You're reading from Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

Product type Paperback

Published in Mar 2017

Publisher Packt

ISBN-13 9781787127487

Length 330 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Analysis

Author (1):

Ivan Idris

View More author details

Table of Contents (16) Chapters

Preface

1. Getting Started with Python Libraries

2. NumPy Arrays FREE CHAPTER

3. The Pandas Primer

4. Statistics and Linear Algebra

5. Retrieving, Processing, and Storing Data

6. Data Visualization

7. Signal Processing and Time Series

8. Working with Databases

9. Analyzing Textual Data and Social Media

10. Predictive Analytics and Machine Learning

11. Environments Outside the Python Ecosystem and Cloud Computing

12. Performance Tuning, Profiling, and Concurrency

A. Key Concepts

B. Useful Functions

C. Online Resources

Chapter 1. Getting Started with Python Libraries

Welcome! Let's get started. Python has become one of the de facto standard language and platform for data analysis and data science. The mind map that you will see shortly depicts some of the numerous libraries available in the Python ecosystem that are used by data analysts and data scientists. NumPy, SciPy, Pandas, and Matplotlib libraries lay the foundation of Python data analysis and are now part of SciPy Stack 1.0 (http://www.scipy.org/stackspec.html). We will learn how to install SciPy Stack 1.0 and Jupyter Notebook, and write some simple data analysis code as a warm-up exercise.

The following are the libraries available in the Python ecosystem that are used by data analysts and data scientists:

NumPy: This is a general-purpose library that provides numerical arrays, and functions to manipulate the arrays efficiently.
SciPy: This is a scientific computing library that provides science and engineering related functions. SciPy supplements and slightly overlaps NumPy. NumPy and SciPy historically shared their code base but were later separated.
Pandas: This is a data-manipulation library that provides data structures and operations for manipulating tables and time series data.
Matplotlib: This is a 2D plotting library that provides support for producing plots, graphs, and figures. Matplotlib is used by SciPy and supports NumPy.
IPython: This provides a powerful interactive shell for Python, kernel for Jupyter, and support for interactive data visualization. We will cover the IPython shell later in this chapter.
Jupyter Notebook: This provides a web-based interactive shell for creating and sharing documents with live code and visualizations. Jupyter Notebook supports multiple versions of Python through the kernel provided by IPython. We will cover the Jupyter Notebook later in this chapter.

Installation instructions for the other required software will be given throughout the book at the appropriate time. At the end of this chapter, you will find pointers on how to find additional information online if you get stuck or are uncertain about the best way of solving problems: