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 focused on how to perform parallel computation on basic data science Python libraries such as pandas, Numpy, and scikit-learn. Dask provides a complete abstraction for DataFrames and Arrays for processing moderately large datasets over single/multiple core machines or multiple nodes in a cluster.

We started this chapter by looking at Dask data types such as DataFrames, Arrays, and Bags. After that, we focused on Dask Delayed, preprocessing, and machine learning algorithms in a parallel environment.

This was the last chapter of this book, which means our learning journey ends here. We have focused on core Python libraries for data analysis and machine learning such as pandas, Numpy, Scipy, and scikit-learn. We have also focused on Python libraries that can be used for text analytics, image analytics, and parallel computation such as NLTK, spaCy, OpenCV, and Dask. Of course, your learning process doesn't need to stop here; keep learning new things and about...