Exploring data wrangling
Data wrangling, also known as data munging or data preprocessing, refers to the process of cleaning, transforming, and preparing raw data for analysis. It involves several tasks, such as handling missing or inconsistent data, removing duplicates, reshaping data formats, and merging multiple datasets. Common techniques used in data wrangling include the following:
- Data cleaning: Identifying and handling missing values, outliers, and errors in the dataset. This may involve imputing missing values, removing outliers, or correcting errors.
- Data transformation: Modifying the structure or format of the data to make it compatible with the desired analysis or modeling techniques. This can include tasks such as changing variable types, scaling numerical values, or encoding categorical variables.
- Data integration: Combining multiple datasets or data sources into a single unified dataset. This may involve joining datasets based on common variables or merging...
