This is the second edition of the book titled “Python Data Cleaning Cookbook,” which provides you with a guide on how to clean and handle data in order to obtain meaningful analysis in Python. It also includes the fundamental and recent methods such as machine learning and AI to preprocess and clean the data. It offers advice and approachable techniques specifically for machine learning and NLP models, which will assist you in parsing, synthesizing, and analyzing your data.
This new edition is brought to date with the newest Python tools and puts more weight on sophisticated data-cleaning techniques. You will learn to detect and fix typical problems, such as dates and IDs, and create visualizations for further EDA. The book also focuses on applying supervised learning and Naive Bayes analysis to identify outliers and classification errors and improve your data diagnostic capability. Further, it shows how to create reusable functions and classes for the subsequent datasets, saving you time and effort.
After reading the book “Python Data Cleaning Cookbook - Second Edition”, you can clean, monitor, and validate massive datasets by applying modern techniques. Here, you can create indexes, apply validation, and preprocess your data for Machine Learning and AI models. This book will help you become more or less proficient in diagnosing and solving data issues you will face while using Python Pandas and optimizing your work with method chaining and other useful techniques.
Python Data Cleaning Cookbook - Second Edition Table of Contents:
- Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
- Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
- Measuring Your Data
- Identifying Outliers in Data Subsets
- Using Visualizations to Identify Unexpected Values
- Cleaning and Exploring Data with Series Operations
- Identifying and Fixing Missing Values
- Encoding, Transforming, and Scaling Features
- Fixing Messy Data During Aggregation
- Addressing Issues When Combining Data Frames
- Tidying and Reshaping Data
- Automating Data Cleaning with User-Defined Functions, Classes, and Pipelines
Who is this course for?
- Every person is interested in ways to deal with disordered, redundant, and low-quality data with the help of Python and its tools and approaches.
- Those who would like to have step-by-step instructions and recipes for cleaning and preparing data for analysis.
- People with a basic understanding of the Python programming language to be able to comprehend the content of the text.
