Python 3.x datetime64[ns]的逻辑语句

我在使用

df.loc

时遇到以下错误。我有一个参数s，它是datetime64[ns]，我有一个数据帧，它有两个列MDT1和MTD2，这也是datetime64[ns]类型。当我使用df.loc使用以下语句时

s= df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
s = pd.to_datetime(s)
df_1x2.loc[(s>df_1x2['MDT1'])&(s<df_1x2['MDT2']),'Morning']= s

但是，当我第二次运行时，它会按预期工作。只是Jupyter笔记本的第一次运行让我犯了错误

以下是下载示例数据文件的链接：

代码如下：

s = pd.to_datetime(df['Date']).astype(str)+' '+df['Time.1'].astype(str)+':00'
df['Matchdate'] = pd.to_datetime (s)

d =np.reciprocal(df_1x2[['Home','Draw','Away']].astype(float))

df_1x2['Margin'] = d.sum(axis = 1)-1
df_1x2['Open_DT'] = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
df_1x2['Open_DT'] = pd.to_datetime(df_1x2['Open_DT'])
df_1x2['Open_DT1'] = df_1x2.groupby(['Match','Odds_Type'])['Open_DT'].transform(min)
s = df_1x2['Open_DT'] == df_1x2['Open_DT1']
df_1x2.loc[s,'Open'] ='Opening'


df_1x2['Current_DT'] = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
df_1x2['Current_DT'] = pd.to_datetime(df_1x2['Current_DT'])
df_1x2['Current_DT1'] = df_1x2.groupby(['Match','Odds_Type'])['Current_DT'].transform(max)
sc = df_1x2['Current_DT'] == df_1x2['Current_DT1']
df_1x2.loc[sc,'Current'] ='Current'
df_1x2.loc[df_1x2.Open == 'Opening','Current']='Opening'

df_1x2 ['morning Date'] = pd.Timestamp.today()
Time1 = '08:00:00'
Time2 = '11:00:00'
s = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
s = pd.to_datetime(s)
df_1x2.reset_index(drop = True, inplace = True)

df_1x2 ['morning Date'] = pd.to_datetime(df_1x2 ['morning Date']).dt.date
df_1x2['MDT1'] = pd.to_datetime(df_1x2['morning Date'].astype(str)+' '+Time1)


df_1x2['MDT2'] = pd.to_datetime(df_1x2['morning Date'].astype(str)+' '+Time2)

df_1x2 .reset_index(drop= True, inplace = True)



df_1x2.loc[(s>df_1x2['MDT1'])&(s<df_1x2['MDT2']),'Morning']= s

s=pd.to_datetime（df['Date']）.astype（str）+''+df['Time.1']）.astype（str）+':00'
df['Matchdate']=pd.to_日期时间
d=np.倒数（df_1x2[[Home'，'Draw'，'Away']].aType（float））
df_1x2[‘裕度’]=d.和（轴=1）-1
df_1x2['Open_DT']=df_1x2['date'].astype（str）+'-'+df_1x2['Time'].astype（str）
df_1x2['Open_DT']=pd.to_datetime（df_1x2['Open_DT']））
df_1x2['Open_DT1']=df_1x2.groupby（['Match'，'doubs_Type']）['Open_DT'].转换（分钟）
s=df_1x2['Open_DT']==df_1x2['Open_DT1']
df_1x2.loc[s，'Open']=“Opening”
df_1x2['Current_DT']=df_1x2['date'].astype（str）+'-'+df_1x2['Time'].astype（str）
df_1x2['Current_DT']=pd.to_datetime（df_1x2['Current_DT']））
df_1x2['Current_DT1']=df_1x2.groupby（['Match'，'doffics_Type']）['Current_DT'].transform（max）
sc=df_1x2['Current_DT']==df_1x2['Current_DT1']
df_1x2.loc[sc，'Current']='Current'
df_1x2.loc[df_1x2.Open=='Opening'，'Current']='Opening'
df_1x2['morning Date']=pd.Timestamp.today（）
时间1='08:00:00'
时间2='11:00:00'
s=df_1x2['date'].astype（str）+'-'+df_1x2['Time'].astype（str）
s=pd.to_日期时间（s）
df_1x2.重置_索引（drop=True，inplace=True）
df_1x2[“上午日期”]=pd.to_datetime（df_1x2[“上午日期”]）.dt.Date
df_1x2['MDT1']=pd.to_datetime（df_1x2['morning Date'].astype（str）+''+Time1）
df_1x2['MDT2']=pd.to_datetime（df_1x2['morning Date'].astype（str）+''+Time2）
df_1x2.重置_索引（drop=True，inplace=True）
df_1x2.loc[（s>df_1x2['MDT1']）和（s您正在将整个系列
s
应用于过滤后的数据帧列。我认为只使用
mask
或
np.where
而不是
。loc
：

