What is Python and how Python Works?
Python is a high-level, object-oriented programming language. Most beginners in the development field prefer Python as one of the first languages to learn because of its simplicity and versatility. It is also well supported by the community and keeps up with its increasing popularity. In this Python Tutorial for beginners, we will learn the basics of Python as a programming language, and understand how to get started with it. We will see how to download and install Python and use the popular IDEs to begin coding. We will also discuss jupyter functionality in detail.
What is Python used for?
Next time you are browsing through Google, indulging in your daily dose of Instagram, spending hours watching videos on Youtube, or listening to your favourite music on Spotify, remember that all of them use Python for their programming needs. Python has various uses across applications, platforms, and services. Let us talk about a few here.
The large selection of pre-built Python libraries makes web development a much simpler task. Writing a Python code is less time-consuming due to its clean and simple syntax. This helps with quick prototyping accelerating the ROI of commercial products. The built-in testing frameworks help in shipping bug-free codes. A large selection of well-supported frameworks help in facilitating speedy implementation without compromising on the performance of the solution.
Internet of Things
For the sake of simplicity, let us consider the Internet of Things to be the ‘physical objects connecting an embedded system to the internet’. It plays a vital role in projects that involve big data, machine learning, data analytics, wireless data networks, and cyber-physical systems. IoT projects also deal with real-time analytics.
A programming language should be a bold choice keeping in mind the aforementioned fields of application. This is where Python ticks off all the check-boxes. Additionally, Python is also scalable, expandable, portable, and embeddable. This makes Python system-independent and allows it to accommodate multiple single board computers, irrespective of the operating system or architecture.
Also, Python is excellent for managing and organising complex data. It is particularly useful for IoT systems that are data-heavy. Another reason for Python to be the ideal programming language for IoT applications is its close relation with scientific computing.
Machine Learning has offered a whole new approach to problem-solving. Python is at the forefront of Machine Learning and Data Science due to the following reasons:
- Extensive open-source library support
- Efficient and precise syntax
- Easy integration with other programming languages
- Python has a low entry-point
- Scalable to different operating systems and architectures
How to download Python?
If you are a Windows user and if you have installed Python using Anaconda distribution package which is available at Anaconda.org, you need to go to “Download Anaconda” and then download the latest version for Python 3.6.
Once you download this, it is a pretty simple and straightforward process to follow, and you will have Python installed for you. The next step is to power up an IDE to start coding in Python.
So once you install Python, you can have multiple IDEs or text editors on top of your Python installation.
For text editors, you can use something like Sublime or Notepad++. If you are comfortable using an Integrated Development Environment, then you can use Jupyter. Also, there are other options like Wingware, Komodo, Pycharm, and Spyder.
There are multiple packages available in Python. Some of the instrumental libraries are numpy, pandas, seaborn for visualisation and scipy for calculations and statistics. Others are xlrb, openpyxl, matplotlib, and io.
Python has become the most preferred programming language for enabling data science and machine learning applications. Of course, Python has its advantages; it is swift as compared to other programming languages, even R for that matter.
We can easily say that Python is a swift compiler. Since it is a Java-based programming language, you will be able to extend its applications beyond analytical research, analytical modelling, and statistical modelling. You will be able to create web applications using Python and integrate these web applications directly to your analytical models in the background.
Python is also very easy to integrate with other platforms and other programming languages. It has a common object-oriented programming architecture wherein existing IT developers, IT analysts, and IT programmers find it very easy to transition to the analytics domain.
Because the structure of coding in Python is object-oriented programing architecture, it has excellent documentation support.
7 Reasons Why You Should Use Python
- Readable and Maintainable Code
- Multiple Programming Paradigms
- Compatible with Major Platforms and Systems
- Robust Standard Library
- Open Source Frameworks and Tools
- Simplified Software Development
- Test-Driven Development
R vs Python?
R was developed for statistical analysis applications; on the other hand; Python was developed as a general-purpose programming language. Both of these are essential for those who work with large data-sets, solve machine learning problems, and create complex data visualizations.
Let us have a look at the differences between R and Python
Read more about the difference between R and Python, and which is a better alternative.
How fast we can learn python?
The ease of learning is the main attribute behind Python’s popularity. It is a simple and type free programming language and hence easy to learn. The time taken to learn the language depends on the level you want to achieve with Python. Also, the learning curve could be shorter or longer depending on individual ability.
