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


In this chapter, we discussed visualizing data using plotting with Matplotlib, pandas, Seaborn, and Bokeh. We covered various plots, such as line plots, pie plots, bar plots, histograms, scatter plots, box plots, bubble charts, heatmaps, KDE plots, violin plots, count plots, joint plots, and pair plots. We focused on accessories for charts, such as titles, labels, legends, layouts, subplots, and annotations. Also, we learned about interactive visualization using Bokeh layouts, interactions, hover tools, and widgets.

The next chapter, Chapter 6, Retrieving, Processing, and Storing Data, will teach us skills of data reading and writing from various sources such as files, objects, and relational and NoSQL databases. Although some people don't consider these skills for data analysis, an independent or assistant data analyst must know how they can fetch data from various file formats and databases for analysis purposes.