Data Cleaning (also called data cleansing or data preprocessing) is the process of identifying and correcting errors, inconsistencies, and missing values in a dataset to improve its quality before analysis or modeling.
✅ Why Data Cleaning Is Important
Clean data ensures:
- Accurate analysis
- Reliable insights
- Better model performance
- Reduced errors and bias
🔧 Common Data Cleaning Tasks
1. Handling Missing Data
- Remove rows with missing values
- Fill missing values using:
- Mean/Median (numerical data)
- Mode (categorical data)
- Forward/Backward fill (time series)
- Predictive imputation
2. Removing Duplicates
Duplicate rows can distort results.
Use methods like:
drop_duplicates()in Python (Pandas)- SQL
DISTINCT
3. Correcting Data Types
Examples:
- Converting strings to dates
- Changing numbers stored as text
- Ensuring categorical fields use category type
4. Handling Outliers
Outliers can be:
- Removed
- Capped using percentiles (Winsorizing)
- Transformed (log, square root)
- Investigated for correctness
5. Standardizing & Normalizing Data
- Standardization (Z-score normalization)
- Min-Max scaling
Useful for machine learning models.
6. Fixing Inconsistent Formatting
Examples:
- “USA”, “U.S.A.”, “US”
- Uppercase/lowercase text
- Date formats (DD/MM/YYYY vs MM/DD/YYYY)
7. Cleaning Text Data
- Removing punctuation
- Removing stopwords
- Lemmatization/Stemming
- Lowercasing
- Removing special characters
8. Validating Data
Check for:
- Invalid values (e.g., age = 400)
- Incorrect categories
- Logical errors (end date < start date)
🛠Common Tools Used
- Python (Pandas, NumPy, Scikit-learn)
- Excel / Google Sheets
- SQL
- R
- ETL tools (Power BI, Tableau Prep, Talend)
📌 If you want, I can show you:
✔ Data cleaning examples in Python
✔ A step-by-step data cleaning workflow
✔ Data cleaning interview questions
✔ Real-life datasets for practice
Just tell me what you prefer!
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