1. There are two types of magic commands available with Jupyter Notebook/Lab: Line Magic Commands: It applies the command to one line of the Jupyter cell as its name suggests. Cell Magic Commands: It applies the command to the whole cell of the notebook and needs to be kept at the beginning of the cell.
2. Markdown cell displays text which can be formatted using markdown language. In order to enter a text which should not be treated as code by Notebook server, it must be first converted as markdown cell either from cell menu or by using keyboard shortcut M while in command mode.
3. The text of the quick referance sheets comes from the IPython%quickref magic command. To download an image below, right click on the link and select 'Save Link As'. Basic Help.svg Download Magic Help.svg Download For more details of magic usage see the Built-in magic commands page in IPython's documentation.

• Jupyter Tutorial

Jupyter Notebook Cheat Sheet Jupyter IPython Notebook, Here are some of the commonly used Magic commands in jupyter Notebook. Statement, Explanation, Example.%magic, Comprehensively lists The text of the quick referance sheets comes from the IPython%quickref magic command. JUPYTER NOTEBOOK CHEAT SHEET Learn PYTHON from experts at https. Jupyter Notebook Create new. Saving/Loading Notebook Edit Cells Magic Commands View Cells.

• IPython

• Jupyter

• QtConsole

• JupyterLab

• Jupyter Resources

• Selected Reading

Magic commands or magic functions are one of the important enhancements that IPython offers compared to the standard Python shell. These magic commands are intended to solve common problems in data analysis using Python. In fact, they control the behaviour of IPython itself.

Magic commands act as convenient functions where Python syntax is not the most natural one. They are useful to embed invalid python syntax in their work flow.

Types of Magic Commands

There are two types of magic commands −

• Line magics
• Cell magics

Line Magics

They are similar to command line calls. They start with % character. Rest of the line is its argument passed without parentheses or quotes. Line magics can be used as expression and their return value can be assigned to variable.

Cell Magics

They have %% character prefix. Unlike line magic functions, they can operate on multiple lines below their call. They can in fact make arbitrary modifications to the input they receive, which need not even be a valid Python code at all. They receive the whole block as a single string.

To know more about magic functions, the built-in magics and their docstrings, use the magic command. Information of a specific magic function is obtained by %magicfunction? Command. Let us now describe some of the built-in line and cell magic commands.

Built-in line magics

This magic function makes a function automatically callable without having to use parentheses. It takes three possible mode parameters: 0 (off), 1 (smart) is default or 2 (always on).

%automagic

Magic functions are callable without having to type the initial % if set to 1. Without arguments it toggles on/off. To deactivate, set to 0.

The following example shows a magic function %pwd (displays present working directory) being called without leading % when %automagic set to 1

%cd

This line magic changes the current directory. This command automatically maintains an internal list of directories you visit during your IPython session, in the variable _dh. You can also do ‘cd -<tab>’ to see directory history conveniently.

Usage

The %cd command can be used in the following ways −

• %cd <dir> − Changes current working directory to <dir>

• %cd. − Changes current directory to parent directory

• %cd − changes to last visited directory.

%dhist

This magic command prints all directories you have visited in current session. Every time %cd command is used, this list is updated in _dh variable.

%edit

This magic command calls upon the default text editor of current operating system (Notepad for Windows) for editing a Python script. The script is executed as the editor is closed.

%env

This magic command will list all environment variables. It also reads value of particular variable or set the value of environment variable.

Usage

The %cd command can be used in the following ways −

• %env − Lists all environment variables

• %env var − Gets value for var

• %env var val − Sets value for var

%gui [GUINAME]

When used without argument this command enables or disables IPython GUI event loop integration. With GUINAME argument, this magic replaces the default GUI toolkits by the specified one.

%lsmagic

Displays all magic functions currently available

%matplotlib

This function activates matplotlib interactive support during an IPython session. However, it does not import matplotlib library. The matplotlib default GUI toolkit is TkAgg. But you can explicitly request a different GUI backend. You can see a list of the available backends as shown −

The IPython session shown here plots a sine wave using qt toolkit −

While using Jupyter notebook, %matplotlib inline directive displays plot output in the browser only.

%notebook

This function converts current IPython history into an IPython notebook file with ipynb extension. The input cells in previous example are saved as sine.ipynb

%pinfo

This function is similar to object introspection ? character. To obtain information about an object, use the following command −

This is synonymous to object? or ?object.

%precision

This magic function restricts a floating point result to specified digits after decimal.

%pwd

This magic function returns the present working directory.

%pylab

This function populates current IPython session with matplotlib, and numpy libraries.

%recall

When executed without any parameter, this function executes previous command.

Note that in %recall n, number in front of it is input cell number. Hence the command in the nth cell is recalled. You can recall commands in section of cells by using command such as %recall 1-4. Current input cell is populated with recalled cell and the cursor blinks till the enter key is pressed.

%run

This command runs a Python script from within IPython shell.

%time

This command displays time required by IPython environment to execute a Python expression.

