Numpy and Pandas Help guide.

Given the fact that it’s one of the fundamental packages for scientific computing, NumPy is one of the packages that you must be able to use and know if you want to do data science with Python. It offers a great alternative to Python lists, as NumPy arrays are more compact, allow faster access to reading and writing items, and are more convenient and more efficient overall.

In addition, it’s (partly) the fundament of other important packages that are used for data manipulation and machine learning which you might already know, namely, Pandas, Scikit-Learn, and SciPy:

• The Pandas data manipulation library builds on NumPy, but instead of the arrays, it makes use of two other fundamental data structures: Series and DataFrames,
• SciPy builds on Numpy to provide a large number of functions that operate on NumPy arrays, and
• The machine learning library Scikit-Learn builds not only on NumPy, but also on SciPy and Matplotlib.

The above guide provides a brief explanation of what the Python library has to offer and what the array data structure looks like, and goes on to summarize topics such as array creation, I/O, array examination, array mathematics, copying, and sorting arrays, selection of array elements and shape manipulation.

NumPy arrays are often preferred over Python lists, and you’ll see that selecting elements from arrays is very similar to selecting elements from lists.

Pandas: Workflow and Help Guide

The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built.

The fast, flexible, and expressive Pandas data structures are designed to make real-world data analysis significantly easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming.

The Pandas help guide will guide us through the basics of the Pandas library, going from the data structures to I/O, selection, dropping indices or columns, sorting and ranking, retrieving basic information of the data structures you’re working with to applying functions and data alignment.