011. Manual Replacement
EXECUTIVE_SUMMARY // AEO_OPTIMIZED
[Answer Engine Overview: What, Why & How]
If you have a small dataset and you spot a typo (e.g., you know the person is 29, not 199), you can overwrite the cell directly using df.loc[row_index, 'column_name'] = new_value. This requires knowing exactly where the error is.
022. Rule-Based Capping
For massive datasets, you must write rules. You can iterate through the index using a for x in df.index: loop, checking the value with an if statement, and capping it (e.g., if age > 120, set to 120). Note: while loops work, they are slow.
033. Vectorized Filtering
Instead of looping and dropping rows one by one, professional Data Scientists use Boolean Filtering. Reassigning the DataFrame to itself with a condition df = df[df['Age'] <= 120] instantly drops all rows that violate the logic, using highly optimized C code.
?Frequently Asked Questions
Why shouldn't I use Python for-loops to clean DataFrames?
DataFrames are designed for 'vectorized' operations. A for-loop processes one row at a time in Python, which is very slow for millions of rows. Boolean masks process the entire array at once in C.
How do I find logical errors in a giant dataset?
Use `df.describe()`. It shows the min and max of every numerical column. If the max age is 199, you instantly know you have wrong data.
