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“NumPy” · 共 6 个 Ree按问题匹配知识，显示最相关的 10 个；摘录来自命中的那段知识。
Answers questions on NumPy: arrays, indexing, broadcasting, ufuncs, dtypes and IO, citing the exact file in the user guide of the GitHub repo numpy/numpy (BSD-3-Clause). Not an official NumPy product.
“################# Using NumPy C-API ################# .. toctree:: c-info.how-to-extend c-info.python-as-glue c-info.ufunc-tutorial c-info.reduction-loop-tutori…”
Answers questions on pandas: DataFrames, indexing, groupby, merging, reshaping, IO and time series, citing the exact file in the public docs of the GitHub repo pandas-dev/pandas (BSD-3-Clause). Not an official pandas product.
“…t = apply_integrate_f_numba( df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy() ) return pd.Series(result, index=df.index, name="result") .. code-block:…”
Answers questions on Hugging Face Transformers: pipelines, models, tokenizers, training and generation, citing the exact file in the public docs of the GitHub repo huggingface/transformers (Apache-2.0). Not an official Hugging Face product.
“return np.asarray([ [0, 0, 0], [120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230,…”
Answers questions on Pydantic models, validation, fields, validators and serialization, citing the exact file in the public docs of the GitHub repo pydantic/pydantic (MIT). Not an official Pydantic product.
“## Numbers Pydantic supports the following numeric types from the Python standard library: <!-- old anchor added for backwards compatibility --> <!-- markdownli…”
Answers questions on reproducible, ethical and collaborative data science from The Turing Way handbook, citing the exact chapter in the GitHub repo the-turing-way/the-turing-way (CC BY 4.0). Not an official Turing Way product.
“# Use the Python package numpy and use version 1.16.1 packages: numpy: version: 1.16.1 ``` Note that comments can be added by preceding them with a `#`. (rr-ren…”
Answers questions on Microsoft's open course ML for Beginners: regression, classification, clustering, NLP and time series, citing the exact lesson in the GitHub repo microsoft/ML-For-Beginners (MIT). Not an official Microsoft product.
“scikit-learn pandas numpy flask”
