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python - Pandas：根据其他列的值创建一个新列（按行）

问题描述

我正在寻找基于几列的自定义函数（

`TOTAL_HH_INCOME','HH_SIZE'

'Eligible Household Size', 'income_min1', 'income_max1', 'hh_size2','income_min2', 'income_max2', 'hh_size3', 'income_min3', 'income_max3', 'hh_size4', 'income_min4', 'income_max4', 'hh_size5', 'income_min5', 'income_max5', 'hh_size6', 'income_min6', 'income_max6'`

我正在寻找比较 HH Size 与每个 HH size# 变量和 TOTAL_HH_INCOME 与我数据框中每一行的每个 income_min 和income_max 变量。

我做了这个功能作为尝试

def eligibility (row):
    
    if df['HH_SIZE']== df['Eligible Household Size'] & df['TOTAL_HH_INCOME'] >= df['income_min1'] & df['TOTAL_HH_INCOME'] <=row['income_max1'] :
        return 'Eligible'
    
    if df['HH_SIZE']== df['hh_size2'] & df['TOTAL_HH_INCOME'] >= df['income_min2'] & df['TOTAL_HH_INCOME'] <=row['income_max2'] :
        return 'Eligible'
    
    if df['HH_SIZE']== df['hh_size3'] & df['TOTAL_HH_INCOME'] >= df['income_min3'] & df['TOTAL_HH_INCOME'] <=row['income_max3'] :
        return 'Eligible'

    if df['HH_SIZE']== df['hh_size4'] & df['TOTAL_HH_INCOME'] >= df['income_min4'] & df['TOTAL_HH_INCOME'] <=row['income_max4'] :
        return 'Eligible'

    if df['HH_SIZE']== df['hh_size5'] & df['TOTAL_HH_INCOME'] >= df['income_min5'] & df['TOTAL_HH_INCOME'] <=row['income_max5'] :
        return 'Eligible'

    if df['HH_SIZE']== df['hh_size6'] & df['TOTAL_HH_INCOME'] >= df['income_min6'] & df['TOTAL_HH_INCOME'] <=row['income_max6'] :
        return 'Eligible'
    
    return 'Ineligible'

如您所见，如果该行符合条件，我希望该行被标记为“合格”，否则应标记为“不合格”

我将此功能应用于我的df

df['Eligibility']= df.apply(eligibility, axis=1)

但是，我收到一个错误：

ValueError: ('The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().', 'occurred at index 0')

为什么？我的功能不正常吗？

编辑：

====================== DATAFRAME ===========================

在此处输入图像描述

标签： pythonpandas

解决方案

问题似乎出在 if 语句中的比较运算符：因为您正在比较数据框的列，所以不仅有一个 True 值，而且还有与列中的项目一样多的 True 值。

如果您希望所有元素都相同，请尝试使用 a.all()。请参考以下示例：

import pandas as pd
dict1 = {'name1': ['tom', 'pedro'], 'name2': ['tom', 'pedro'],
         'name3': ['tome', 'maria'], 'name4': ['maria', 'marta']}
df1 = pd.DataFrame(dict1)

# This produce a ValueError as the one you have
# if df1['name1'] == df1['name2']:
#     pass
# To see why this produce an error try printing the following:
print('This is a DataFrame of bool values an can not be handle by an if statement: \n',
      df1['name1'] == df1['name2'])

# This check if all the elements in 'name1' are the same as in 'name2'
if (df1['name1'] == df1['name2']).all():
    print('\nEligible')

输出：

This is a DataFrame of bool values an can not be handle by an if statement: 
 0    True
 1    True
dtype: bool

Eligible

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