A Beginner’s Guide to Python for Data Science - Part 3 Introduction to Jupyter Notebooks

 


In the last article, we introduced the method to create a python project in PyCharm.


Now, it’s time to turn our attention to the jupyter notebook itself. We see different options, some code lines, something named “Untitled”, informing us about the last checkpoint, etc.


The first thing we can do is give our notebook a name.


We can do this by clicking on the ‘untitled’ area, and as soon as we click that, jupyter provides us with an input box to rename the file, as shown below:




We will name the notebook “Intro to Python”.

Now we will look at the options provided in the ribbon just before the quick options.
The first option in the ribbon is “File”. As soon as we click on it, we can see that all options relevant to handling the file, such as creating a new file, opening an existing file, and duplicating and saving files in multiple different formats, are available with a single button press.

The following screenshot gives you a quick view of all the options discussed above:




One of my favourite shortcuts is the “Shift + Enter” for running the current cell. This command runs the cell and generates a new empty cell below for us to keep working.


As we can see in the above image, as soon as we run the first cell using “Shift + Enter”, we receive an empty cell below, highlighted as the current cell indicated by the blue box surrounding it.
Now let’s say we want to insert cells above the current cell. We can achieve this by pressing the “A” key when the cell is in blue, also known as the “Command mode”.
When you click inside the cell, you will observe that the cell boundary has turned green from the previous colour of blue.
The other way to put across the same information is to say that the cell has changed from “Command Mode” to “Edit Mode”. You can see this in the below image:

We can shift from the edit mode to the command mode by pressing the escape key on the keyboard.
And there we have it. I hope you have found this useful. Thank you for reading.


Until next time!

Comments

Post a Comment

Popular posts from this blog

A Beginner’s Guide to Python for Data Science - Part 1 Installing the required software

Image
  Let’s discuss how someone interested in coding and data science can begin their journey. You see many people learning Python and data science. Once they have their data analyst or data scientist jobs, they lead a comfortable lifestyle thanks to the value their skillset generates for society, directly allowing the individual to earn a lot of money. You decide to give it a try, and you get yourself a laptop. Now, the questions you get are something like the following: Where do you begin? Which course do you take? These are critical questions to be answered if you want to reach your desired goal. With the available options on the internet, you might feel a little dizzy just going through choices of the various courses. This article is my attempt to make your journey a little more comfortable. I will focus on the Python language, but be aware that we have many coding languages to choose from for learning this skill set. We will begin by installing the required software for learning data
Read more

A Beginner’s Guide to Python for Data Science - Part 5 Adding Comments in Python

Image
  In the  last article , we introduced statements and indentation in Python. Next, let’s discuss commenting in Python. Comments are   descriptions of the code   under consideration. So we can write one or more lines of explanation. There can be times when you need to write some long and complicated code for a task. After it is complete, you might save the file somewhere, but when you refer to the code sometime in the future, let’s say, after three or four months, you may not be able to understand the code you have written yourself! To help yourself and anyone going through your code, in the hope of understanding its utility, you should generously use comments to clarify the written code. Commenting is necessary, especially for the complicated functions you might have created for a specific task. There are a few ways to declare a comment inside Python. The easiest way is to use a hash symbol (#). When we use the hash symbol inside our code, the python interpreter ignores whatever is pre
Read more

A Beginner’s Guide to Python for Data Science - Part 4 Statements and Indentation in Python

Image
  In the  last article , we introduced the jupyter notebook and its functionality. Now that we have learnt the basics of the jupyter notebook, it’s time we get to the meat of the matter, i.e. time to start learning Python. Statements: The first thing we will discuss is “Statements and Indentation”. A Statement is any instruction or set of commands given to Python, or we can say that a statement is any code executable inside Python. So a simple addition that we performed while checking the installation is an example of a statement. There are two types of statements that you would generally see in Python, they are: Single-line statement. Multi-line statement. Let’s see an example of a multi-line statement. We will add the first six numbers for the task. We will run the following command: my_variable = (1 + 2 + 3 + 4 + 5 + 6) As soon as we print the variable we see that we get the sum of desired numbers. In the above code, I entered the entire addition in one line, which could also have b
Read more