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Project description

Copyright (c) 2019 The Python Packaging Authority

The package is used for risk control modeling in Python. It mainly provides some basic and commonly data analysis methods for partners who want to learn Python data analysis or machine learning in the Internet financial industry. It also contains a large number of solutions to the problems encountered by the authors in their daily work. I hope you can actively use it. If you have any questions, please contact me at



Riskcontrol is used for risk control modeling in Python.

It provides intuitive tools for

  • feature information-value (iv)
  • plot bad rate of bins based on decision tree
  • ks plot
  • feature info describe for external data validation(contain missing analysis)
  • two feature heat map for cross analysis
  • logistic credit card
  • gps near geohash coding


pip install riskcontrol


import riskcontrol as rc
import pandas as pd

data = pd.read_csv('test.csv')
rc.feature_miss_ana(data) # data analysis report
col col_type iv ... mode_v mode_pect mode_bad_rate risk_monotonicity
ios float64 0.0273 ... 0 0.528996 0.054911
sex_m float64 0.0486 ... 1 0.723311 0.053726
age float64 0.0408 ... 22 0.095423 0.053391
income float64 0.0302 ... 100000 0.147488 0.041681
edu float64 0.0367 ... 0 0.828286 0.051514
freeram float64 0.0087 ... 482864 0.000104 0.125
joblevel float64 0.0411 ... 4 0.383198 0.056743
  • miss_bad_rate:缺失值对应的bad_rate
  • notnull_bad_rate:非缺失值对应的bad_rate
  • zero_pect:0值占比
  • zero_bad_rate:0值bad_rate
  • mode_v:众数
  • mode_pect:众数占比
  • mode_bad_rate:众数对应的bad_rate
  • risk_monotonicity:分箱后的逾期单调性
rc.split_box_plot_new(data, col_name) # bins bad rate plot


rc.create_geohash(latitude, longitude, radius, precision)


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