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Simran Kaur Arora | 15 Nov, 2022

Python for Data Analysis

Data analysis in programming, as we know, is the procedure by which collected data and information are processed, arranged, and sorted with the help of different software and programs so that they can be scrutinized and used for insight. In simpler words, it is the transformation of raw data into studies, observations, and input. For a company to function, they need to be introspective, and they achieve this through an established mechanism that involves the collection of data. The said data can be feedback, pattern observation, or a simple insight into the financial functioning of the company.

An organized study and analysis of this data can help the company look into its functioning, see how the customers and clients are reacting to it, and also gives a third-party lens to its performance. What we are trying to say is that data analysis is an essential tool for the operation of any company and should be treated in the same way. The best of the software and programming should be at the disposal of its function so that the required nuance can be achieved.

Consider the central place of data analysis in a company, and there are very few programming languages that can be trusted with the same, and Python has emerged as one of the most trusted ones.

Python for Data Analysis

Python stands as one of the most reliable programming languages in the worldwide industry, and as much as 44% of data analysts trust it. It has shown an unprecedented growth in its customer loyalty, and all that comes from the varied functions it serves and the holistic approach it has adopted to data deciphering. It does not limit itself to a simple task but comes with a varied number of applications and other programs that make your work organized and efficient. It is also a general-purpose programming language, which means that it can be used for several purposes and does not limit itself to anyone's kind of operation.

Python for Data Analysis

Setting up and getting started with Python is not difficult at all, but if you're new to the entire domain, you may find yourself at a loss in many instances. Even if you have set up the program and got it running, there might be many aspects of it that you might not be aware of, and you might not utilize the full efficiency of Python. Hence we have laid down a step by step guideline that you can follow to set up the program and get it running.

Step 1: Setting up a Python Environment

Before you can run the Python programs, you would need to set up an environment within which it can run and function. It is the language ambit of the program and a relatively simple process. Getting an Anaconda package is free, and it contains the language and also the many libraries such as NumPy, Pandas, SciPy, Matplotlib. Setting up the package will get the many programs running on your computer, the most important of which is the iPython notebook. This does not require an internet connection as it uses the computer browser to get started.

Step 2: Learning the Basics and the Fundamentals

Python is a computer language, the depths of which cannot be understood through a simple manual or guide. It requires professional training and guidance, and several online courses can acquire the same. If you are not fluent in this program, you can always apply for these courses, and they are sufficient and accommodating. Some of these courses are also free, and they will teach you the basics of operating Python and get you around the fundamentals of the same. This will help you get the hang of it, and you will be better equipped at handling and utilizing it for your maximum benefit.


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Step 3: Know About the Different Python Packages

As we mentioned, Python is a general programming language and not a specific one. This is why it is used for not just decoding data or data organization but encompasses a wide range of libraries and programs that makes the data analysis process whole and complete. Therefore, you must make yourself aware of the different Python packages that contain different programs within it. You must know the features of different programs and the purposes they fulfill. This way, you will be able to get a package that suits your needs the best, and you can curate a viable package for yourself.

Step 4: Practicing with Datasets

The most effective way to master any programming language is through practice, and one can do the same situation in Python. Getting one's hand on datasets and practicing on it will not only give you the skill to work with it but explore the many ways in which you can use it. You can explore your skills and not be limited by anyone else's operation of the same program. You can also get an understanding of your strengths and weaknesses and work on the exemplification and elimination of the particular ones to master Python.

Step 5: Operate the Data

Once you have learned how to deal with data, you can do the same with the real data that you will be receiving. The data that you receive will often be in a very raw state and not make much sense unless it is organized and labeled. This organization, cleaning, and assessment of this data can be done with the help of various programs within Python. You must acquaint yourself with the different functions of the different programs and feed the data to the same so that you can process and operate the data that you receive.

Step 6: Visualizing the data

The use of visuals in data analysis is only gaining more ground every day. This is because of the many platforms and ways in which it can be presented and also in the way it interprets data securely and accessible. Matplotlib is the Python library used for this very function as it executes the data in a visual form such as in graphs, digraphs, flow charts, etc. This makes the reading of the data fun and also provides you with a producer that can be used on a layman's platform, such as your website. It makes it more visually appealing and creates an image of transparency.

Step 7: Analysis of the data

The core use of Python is not only in the presentation and deciphering of data but in understanding it. The processes mentioned above are part of the larger goal of understanding one's business. A lot of this understanding can be manual, but with the use of programs like the Sci-kit Learn and Stats Model, you can get the result for your data. You will be provided with a report that can be later discussed and ascertained. The use of data analysis lies here, where the numbers do not make sense, but with a little help, you get an outlook on how they do.


The steps above will get you started on using Python for Data Analysis. It is a straightforward mechanism, and all it requires is practice for one to master it. Using data sheets and samples to get your hand running is the best way around it. We all know how important the use of data analysis is in a company. A company that runs without it leaves no scope for development and improvement. Hence data analysis is the arc of growth that a company adopts. There are my agencies that also provide the same services, and getting their help is not an issue. They would have the professional experience and the required skill to deliver to you what you are looking for. However, it is not a financially viable option for many small firms and businesses. Following the procedures can be handy for you to master it or can also be used to develop an in-house team that delves into it for you.

The data science community also swears for the simplicity and the ease of this program. Python stands as one of the first general programming languages in the world. It has earned its reputation for too many reasons. Firstly, it has a varied library that serves the user at different stages of analysis and aids them in a lot of other aspects as well. Secondly, the variety of its functions has made it famous, and that is why it is relatively easy and handy to use the program. The number of tutorials and help available online makes it very accessible. Thirdly, it is very user-friendly and stands to serve at simple commands. These many reasons are enough to support its popularity and extent too.

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By Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.

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