fxy 0.3.3

Convenience imports and scientific functions.

Just a convenience imports for scientific functions and packages for calculation.

• pip3 install fxy to get the import shortcuts.

• pip3 install fxy[main] to install all libraries except xgboost,

• pip3 install fxy[all] (slow) to install all libraries for which the shortcuts exist.

Usage

If you are in command line, and just want Python with something, run:

• $ fxy -[n|s|a|p|l] - to do with Python

• $ fxy -i[n|s|a|p|l] - to do with IPython

• $ fxy -b[n|s|a|p|l] - to do with BPython

If you are in existing environment of some kind, just do, to import:

• from fxy.n import *, if you need mpmath and plotting.

• from fxy.s import *, if you need isympy imports.

• from fxy.a import *, if you need numpy, pandas, xarray, scipy, statsmodels and matplotlib, seaborn.

• from fxy.p import *, if you need matplotlib and seaborn.

• from fxy.l import *, if you need sklearn.* as sklearn and xgboost as xgb.

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, 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, sp: scipy, st: scipy.stats, 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 does cataloguing implementations of advanced computations to be reused.