Do you agree with the fact?
#20 WEEKS of Data Science.
#Week 1 — PART 2
“Jupyter NB is most of the data scientists’ best computational notebook of choice”
Everyone has their own views on it but as budding Data Scientists, we prefer to kickstart with JUPYTER Notebook as your tool for programming and computation.
Wanna know Why did we prefer JUPYTER NB?
Jupyter Notebook has seen a strong adoption among the data science community, to an extent where it has become a default environment for research, says Tech resources.
This creative tool supports multi-language programming and therefore became the de facto choice for data scientists for practicing and sharing various codes, quick prototyping, and exploratory analysis.
“If there is one comfortable and easy to use tool that any noob or pro points out to be the best ,it is Jupyter Notebook.”
— 7/10 Scientists
Data scientists are techie peeps who often deal the data with Mathematics, Statistical concepts, and Visualization. Jupyter Notebook plays its prominent role here as it provides numerical simulation, live code, equations, and explanatory text.
Here is the best part!
We will provide you a clear vision of the Jupyter Notebook!
By the time you reach the end of the article, you will have good knowledge about JUPYTER Notebook!
Table of Contents:
- How to install JUPYTER Notebook?
- Jupyter Notebook — Dashboard.
- Understanding the Notebook Interface.
- Creating Your First Notebook.
- What is an ipynb File?
- Cells.
- Keyboard Shortcuts.
- Magic functions.
- Privacy concern.
1. How to install jupyter Notebook?
Click Here # The procedure given is straight forward and you can do it on your own!
or give it a try in your browser itself HERE
2. Jupyter Notebook — Dashboard
The below image is the dashboard of the JUPYTER notebook.
On Windows, you can run Jupyter via the shortcut Anaconda that automatically adds to your start menu, which will open a new tab in your default web browser named(localhost8888/tree)that should look something like the following screenshot.
A Dashboard is nothing but we can Think of it as the launchpad for exploring, editing, and creating our notebooks.
3. Understanding the Notebook Interface
Lets understand the interface by ourselves.
Now that you have an open notebook in front of you take a look around. Check out the menus to see what the different options and functions are readily available, especially take some time out to scroll through the list of commands in the command palette, the small button with the keyboard icon (or just press Ctrl + Shift + P )
4. Creating Your First Notebook
How Belissimo will you feel when you create something on your own! It is just as simple as it looks in the below Gif!
Hooray, You have started with your own notebook!
5. What is an ipynb File?
It will be useful to understand what this file really is.
Each .ipynb file is a text file that describes the contents of your notebook in a format called JSON.
The Python Notebooks that we save using the Jupyter Notebook is in the format ipynb file.
6. Cells
Cells in Jupyter notebook are of three types − Code, Markdown, and Raw.
7. Keyboard Shortcuts
Shortcut keys provide an easier and quicker method of navigating and executing commands in any software or IDE.
To know More about Jupyter Notebook Shortcuts click HERE
8. Magic functions
In this section of the article, we will let you understand magic functions and their functionalities.
9. Privacy
Data privacy is a huge public concern of the digital age, because data breaches continue exposing the personal data of millions of people.
Because you use Jupyter in a web browser, some people are understandably concerned about using it with sensitive data.
However, if you followed the standard install instructions, Jupyter is actually running on your own computer.
If the URL in the address bar starts with http://localhost: or http://127.0.0.1:, it’s your computer acting as the server.
Jupyter Notebook doesn’t send your data anywhere else—and as it’s open-source, other people can check that. (honest about this)
Tips and Tricks
Having a simple trick is always an added advantage. Here we are listing out a few tips and tricks of Jupyter Notebook.
- Comment line and Docstring:
One of the mandatory habits to develop as a programmer is — always try to use or add a comment line to your program even if it’s a few lines of code.
But at the same time keep in mind that don’t overwrite too much of comment this will surely mess up your code and the idea of comment line might get into a dip.(go wrong!)
Any line of strings with # symbol before it is called a single comment line
Similarly, you can perform multi-line comments by using a required multi-line comment within “ “ “ ” ” ” [triple quotes] as a description about the program which is also called as docstring
Single-Line Docstring :
Multi-Line Docstring :
You can access docstrings with the built-in __doc __ attribute and help function.
2. Naming Variables:
Stick on to the naming scheme throughout the code to ensure consistency. This makes anyone understand your code, similar to the comment line, Naming variables play a mandatory role.
[Stay tuned to see more about this in the Python modules Coming Weeeeeeeeeeeeeek !]
3. Use of Libraries:
Your code may require different libraries, whatever libraries you require for your code make sure you import them at the beginning itself this may avoid confusion later on.
[Stay tuned to see more about this in the Python modules Coming Weeeeeeeeeeeeeek !]
4. Code Hiding:
In case of heavy code, You can hide the code if it's not that necessary, this makes your code much tidier and cleaner
Conclusion:
Play around with all techniques mentioned above for better understanding.
“Nothing comes handy unless you practice it.”
Ps :) This is not a comprehensive list of things to do with JUPYTER NB, there are still a lot many things to know about and explore the fun facts!
It is just a warm-up session to get started, and We are delighted you are taking up with us!!
Coming up topics for the Next week in #20 weeks of Data Science.
Launching in T — Minus 1 Weeeeeeeek!
Python Language basic & intermediate,
Useful resources & Blogs,
Paths to learn Python,
Simple hands on step by step Projects with Python.
Feel free to post your comments and we will give our best to bring you the right solution!
Ping it to a friend and create positive vibes that might help you and them! :)
This is the 2 budding data scientists Signing off this WEEK 1 of the 20 weeks of Data Science !!!!!!!!!!!
