Pandas扔铁轨

Question

我正在尝试对两个数据帧进行更改数据捕获。逻辑是合并两个数据帧并按一个键分组，然后对计数大于1的组运行循环，以查看哪个列“更新”。我犯了个奇怪的错误。感谢任何帮助。 代码

import pandas as pd
import numpy as np

pd.set_option('display.height', 1000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
print("reading wolverine xlxs")


# defining metadata

df_header = ['DisplayName','StoreLanguage','Territory','WorkType','EntryType','TitleInternalAlias',
         'TitleDisplayUnlimited','LocalizationType','LicenseType','LicenseRightsDescription',
         'FormatProfile','Start','End','PriceType','PriceValue','SRP','Description',
         'OtherTerms','OtherInstructions','ContentID','ProductID','EncodeID','AvailID',
         'Metadata', 'AltID', 'SuppressionLiftDate','SpecialPreOrderFulfillDate','ReleaseYear','ReleaseHistoryOriginal','ReleaseHistoryPhysicalHV',
          'ExceptionFlag','RatingSystem','RatingValue','RatingReason','RentalDuration','WatchDuration','CaptionIncluded','CaptionExemption','Any','ContractID',
          'ServiceProvider','TotalRunTime','HoldbackLanguage','HoldbackExclusionLanguage']
df_w01 = pd.read_excel("wolverine_1.xlsx", names = df_header)

df_w02 = pd.read_excel("wolverine_2.xlsx", names = df_header)

df_w01['version'] = 'OLD'
df_w02['version'] = 'NEW'

#print(df_w01)
df_m_d = pd.concat([df_w01, df_w02], ignore_index = True)

first_pass = df_m_d[df_m_d.duplicated(['StoreLanguage','Territory','TitleInternalAlias','LocalizationType','LicenseType','FormatProfile'], keep=False)]

first_pass_keep_duplicate = df_m_d[df_m_d.duplicated(['StoreLanguage','Territory','TitleInternalAlias','LocalizationType','LicenseType','FormatProfile'], keep='first')]

group_by_1 = first_pass.groupby(['StoreLanguage','Territory','TitleInternalAlias','LocalizationType','LicenseType','FormatProfile'])
for i,rows in group_by_1.iterrows():
    print("rownumber", i)
    print (rows)


print(first_pass)

我得到的错误是：

^{pr2}$

任何帮助都是非常感谢的。在

Answer 1

您的GroupBy对象支持迭代，因此

for i,rows in group_by_1.iterrows():
    print("rownumber", i)
    print (rows)

你需要做点什么

^{pr2}$

然后您可以对每个group执行所需的操作

见the docs

Answer 2

为什么不照建议做并使用apply？比如：

def print_rows(rows):
    print rows

group_by_1.apply(print_rows)