Cafe Sale Data Cleaning

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Cafe Sale Data Cleaning

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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

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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

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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 missed

Recovering 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

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