Data cleaning for analytics involves identifying and fixing issues in raw data to ensure accuracy and consistency. The first step is handling missing data. You can either remove incomplete records or fill gaps using methods like mean/median imputation. For example, in Python’s pandas library, df.dropna() removes rows with missing values, while df.fillna(mean_value) replaces them. The choice depends on the dataset size and the impact of missing data. Next, remove duplicates to avoid skewed analysis. Tools like pandas’ df.drop_duplicates() help eliminate repeated entries. For instance, sales data might have duplicate transactions due to system errors, which can inflate revenue figures if not addressed. Finally, check for invalid values, such as negative ages or nonsensical dates, and correct them using domain-specific rules or by cross-referencing trusted sources.
Standardizing data formats ensures uniformity. Dates, currencies, and categorical values often vary in raw data. For example, converting all date strings to YYYY-MM-DD format using pandas’ to_datetime() avoids parsing errors. Categorical data like country names (“US,” “USA,” “United States”) should be mapped to a single standard. Regular expressions or string functions can fix typos in text fields. Another common issue is inconsistent units, such as mixing kilograms and pounds in weight data. Converting all values to a single unit prevents miscalculations during analysis. For numerical data, scaling or normalization (e.g., using sklearn.preprocessing.StandardScaler) might be necessary to ensure comparability. Address outliers using statistical methods like the interquartile range (IQR) or domain knowledge—for example, capping unrealistic transaction amounts in financial data.
After cleaning, validate the dataset to ensure correctness. Check data types (e.g., ensuring numeric columns aren’t stored as strings) and verify ranges (e.g., ages shouldn’t be negative). Automated scripts in Python or SQL make the process repeatable. For example, a PySpark job can clean large datasets by filtering outliers, enforcing schemas, and logging errors. Documenting each step ensures transparency, and validation checks post-cleaning confirm readiness for analysis. Tools like Great Expectations or custom unit tests can automate validation, reducing manual oversight. For instance, a test could assert that all timestamps fall within an expected date range. By systematically addressing these issues, developers ensure the data is reliable for downstream tasks like reporting, machine learning, or business intelligence dashboards.