What Is Data Wrangling?
Data Wrangling is the process of cleaning, structuring, and enriching raw data into a usable format for analysis. It involves transforming data from its initial, often unstructured form into a format that can be easily analyzed and used for further processing.
Data Wrangling is an essential step in the data analysis process, as raw data is typically messy and inconsistent. This can include missing values, incorrect formatting, or duplicated entries. By performing Data Wrangling, we can ensure that the data is accurate and reliable before conducting any analyses.
Importance of Data Wrangling
Data is the foundation of any analysis or decision-making process. Without properly wrangling the data beforehand, any insights or conclusions drawn from the analysis could be flawed or inaccurate. Data Wrangling ensures that the data used for analysis is clean and organized, allowing for more accurate and reliable results.
Moreover, data wrangling also helps to save time and resources in the long run. By identifying and addressing any issues with the data early on, it prevents potential problems or errors from arising during the analysis stage. This helps to streamline the entire process and avoid rework or delays due to faulty data.