python - 在 pandas df 中创建新列并有条件地分配字符串值

Question

我是 pandas 的新手，试图在 Pandas Dataframe 中创建一个新列，并根据函数分配一个字符串值，但结果只输出 1 个值('residential)给所有5,000 列。知道我的代码有什么问题吗？谢谢

def programType(c):
if c['Primary Property Type - Self Selected'] == 'Multifamily Housing' or 'Residence Hall/Dormitory':
    return 'Residential'

elif c['Primary Property Type - Self Selected'] == 'Bank Branch' or 'Hotel' or 'Financial Office' \
or 'Retail Store' or 'Distribution Center' or 'Non-Refrigerated Warehouse' or 'Fitness Center/Health Club/Gym' \
or 'Mixed Use Property' or 'Self-Storage Facility' or 'Wholesale Club/Supercenter' or 'Supermarket/Grocery Store':
    return 'Commercial'  

elif c['Primary Property Type - Self Selected'] == 'Senior Care Community' or 'K-12 School' or 'College/University' \
or 'Worship Facility' or 'Medical Office' or 'Hospital (General Medical & Surgical)':
    return 'Institutional'

elif c['Primary Property Type - Self Selected'] == 'Manufacturing/Industrial Plant': 
    return 'Industrial'

else:
    return 'Other'

新列称为“程序类型”

datav3['Program Type'] = datav3.apply(programType, axis=1)

Answer 1

如果存在矢量化解决方案，在 pandas 中最好避免循环(apply 是引擎盖下的循环)，因为循环很慢。

我尝试重写您的代码 - 创建包含输出和值列表的字典，将键与值交换并调用 map , 最后为不匹配的值添加 fillna :

d = {'Residential' :['Multifamily Housing', 'Residence Hall/Dormitory'],
     'Commercial' : ['Bank Branch', 'Hotel' , 'Financial Office' , 'Retail Store', 'Distribution Center', 
                   'Non-Refrigerated Warehouse', 'Fitness Center/Health Club/Gym', 'Mixed Use Property',
                   'Self-Storage Facility', 'Wholesale Club/Supercenter', 'Supermarket/Grocery Store'],
     'Institutional':['Senior Care Community', 'K-12 School', 'College/University', 'Worship Facility',
                     'Medical Office', 'Hospital (General Medical & Surgical)'],
     'Industrial':  ['Manufacturing/Industrial Plant'] }

---

d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}
print (d1)

{
    'Multifamily Housing': 'Residential',
    'Residence Hall/Dormitory': 'Residential',
    'Bank Branch': 'Commercial',
    'Hotel': 'Commercial',
    'Financial Office': 'Commercial',
    'Retail Store': 'Commercial',
    'Distribution Center': 'Commercial',
    'Non-Refrigerated Warehouse': 'Commercial',
    'Fitness Center/Health Club/Gym': 'Commercial',
    'Mixed Use Property': 'Commercial',
    'Self-Storage Facility': 'Commercial',
    'Wholesale Club/Supercenter': 'Commercial',
    'Supermarket/Grocery Store': 'Commercial',
    'Senior Care Community': 'Institutional',
    'K-12 School': 'Institutional',
    'College/University': 'Institutional',
    'Worship Facility': 'Institutional',
    'Medical Office': 'Institutional',
    'Hospital (General Medical & Surgical)': 'Institutional',
    'Manufacturing/Industrial Plant': 'Industrial'
}

---

datav3 = pd.DataFrame({'Program':['Medical Office','Hotel',
                                       'Residence Hall/Dormitory',
                                       'Manufacturing/Industrial Plant','House']})
datav3['Program Type'] = datav3['Program'].map(d1).fillna('Other')
print (datav3)
                          Program   Program Type
0                  Medical Office  Institutional
1                           Hotel     Commercial
2        Residence Hall/Dormitory    Residential
3  Manufacturing/Industrial Plant     Industrial
4                           House          Other