求指导
# coding=utf-8# import pandas as pd
# may_df = pd.read_csv('may.csv')
# june_df = pd.read_csv('june.csv')
#
# merged = pd.merge(may_df,june_df,on="states",how = 'outer',suffixes=('_ahead','behind'),indicator=True)
# # find change
# changed_rows = merged!='both']
# # find new lines
# new_rows = merged=='right_only']
# print(new_rows)
#
import pandas as pd
from tqdm import tqdm
# # 读取两张表
# df1 = pd.read_csv('may.csv')
# df2 = pd.read_csv('june.csv')
#
# # 筛选出location、project_name、building、unit、high和room相同的行
# cols = ['location', 'project_name', 'building', 'unit', 'high', 'room']
# df1_grouped = df1.groupby(cols).first().reset_index()
# df2_grouped = df2.groupby(cols).first().reset_index()
#
# # 将df1_grouped和df2_grouped合并，并比较states列的值
# df = pd.DataFrame(columns=df1.columns)
# for index, row1 in tqdm(df1_grouped.iterrows(), total=len(df1_grouped)):
# for _, row2 in df2_grouped.iterrows():
# if row1['location'] == row2['location'] and \
# row1['project_name'] == row2['project_name'] and \
# row1['building'] == row2['building'] and \
# row1['unit'] == row2['unit'] and \
# row1['high'] == row2['high'] and \
# row1['room'] == row2['room'] and \
# row1['states'] != row2['states']:
# df = df.append(row1)
#
# # 输出结果
# print(df)
import pandas as pd
from tqdm import tqdm
# 读取两张表
df1 = pd.read_excel('D:\一介书生资料库\爬虫：八爪鱼\各市县整体市场\shujuchuli\七月三亚.xlsx')
df2 = pd.read_excel('D:\一介书生资料库\爬虫：八爪鱼\各市县整体市场\shujuchuli\八月三亚.xlsx')
# 筛选出location、project_name、building、unit、high和room相同的行，并选择指定的列
cols = ['区域', '项目名称', '楼盘', '单元', '楼层', '房间', '建筑面积', '房型', '挂牌清水价', '挂牌装修价']
df1_grouped = df1.groupby(cols).first().reset_index()
df2_grouped = df2.groupby(cols).first().reset_index()
# 合并两张表，并筛选出states有变化的行
df_merged = pd.concat()
df_duplicates = df_merged
df_changed = df_duplicates.loc != df_duplicates['状态']]
# 输出结果
df_changed.to_excel('D:\pydata\data.xlsx', index=False)
print(df_changed)
表格标题为：区域 项目名称 楼盘 单元 楼层 房间 建筑面积 房屋用途 房型 挂牌清水价 挂牌装修价 产品类型 状态 商品房预售许可证 发证日期
提示错误：
D:\PythonProject\pythonProject\Scripts\python.exe E:\qycache\xuexi\pythonProject\房地产\process（版本2.3）.py
D:\PythonProject\pythonProject\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\PythonProject\pythonProject\lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
D:\PythonProject\pythonProject\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Traceback (most recent call last):
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\indexes\base.py", line 3652, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas\_libs\hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '状态'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\qycache\xuexi\pythonProject\房地产\process（版本2.3）.py", line 57, in <module>
df_changed = df_duplicates.loc != df_duplicates['状态']]
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\frame.py", line 3761, in __getitem__
indexer = self.columns.get_loc(key)
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\indexes\base.py", line 3654, in get_loc
raise KeyError(key) from err
KeyError: '状态'
进程已结束,退出代码1
根据您提供的代码和错误提示，问题出在 df_duplicates.loc != df_duplicates['状态']] 这一行。错误提示显示KeyError: '状态'，说明在DataFrame中找不到名为'状态'的列。
解决方法：
