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Libraries for cleaning, transforming, and preparing data.
Data wrangling tools in Python are essential libraries that assist in cleaning, transforming, and preparing data, making it more suitable for analysis. These tools handle tasks like dealing with missing values, normalizing data, converting data formats, merging datasets, and more, which are crucial steps before any serious data analysis or machine learning model training. Pandas is widely used for its powerful data structures like DataFrames, Dask extends Pandas' capabilities to larger datasets, NumPy supports numerical data wrangling, and Beautiful Soup and Scrapy are specialized towards web data extraction and transformation.
Web Scraping & HTML Parsing
Library for web scraping and parsing HTML/XML documents. Extensively used in data wrangling to clean, parse, and extract data from web sources.
Data Analysis & Manipulation
Foundational library for data manipulation and analysis in Python. Provides fast, flexible, and expressive data structures (DataFrames) designed for working with structured, tabular, and time series data. Essential tool for data wrangling with comprehensive features for indexing, grouping, merging, and filtering.
Data Cleaning & Transformation
Powerful tool for working with messy data, cleaning it, transforming from one format to another, and extending it with web services or external data. Although not a Python library, it's valuable for advanced data wrangling alongside Python tools.