# The jupyter ecosystem & notebooks logo

Before we get started 1…#

most of what you’ll see within this lecture was prepared by Ross Markello, Michael Notter and Peer Herholz and further adapted for this course by Peer Herholz
based on Tal Yarkoni’s “Introduction to Python” lecture at Neurohackademy 2019
based on IPython notebooks from J. R. Johansson
based on http://www.stavros.io/tutorials/python/ & http://www.swaroopch.com/notes/python
based on oesteban/biss2016 & jvns/pandas-cookbook

Objectives 📍#

learn basic and efficient usage of the jupyter ecosystem & notebooks
- what is Jupyter & how to utilize jupyter notebooks

To Jupyter & beyond#

a community of people
an ecosystem of open tools and standards for interactive computing
language-agnostic and modular
empower people to use other open tools

To Jupyter & beyond#

Before we get started 2…#

We’re going to be working in Jupyter notebooks for most of this presentation!

To load yours, do the following:

Open a terminal/shell & navigate to the folder where you stored the course material (cd)

Type jupyter notebook

If you’re not automatically directed to a webpage copy the URL (https://....) printed in the terminal and paste it in your browser

Files Tab#

The files tab provides an interactive view of the portion of the filesystem which is accessible by the user. This is typically rooted by the directory in which the notebook server was started.

The top of the files list displays clickable breadcrumbs of the current directory. It is possible to navigate the filesystem by clicking on these breadcrumbs or on the directories displayed in the notebook list.

A new notebook can be created by clicking on the New dropdown button at the top of the list, and selecting the desired language kernel.

Notebooks can also be uploaded to the current directory by dragging a notebook file onto the list or by clicking the Upload button at the top of the list.

The Notebook#

When a notebook is opened, a new browser tab will be created which presents the notebook user interface (UI). This UI allows for interactively editing and running the notebook document.

A new notebook can be created from the dashboard by clicking on the Files tab, followed by the New dropdown button, and then selecting the language of choice for the notebook.

An interactive tour of the notebook UI can be started by selecting Help -> User Interface Tour from the notebook menu bar.

Body#

The body of a notebook is composed of cells. Each cell contains either markdown, code input, code output, or raw text. Cells can be included in any order and edited at-will, allowing for a large amount of flexibility for constructing a narrative.

Markdown cells - These are used to build a nicely formatted narrative around the code in the document. The majority of this lesson is composed of markdown cells.
to get a markdown cell you can either select the cell and use esc + m or via Cell -> cell type -> markdown

Code cells - These are used to define the computational code in the document. They come in two forms:
- the input cell where the user types the code to be executed,
- and the output cell which is the representation of the executed code. Depending on the code, this representation may be a simple scalar value, or something more complex like a plot or an interactive widget.
to get a code cell you can either select the cell and use esc + y or via Cell -> cell type -> code

Raw cells - These are used when text needs to be included in raw form, without execution or transformation.

Modality#

The notebook user interface is modal. This means that the keyboard behaves differently depending upon the current mode of the notebook. A notebook has two modes: edit and command.

Edit mode is indicated by a green cell border and a prompt showing in the editor area. When a cell is in edit mode, you can type into the cell, like a normal text editor.

Command mode is indicated by a grey cell border. When in command mode, the structure of the notebook can be modified as a whole, but the text in individual cells cannot be changed. Most importantly, the keyboard is mapped to a set of shortcuts for efficiently performing notebook and cell actions. For example, pressing c when in command mode, will copy the current cell; no modifier is needed.

Mouse navigation#

The first concept to understand in mouse-based navigation is that cells can be selected by clicking on them. The currently selected cell is indicated with a grey or green border depending on whether the notebook is in edit or command mode. Clicking inside a cell’s editor area will enter edit mode. Clicking on the prompt or the output area of a cell will enter command mode.

