NumPy, short for Numerical Python is a library for scientific computing in Python. As the name suggests, it provides a host of tools to conduct mathematical and numerical routines.
One amongst these high-performing tools is the NumPy array. This multidimensional array object is a powerful data structure for efficient computation on vectors and matrices. In this article, we will explore these arrays and their power-packed functionalities.
You are encouraged to follow along with the tutorial and play around with NumPy, trying various things and making sure you're getting the hang of it. Let's get started!
As with any other package we start off by importing the library, NumPy in this case, by its most commonly used alias, np.
Introduction to Data Visualization with Matplotlib
Matplotlib is the most popular Python package for data visualization. It provides a quick way to visualize data from Python and create publication-quality figures in various different formats. Matplotlib is a multi-platform data visualization library built on NumPy arrays. This allows it to work with the broader SciPy stack.
In this article, we are going to explore matplotlib in interactive mode covering 7 basic cases. You are encouraged to follow along with the tutorial and play around with Matplotlib, trying various things and making sure you're getting the hang of it. Let's get started!
Matplotlib and Pyplot
Just as we use the np shorthand for NumPy and the pd...
If real world data-sets contain numeric, texts, alpha-numeric, time-stamps, and various other unstructured data types, then how does one store, retrieve and easily manipulate these multidimensional data-sets? The answer is a data science library like Pandas! Pandas is a powerful data analysis toolkit with high-performance and easy-to-use data structures. Unlike Excel and SQL, it carries a host of useful tools, methods and other functionalities that set it apart when it comes to row-wise and column-wise data manipulations. We will visit these functionalities in this tutorial.