df_1x2['Morning'] = df_1x2['Morning'].mask((s > df_1x2['MDT1']) & (s < df_1x2['MDT2']), s)


如果该列尚不存在，请使用：

df_1x2['Morning'] = np.where((s > df_1x2['MDT1']) & (s < df_1x2['MDT2']), s, np.datetime64('NaT'))

你能复制粘贴
df_1x2
@wwnde你是说整个数据帧吗？如果你没有找到解决方案，只需头部就可以了。我将上传csv文件粘贴问题中的代码。嗨@David Erickson，很遗憾，“早上”一栏在此步骤之前不存在，因此我也得到了错误。我将附加包含data@Zephyr在您的条件之外，另一种结果是什么？您刚才提到的是您的问题。您不能将基于带条件的筛选数据帧的列添加到数据帧，因为长度不相等。例如，如果备选结果为
np.nan
，请查看我的更新答案。不幸的是，它仍然给了我相同的错误值错误：只能比较标记相同的系列对象。我认为它在重新运行两次后有效。非常感谢。这意味着我仍然可以应用df.loc，但重置后索引给我带来了问题。非常感谢David。
df_1x2['Morning'] = np.where((s > df_1x2['MDT1']) & (s < df_1x2['MDT2']), s, df_1x2['Morning'])

df_1x2['Morning'] = np.where((s > df_1x2['MDT1']) & (s < df_1x2['MDT2']), s, np.datetime64('NaT'))

s = pd.to_datetime(df['Date']).astype(str)+' '+df['Time.1'].astype(str)+':00'
df['Matchdate'] = pd.to_datetime (s)

d =np.reciprocal(df_1x2[['Home','Draw','Away']].astype(float))

df_1x2['Margin'] = d.sum(axis = 1)-1
df_1x2['Open_DT'] = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
df_1x2['Open_DT'] = pd.to_datetime(df_1x2['Open_DT'])
df_1x2['Open_DT1'] = df_1x2.groupby(['Match','Odds_Type'])['Open_DT'].transform(min)
s = df_1x2['Open_DT'] == df_1x2['Open_DT1']
df_1x2.loc[s,'Open'] ='Opening'


df_1x2['Current_DT'] = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
df_1x2['Current_DT'] = pd.to_datetime(df_1x2['Current_DT'])
df_1x2['Current_DT1'] = df_1x2.groupby(['Match','Odds_Type'])['Current_DT'].transform(max)
sc = df_1x2['Current_DT'] == df_1x2['Current_DT1']
df_1x2.loc[sc,'Current'] ='Current'
df_1x2.loc[df_1x2.Open == 'Opening','Current']='Opening'

df_1x2 ['morning Date'] = pd.Timestamp.today()
Time1 = '08:00:00'
Time2 = '11:00:00'

########### I moved s from here...........

df_1x2.reset_index(drop = True, inplace = True)

df_1x2 ['morning Date'] = pd.to_datetime(df_1x2 ['morning Date']).dt.date
df_1x2['MDT1'] = pd.to_datetime(df_1x2['morning Date'].astype(str)+' '+Time1)


df_1x2['MDT2'] = pd.to_datetime(df_1x2['morning Date'].astype(str)+' '+Time2)

df_1x2 .reset_index(drop= True, inplace = True)

########### .........to here. 
##### Resetting the  index after defining s, means you can no longer compare directly to your dataframe as s has a different index than your dataframe now.

s = df_1x2['date'].astype (str)+'-'+ df_1x2['Time'].astype(str)
s = pd.to_datetime(s)


df_1x2.loc[(s>df_1x2['MDT1'])&(s<df_1x2['MDT2']),'Morning']= s