Data wrangling, also known as data cleaning or data preparation, is the process of preparing raw data for analysis. It involves a range of tasks such as accessing and collecting data, checking for errors or inconsistencies, and formatting the data in a way that is suitable for analysis.
Some common data wrangling tasks include:
Gathering data from multiple sources: This may involve collecting data from different formats, such as text files, Excel spreadsheets, or databases.
Checking for errors and inconsistencies: This may involve identifying and correcting errors such as typos or missing values, or checking for inconsistencies such as duplicate records or contradictory data.
Reformatting data: This may involve reshaping data from a wide format to a long format, or merging data from multiple sources into a single dataset.
Creating new variables: This may involve creating new variables based on existing variables, such as creating a new variable that represents the sum of two other variables.
Filtering data: This may involve selecting a subset of rows or columns based on certain criteria, such as selecting only rows where a certain variable has a certain value.
Data wrangling can be a time-consuming and tedious process, but it is an essential step in the data analysis workflow. By thoroughly cleaning and preparing your data, you can ensure that your analysis is accurate and reliable.