对于分箱后得到的结果给出python代码计算它的iv值

IV（Information Value）信息价值是一种用于评估单个预测变量的预测能力的指标，常用于特征筛选。下面给出一个简单的Python函数来计算IV值：

import pandas as pd
import numpy as np

def calc_iv(df, feature, target):
    """
    计算IV值
    :param df: 数据框
    :param feature: 待计算IV值的特征
    :param target: 目标变量
    :return: IV值
    """
    lst = []
    cols = ['Variable', 'Value', 'All', 'Bad']
    for i in range(df[feature].nunique()):
        val = list(df[feature].unique())[i]
        lst.append([feature, val, len(df[df[feature] == val]), len(df[(df[feature] == val) & (df[target] == 1)])])

    data = pd.DataFrame(lst, columns=cols)
    data = data[data['Bad'] > 0]

    data['Share'] = data['All'] / data['All'].sum()
    data['Bad Rate'] = data['Bad'] / data['All']
    data['Distribution Good'] = (data['All'] - data['Bad']) / (data['All'].sum() - data['Bad'].sum())
    data['Distribution Bad'] = data['Bad'] / data['Bad'].sum()
    data['WoE'] = np.log(data['Distribution Good'] / data['Distribution Bad'])
    data['IV'] = (data['WoE'] * (data['Distribution Good'] - data['Distribution Bad'])).sum()

    return data['IV'].iloc[0]

其中，`df`为数据框，`feature`为待计算IV值的特征，`target`为目标变量。函数中，首先统计出每个特征值的样本数量、坏样本数量，然后计算每个特征值的占比、坏样本率、好样本分布率、坏样本分布率、WOE值和IV值，最后返回IV值即可。

使用该函数计算分箱后的IV值：

iv = calc_iv(df, 'feature', 'target')
print(iv)

其中，`df`为分箱后的数据框，`feature`为待计算IV值的特征，`target`为目标变量。