Cocalc Importing Notebooks Ipynb

It is a common problem that people want to import code from IPython Notebooks. This is made difficult by the fact that Notebooks are not plain Python files, and thus cannot be imported by the regular Python machinery. Fortunately, Python provides some fairly sophisticated hooks into the import machinery, so we can actually make IPython notebooks importable without much difficulty, and only using public APIs. Import hooks typically take the form of two objects: a Module Loader, which takes a module name (e.g. 'IPython.display'), and returns a Module

a Module Finder, which figures out whether a module might exist, and tells Python what Loader to use Before you start, you need to create a Cocalc account and add your partner as a collaborator [TODO: Link to instructions] Navigate to www.cocalc.com and sign in using your university email address. Click on project NSCI0007_20_21 in the project list to load your project. Your project is essentially a virtual computer hosted in the cloud, and it comes preinstalled with all the software and tools you need to get Python programming straight away! Click on the Handouts folder then the 01_Introduction folder.

DEFINITIONS: Cocalc - the online platform we will be using, providing access to virtual computers hosted in the cloud. Project - Every student has a Cocalc account allowing access to a project, which is a virtual computer including operating system (Linux) and software libraries. Jupyter Notebook - a type of file which contains Python code and formatted text, allowing us to combine computations, results and descriptive text in a single file. It is also sometimes called an IPython Notebook, and has the extension .ipynb. Python - the programming language allowing us to perform scientific computing. Let's build up from the basics: what is a Jupyter Notebook?

A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this: Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. Try it yourself now. Click "Copy & Edit" in the top right to get your own editable version of this notebook, then click the cell above and hit Shift-Enter.

Other renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists use Jupyter Notebooks for their experiments and some suggest that Jupyter Notebooks will be the new format for research papers. A type of cell in which you can write text is called a Markdown cell. Markdown is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown. As the official page of the Jupyter project states:

A notebook is a shareable document that combines computer code, plain language descriptions, data, rich visualizations like 3D models, charts, graphs and figures, and interactive controls. A notebook, along with an editor (like JupyterLab), provides a fast interactive environment for prototyping and explaining code, exploring and visualizing data, and sharing ideas with others. Nowadays a Jupyter Notebook is a de facto standard document format, typically stored in .ipynb files, which records interactive sessions with a kernel. It is made up of cells, which can either store one or more lines of code or formatted text. When you run a code cell, the active kernel session evaluates the piece of code in it and the resulting output is shown below. The combination of communicating back and forth with a kernel and adding descriptive text makes this form of document very attractive.

CoCalc has its own implementation of the user interface to work with Jupyter Notebooks, which supports our unique features such as real-time collaboration, AI Assistant, and TimeTravel. However, the underlying format of .ipynb files is exactly the same. You can bring your existing notebooks to CoCalc and resume working, or you can download your work from CoCalc to a local computer or another cloud platform, there is no vendor-lock-in! Most of the time you don’t have to think much about the Jupyter kernel which you are using, because your notebook already has the right kernel selected or your default kernel is picked automatically... But when you want or need to, you can choose the programming language and environment by selecting a kernel explicitly. See Jupyter Kernel Selection

This page describes how to install a Python package in a CoCalc project. CoCalc already includes hundreds of packages for several Python environments. Check first if the lib you’re looking for is already installed! Related: Custom Jupyter Kernel, Setup Jupyter Extensions and Install R Packages. pip install directly from git repository Configure a Jupyter kernel for my custom Anaconda environment

Jupyter Notebooks are an emerging format for sharing scientific calculations in a standardized document. It is made up of a list of input-cells (usually containing code) and corresponding output cells (containing text and/or graphics). There are also "Markdown" cells, like the one you're reading right now, containing formatted text to explain the content. To evaluate a cell, select the input cell (click on it, or put the cursor inside of it), and either press the play-button in the button row or press the Shift+Return keys. Usually, you should be able to go through a full notebook by pressing Shift+Return for evaluating one cell after another. Try it here after reading this introduction!

Each Jupyter Notebook has a corresponding "Kernel". It defines the language and back-end you are using when running code in a cell. Look at the top right, there it tells you what the current one is! In the menu, you can select the "Kernel" you want to use. For this notebook here, it needs to be Python 3 by Anaconda. Our Jupyter notebooks are an enhanced version of the legacy implementation.

In addition to the standard features, we offer concurrent editing (Google Docs style), integrated chat, detailed revision history, and a faster more scalable interface. Jupyter Notebooks (.ipynb files) are a cornerstone of data science, machine learning, and interactive programming. They allow you to combine code, visualizations, and narrative text in a single, shareable document. However, one common challenge is reusing code from one notebook in another. The default workaround—converting the source notebook to a .py file—can be tedious, especially if the source notebook contains interactive elements (e.g., widgets, plots) or relies on Jupyter-specific magic commands that don’t translate cleanly to... In this guide, we’ll explore four methods to import an .ipynb file directly into another notebook without converting it to a .py file.

Whether you need to reuse functions, variables, or entire workflows, these methods will help you keep your code modular and avoid redundant work. Converting .ipynb to .py works for simple cases, but it has limitations: Directly importing .ipynb files avoids these issues, keeping your workflow streamlined and your project organized. We’ll cover four methods, ranging from simple built-in commands to dedicated libraries. Choose the one that best fits your workflow!

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