One would require 6-8 weeks to learn the basics of Python. This will include learning the syntax, key-words, functions and classes, data types, basic coding, and exception handling.
Advanced Python skills are not necessary for all Python professionals. Depending on the nature of your work, you can learn skills such as database programming, socket programming, multithreading, synchronisation techniques etc.
The highly sophisticated Python skills include concepts of Data Analytics, hands-on experience of the required libraries, image processing etc. Each of the specialised skill would need around one week to master.
Read our blog on top 50 interview questions for Python to test your knowledge. It will give you an idea about how much you know about Python and what else is there to learn.
What are the top Python IDE?
There are 7 top IDE’s for Python
Which is the best IDE for Python?
Jupyter is the best IDE for Python, and one of the most widely used IDE for Python. Let us have a look at how to set-up the Jupyter Notebook. Also, let us see what the functionalities of a Jupyter Notebook.
How to Power Jupyter Notebook
Below are the guided steps to power up a Jupyter notebook:
- Open the Anaconda prompt. This is available to you if you have done the installation through the Anaconda installer.
- Once you open the Anaconda Command Prompt, you will see a default path assigned to you. This is the username for the computer that you are using.
- Add the folder paths to this default path (e.g., cd Desktop → cd Python), where you want to open the notebook
- Once you set the path, add the Jupyter notebook using the command jupyter notebook
- Hit enter. This will open the notebook in your local host, i.e., your system
- The path described in the Anaconda prompt will now come on your jupyter notebook home page
- Next step is to open a new Python Notebook. This is your environment to carry out all the coding. You can rename the new notebook (untitled) to what you want and hit ‘rename’.
Keep the anaconda prompt active, the one which you used to power up your Jupyter notebook, while you are working with your Jupyter in your local. If the anaconda prompt is closed, the python is no longer running on your system, and the kernel gets disconnected.
Functionalities in a Python Notebook (Jupyter)
There are multiple options on the toolbar, i.e., File, Edit, View, Insert, Cell, Kernel, Widgets and Help. Let us have a look at some of the features and functionalities one by one.
Save and Checkpoint – Setting a Checkpoint is a fascinating concept. The file is Autosaved at regular intervals, and by setting a check-point, you can skip back a few auto-saves to the set checkpoint. This helps in case you made a mistake in the past couple of minutes or hours. You can always revert to a more stable checkpoint and proceed with your code from there, rather than starting from scratch.
Download as – There are different ways in which you can download a Jupyter Notebook. First is the Classic Notebook, which is the ipynb extension. Before being called a jupyter notebook, it was an Ipython notebook. That is why this extension.
Then you have your .py extension. Save the file with .py extension, and you can import the same to a different IDE for easier use.
Close and Halt – This command closes whatever kernel is running at this particular point in time and halts all the processes.
It includes Cut Cells, Copy Cells, Paste, Delete, Splitting a Cell, Moving up, down, so on and so forth.
So, What is a cell?
Cells are nothing but the code that you type in the dialogue box present on the window. This is a cell, where you type in your code — each cell when run will give you an output.
To run this particular piece of code, you can either click the specific option which says, Run cell or the shortcut key for the same is Shift + Enter.
If you want to explore the other available shortcut options, you can get under Help in Keyboard Shortcuts.
You can cut these cells, paste them later on. You can merge, split, so on and so forth. These are simple items.
You can Toggle your Headers, Toolbars, and Line numbers as well.
These are basic insert operations. You can insert a cell above or below as per the requirement of your code.
If you hit Run All, it runs all the cells that are present in this entire workbook. When you click ‘Run All Above’, it runs all the cells above the selected cell. Similarly, if you click ‘Run All Below’, it runs all the cells that are below the selected cell.
The different types of cells, i.e., Code, Markdown and Raw Convert Files.
One exciting feature that we will be using much in our code files is something called Markdown file. A markdown is nothing but converting whatever you have typed in a cell into a text message.
The cells that you have converted as a Markdown will not be run or considered as a line of code. When you run this cell, it is taken as a text field, and the output is text too. No computation is carried out on this cell.
Here you can see the usual libraries and packages that are available.
You can click on these options, and it will open a guidebook or the reference book, where you can have a look at the various methods that are available within the selected package.
There are various other options you can experiment with when you are working with Jupyter.
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