<p>您的数据在最后一列中不是数字,这是一个问题。你知道吗</p>
<p>解决方案是使用<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_numeric.html" rel="nofollow noreferrer">^{<cd1>}</a>将坏数据转换成<code>NaN</code>:</p>
<p>为了更好地使用数据帧,还可以为列名添加参数<code>names</code>到<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer">^{<cd4>}</a>。你知道吗</p>
<pre><code>import pandas as pd
from pandas.compat import StringIO
temp=u"""02/01/2016;05:15:00;10.800
02/01/2016;05:30:00;10.300
02/01/2016;05:45:00;9.200
02/01/2016;06:00:00;9.200
02/01/2016;06:15:00;8.900
02/01/2016;06:30:00;8.900
02/01/2016;06:45:00;9.400
03/01/2016;07:00:00;9.000
03/01/2016;07:15:00;9.200
03/01/2016;07:30:00;11.100
04/01/2016;07:45:00;13.000
04/01/2016;08:00:00;14.400
04/01/2016;08:15:00;a"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df_intraday = pd.read_csv(StringIO(temp),
sep=";",
names=['date','time','val'],
parse_dates=[0])
print (df_intraday)
date time val
0 2016-02-01 05:15:00 10.800
1 2016-02-01 05:30:00 10.300
2 2016-02-01 05:45:00 9.200
3 2016-02-01 06:00:00 9.200
4 2016-02-01 06:15:00 8.900
5 2016-02-01 06:30:00 8.900
6 2016-02-01 06:45:00 9.400
7 2016-03-01 07:00:00 9.000
8 2016-03-01 07:15:00 9.200
9 2016-03-01 07:30:00 11.100
10 2016-04-01 07:45:00 13.000
11 2016-04-01 08:00:00 14.400
12 2016-04-01 08:15:00 a
</code></pre>
<hr/>
<pre><code>df_daily = df_intraday.groupby('date', as_index=False)['val'].max()
print (df_daily)
date val
0 2016-02-01 9.400
1 2016-03-01 9.200
2 2016-04-01 a
#check dtypes - object is obviusly string
print (df_intraday['val'].dtypes)
object
df_intraday['val'] = pd.to_numeric(df_intraday['val'], errors='coerce')
print (df_intraday)
date time val
0 2016-02-01 05:15:00 10.8
1 2016-02-01 05:30:00 10.3
2 2016-02-01 05:45:00 9.2
3 2016-02-01 06:00:00 9.2
4 2016-02-01 06:15:00 8.9
5 2016-02-01 06:30:00 8.9
6 2016-02-01 06:45:00 9.4
7 2016-03-01 07:00:00 9.0
8 2016-03-01 07:15:00 9.2
9 2016-03-01 07:30:00 11.1
10 2016-04-01 07:45:00 13.0
11 2016-04-01 08:00:00 14.4
12 2016-04-01 08:15:00 NaN
print (df_intraday['val'].dtypes)
float64
</code></pre>
<hr/>
<pre><code>#simplier way for aggregating max
df_daily = df_intraday.groupby('date', as_index=False)['val'].max()
print (df_daily)
date val
0 2016-02-01 10.8
1 2016-03-01 11.1
2 2016-04-01 14.4
</code></pre>