Scikit Learn, Pandas, and NumPy Usage Questions
Practical proficiency with these core libraries. Pandas: DataFrames, data manipulation, handling missing values. NumPy: arrays, vectorized operations, mathematical functions. Scikit-learn: preprocessing, model fitting, evaluation metrics, pipelines. Knowing standard patterns and APIs. Writing efficient, readable code using these libraries.
HardTechnical
112 practiced
Write pytest unit tests for a pandas transformation function transform(df) that imputes missing numeric values with median, encodes a categorical column with one-hot encoding, and scales numerics. Provide sample fixture DataFrames covering normal cases and edge cases (all-NaN column, unseen category) and examples of assertions you would make (shapes, column names, non-null numeric columns).
EasyTechnical
65 practiced
Given a transactions DataFrame with columns ['user_id', 'transaction_id', 'amount', 'transaction_date'], write pandas code to compute for each user:- total_amount- transaction_count- average_amount- most_recent_transaction_dateReturn a DataFrame indexed by user_id with these columns. Provide an explanation of any aggregation choices.
MediumTechnical
67 practiced
Discuss trade-offs between one-hot encoding and target encoding for a categorical feature with high cardinality (~10,000 categories). In what situations is target encoding appropriate? How do you avoid target leakage when applying target encoding in cross-validation or production?
MediumTechnical
117 practiced
Design a scikit-learn pipeline using ColumnTransformer to handle a dataset with mixed features: numeric_cols = ['age','income'], categorical_cols = ['region','plan'], with missing values. Pipeline should:- Impute numeric with median and scale- Impute categorical with 'missing' and OneHotEncode (handle_unknown='ignore')- Fit a RandomForestClassifier on processed featuresProvide complete Python code for the pipeline.
MediumTechnical
64 practiced
Implement a custom scikit-learn Transformer named InteractionFeatures that takes a list of numeric columns and returns their pairwise product interactions (e.g., x1*x2, x1*x3...). The transformer should implement fit/transform, be compatible with pandas DataFrames preserving column names, and integrate into a Pipeline. Provide Python code.
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