Cocalc Jupyter Notebook Ipynb

CoCalc: Collaborative Calculations and Data Science 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 are a powerful web interface designed for interactive computing in multiple programming languages. Although it was initially designed for Python, its functionalities have quickly permeated other programming languagues. It have evolved from IPython, a replacement for the official Python shell in the console It is worth mentioning the main author of IPython is Fernando Perez, a physicist graduate from the Universidad de Antioquia and currently Professor in University of California, Berkeley.

Jupyter provides a rich architecture for interactive computing with: Powerful interactive shells (terminal and Qt-based). A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media. 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 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 By the end of this lecture you will be able to:

Define variables and differentiate between global and local variables. Identify and use different object types in python. Use some of the python's default functions and define your own functions. Introduction to numpy and matplotlib libraries 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. Quick overview of using Jupyter Notebooks in CoCalc. 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 (like this one). 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! Jupyter notebook python_in_jupyter_1/Python in Jupyter Notebook a tutorial.ipynb Some basic data analysis and plotting with pandas and matplotlib Installing python/anaconda/etc.

(we can help you offline) Serious python programming (functions, classes, lambda functions, list comprehensions) How to write, manage, deploy software (e.g. git)

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