<p>我不是最擅长约会的人,但可能是这样的:</p>
<pre><code>import pandas as pd
from datetime import timedelta
df = pd.read_csv("hourmelt.csv", sep=r"\s+")
df = pd.melt(df, id_vars=["Date"])
df = df.rename(columns={'variable': 'hour'})
df['hour'] = df['hour'].apply(lambda x: int(x.lstrip('h'))-1)
combined = df.apply(lambda x:
pd.to_datetime(x['Date'], dayfirst=True) +
timedelta(hours=int(x['hour'])), axis=1)
df['Date'] = combined
del df['hour']
df = df.sort("Date")
</code></pre>
<hr/>
<p>下面是一些解释。</p>
<p>从</p>
<pre><code>>>> import pandas as pd
>>> from datetime import datetime, timedelta
>>>
>>> df = pd.read_csv("hourmelt.csv", sep=r"\s+")
>>> df
Date h1 h2 h3 h4 h24
0 14.03.2013 60 50 52 49 73
1 14.04.2013 5 6 7 8 9
</code></pre>
<p>我们可以使用<code>pd.melt</code>将小时列变成具有该值的列:</p>
<pre><code>>>> df = pd.melt(df, id_vars=["Date"])
>>> df = df.rename(columns={'variable': 'hour'})
>>> df
Date hour value
0 14.03.2013 h1 60
1 14.04.2013 h1 5
2 14.03.2013 h2 50
3 14.04.2013 h2 6
4 14.03.2013 h3 52
5 14.04.2013 h3 7
6 14.03.2013 h4 49
7 14.04.2013 h4 8
8 14.03.2013 h24 73
9 14.04.2013 h24 9
</code></pre>
<p>摆脱那些<code>h</code>:</p>
<pre><code>>>> df['hour'] = df['hour'].apply(lambda x: int(x.lstrip('h'))-1)
>>> df
Date hour value
0 14.03.2013 0 60
1 14.04.2013 0 5
2 14.03.2013 1 50
3 14.04.2013 1 6
4 14.03.2013 2 52
5 14.04.2013 2 7
6 14.03.2013 3 49
7 14.04.2013 3 8
8 14.03.2013 23 73
9 14.04.2013 23 9
</code></pre>
<p>将这两列合并为日期:</p>
<pre><code>>>> combined = df.apply(lambda x: pd.to_datetime(x['Date'], dayfirst=True) + timedelta(hours=int(x['hour'])), axis=1)
>>> combined
0 2013-03-14 00:00:00
1 2013-04-14 00:00:00
2 2013-03-14 01:00:00
3 2013-04-14 01:00:00
4 2013-03-14 02:00:00
5 2013-04-14 02:00:00
6 2013-03-14 03:00:00
7 2013-04-14 03:00:00
8 2013-03-14 23:00:00
9 2013-04-14 23:00:00
</code></pre>
<p>重新组装和清理:</p>
<pre><code>>>> df['Date'] = combined
>>> del df['hour']
>>> df = df.sort("Date")
>>> df
Date value
0 2013-03-14 00:00:00 60
2 2013-03-14 01:00:00 50
4 2013-03-14 02:00:00 52
6 2013-03-14 03:00:00 49
8 2013-03-14 23:00:00 73
1 2013-04-14 00:00:00 5
3 2013-04-14 01:00:00 6
5 2013-04-14 02:00:00 7
7 2013-04-14 03:00:00 8
9 2013-04-14 23:00:00 9
</code></pre>