The second concept to understand in mouse-based navigation is that cell actions usually apply to the currently selected cell. For example, to run the code in a cell, select it and then click the Run button in the toolbar or the Cell -> Run menu item. Similarly, to copy a cell, select it and then click the copy selected cells button in the toolbar or the Edit -> Copy menu item. With this simple pattern, it should be possible to perform nearly every action with the mouse.

Markdown cells have one other state which can be modified with the mouse. These cells can either be rendered or unrendered. When they are rendered, a nice formatted representation of the cell’s contents will be presented. When they are unrendered, the raw text source of the cell will be presented. To render the selected cell with the mouse, click the button in the toolbar or the Cell -> Run menu item. To unrender the selected cell, double click on the cell.

Keyboard Navigation#

The modal user interface of the IPython Notebook has been optimized for efficient keyboard usage. This is made possible by having two different sets of keyboard shortcuts: one set that is active in edit mode and another in command mode.

The most important keyboard shortcuts are Enter, which enters edit mode, and Esc, which enters command mode.

In edit mode, most of the keyboard is dedicated to typing into the cell's editor. Thus, in edit mode there are relatively few shortcuts. In command mode, the entire keyboard is available for shortcuts, so there are many more possibilities.

The following images give an overview of the available keyboard shortcuts. These can viewed in the notebook at any time via the Help -> Keyboard Shortcuts menu item.

The following shortcuts have been found to be the most useful in day-to-day tasks:

Basic navigation: enter, shift-enter, up/k, down/j
Saving the notebook: s
Cell types: y, m, 1-6, r
Cell creation: a, b
Cell editing: x, c, v, d, z, ctrl+shift+-
Kernel operations: i, .

Markdown Cells#

Text can be added to IPython Notebooks using Markdown cells. Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:

http://daringfireball.net/projects/markdown/

You can view the source of a cell by double clicking on it, or while the cell is selected in command mode, press Enter to edit it. Once a cell has been edited, use Shift-Enter to re-render it.

Markdown basics#

You can make text italic or bold.

You can build nested itemized or enumerated lists:

One
- Sublist
  - This
- Sublist - That - The other thing
Two
- Sublist
Three
- Sublist

Now another list:

Here we go
1. Sublist
2. Sublist
There we go
Now this

You can add horizontal rules:

Here is a blockquote:

Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one– and preferably only one –obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than right now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea – let’s do more of those!

You can add headings using Markdown’s syntax:

# Heading 1

# Heading 2

## Heading 2.1

## Heading 2.2

Embedded code#

You can embed code meant for illustration instead of execution in Python:

def f(x):
    """a docstring"""
    return x**2

or other languages:

if (i=0; i<n; i++) {
  printf("hello %d\n", i);
  x += 4;
}

Github flavored markdown (GFM)#

The Notebook webapp supports Github flavored markdown meaning that you can use triple backticks for code blocks

```python
print "Hello World"
```

```javascript
console.log("Hello World")
```

Gives

print "Hello World"

console.log("Hello World")

And a table like this :

| This | is   |
|------|------|
|   a  | table|

A nice HTML Table

This	is
a	table

General HTML#

Because Markdown is a superset of HTML you can even add things like HTML tables:

Header 1	Header 2
row 1, cell 1	row 1, cell 2
row 2, cell 1	row 2, cell 2

Local files#

If you have local files in your Notebook directory, you can refer to these files in Markdown cells directly:

[subdirectory/]<filename>

For example, in the static folder, we have the logo:

<img src="static/NOWA_logo.png" />

These do not embed the data into the notebook file, and require that the files exist when you are viewing the notebook.

Security of local files#

Note that this means that the IPython notebook server also acts as a generic file server for files inside the same tree as your notebooks. Access is not granted outside the notebook folder so you have strict control over what files are visible, but for this reason it is highly recommended that you do not run the notebook server with a notebook directory at a high level in your filesystem (e.g. your home directory).

When you run the notebook in a password-protected manner, local file access is restricted to authenticated users unless read-only views are active.

