Metadata-Version: 2.1
Name: fxy
Version: 0.1.7
Summary: Convenience imports and scientific functions.
Home-page: https://github.com/mindey/fxy
Author: Mindey
Author-email: mindey@qq.com
License: UNLICENSE
Description: fxy
        ===
        
        Just a convenience imports for scientific functions and packages for
        calculation.
        
        ``pip install fxy`` to get the import shortcuts.
        ``pip install fxy[main]`` to install all libraries except ``xgboost``,
        ``pip install fxy[all]`` to install all libraries for which the
        shortcuts exist.
        
        Usage
        -----
        
        -  ``from fxy.n import *``, if you need ``mpmath`` and plotting.
        -  ``from fxy.s import *``, if you need ``isympy`` things.
        -  ``from fxy.a import *``, if you need ``numpy``, ``pandas``,
           ``scipy``, ``statsmodels`` and ``matplotlib``.
        -  ``from fxy.p import *``, if you need ``matplotlib``.
        -  ``from fxy.l import *``, if you need ``sklearn.* as sklearn``.
        
        About
        -----
        
        This package may be useful for computing basic things, doing things to
        emulate Python's capabilities in computational and symbolic mathematics
        and statistics, so this package will introduce just convenient imports
        so that one doesn't have to `configure Jupyter notebook
        profile <https://mindey.com/blog/how_to_set_up_ipython_for_statistics_on_linux>`__,
        to have those imports every time, and works well as an on-the-go
        calculator.
        
        This package does not assume versions of the imported packages, it just
        performs the basic imports, assuming that those namespaces within those
        packages will exist for a long time to come, so it is
        *dependencies-agnostic*.
        
        ::
        
            # Numeric (mpmath.*)
            >>> from fxy.n import * (394 functions)
            >>> pi
            <pi: 3.14159~>
        
            # Symbolic (sympy.*)
            >>> from fxy.s import * (915 functions, and "isympy" imports)
            >>> f = x**4 - 4*x**3 + 4*x**2 - 2*x + 3
            >>> f.subs([(x, 2), (y, 4), (z, 0)])
            -1
            >>> plot(f)
        
            # Actuarial (np: numpy, pd: pandas, sm: statsmodels.api, st: scipy.stats, scipy, smf: statsmodels.formula.api, statsmodels)
            >>> from fxy.a import *
            >>> df = pandas.DataFrame({'x': numpy.arange(10), 'y': np.random.random(10)})
            >>> df.sum()
            x    45.000000
            y     4.196558
            dtype: float64
        
            # Learning (sklearn.* as sklearn)
            >>> from fxy.l import *
            >>> X = [[0], [1], [2], [3]]
            >>> y = [0, 0, 1, 1]
            >>> neigh = skl.neighbors.KNeighborsClassifier(n_neighbors=3)
            >>> neigh.fit(X, y)
            >>> print(neigh.predict([[1.1]]))
            [0]
            >>> print(neigh.predict_proba([[0.9]]))
            [[0.66666667 0.33333333]]
        
            # Plotting (plt, matplotlib)
            >>> from fxy.p import *
            >>> plt.plot([1, 2, 3, 4])
            >>> plt.ylabel('some numbers')
            >>> plt.show()
            <image>
        
        I often collect convenient computations and functions in various fields,
        like what **`WolframAlpha <https://www.wolframalpha.com>`__**
        `does <https://wiki.mindey.com/shared/screens/Screenshot_2021-02-28_06-16-43.png>`__
        cataloguing implementations of advanced computations to be reused.
        
Platform: UNKNOWN
Provides-Extra: all
Provides-Extra: main
Provides-Extra: test
