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Become a Python Data Analyst

By : Alvaro Fuentes

4.3 (510)

Buy this Book

Become a Python Data Analyst

4.3 (510)

By: Alvaro Fuentes

Buy this Book

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

Who this book is for

What this book covers

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Free Chapter

The Anaconda Distribution and Jupyter Notebook

The Anaconda distribution

Jupyter Notebook

Using the Jupyter Notebook

Summary

Vectorizing Operations with NumPy

Introduction to NumPy

NumPy arrays

Using NumPy for simulations

Summary

Pandas - Everyone's Favorite Data Analysis Library

Introduction to the pandas library

Operations and manipulations of pandas

Answering simple questions about a dataset

Answering further questions

Summary

Visualization and Exploratory Data Analysis

Introducing Matplotlib

Introduction to pyplot

Object-oriented interface

Common customizations

EDA with seaborn and pandas

Analyzing variables individually

Relationships between variables

Summary

Statistical Computing with Python

Introduction to SciPy

Hypothesis testing

Summary

Introduction to Predictive Analytics Models

Predictive analytics and machine learning

Understanding the scikit-learn library

Building a regression model using scikit-learn

Regression model to predict house prices

Summary

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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: