Pandas - quick overview

Pandas is quite a game changer when it comes to analyzing data with Python and it is one of the most preferred and widely used tools in data munging/wrangling if not THE most used one. Pandas is an open source, free to use (under a BSD license) and it was originally written by Wes McKinney.

This week, we have covered the fundamentals of pandas library. Hopefully, you are already well aware of it's basics. To sum up, here are some links below that might come in handy when dealing with the library:

• https://medium.com/swlh/7-pandas-tricks-cc197174a6ae - 7 Pandas Tricks
• https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf - Cheat sheet (extremely useful)

Since previous week was devoted to NumPy library, let's now compare the two with each other.

Numpy vs Pandas

Unlike NumPy, the Pandas library does not require knowledge of linear algebra or the ability to work with multidimensional arrays (tensors). A DataFrame can be thought of as a table where each row represents a single observation. It is also convenient to work with rows and columns. The Pandas Python library supports the same operations as NumPy: array indexing, statistical research, working with empty (Nan) values, reshaping arrays, extracting individual elements, concatenation. Apart from this, Pandas also allows you to:

reading and writing data, for example, csv or excel table; data grouping; data visualization tools; work with dates; work with string values, etc.

Numpy and Pandas are best friends of a Data Scientist.

In practice, when solving Data Science and Machine Learning problems, one has to work with both considered libraries, because they allow you to quickly and easily process large amounts of data.

For more detailed comparison, check out the website: https://favtutor.com/blogs/numpy-vs-pandas

WRITTEN BY

Mashkhura Eshmatova