<p>我的测试数据:</p>
<pre><code>df:
EnNo DaiGong DateTime
2 2019-10-12 09:53:56
2 2019-10-13 09:42:00
2 2019-10-14 12:00:01
1 2019-11-01 11:12:00
1 2019-11-02 10:13:45
</code></pre>
<p>创建助手数据:</p>
<pre><code> tdr=pd.timedelta_range("09:00:00","12:30:00",freq="30T")
s=pd.Series( len(tdr)*["-"] )
s[0]=1
cls=[ t.rsplit(":",maxsplit=1)[0] for t in tdr.astype(str) ]
cols=[ t1+"-"+t2 for (t1,t2) in zip(cls,cls[1:]) ]
cols.append(cls[-1]+"-")
tdr:
TimedeltaIndex(['09:00:00', '09:30:00', '10:00:00', '10:30:00', '11:00:00', '11:30:00', '12:00:00', '12:30:00'], dtype='timedelta64[ns]', freq='30T')
cols:
['09:00-09:30', '09:30-10:00', '10:00-10:30', '10:30-11:00', '11:00-11:30', '11:30-12:00', '12:00-12:30', '12:30-']
s:
0 1
1 -
2 -
3 -
4 -
5 -
6 -
7 -
dtype: object
</code></pre>
<p>使用“apply”和“searchsorted”获取时间段:</p>
<pre><code>df2= df.DateTime.apply(lambda t: \
s.shift(tdr.searchsorted(t)-1,fill_value="-"))
df2.columns=cols
df2:
09:00-09:30 09:30-10:00 10:00-10:30 10:30-11:00 11:00-11:30 11:30-12:00 12:00-12:30 12:30-
0 - 1 - - - - - -
1 - 1 - - - - - -
2 - - - - - - 1 -
3 - - - - 1 - - -
4 - - 1 - - - - -
</code></pre>
<p>最后,连接两个数据帧:</p>
<pre><code>df_rslt= pd.concat([df,df2],axis=1)
df_rslt:
EnNo DaiGong DateTime 09:00-09:30 09:30-10:00 10:00-10:30 10:30-11:00 11:00-11:30 11:30-12:00 12:00-12:30 12:30-
0 2 2019-10-12 09:53:56 - 1 - - - - - -
1 2 2019-10-13 09:42:00 - 1 - - - - - -
2 2 2019-10-14 12:00:01 - - - - - - 1 -
3 1 2019-11-01 11:12:00 - - - - 1 - - -
4 1 2019-11-02 10:13:45 - - 1 - - - - -
</code></pre>