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.

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Table of Contents (8 chapters)
Preface
Preface
Who this book is for
What this book covers
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1
The Anaconda Distribution and Jupyter Notebook
The Anaconda Distribution and Jupyter Notebook
The Anaconda distribution
Jupyter Notebook
Using the Jupyter Notebook
Summary
2
Vectorizing Operations with NumPy
Vectorizing Operations with NumPy
Introduction to NumPy
NumPy arrays
Using NumPy for simulations
Summary
3
Pandas - Everyone's Favorite Data Analysis Library
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
4
Visualization and Exploratory Data Analysis
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
5
Statistical Computing with Python
Statistical Computing with Python
Introduction to SciPy
Hypothesis testing
Summary
6
Introduction to Predictive Analytics Models
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
7
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Visualization and Exploratory Data Analysis

Visualization is a key topic for data science and data analysis, and Python provides a lot of options in terms of executing visualizations for different purposes. In this chapter, we will talk about the two most popular libraries for doing visualization in Python, namely, matplotlib and seaborn. We will also talk about the pandas capabilities for doing visualizations.

Let's look into the following various topics that we will discuss in this chapter:

  • Introducing matplotlib
  • Introducing pyplot
  • Object-oriented interfaces
  • Common customizations
  • Exploratory data analysis with seaborn and pandas
  • Analyzing the variables individually
  • The relationship between variables
Previous Section
End of Section 1
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