Jupiter Notebook Magic Commands Cheat Sheet Printable

%timeit

This function also displays time required by IPython environment to execute a Python expression. Time execution of a Python statement or expression uses the timeit module. This function can be used both as a line and cell magic as explained here −

• In line mode you can time a single-line.

• In cell mode, the statement in the first line is used as setup code and the body of the cell is timed. The cell body has access to any variables created in the setup code.

%who

This line magic prints all interactive variables, with some minimal formatting. If any arguments are given, only variables whose type matches one of these are printed.

IPython Custom Line Magic function

IPython’s core library contains register_line_magic decorator. A user defined function is converted into a line magic function using this decorator.

Section author: Robert Nikutta <nikutta@noao.edu>

• Basic notebook usage
• Troubleshooting

Since October 2020 JupyterLab is the default notebook interface at Astro Data Lab. However, the classiceJupyter Notebook interface is still available if you need it, and thispage refers to that classic interface.

To switch from JupyterLab to Jupyter Notebook, click in the Menu on Help-->LaunchClassicNotebook

It will open in a separate browser tab.

A very convenient way to do data science is interactively in yourbrowser, for instance through Jupyter notebooks.

Jupyter notebooks are self-contained programs (for Data Lab they arewritten in Python), which can be loaded and run right inside yourbrowser. This is very convenient, as you can use the entire power ofData Lab and of the Python language, without giving much thought tothe installation and configuration of software. Gateway hd display driverdigitalfront.

You can recognize Python notebooks by their file name suffix.ipynb, i.e. my_first_notebook.ipynb. This file stores all theinput commands that you use in the notebook, as well as the output thatthese commands generate (for instance, tables and graphs).

Data Lab was built from the start with Python notebooks in mind, andprovides a containerized notebook server for registered users, as well as a more restrictedpublic notebook server (if youwant to try it out before signing up).

There are three ways of using Data Lab through a Jupyter notebook:

1. Data Lab’s public notebook server

• No user account required
• Your work will only persist for the duration of the session. Download the notebook if you want to preserve it.
• No virtual storage allocation
• Access is limited to some databases

2. Data Lab’s registered users notebook server

• User account registrationrequired
• Saved notebooks will persist as long as you want to keep them
• Your account comes with a generous allocation of virtual storagespace. You can use it to store query results, upload/downloadfiles to/from, share files with collaborators, perform deeperanalysis on data, etc.
• Access to all database holdings at Data Lab

3. Run a notebook server on your own computer

• You need to install the Data Lab client and interfaces on your computer

Launch any of these notebook servers (as described in the abovesection):

1. Data Lab’s public notebook server: https://datalab.noao.edu/notebooks
2. Data Lab’s notebook server for registered users: https://datalab.noao.edu/devbooks
3. Launch a notebook server on your own computer: type jupyternotebook in a terminal; a browser window pops up

Data Lab’s notebook server for registered users (2nd option above)will prompt you for your registered user name and password.

If you don’t have a Data Lab account yet, you can register for oneright away.

In all three cases you should see a notebook “dashboard” that lookssimilar to this:

Jupyter Cheat Sheet Pdf

If you’re using the Data Lab notebook servers (public or registered),the dashboard will be pre-populated with several example notebooks,ranging from simple “getting started” notebooks, through moretechnical “how to” notebooks, to “science example notebooks” thatguide you from a science questions, through writing data queries, todata analysis and visualization.

• The notebooks/ directory has the default set notebooks, as theywere when you first created your account.
• The notebooks-latest/ directory is read-only, and contains thedefault set notebooks as of right now. You can get a copy of them easily.
• The vospace/ directory is a read-only link to your virtualstorage directory.

You can either load any of the notebooks by navigating through thedirectories and clicking on its name, or create a new notebook to start with a clean slate. You can alsoorganize your notebooks into new sub-directories.

Data Lab’s Jupyter environment comes with a simple terminal emulatorbuilt-in. It is somewhat limited in the commands that it will let youexecute, but is very useful in many cases. To bring up the terminal,click in the Jupyter dashboard on the “New” button (top-right corner),then on “Terminal”:

A new browser tab will open, with the terminal and a friendly andinformative welcome message:

You can use the terminal to get the newest versions of all defaultnotebooks (which may have changed since your notebooks/ directorywas first created.

A built-in shell function getlatest does the work. To get thelatest default notebooks, simply type

This copies the content of notebooks-latest/ to a new directorynamed with the current date and time.

You can also provide an explicit target directory name:

The target directory will be created if it doesn’t existyet. Otherwise, for every file that is already present in the targetdirectory, you will be asked whether you wish to overwrite it.

Many of the notebook functions can be accessed through quick andconvenient keyboard shortcuts. These are available when a notebook isloaded and/or edited (see below). Most shortcuts are invoked bypressing the ESC key (release it after pressing), which enters thecommand mode of the notebook, followed by one or two additionalkeys, which are pressed simultaneously or in sequence (releasing theprevious key before pressing the next one), depending on the functionyou wish to invoke.