Markdown attachments#

Since Jupyter notebook version 5.0, in addition to referencing external files you can attach a file to a markdown cell. To do so drag the file from e.g. the browser or local storage in a markdown cell while editing it:

![NOWA_logo.png](attachment:NOWA_logo.png)

Files are stored in cell metadata and will be automatically scrubbed at save-time if not referenced. You can recognize attached images from other files by their url that starts with attachment. For the image above:

![NOWA_logo.png](attachment:NOWA_logo.png)

Keep in mind that attached files will increase the size of your notebook.

You can manually edit the attachement by using the View > Cell Toolbar > Attachment menu, but you should not need to.

Code cells#

When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient. First, we need to explain how to run cells. Try to run the cell below!

import pandas as pd

print("Hi! This is a cell. Click on it and press the ▶ button above to run it")

Hi! This is a cell. Click on it and press the ▶ button above to run it

You can also run a cell with Ctrl+Enter or Shift+Enter. Experiment a bit with that.

Tab Completion#

One of the most useful things about Jupyter Notebook is its tab completion.

Try this: click just after read_csv( in the cell below and press Shift+Tab 4 times, slowly. Note that if you’re using JupyterLab you don’t have an additional help box option.

pd.read_csv(

After the first time, you should see this:

After the second time:

After the fourth time, a big help box should pop up at the bottom of the screen, with the full documentation for the read_csv function:

This is amazingly useful. You can think of this as “the more confused I am, the more times I should press Shift+Tab”.

Okay, let’s try tab completion for function names!

pd.r

You should see this:

Get Help#

There’s an additional way on how you can reach the help box shown above after the fourth Shift+Tab press. Instead, you can also use obj? or obj?? to get help or more help for an object.

pd.read_csv?

Writing code#

Writing code in a notebook is pretty normal.

def print_10_nums():
    for i in range(10):
        print(i)

print_10_nums()

If you messed something up and want to revert to an older version of a code in a cell, use Ctrl+Z or to go than back Ctrl+Y.

For a full list of all keyboard shortcuts, click on the small keyboard icon in the notebook header or click on Help > Keyboard Shortcuts.

The interactive workflow: input, output, history#

Notebooks provide various options for inputs and outputs, while also allowing to access the history of run commands.

2+10

_+10

You can suppress the storage and rendering of output if you append ; to the last cell (this comes in handy when plotting with matplotlib, for example):

10+20;

Previous inputs are available, too:

In[9]

'_'

_i

'In[9]'

%history -n 1-5

   1:
import pandas as pd

print("Hi! This is a cell. Click on it and press the ▶ button above to run it")
   2:
import pandas as pd

print("Hi! This is a cell. Click on it and press the ▶ button above to run it")
   3: pd.read_csv?
   4:
def print_10_nums():
    for i in range(10):
        print(i)
   5: print_10_nums()

Accessing the underlying operating system#

Through notebooks you can also access the underlying operating system and communicate with it as you would do in e.g. a terminal via bash:

!pwd

/Users/peerherholz/google_drive/GitHub/nowaschool_material/school/materials/python

files = !ls
print("My current directory's files:")
print(files)

My current directory's files:
['intro_jupyter.ipynb', 'overview_python.md']

!echo $files

[intro_jupyter.ipynb, overview_python.md]

!echo {files[0].upper()}

INTRO_JUPYTER.IPYNB

Magic functions#

IPython has all kinds of magic functions. Magic functions are prefixed by % or %%, and typically take their arguments without parentheses, quotes or even commas for convenience. Line magics take a single % and cell magics are prefixed with two %%.

Some useful magic functions are:

Magic Name	Effect

You can run %magic to get a list of magic functions or %quickref for a reference sheet.

