Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
About the Author
About the Reviewers
Customer Feedback
Key Concepts
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 ( 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:

In this chapter, we will cover the following topics:

  • Installing Python 3

  • Using IPython as a shell

  • Reading manual pages

  • Jupyter Notebook

  • NumPy arrays

  • A simple application

  • Where to find help and references

  • Listing modules inside the Python libraries

  • Visualizing data using matplotlib