I have used Python in my previous projects from a previous organization. I got an opportunity to work on existing APIs written in Flask, a Python framework. I made the best use of this opportunity to explore the language and wrote many APIs to supply data to the Front End. Since then I've been loving using Python in my personal projects too. Although I had used node js for the backend earlier, which was quite comfortable for me as a javascript developer, I didn't look back after being introduced to Python. It is an amazing programming language and anyone who is new to the computer science industry can take it up to have a smooth learning experience. For writing a Python program, you need python installed on your computer, which is by default available in most operating systems. Open a terminal and type 'python -v' to know the version which is currently available on your machine. You would also need an IDE or an editor. However, to get started I recommend going with Anaconda Installer for Data Science projects.

To begin the setup, go to anaconda.com/download. Here, you can find the installers for all machine types - Windows, Unix, and Mac. Download the respective installer. Anaconda is a Python distribution that aims to provide everything we need to do data science tasks. Follow the instructions in the installer wizard to complete the installation. Now you are good to begin. Open a terminal and type 'jupyter notebook'. This will launch jupyter notebook in localhost:8888/ port and opens the file directory by default. You will have two more tabs - 'Running' and 'Clusters'. On the top right, select New -> Jupyter Notebook. You can rename the filename. These files would have an ipynb extension.

In order to print a message and check the output, type 'print ('HELLO WORLD')' without quotes. Then click on the Run button on top to see the output right under the program.

Let us now use a dataset from Kaggle. You need to sign in for downloading any dataset. There are a few ways you can sign up, check them and use a way of your comfort to sign up and proceed. Now search for 'video game sales'. Click on the first dataset that has some 16 thousand rows and 11 columns. Download the .csv file and save it in the same folder where the respective Jupyter notebook is saved. Now let us come back to the notebook at localhost:8888 and type the following instructions:

import pandas as pd
fileDetails = pd.read_csv('vgsales.csv')
fileDetails

Now, run the file to see the table from vgsales file, as shown in the below image.

It was great to get started with pandas. I am learning more about Python Pandas and will post more about that in the next posts. Thank you for reading till the end. I strongly suggest you just get started with it. An effort is more important than success and the journey should be the focus and things will fall in place.