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)

Understanding the scikit-learn library

In this section, we will look at the scikit-learn library and will use it to implement a simple predictive model. To do this, we need to understand scikit-learn and how to load the iris dataset to the Jupyter Notebook. We will then take a closer look at how to build a supervised machine learning model using scikit-learn and, using this, we will build a simple predictive model.


scikit-learn is the most popular Python library for doing machine learning. It provides a simple and efficient API with tools for data modeling and data analysis. It is built on top of NumPy, SciPy, and Matplotlib. The following is a screenshot of a Jupyter Notebook:

We do not import the entire...