon Thomm 3.1k Jan 06, 2023
Simple Python interface for Graphviz

Simple Python interface for Graphviz

Sebastian Bank 1.3k Dec 26, 2022
A simple agent-based model used to teach the basics of OOP in my lectures

Pydemic A simple agent-based model of a pandemic. This is used to teach basic principles of object-oriented programming to master students. It is not

Fabien Maussion 2 Jun 08, 2022
Joyplots in Python with matplotlib & pandas :chart_with_upwards_trend:

JoyPy JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots). The code f

Leonardo Taccari 462 Jan 02, 2023
Generate SVG (dark/light) images visualizing (private/public) GitHub repo statistics for profile/website.

Generate daily updated visualizations of GitHub user and repository statistics from the GitHub API using GitHub Actions for any combination of private and public repositories, whether owned or contri

Adam Ross 2 Dec 16, 2022
Learning Convolutional Neural Networks with Interactive Visualization.

CNN Explainer An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs) For more information,

Polo Club of Data Science 6.3k Jan 01, 2023
Data visualization using matplotlib

Data visualization using matplotlib project instructions Top 5 Most Common Coffee Origins In this visualization I used data from Ankur Chavda on Kaggl

13 Oct 27, 2021
Fast scatter density plots for Matplotlib

About Plotting millions of points can be slow. Real slow... 😴 So why not use density maps? ⚡ The mpl-scatter-density mini-package provides functional

Thomas Robitaille 473 Dec 12, 2022