codeplot 0.4.5

codeplot is a canvas designed for code-driven data exploration where you can plot graphs, data frames, markdown and much more using plain Python

Demo · Discord

---

codeplot is a dynamic spatial canvas for data exploration, offering an interactive environment for graphing and visualizing data with Python.

Created by @antl3x, read more about its inception.

Video Demo

Why Choose codeplot?

• Dynamic Visualization: Break free from static images and rigid layouts. codeplot brings your data to life on an interactive canvas.

• Easy Integration: Directly plot from your Python code or REPL into your canvas at codeplot.co.

• Varied Visualizations: From basic charts to advanced widgets, codeplot supports a wide range of data representations.

• Flexible Layouts: Arrange your visualizations to suit your workflow, with draggable and movable plots.

• Open to Everyone: Designed for data scientists and enthusiasts alike, codeplot aims to enhance your data exploration experience.

Getting Started (IPython Extension)

To use codeplot in a IPython Notebook like Jupyter, Google Colab, etc, you can install the IPython Extension using pip:

pip install codeplot-ipython

After install you can load the extension and connect to a new room:

%load_ext codeplot-ipython
%cP_connect ws://your-ws-url/your-room-id

Now the output of your cells will be automatically plotted in the codeplot canvas! So you don't need to use the cP.plot function.

Thats all!

Getting Started (Python SDK)

If you want to use codeplot in a Python script, and have a more "fine-grained" control over the plots, you can use the Python SDK.

To get started with codeplot, you can install the package using pip:

pip install codeplot

Once installed, you can start using codeplot by importing the package and connectig to a new room:

import asyncio
import codeplot

async def main():
    cP = await codeplot.connect("ws://your-ws-url/your-room-id")

    # Now you can start plotting
    await cP.plot(df.describe())
    await cP.plot(df.head(10))
    await cP.plot(df)

asyncio.run(main())

You can use the public codeplot client & server to start plotting right away:

1. Join the codeplot room at codeplot.co
2. Use the room id to connect to the room using the code above

If you want to use codeplot in a Jupyter Notebook, you can use the following code:

import codeplot
cP = await codeplot.connect("ws://your-ws-url/your-room-id")

# Now you can start plotting
await cP.plot(df.describe())
await cP.plot(df.head(10))
await cP.plot(df)

Run Codeplot on Docker

Instead of using the public codeplot server, you can self-host and run codeplot on your local machine using Docker. To do so, you can use the following command:

curl -s https://raw.githubusercontent.com/codeplot-co/codeplot/master/minirepos/@codeplot-docker/docker-compose.yaml | docker-compose -f - up

Or if you are using docker-compose v2, you can use the following command instead

curl -s https://raw.githubusercontent.com/codeplot-co/codeplot/master/minirepos/@codeplot-docker/docker-compose.yaml | docker compose -f - up

This will start a codeplot server and a client on your local machine, and you can access it at:

• Client: http://localhost:9107
• Server: ws://localhost:9108

Join the codeplot Community

Become part of a forward-thinking community dedicated to advancing data visualization. Connect, engage, and grow with peers on Discord. With codeplot, data visualization is a shared journey. Let's explore new insights together!

License and Pricing

Codeplot is crafted to support a wide range of users, from individuals exploring their personal projects to enterprises seeking to enhance their business processes. To accommodate this diversity, Codeplot adopts a dual-license approach.

Codeplot is free to use for personal and non-commercial purposes.

Only pay if you use Codeplot commercially.

Read more about License and Pricing here.