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)
1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing
Statistics

Exploratory data analysis (EDA) is the first step toward data analysis and building a machine learning model. Statistics provide fundamental knowledge and a set of tools for exploratory or descriptive data analysis. This chapter is designed to make you data-ready. For any kind of data professional role, you need to understand real-world data that is generally noisy, has missing values, and is collected from various sources.

Before performing any kind of preprocessing and analysis, you need to get familiar with the data present, and statistics is the only tool that will help you here. This makes statistics a primary and very necessary skill for data professionals, helping them gain initial insights and an understanding of the data. For example, the arithmetic mean of the monthly working hours of an employee can help us to understand the load of an employee in an organization...