%magic

Line vs cell magics:

%timeit list(range(1000))

13.6 µs ± 1.56 µs per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

%%timeit
list(range(10))
list(range(100))

1.16 µs ± 63.1 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

Line magics can be used even inside code blocks:

for i in range(1, 5):
    size = i*100
    print('size:', size, end=' ')
    %timeit list(range(size))

size: 100 904 ns ± 57.9 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
size: 200 1.42 µs ± 109 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
size: 300 2.37 µs ± 299 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
size: 400 3.62 µs ± 68 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Magics can do anything they want with their input, so it doesn’t have to be valid Python:

%%bash
echo "My shell is:" $SHELL
echo "My disk usage is:"
df -h

My shell is: /bin/bash
My disk usage is:
Filesystem        Size    Used   Avail Capacity iused ifree %iused  Mounted on
/dev/disk1s1s1   466Gi   9.4Gi    33Gi    23%    394k  347M    0%   /
devfs            206Ki   206Ki     0Bi   100%     714     0  100%   /dev
/dev/disk1s3     466Gi   1.9Gi    33Gi     6%    1.6k  347M    0%   /System/Volumes/Preboot
/dev/disk1s5     466Gi    11Gi    33Gi    25%      11  347M    0%   /System/Volumes/VM
/dev/disk1s6     466Gi   6.8Mi    33Gi     1%      17  347M    0%   /System/Volumes/Update
/dev/disk1s2     466Gi   409Gi    33Gi    93%    3.4M  347M    1%   /System/Volumes/Data
map auto_home      0Bi     0Bi     0Bi   100%       0     0     -   /System/Volumes/Data/home

Another interesting cell magic: create any file you want locally from the notebook:

%%writefile test.txt
This is a test file!
It can contain anything I want...

And more...

Writing test.txt

!cat test.txt

This is a test file!
It can contain anything I want...

And more...

Let’s see what other magics are currently defined in the system:

%lsmagic

Available line magics:
%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %code_wrap  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %mamba  %man  %matplotlib  %micromamba  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode

Available cell magics:
%%!  %%HTML  %%SVG  %%bash  %%capture  %%code_wrap  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile

Automagic is ON, % prefix IS NOT needed for line magics.

Writing latex#

Let’s use %%latex to render a block of latex:

%%latex
$$F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} \mathrm{d} x$$

\[F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} \mathrm{d} x\]

Running normal Python code: execution and errors#

Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:

>>> # Fibonacci series:
... # the sum of two elements defines the next
... a, b = 0, 1
>>> while b < 10:
...     print(b)
...     a, b = b, a+b

In [1]: for i in range(10):
   ...:     print(i, end=' ')
   ...:     

0 1 2 3 4 5 6 7 8 9

And when your code produces errors, you can control how they are displayed with the %xmode magic:

%%writefile mod.py

def f(x):
    return 1.0/(x-1)

def g(y):
    return f(y+1)

Writing mod.py

Now let’s call the function g with an argument that would produce an error:

import mod
mod.g(0)

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
Cell In[34], line 2
      1 import mod
----> 2 mod.g(0)

File ~/google_drive/GitHub/nowaschool_material/school/materials/python/mod.py:6, in g(y)
      5 def g(y):
----> 6     return f(y+1)

File ~/google_drive/GitHub/nowaschool_material/school/materials/python/mod.py:3, in f(x)
      2 def f(x):
----> 3     return 1.0/(x-1)

ZeroDivisionError: float division by zero

%xmode plain
mod.g(0)

%xmode verbose
mod.g(0)

The default %xmode is “context”, which shows additional context but not all local variables. Let’s restore that one for the rest of our session.

%xmode context

Running code in other languages with special `%%` magics#

%%perl
@months = ("July", "August", "September");
print $months[0];

%%ruby
name = "world"
puts "Hello #{name.capitalize}!"

