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        "description": "Quick: Applied Numba\n====================\n\nOptimized numerical computation using Continuum's Numba. Intended as a drop-in replacement\nfor numerical functions in numpy, scipy, or builtins. Provides strong performance boosts.\n\n`Numba website <http://numba.pydata.org/>`_\n\nInputs use numpy arrays, not lists.\nRough/early release - Open to suggestions and bug reports.\n\nIncluded functions\n------------------\n\n- sum: Similar to builtin sum, or numpy.sum\n- mean: Similar to numpy.mean\n- var: Variance test, similar to numpy.var\n- cov: Covariance estimation, similar to numpy.cov\n- std: Standard deviation, similar to numpy.std\n- corr: Pearson correlation test, similar to scipy.stats.pearsonr\n- bisect: Similar to standard library bisect.bisect\n- bisect_left: Similar to standard library builtin.bisect_left\n- interp: Linear interpoliation, similar to numpy.interp. x is an array.\n- interp_one: Linear interpolation, similar to numpy.interp. x is a single value.\n- detrend: Similar to scipy.signal.detrend. Linear or constant trend.\n- ols: Simple Ordinary Least Squares regression for two data sets.\n- ols_single: Simple Ordinary Least Squares regression for one data set.\n- lin_resids: Residuals calculation from a linear regression with two data sets\n- lin_resids_single: Residuals calculation from a linear regression with one data set.",
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