Data Cleaning Techniques for Beginners
Data cleaning (also called data cleansing) is the process of fixing or removing incorrect, incomplete, or inconsistent data. Clean data is essential for accurate analysis, reporting, and machine learning.
1. Remove Duplicate Data
Duplicates occur when the same record appears more than once.
Why it matters:
Duplicates can distort analysis results.
How to clean:
Identify duplicate rows
Keep only one instance of each record
Example:
Two identical customer entries → keep one.
2. Handle Missing Values
Missing data may appear as blank cells, NULL, or NaN.
Common approaches:
Remove rows with missing values (if few)
Fill with a value, such as:
Mean or median (for numbers)
Mode or “Unknown” (for categories)
Example:
If age is missing → replace with the average age.
3. Correct Data Types
Sometimes data is stored in the wrong format.
Examples:
Numbers stored as text
Dates stored as strings
Why it matters:
Wrong data types prevent proper calculations and analysis.
4. Fix Inconsistent Data
Inconsistent data occurs when values don’t follow the same format.
Examples:
“USA”, “U.S.A”, “United States”
“Male”, “male”, “M”
Solution:
Standardize values into one format
5. Remove Irrelevant Data
Some columns or rows may not be useful.
Examples:
Unnecessary ID columns
Columns with too many missing values
Tip:
If it doesn’t help answer your question, consider removing it.
6. Handle Outliers
Outliers are values that are unusually high or low.
Example:
Salary = $1,000,000 when most are under $100,000
Options:
Investigate and correct errors
Remove if clearly incorrect
Keep if valid but rare
7. Correct Spelling Errors
Spelling mistakes can create duplicate categories.
Example:
“Califronia” instead of “California”
Solution:
Manually correct
Use automated text-matching tools
8. Standardize Text Formatting
Make text consistent.
Examples:
Convert all text to lowercase or uppercase
Remove extra spaces
9. Validate Data Ranges
Ensure values fall within logical limits.
Examples:
Age should not be negative
Percentage should be between 0 and 100
10. Document Your Changes
Always keep track of:
What was changed
Why it was changed
This helps with transparency and reproducibility.
Common Tools for Data Cleaning
Excel / Google Sheets (for beginners)
Python (Pandas)
R
SQL
Final Tip for Beginners
Start simple. Clean data step by step, and always understand why you are making each change. Clean data leads to better insights and better decisions.
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