Logical statement for datetime64[ns]

You are applying an entire series s to a filtered dataframe column. I think it is easier to just use mask or np.where instead of .loc:

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

or np.where:

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

If the column doesn’t exist yet, then use:

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

.loc would be okay for this use case if you were assigning a single value like 10 rather than a series that has a longer length than your filtered dataframe.

The issue is that you reset_index() after defining s, which means it is not comparable to your dataframe:

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