'不再支持将缺少列表的标签传递给.loc或[]且缺少标签的情况，请参见

如何解决'不再支持将缺少列表的标签传递给.loc或[]且缺少标签的情况，请参见

如何纠正错误：

KeyError: 'Passing list-likes to .loc or [] with any missing labels 
is no longer supported,see 
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'

我只想获取索引“ ix”中数据框的位置。

这是可重现的样品：

import pandas as pd
ix = pd.DatetimeIndex(["2019-07-19 08:47:00","2019-07-19 08:48:00","2019-07-19 08:49:00",'2019-07-19 13:43:00','2019-07-19 13:44:00','2019-07-19 13:45:00','2019-07-19 13:46:00','2019-07-19 13:47:00','2019-07-19 13:48:00','2019-07-19 13:49:00','2019-07-19 13:50:00','2019-07-19 13:51:00','2019-07-19 13:52:00'],dtype='datetime64[ns]',name='Time',freq=None)
df = pd.DataFrame({"result":[7.445043,0.585584,1.735565,3.217186,0.211871,0.000000,0.180448,21.645403,22.724170,304.292450]},index = [
"2019-07-19 08:47:00","2019-07-19 08:50:00","2019-07-19 08:51:00","2019-07-19 08:52:00","2019-07-19 08:53:00","2019-07-19 08:54:00","2019-07-19 08:55:00","2019-07-19 08:56:00"   ])
df.index = pd.to_datetime(df.index)

result = df.loc[ix]
print(result)

解决方法

要搜索数据框索引，只需将标准列表与df.index.isin一起使用。

尝试以下代码：

import pandas as pd
from datetime import datetime

# string dates
dtsearch = ["2019-07-19 08:47:00","2019-07-19 08:48:00","2019-07-19 08:49:00",'2019-07-19 13:43:00','2019-07-19 13:44:00','2019-07-19 13:45:00','2019-07-19 13:46:00','2019-07-19 13:47:00','2019-07-19 13:48:00','2019-07-19 13:49:00','2019-07-19 13:50:00','2019-07-19 13:51:00','2019-07-19 13:52:00']

# convert to datetime
dts = [datetime.strptime(d,'%Y-%m-%d %H:%M:%S') for d in dtsearch]

# build dataframe
df = pd.DataFrame({"result":[7.445043,0.585584,1.735565,3.217186,0.211871,0.000000,0.180448,21.645403,22.724170,304.292450]},index = ["2019-07-19 08:47:00","2019-07-19 08:50:00","2019-07-19 08:51:00","2019-07-19 08:52:00","2019-07-19 08:53:00","2019-07-19 08:54:00","2019-07-19 08:55:00","2019-07-19 08:56:00"])

# set df index
df.index = pd.to_datetime(df.index)

# search index using date list
df2 = df[df.index.isin(dts)]

print(df2)

输出

                       result
2019-07-19 08:47:00  7.445043
2019-07-19 08:48:00  0.585584
2019-07-19 08:49:00  1.735565

,

您可以尝试如下操作：

result = df.loc[df.index.intersection(ix)]