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Cafe Sale Data Cleaning A data cleaning project on a synthetic cafe sales dataset with 10,000 transactions. The dataset contained fake null values disguised as strings, wrong data types, and high volumes of missing data.
Goals
Handle fake null values (ERROR, UNKNOWN) alongside real nulls
Recover missing values using mathematical relationships between columns
Apply appropriate strategies per column based on context and null percentage
Produce a clean, analysis-ready dataset with correct data types
Process
Handled nulls per column based on context — filled, calculated, or dropped where justified.
Replaced ERROR and UNKNOWN strings with np.nan to standardize all missing values
Recovered price_per_unit, quantity, and total_spent using a price chart and column arithmetic
Filled high-null categorical columns (payment_method, location) with "unknown" rather than dropping
Insights
25% of payment methods and 32% of locations were missing — dropping would have destroyed the dataset
errors='coerce' revealed 301 extra nulls hiding as ERROR/UNKNOWN in the date column that isna() alone would have missedRecovering values mathematically (quantity × price = total) is always better than filling with a statistic
Only 60 rows (0.6%) were truly unrecoverable — a sign that most missing data had a recovery path worth exploring
This case study documents a portfolio analysis. Findings describe this dataset and should be interpreted within its limitations.
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