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
Retrieving, Processing, and Storing Data

Data can be found everywhere, in all shapes and forms. We can get it from the web, IoT sensors, emails, FTP, and databases. We can also collect it ourselves in a lab experiment, election polls, marketing polls, and social surveys. As a data professional, you should know how to handle a variety of datasets as that is a very important skill. We will discuss retrieving, processing, and storing various types of data in this chapter. This chapter offers an overview of how to acquire data in various formats, such as CSV, Excel, JSON, HDF5, Parquet, and pickle.

Sometimes, we need to store or save the data before or after the data analysis. We will also learn how to access data from relational and NoSQL (Not Only SQL) databases such as sqlite3, MySQL, MongoDB, Cassandra, and Redis. In the world of the21st-century web, NoSQL databases are undergoing...