A very useful keyboard shortcut combination is ESC,h (i.e. pressthe ESC key, release it, then press the h key), which bringsup a window with all major keyboard shortcuts… how convenient!

If you want to load a notebook that is visible in the dashboard,simply click on its name. A new browser tab will open and the notebookloaded.

If your notebook file is located under one or several sub-directoriesin the dashboard, simply click your way through to it by traversingthe directories in the dashboard view.

You cannot access .ipynb files that are outside (i.e. above) theroot of the directory tree in the dashboard. You can always jumpback by clicking on the “home” icon in the dashboard view:

Upon opening a notebook file, the upper right corner of the new tabwill display status notifications, including (very briefly) a Kernelready status message.

This means that the Python kernel is ready for your input.

To create a new notebook, click on the New button in the upperright corner of the dashboard, and select a Python kernel (herePython3).

A new browser tab will open with an empty notebook awaiting your input:

When you create a new notebook, it’s title will be Untitled(possibly with an auto-incrementing counter appended to it,e.g. Untitled3, etc.).

The title of a notebook also serves as the root of the notebook filename to be saved. It is a good idea to give the notebook a descriptivetitle, e.g. one that helps you recognize later what this notebook wasall about.

To change the notebook title, click on the current title (which is“Untitled”). A small window will pop up:

You can now edit the highlighted text (here Untitled), then click on the OK button to confirm your change

Note the changed title (here Pythagoras). On the next save, thefile name of this notebook will be Pythagoras.ipynb. (The standard extension is automatically forced.)

Commands and text are entered into so-called cells inside a notebook.

Each cell can contain one or more (or many!) lines of input. Thecontent of the cell will be only executed when you explicit tell it todo so (via keyboard shortcut Shift+ENTER).

In the example above we have instructed the notebook to import theData Lab authentication client, under a shortcut name ac. Once theinput was typed in the cell, a Shift+Enter (pressedsimultaneously) executed the content of the cell. The import wasperformed, and a new empty cell appeared beneath the first cell,awaiting new input.

When the execution of a cell does not return immediately, the cell number at the left changes temporarily to [*] during processing - during a long task this might be your only evidence that something is happening.

Each cell can be set to one of several input modes (e.g. code,markdown, etc.). The default is code (meaning valid Pythoncommands). In the example below we switch to markdown which isgood for formatting, headers, descriptive text, etc.

After changing the cell type to markdown we enter a large title(H1 size):

Finally, a Shift+ENTER keyboard shortcut executes this cell aswell, rendering the markdown content:

As before, a new empty cell is created right underneath the renderedone.

You can also use keyboard shortcuts to switch the cell type; e.g., tochange a cell type to markdown, press ESC,m. To change it to code,type ESC,y.

To run all cells in a notebook, one after another, starting at thetop, click on the Cell entry in the menu bar, then on Runall,as shown below:

The entire notebook will be executed.

Your notebook is automatically saved after a set period of time(typically 120 seconds). If you wish to save the file manually at anygiven time, either type ESC,s or click on the Save andCheckpoint icon (left-most icon that looks like a floppy disk).

A notebook .ipynb file also saves all output generated by thenotebook cells. This makes a notebook very portable, and you caneasily share it with others, email it to yourself, etc.

To logout from any of the Data Lab notebook servers, click on the Logout button in the upper-right corner (either from a notebook browser tab, or from the notebookdashboard tab).

The public server does not have a logout option - logout is automatic when the last open window is closed.

Sometimes you may wish to restart a kernel, i.e. restart the Pythonnotebook you are currently running.

One reason for doing so is when you have changed some external .pyfile that your notebook is importing. If you try to import that fileagain, the new changes won’t be picked up (unless you explicitlyreload that file). Another common reason to restart a runningkernel is when there is some technical hiccup with the notebook; arestart of the kernel can help resolve the issues.

To restart a kernel, either type ESC,0,0 (Escape,zero,zero keys),or click in the menu bar on Kernel, then on Restart:

Whether you used the mouse or the keyboard shortcut, a confirmationdialog will pop up:

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Confirm your wish to indeed restart the notebook kernel by eitherclicking on the Restart button, or by hitting the ENTER key.

The kernel will be restarted, and you can re-run all relevant cells init. (You must re-run the cells, since a kernel restart forgets thecurrent state of memory variables).

If you experience technical difficulties in executing your notebook,and you have already tried to remedy them by restarting thekernel (to no effect), then the problem might beon the Data Lab notebook server side.

if you are logged in as a registered user, then you can then try to restart the notebook server. For this, click onthe ControlPanel button in the upper-right corner (either from anotebook browser tab, or from the dashboard tab):

You should see two buttons. Click on the red one saying StopMyServer:

Wait a few seconds until the red button disappears. The notebookserver has been shut down, and you can restart it my clicking on theremaining green button MyServer:

On the public server, quitting all windows has the effect of restarting the server - at the price of losing unsaved work!