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.
MediumTechnical
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Implement a scikit-learn compatible transformer class named DateFeatureExtractor that accepts a datetime column name(s) and on transform returns numeric columns year, month, dayofweek, is_weekend, and days_since_first for each row. The transformer should implement fit and transform and work inside a Pipeline with other scikit-learn transformers. Provide the class skeleton and a short usage example with a pandas DataFrame.
EasyTechnical
0 practiced
Explain differences between NumPy slicing (views) and advanced integer indexing (copies). Given a = np.arange(12).reshape(3,4), demonstrate a[1:3,:], a[[0,2],[1,3]], and show how assignment to these views or copies affects the original array. Provide code and explain when a copy is created.
EasyTechnical
0 practiced
Given a DataFrame df with columns ['age','country','score'], write a pandas expression to filter rows where age is between 25 and 40, country is in ['US','CA'], and score is in the top 10% for that country. Provide code that avoids Python-level loops and explain how to compute per-country quantiles efficiently.
HardSystem Design
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You must train a model on 100 million rows that do not fit into memory. Describe concrete strategies using pandas and numpy to preprocess and train: out-of-core learning with partial_fit, chunked preprocessing and feature extraction, using numpy.memmap for large arrays, and when to adopt Dask or Spark. Provide example code demonstrating chunked training with sklearn.linear_model.SGDClassifier.partial_fit.
MediumTechnical
0 practiced
You need to compute column z = sqrt(x**2 + y**2) for 20 million rows stored in a pandas DataFrame with columns 'x' and 'y'. Compare three approaches: Python loop, df.apply(row-wise), and NumPy vectorized computation. Provide code for each approach, report expected relative timings, and implement the fastest approach using numpy for best speed and minimal memory overhead.
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