iPython and Jupyter Notebook with Embedded D3.js

IPython (Jupyter Notebook) Reference Manual. One of Python’s most useful features is its interactive interpreter. Today I'm going to write about Jupyter Notebook, previously known as IPython Notebook. It's a tool that allows you to create interactive python sessions, visualize results, and easily distribute.

When you create a new notebook document, you will be presented with the notebook name, a menu bar, a toolbar and an empty code cell. Notebook name: The name displayed at the top of the page, next to the Jupyter logo, reflects the name of the.ipynb file. Clicking on the notebook name brings up a dialog which allows you to rename it. Debugging, code formatting, Jupyter notebook support. 2 days ago 177 used Verified. Working with Jupyter code cells in the Python Interactive.

Jupyter Notebook Formatting Markdown

There have been several tries to incorporate D3 into IPython:

Though quite progresses have been made in those approaches, they were kind of hacks. Now, we have language agnostic Jupyter which was forked from IPython, we can take the D3 into Notebook without lots of effeorts.

We can start implement D3 into Jupyter from this repo: PyGoogle/PyD3.

The repo is based on this presentation:

The primary idea looks like this:

1. Jupyter reads in HTML DOM as a string via IPython.core.display
So, Jupyter simply imports D3 using HTML API

2. DOM elements manipulation using Python's string.Template.substitute


3. Data format - Panda's JSON

Before we use Jupyter, we'll use the following D3 animation.

Here is the pure D3 animation.

Here is the code for D3 animaition:

We're using the same animation showed in the previous section.

The code for Notebook looks like this:

Notebook is available at Github:
PyD3/D3-Circle-Animation.ipynb

Note: This is an email I got from a reader:

hi K Hong,
I was just reading your post regarding embedding d3 in jupyter notebooks:
http://www.bogotobogo.com/python/IPython/iPython_Jupyter_Notebook_with_Embedded_D3.php
It seems there is an issue importing d3 as an external library:
see https://github.com/mpld3/mpld3/issues/33#issuecomment-32101013
(I had the same issue)
the solution was to place the usage of d3 within a require:
this worked for me.
you can se an example for how to get your code to work with this fix here:
https://github.com/fensterheim/DataProjects/blob/master/D3_example/D3Test.ipynb
according to the issue that was opened it seems that this is a problem with newer versions of d3, therefore it might be worth while noting this in your blog post.

02 Feb 2019

Are you working with Jupyter Notebook and Python? Do you also want to benefit from virtual environments? In this tutorial you will see how to do just that with Anaconda or Virtualenv/venv.

Before we start, what is a virtual environment and why do you need it? A virtual environment is an isolated working copy of Python. This means that each environment can have its own dependencies or even its own Python versions. This is useful if you need different versions of Python or packages for different projects. This also keeps things tidy when testing packages and making sure your main Python installation stays healthy.

A commonly used tool for virtual environments in Python is virtualenv. Since Python 3.3, a subset of virtualenv has been integrated in the Python standard library under the venv module. If you are using Python 2, you can install virtualenv with:

Now, you can create a virtual environment with:

where myenv can be replaced with the name you want for your virtual environment. The virtual environment can be found in the myenv folder. For Python >= 3.3, you can create a virtual environment with:

After you have created your virtual environment, you can activate the virtual environment with:

Jupyter Notebook Highlight Text

To deactivate the virtual environment, you can run deactivate. To delete the virtual environment you just need to remove the folder with the virtual environment (e.g. rm -r myenv). For further information, have a read in the virtualenv documentation or venv documentation.

Let’s have a look how to create an virtual environment with Anaconda. Anaconda is a Python (and R) distribution that has the goal to simplify package management and deployment for scientific computing. After the installation you can create the conda virtual environment with:

where myenv is the name of your new environment. If you want a specific Python version that is not your current version, you can type:

The environment is then stored in the envs folder in your Anaconda directory. After you have created the enviroment, you can activate it by typing:

If you now run python, you’ll see that you are in your freshly created virtual environment. To deactivate the environment you can type conda deactivate and you can list all the available environments on your machine with conda env list. To remove an enviroment you can type:

After creating your environment, you can install the packages you need besides the one already installed by conda. You can find more information on how to manage conda environments in this user guide.

Jupyter Notebook makes sure that the IPython kernel is available, but you have to manually add a kernel with a different version of Python or a virtual environment. First, make sure your environment is activated with conda activate myenv. Next, install ipykernel which provides the IPython kernel for Jupyter:

Jupyter Notebook Font

Next you can add your virtual environment to Jupyter by typing:

Formatting In Jupyter Notebook

This should print the following:

In this folder you will find a kernel.json file which should look the following way if you did everything correctly:

That’s all to it! Now you are able to choose the conda environment as a kernel in Jupyter. Here is what that would look like in JupyterLab:

After you deleted your virtual environment, you’ll want to remove it also from Jupyter. Let’s first see which kernels are available. You can list them with:

This should return something like:

Now, to uninstall the kernel, you can type:

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