Wrangling refers to the process of cleaning, organizing, and transforming raw data into a structured format that can be effectively analyzed. It is a critical step in data preparation for activities such as data analysis, machine learning, or reporting.
Key tasks involved in wrangling include:
Data Cleaning: Identifying and correcting errors, inconsistencies, or inaccuracies in the data (e.g., handling missing values, outliers, and duplicates).
Data Transformation: Converting data into a suitable format, such as normalizing values, encoding categorical variables, or aggregating data.
Data Integration: Combining data from different sources into a cohesive dataset.
Feature Engineering: Creating new variables or features that help enhance the predictive power of models.
Filtering and Subsetting: Selecting relevant rows or columns that align with the analysis goals.
Tools and techniques for data wrangling range from spreadsheet software like Excel to programming languages like Python (using libraries such as Pandas and NumPy) or R, as well as specialized software platforms like Tableau Prep or Alteryx.
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