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
Parallel Computing Using Dask

Dask is one of the simplest ways to process your data in a parallel manner. The platform is for pandas lovers who struggle with large datasets. Dask offers scalability in a similar manner to Hadoop and Spark and the same flexibility that Airflow and Luigi provide. Dask can be used to work on pandas DataFrames and Numpy arrays that cannot fit into RAM. It splits these data structures and processes them in parallel while making minimal code changes. It utilizes your laptop power and has the ability to run locally. We can also deploy it on large distributed systems as we deploy Python applications. Dask can execute data in parallel and processes it in less time. It also scales the computation power of your workstation without migrating to a larger or distributed environment.

The main objective of this chapter is to learn how to perform flexible parallel...