pandas

Working with large csv files would be a real pain for me if it weren't for pandas. I have used pandas in college, in hobby projects and professionally to manipulate data ranging from a few hundred entries to hundreds of thousands of entries, and the performance has always been very good. Pandas has everything you'd ever want for manipulating large amounts of data: speed, efficiency, ease of use, ability to deal with missing values, and much more.

Pandas is one of the most used package in data science field. I used it almost on daily basis to do store, manipulate and do computation on that numerical data. It is really powerful and flexible at the same time. For example if I have to create a new column in existing dataframe based on other 2 column values, I can just index those column numerically and add a new column with a simple add column method.

Pandas is a wonderful library for working with csv and xml data in python. Generally this library associates itself with the Machine learning domain. But I have used this library apart from this domain also. I have used this to copy to a clipboard a set of excel data. I just have to write 2 calls and it's done. Really a great library to work with Excel and CSV.

I used pandas to transform ohlcv data using the yfinace library and modifying it to calculate moving average convergence divergence, simple moving averages and perform technical analysis on stock market data. very efficient and easy to transform data that never had to look beyond it.

I mostly use it to work with tabular data and use it to convert data from one form to the other in terms of the format and the structure of the data. Financial data analysis like stock price is a good use case where Pandas really shines.

I have mostly used this for my hobby projects, used this to extract data from an excel file and transform data into different forms. Working with large amount of data is easier with this library.