Skip to main content

How to Use Python for Data Analysis?

If you are here for data analysis using Python, then you must be aware that why Python is a great language for developers. So, in short, let me remind you some well-known classic aspects of Python without getting deep into the definition of “What is Python and so on”. Python is open source, free to use, have great collection of libraries, it is structured as well as object-oriented language, and has on offer great readability (unlike other languages) and of course great community support.

Well, you agree that Python is great and now you need to know how to use this language for data analysis. The process involves various steps, let’s get on the route.

Understanding the Type of Data:
The foremost step is to identify the type of data available for analysis. Assume we have a huge data in excel sheets, with millions of rows and columns. Imagine, can you drive value from this data by using basic search and find commands in excel, may be you can. But it is going to be messy and time-consuming. Well, with Python you can use libraries like Numpy and Pandas for parallel processing.

Availability of Data:
Often the data we need to process is not so easily available. It is either to be scraped form web or half-prepared. Well, at times, we need to fetch or scrap data from the web and for that we use libraries like Beautifulsoup and Scrapy: they are good at scraping data from web.

Visualization of Data:
After having arranged data using relevant libraries, you need to see that huge amount of data in charts, histogram, pie-charts, plots and so on. As a matter of fact, at this point you need to have visualization of data. Well, that you can do by using libraries like Matplotlib and Seaborn.

Machine Learning:
ML is very important stage in data analysis, as you know that ML is an exceptionally high computational technique full of mathematics essentials (probability, calculus and matrix operations) that run over millions of data presented in rows and columns. All this computational for ML becomes easy by using scikit-learn, a ML library in Python.

Overcoming Inconsistent Data:
What if you get data in image or text format? It is not plain as you wished in your huge excel sheet, with decimal and integer values. Well worry not; Python can process that as well with the help of open-source library named opencv, another Python library devoted for image processing.

From the above steps, it becomes clear that developers use mostly Python for data cleansing. Data analysis becomes a cake walk if you can clean it like a pro. But still, I will recommend you to watch some YouTube videos for clarity or in case of doubt.


Popular posts from this blog

Six, Five by Binary | Book Review

A few years ago I accidentally came across a novel by William Kent Krueger titled Ordinary Grace. Unaware of my expectations, it turned out to be the best crime cum detective novel I had ever read in my life. So, after that I read many more crime, suspense, and detective fictions, but every time I bring Ordinary Grace for comparison. And this time too with this new novel ‘Six, Five’ written by an Indian writer Binary (probably pen name).

It is a pretty daunting book with over 400 pages and it has unwelcoming cover. Having a boy and girl holding each other’s hand did not make the cover very appealing. Blurb indicates that all Sherlock Holmes fans must go through this book once. I picked up thinking I will be, at least for a week or so, routing through different locations, part of outer and underworld, spies, undercover agents, grumbling detectives, good men and evil men. Often with detective stories, you become a part of their world; instead they enter your world. Much to my surprise,…

Why is Python becoming a Trend among Data Scientists?

Internet technology has set the world on fire. New revolutions are always around the corner. But did you ever notice that nowadays new revolutions are mostly based on technology and driven by data. It is data that is being generated everywhere via the internet. So what’s big deal about it? Well, the data we get from Internet is big data. Websites, social media, servers and so on...all contribute for data. It is data that is driving the demand-supply chain that serves the human race. Since we have been generating humongous amount of data every day, we have data scientists who drive value from it, so that humans can lead life of meaning and purpose and of convenient.

We now got hunch that Python has something to do with big data and work profile of data scientists. Now let’s get back to the point and seek answers as why data scientists are loving languages like Python and R over the traditional programming languages.
Let the pictures below speak for them, as a picture speaks a thousan…

What is Apache Cassandra?