Data cleaning is the process of preparing raw data so it becomes accurate, consistent, and ready for analysis. In real projects, datasets often contain missing values, duplicates, inconsistent labels, wrong data types, and invalid or extreme values. If these issues are not fixed early, your charts and summaries can give incorrect results.
A typical data cleaning workflow starts with checking the dataset structure. You review column names, remove unnecessary columns, and standardise naming so it is easy to work with. Next, you handle missing values based on the meaning of the data. Sometimes you remove rows with missing critical fields, and sometimes you fill missing values using sensible rules (for example, median for numeric columns or a placeholder category for text fields).
Duplicates are another common problem. You identify duplicate rows and decide whether to drop them completely or keep the most relevant record (for example, the latest entry based on a timestamp). Data type correction is also essential. Many datasets store numbers as text or dates as strings, so you convert them into the correct formats to ensure calculations and filtering work properly.
Text cleaning is important for category columns. You may remove extra spaces, standardise case, and fix spelling variations so categories do not get split into multiple versions of the same value. Finally, you validate the cleaned dataset by re-checking missing values, duplicates, and summary statistics, and then save a clean version for analysis.
Data cleaning is not a one-time step. It is an ongoing habit that makes your analysis reliable and your results trustworthy.
