Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing
Getting Started with Python Libraries

As you already know, Python has become one of the most popular, standard languages and is a complete package for data science-based operations. Python offers numerous libraries, such as NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib, Seaborn, and Plotly. These libraries provide a complete ecosystem for data analysis that is used by data analysts, data scientists, and business analysts. Python also offers other features, such as flexibility, being easy to learn, faster development, a large active community, and the ability to work on complex numeric, scientific, and research applications. All these features make it the first choice for data analysis.

In this chapter, we will focus on various data analysis processes, such as KDD, SEMMA, and CRISP-DM. After this, we will provide a comparison between data analysis and data science, as well as the roles and different skillsets for data analysts and data scientists. Finally, we will shift our focus and start installing various Python libraries, IPython, Jupyter Lab, and Jupyter Notebook. We will also look at various advanced features of Jupyter Notebooks.

In this introductory chapter, we will cover the following topics:

  • Understanding data analysis
  • The standard process of data analysis
  • The KDD process
  • Comparing data analysis and data science
  • The skillsets of data analysts and data scientists
  • Installing Python 3
  • Software used in this book
  • Using IPython as a shell
  • Using Jupyter Lab
  • Using Jupyter Notebooks
  • Advanced features of Jupyter Notebooks

Let's get started!