#### 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)
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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# Using NumPy for simulations

Now let's learn how to use NumPy in a real-world scenario. Here, we will cover two examples of simulations using NumPy, and in the process, we will also learn about other operations that we can do with arrays.

# Coin flips

We will look into a coin flip, or coin toss, simulation using NumPy. For this purpose, we will use the randint function that comes in the random submodule from NumPy. This function takes the low, high, and size arguments, which will be the range of random integers that we want for the output. So, in this case, we want the output to be either 0 or 1, so the value for low will be 0 and high will be 2 but not including 2. Here, the size argument will define the number of random...