Why Pandas Library Has Replaced MS Excel For Data Analysis?

Gone were the days when people used to work with hundreds of Excel sheets to maintain the records and analyze the data. In the era of data, programming languages like Python already contain more than 1 lakhs of libraries. Pandas library is one such library that has been used nowadays for data analysis.

Data analysis tasks are getting huge with each passing day, as the amount of data is increasing exponentially due to the availability of the internet and mobile phones.

Big companies like Google, Meta, Microsoft, and many more are generating millions of bytes of data on any given day. With such humongous data, there occurred a need to analyze the data with more advanced tools than the ones we were using 10 years ago.

Pandas is a fast, easy to use, powerful, and flexible library that has been written in Python programming language and is in the trend for now. It is a software library that is used to analyze unstructured data using pre-built functions.

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Consequences of MS Excel

MS Excel is undoubtedly one of the most powerful tools that we have used but it has got a lot of limitations. While working with Bigdata, it is nearly impossible to work on MS Excel.

Microsoft Excel has a limit of 10,48,576 rows as of 2010. This number keeps increasing over a few years but the last update was in the year 2010. As of the 2010 update, there can be a total of 16,384 columns.

With this limitation, MS Excel loses its power in front of Bigdata.

Pandas Library Has Replaced MS Excel

While data professionals work on a huge amount of data, Pandas library comes into the picture for its powerful abilities. Pandas library has been used for data analysis by almost 60% of companies around the globe.

The development of Pandas library started in the year 2008 and it went open-source in the year 2009. In the year 2012, the first edition of Python For Data Analysis was published.

Whether your data is in CSV format, text files, ms excel, SQL database, or HDF5 format, Pandas library can work incompatibility with all of these and manipulate the data according to your needs.

Working with high-performing datasets, the Pandas library can be a life-saver.

What is the scope of Pandas library?

Talking about the data analytics domain, it is one of the most trending job profiles on the globe because of the change in the business environment. With the rise of machine learning and artificial intelligence, the importance of data has increased significantly and is only going to increase in the future.

Whether you’re a working professional, a student, or a fresher, you can start by learning Python programming language to get started with your career as a data professional.

After learning Python, you can move ahead with the Python libraries like NumPy, Pandas, Matplotlib, and Seaborn. All of these libraries are developed in Python programming language and have been used for data analysis.

Also, all of these libraries are used as standalone technologies and have various job vacancies available in the market. The minimum package of a data analyst with the knowledge of Pandas library is somewhere around 6-10 Lacs in the market.

Pandas Library

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