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

Dask data types

In computer programming, data types are basic building blocks for writing any kind of functionality. They help us work with different types of variables. Data types are the kind of values that are stored in variables. They can be primary and secondary.

Primary data types are the basic data types such as int, float, and char, while secondary data types are developed using primary data types such as lists, arrays, strings, and DataFrames. Dask offers three data structures for parallel operations: DataFrames, Bags, and Arrays. These data structures split data into multiple partitions and distribute them to multiple nodes in the cluster. A Dask DataFrame is a combination of multiple small pandas DataFrames and it operates in a similar manner. Dask Arrays are like NumPy arrays and support all the operations of Numpy. Finally, Dask Bags are used to process large Python objects.

Now, it's time to explore these data types. We'll start with Dask Arrays.

Dask Arrays