Book Image

Become a Python Data Analyst

By : Alvaro Fuentes
Book Image

Become a Python Data Analyst

By: Alvaro Fuentes

Overview of this book

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python.
Table of Contents (8 chapters)

Common customizations

One nice thing about matplotlib is that it allows us to tweak every single element of a plot. We will see some of the common customizations that you will always do when working with matplotlib when performing data analysis.

First, let's generate some data to work on using the following lines of code:

# Generating data
x = np.linspace(-np.pi, np.pi, 200)
sine, cosine = np.sin(x), np.cos(x)

We will now look into each customization feature in matplotlib.


Colors are associated with everything that is plotted in the figures. Matplotlib supports a very robust language for specifying colors that should be familiar to a wide variety of users.