monobinpy 0.0.1

Monotonic Binning for Credit Rating Models

monobinpy

The goal of the monobinpy package is to perform monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Missing values and other possible special values are treated separately from so-called complete cases. This is replica of monobin R package.

Installation

To install pypi.org version run the following code:

pip install monobinpy

and to install development (github) version run:

pip install git+https://github.com/andrija-djurovic/monobinpy.git#egg=monobinpy

Example

This is a basic example which shows you how to solve a problem of monotonic binning of numeric risk factors:

import monobinpy as mb
import pandas as pd
import numpy as np

url = "https://raw.githubusercontent.com/andrija-djurovic/monobinpy/main/gcd.csv"
gcd = pd.read_csv(filepath_or_buffer = url)
gcd.head()

res = mb.sts_bin(x = gcd.age.copy(), y = gcd.qual.copy())
res[0]
res[1].value_counts().sort_index()

Besides above example, additional five binning algorithms are available. For details and additional description please check:

help(mb)