#### 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.
Preface
Free Chapter
The Anaconda Distribution and Jupyter Notebook
Vectorizing Operations with NumPy
Pandas - Everyone's Favorite Data Analysis Library
Visualization and Exploratory Data Analysis
Statistical Computing with Python
Introduction to Predictive Analytics Models
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# Vectorizing Operations with NumPy

In this chapter, we will learn about Numerical Python (NumPy) extensions, which is a library for the Python programming language, what it is, and why we need it. We will also look at arrays, which are the most important type of objects in the numpy library. We will learn how to work with arrays, what the most important methods are, and the attributes that we can use with arrays. Then, we will apply our knowledge and do some simulations to see how we use NumPy in the real world. By the end of this chapter, you will know all the foundations that you need to work with other libraries in Python's Data Science Stack, such as Matplotlib. We will also get into some motivating examples to see why we need NumPy and the main problem it solves.

We will cover the following topics:

• Introduction to NumPy
• NumPy arrays creation, methods, and attributes...