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
0 practiced
Describe how to reliably serialize a scikit-learn Pipeline that contains custom transformer classes and third-party objects so it can be loaded on a different machine for production. Discuss pitfalls of pickle/joblib, define best practices (avoid lambdas, ensure import path for classes, include environment dependencies), and explain when converting to ONNX is advantageous.
EasyTechnical
0 practiced
Implement a Python function using NumPy that takes two 1-D arrays a and b of shape (n,) and returns a boolean array indicating whether a is greater than b + tol element-wise. Do this without Python loops, using broadcasting and vectorized operations. Include handling for NaN values so that comparisons with NaN yield False.
MediumTechnical
0 practiced
Describe what a NumPy universal function (ufunc) is and how to write a simple vectorized ufunc using numpy.frompyfunc or np.vectorize. Explain why np.vectorize generally does not give performance benefits and when to use numba or write a C-extension for true speed gains. Provide a small example and describe limitations.
MediumTechnical
0 practiced
Using pandas, implement a per-user 7-day rolling mean for irregular time series. DataFrame has ['user_id','timestamp','metric'] and timestamps are uneven. Provide code that computes the 7-day lookback rolling mean per user aligned to each timestamp and handles users with sparse data.
EasyTechnical
0 practiced
Describe the difference between the legacy numpy.random.seed approach and the new numpy.random.Generator API. Show code to create a reproducible Generator, sample normal variates, and explain why the new API is preferred for modern code and parallel workflows.
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