熊猫：根据其他列中的值创建一个新列（行式）

Question

我希望基于几列来创建自定义函数（

`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'`

我想比较数据框中每一行的HH大小与每个HH大小＃变量以及TOTAL_HH_INCOME与每个收入_最小和收入_最大变量的比较。

我已经尝试过使用此功能

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'

您可以看到该行是否满足条件，我希望将该行标记为“合格”，否则应将其标记为“不合格”

我将此功能应用于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？我的功能不好吗？

编辑：

======================数据框======================== ===

Answer 1

问题似乎是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True值与列中的项一样多。

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

Answer 2

您可以尝试使用df.to_records()：

import re

#df.columns
s=['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']


def func(row):
    totalincome=row[2]
    HHSIZE=row[3]
    indexhhsize=list(map(s.index,re.findall('(hh_size\d+)',''.join(s))))
    indexmax=list(map(s.index,re.findall('(income_max\d+)',''.join(s))))
    indexmin=list(map(s.index,re.findall('(income_min\d+)',''.join(s))))

    if(any(HHSIZE==row[i+1] for i in indexhhsize))\
    |(any(totalincome>=row[i+1] for i in indexmin))\
    |(any(totalincome<=row[i+1] for i in indexmax)):
        return 'Eligible'
    else:
        return 'Ineligible'
    
df['Eligibility']=[func(row) for row in df.to_records()]