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

Python is one of the most popular programming languages among data professionals. Python data science libraries such as Numpy, Pandas, Scipy, and Scikit-learn can sequentially perform data science tasks. However, with large datasets, these libraries will become very slow due to not being scalable beyond a single machine. This is where Dask comes into the picture. Dask helps data professionals handle datasets that are larger than the RAM size on a single machine. Dask utilizes the multiple cores of a processor or uses it as a distributed computed environment. Dask has the following qualities:

  • It is familiar with existing Python libraries
  • It offers flexible task scheduling
  • It offers a single and distributed environment for parallel computation
  • It performs fast operations with lower latency and overhead
  • It can scale up and scale down

Dask offers similar concepts to pandas, NumPy, and Scikit-learn, which makes it easier to learn. It is an open source parallel...