Data Wrangling with Python: Simplify your ETL processes with these hands-on data sanitation tips, tricks and best practices
Tirthajyoti Sarkar, Shubhadeep Roychowdhury
Data is the new oil but it comes as crude, just like oil. To do anything meaningful - modeling, visualization, machine learning, for predictive analysis - you first need to wrestle and wrangle with data. This book teaches the essential basics of data wrangling using Python.
- Focuses on essential basics of wrangling to get you up and running with analysis in no time
- Teaches the tricks and know-how of "how to solve data wrangling problems"
- Added bonus topics - random data generation, data integrity checks
To practice high-quality science with data, first you need to make sure it is properly sourced, cleaned, formatted, and pre-processed. This book teaches you the most essential basics of this invaluable component of the data science pipeline - data wrangling.
What you will learn
- Able to manipulate complex and simple data structure using Python and it's built-in functions
- Use the fundamental and advanced level of Pandas DataFrames and numpy.array
- Manipulate them at run time
- Extract and format data from various formats (textual) - normal text file, SQL, CSV, Excel, JSON, and XML
- Perform web scraping using Python libraries such as BeautifulSoup4 and html5lib
- Perform advanced string search and manipulation using Python and RegEX
- Handle outliers, apply advanced programming tricks, and perform data imputation using Pandas
- Basic descriptive statistics and plotting techniques in Python for quick examination of data
- Practice data wrangling and modeling using the random data generation techniques
Who This Book Is For