Raw Input in the notebook#

Since 1.0 the IPython notebook web application supports raw_input which for example allow us to invoke the %debug magic in the notebook:

mod.g(0)

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
Cell In[35], line 1
----> 1 mod.g(0)

File ~/google_drive/GitHub/nowaschool_material/school/materials/python/mod.py:6, in g(y)
      5 def g(y):
----> 6     return f(y+1)

File ~/google_drive/GitHub/nowaschool_material/school/materials/python/mod.py:3, in f(x)
      2 def f(x):
----> 3     return 1.0/(x-1)

ZeroDivisionError: float division by zero

%debug

> /Users/peerherholz/google_drive/GitHub/nowaschool_material/school/materials/python/mod.py(3)f()
      1 
      2 def f(x):
----> 3     return 1.0/(x-1)
      4 
      5 def g(y):

ipdb> exit

Don’t forget to exit your debugging session. Raw input can of course be used to ask for user input:

enjoy = input('Are you enjoying this tutorial? ')
print('enjoy is:', enjoy)

Are you enjoying this tutorial? 
enjoy is: 

Plotting in the notebook#

Notebooks support a variety of fantastic plotting options, including static and interactive graphics. This magic configures matplotlib to render its figures inline:

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2*np.pi, 300)
y = np.sin(x**2)
plt.plot(x, y)
plt.title("A little chirp")
fig = plt.gcf()  # let's keep the figure object around for later...

../../_images/76c6a7794d30fea9adfa919ed981e43b76237ec0fb1a0f4d1e307260a7d877ec.png

import plotly.figure_factory as ff

# Add histogram data
x1 = np.random.randn(200) - 2
x2 = np.random.randn(200)
x3 = np.random.randn(200) + 2
x4 = np.random.randn(200) + 4

# Group data together
hist_data = [x1, x2, x3, x4]

group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']

# Create distplot with custom bin_size - works when running jupyter notebooks
# comment in when running the tutorial

# fig = ff.create_distplot(hist_data, group_labels, bin_size=.2)
# fig.show()

# Adapted code needed to include interactive graphic in Jupyter Book
fig.write_html("plotly_example.html")
from IPython.display import HTML
HTML(filename="plotly_example.html")

Jupyter Widgets#

Jupyter Widgets, also known as ipywidgets, are interactive HTML widgets for Jupyter notebooks and the IPython kernel. They provide a powerful way to build interactive GUIs for your notebooks. Widgets can be used to create interactive web applications with sliders, buttons, dropdowns, and more. They are instrumental in making notebooks more interactive, allowing users to visualize and manipulate data in real time.

For more detailed information, tutorials, and examples on using Jupyter Widgets, you can visit the official Jupyter Widgets documentation.

A short interactive example with Jupyter Widgets#

Objective: Create a plot of a sine wave.
Interactivity: Use a slider to control the frequency of the sine wave.

import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display

def plot_sine_wave(frequency=1.0):
    t = np.linspace(0, 2*np.pi, 500)
    y = np.sin(frequency * t)
    
    plt.figure(figsize=(10, 4))
    plt.plot(t, y)
    plt.title('Sine Wave')
    plt.xlabel('Time')
    plt.ylabel('Amplitude')
    plt.grid(True)
    plt.ylim(-1.1, 1.1)
    plt.show()

frequency_slider = widgets.FloatSlider(value=1.0, min=0.1, max=5.0, step=0.1, description='Frequency:')
widgets.interactive(plot_sine_wave, frequency=frequency_slider)

The IPython kernel/client model#

%connect_info

{
  "shell_port": 57949,
  "iopub_port": 57950,
  "stdin_port": 57951,
  "control_port": 57953,
  "hb_port": 57952,
  "ip": "127.0.0.1",
  "key": "1394d9c4-67740c364fe2292a01be1999",
  "transport": "tcp",
  "signature_scheme": "hmac-sha256",
  "kernel_name": "nowaschool"
}

Paste the above JSON into a file, and connect with:
    $> jupyter <app> --existing <file>
or, if you are local, you can connect with just:
    $> jupyter <app> --existing kernel-5e991603-0df9-4910-9892-41716c8641be.json
or even just:
    $> jupyter <app> --existing
if this is the most recent Jupyter kernel you have started.