1. 首先，检查您的DataFrame中是否存在名为'状态'的列。可以通过 print(df_duplicates.columns) 打印出所有列名，确认是否存在'状态'列。
2. 如果确实存在'状态'列，那么可能是列名的格式问题。请检查列名是否与代码中的列名一致，包括大小写和空格。
3. 如果列名正确，那么可能是数据中存在缺失值或其他异常值导致的问题。可以尝试使用 df_duplicates.dropna() 删除缺失值，或者使用其他方法处理异常值。
另外，您还提到了警告信息 UserWarning: loaded more than 1 DLL from .libs 。这是由于NumPy加载了多个DLL文件导致的警告，一般不会影响代码的执行，可以忽略该警告。
请根据以上提示检查代码和数据，如果问题仍然存在，请提供更多的信息，例如数据样本和完整的错误提示。 指导：新手乐园
根据你提供的代码和错误信息，我注意到以下问题：
1. 错误信息中提到了一个KeyError，即找不到'状态'列。这可能是因为两个表格中没有名为'状态'的列。请确保表格中存在该列，并使用正确的列名来进行比较。
2. 在合并两张表格之前，你需要确认两个表格中相同行的定义。目前你使用了location、project_name、building、unit、high和room这些列来进行筛选，但我注意到在后续的代码中，你使用了不同的列名。请确认列名的一致性，并将其用于合并和筛选操作。
3. 你可以使用pandas库的duplicated()函数来找到重复行，然后再通过判断'状态'列的值是否相等来确定是否发生变化。请确保在使用duplicated()函数时，传入正确的列名作为参数。
4. 最后，你可以使用to_excel()函数将结果保存到Excel文件中，但请确保你提供的保存路径是有效的，并且具有写入权限。
考虑到以上问题，我修改了你的代码，并进行了注释说明，希望能帮助到你：
import pandas as pd
# 读取两张表格
df1 = pd.read_excel('D:\一介书生资料库\爬虫：八爪鱼\各市县整体市场\shujuchuli\七月三亚.xlsx')
df2 = pd.read_excel('D:\一介书生资料库\爬虫：八爪鱼\各市县整体市场\shujuchuli\八月三亚.xlsx')
# 筛选出相同的行，并选择指定的列
cols = ['区域', '项目名称', '楼盘', '单元', '楼层', '房间', '建筑面积', '房屋用途', '房型', '挂牌清水价', '挂牌装修价']
df1_grouped = df1.groupby(cols).first().reset_index()
df2_grouped = df2.groupby(cols).first().reset_index()
# 合并两张表格
df_merged = pd.concat()
# 找到发生变化的行
df_duplicates = df_merged
df_changed = df_duplicates != df_duplicates['状态_y']]
# 输出结果
df_changed.to_excel('D:\pydata\data.xlsx', index=False)
print(df_changed)
请根据你的实际需求和数据情况进行适当调整。希望这能帮助到你！如果还有其他问题，请随时提问。
球一个最佳答案谢谢啦！这对我非常重要！{:10_254:}{:10_254:}{:10_254:} Mike_python小 发表于 2023-11-3 08:33
指导：新手乐园
根据你提供的代码和错误信息，我注意到以下问题：
不行 还是提示错误 没有找到状态 表格是有状态那列的 一介书生423 发表于 2023-11-3 08:37
不行 还是提示错误 没有找到状态 表格是有状态那列的
错误提示
D:\PythonProject\pythonProject\Scripts\python.exe E:\qycache\xuexi\pythonProject\房地产\process（版本2.3）.py
D:\PythonProject\pythonProject\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\PythonProject\pythonProject\lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
D:\PythonProject\pythonProject\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Traceback (most recent call last):
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\indexes\base.py", line 3652, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas\_libs\hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '状态_x'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:\qycache\xuexi\pythonProject\房地产\process（版本2.3）.py", line 59, in <module>
df_changed = df_duplicates != df_duplicates['状态_y']]
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\frame.py", line 3761, in __getitem__
indexer = self.columns.get_loc(key)
File "D:\PythonProject\pythonProject\lib\site-packages\pandas\core\indexes\base.py", line 3654, in get_loc
raise KeyError(key) from err
KeyError: '状态_x' Mike_python小 发表于 2023-11-3 08:33
指导：新手乐园
根据你提供的代码和错误信息，我注意到以下问题：
表格标题为：区域 项目名称 楼盘 单元 楼层 房间 建筑面积 房屋用途 房型 挂牌清水价 挂牌装修价 产品类型 状态 商品房预售许可证 发证日期 isdkz 发表于 2023-11-3 08:31
根据您提供的代码和错误提示，问题出在 df_duplicates.loc != df_duplicates['状态' ...
可否留个邮箱发送表格和代码给你帮我看看呀 isdkz 发表于 2023-11-3 08:31
根据您提供的代码和错误提示，问题出在 df_duplicates.loc != df_duplicates['状态' ...
求留个邮箱 发送数据源 一介书生423 发表于 2023-11-3 08:53
求留个邮箱 发送数据源
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