Data Wrangling

Definition of Data Wrangling

Data wrangling is the process of manipulating and cleaning data in order to prepare it for analysis. This can involve anything from removing duplicates to transforming data into a different format. Data wrangling can be a time-consuming process, but it is essential for ensuring that data is ready for analysis.

What is Data Wrangling used for?

Data wrangling, also referred to as data munging, is a process used for transforming and mapping raw data into a format that can be used for analysis. It involves selecting, cleaning, restructuring, and integrating data to turn it into an appropriate form for consumption. The main objective of data wrangling is to reduce the time spent on manual inputting of data while improving the accuracy of the results.

Data wrangling is typically used in big data or machine learning contexts when large amounts of unstructured or semi-structured datasets need to be processed and organized into uniform formats. During this process, tools like Python or R programming are often used to apply the necessary transformations such as web scraping, conversion, text mining or natural language processing. The output obtained from these processes is often fed into predictive models that enables us to draw meaningful insights from large datasets.

Data wrangling helps make complex datasets easier to analyze by using various techniques such as removing duplicates and outliers, imputing missing values with more accurate estimates and standardizing different formats like currency or date/time values. It also allows us to effectively join different datasets together by combining their columns in a consistent manner and thereby enabling further processing with techniques like clustering algorithms or regression models. With these techniques companies can gain deeper insight across different areas such as marketing analytics, customer segmentation and fraud detection.