We can connect automatically a Qt Console to the currently running kernel with the %qtconsole magic, or by typing ipython console --existing <kernel-UUID> in any terminal:

%qtconsole

Saving a Notebook#

Jupyter Notebooks autosave, so you don’t have to worry about losing code too much. At the top of the page you can usually see the current save status:

Last Checkpoint: 2 minutes ago (unsaved changes) Last Checkpoint: a few seconds ago (autosaved)

If you want to save a notebook on purpose, either click on File > Save and Checkpoint or press Ctrl+S.

Jupyter Book#

Jupyter Book is an open-source tool that allows you to publish computational narratives using Jupyter notebooks and Markdown files (among other options). It enables you to create a structured and interactive collection of your Jupyter notebooks, making it easier to share your research, data analyses, and other computational work. Jupyter Book uses the Executable Book Project technology to turn your content into interactive, publication-quality documents. Make sure to have a look at the documentation and examples:

from IPython.display import IFrame

IFrame(src='https://executablebooks.org/en/latest/gallery', width=900, height=600)

Key Features:

Interactive Outputs: Your Jupyter notebooks’ output, including widgets, plots, and tables, can be interactive in your published book.
Cross-references and Citations: Support for academic citations and cross-references across your book content.
Export Formats: Export your Jupyter Book into multiple formats, including HTML, PDF, and EPUB.

JupyterLab#

JupyterLab is the next-generation web-based user interface for Project Jupyter. It offers all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. JupyterLab is designed to be extensible and modular, with the ability to integrate new components that are not available in the classic Jupyter Notebook interface.

Key Features:#

Workspaces: JupyterLab allows you to work with multiple notebooks, text files, datasets, and more in a tabbed workspace, which enhances productivity and organization.
Integrated Components: It integrates various components such as a file browser, terminals, and text editors into a single application, making it a comprehensive environment for interactive computing.
Extensibility: A vast ecosystem of extensions is available for JupyterLab, enabling additional functionalities like Git integration, interactive widgets, themes, and much more.

Differences from Jupyter Notebook:#

While JupyterLab and the classic Jupyter Notebook share many core features, JupyterLab’s interface is more modular and feature-rich.

Here are some notable differences:

Interface: JupyterLab provides a more integrated environment with a sidebar for file browsing, running kernels, command palette, and extensions. It supports dragging and dropping cells within a notebook as well as between notebooks.
Extensions: JupyterLab has a more extensive and easily accessible system for extensions, allowing users to customize and enhance their experience directly from the interface.
File Editing: JupyterLab supports editing of a wider range of text files, including Markdown, JSON, and CSV files, with previews and editing features.

Running JupyterLab#

To start using JupyterLab, you need to have Jupyter installed. If you have Jupyter Notebook already installed, you likely already have JupyterLab. If not, you can install it using pip:

pip install jupyterlab

Once installed, you can launch JupyterLab by running:

jupyter-lab

This command starts the JupyterLab server and opens it in your default web browser. You can then create new notebooks, open existing ones, and explore the various features of JupyterLab.

For more detailed information and advanced features, visit the official JupyterLab documentation.

Before we get started 1…

Contents

Before we get started 1…#

Objectives 📍#

To Jupyter & beyond#

To Jupyter & beyond#

To Jupyter & beyond#

Before we get started 2…#

Files Tab#

The Notebook#

Header#

Body#

Modality#

Mouse navigation#

Keyboard Navigation#

Markdown Cells#

Markdown basics#

Embedded code#

Github flavored markdown (GFM)#

General HTML#

Local files#

Security of local files#

Markdown attachments#

Code cells#

Tab Completion#

Get Help#

Writing code#

The interactive workflow: input, output, history#

Accessing the underlying operating system#

Magic functions#

Writing latex#

Running normal Python code: execution and errors#

Running code in other languages with special %% magics#

Raw Input in the notebook#

Plotting in the notebook#

Jupyter Widgets#

A short interactive example with Jupyter Widgets#

The IPython kernel/client model#

Saving a Notebook#

Jupyter Book#

JupyterLab#

Key Features:#

Differences from Jupyter Notebook:#

Running JupyterLab#

Running code in other languages with special `%%` magics#