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Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights

Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights

Michael Walker

E-book
Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data.

By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.
  • 1. Anticipating Data Cleaning Issues when Importing Tabular Data into pandas
  • 2. Anticipating Data Cleaning Issues when Importing HTML and JSON into Pandas
  • 3. Taking the Measure of Your Data
  • 4. Identifying Issues in Subsets of Data
  • 5. Using Visualizations for Exploratory Data Analysis
  • 6. Cleaning and Wrangling Data with Pandas Data Series Operations
  • 7. Fixing Messy Data When Aggregating
  • 8. Addressing Data Issues When Combining Data Frames
  • 9. Tidying and Reshaping Data
  • 10. User Defined Functions and Classes to Automate Data Cleaning
  • Titel: Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights
  • Autor: Michael Walker
  • Originaler Titel: Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights
  • ISBN: 9781800564596, 9781800564596
  • Veröffentlichungsdatum: 2020-12-11
  • Format: E-book
  • Artikelkennung: e_2aig
  • Verleger: Packt Publishing