从数据框中获取最大值以获取另一个数据框中的值

Getting maximum from dataframe for values from another dataframe

我有一个温度数据框:

temp.ix[1:10]
                     KCRP
DateTime                 
2011-01-01 01:00:00  61.0
2011-01-01 02:00:00  60.0
2011-01-01 03:00:00  57.0
2011-01-01 04:00:00  56.0
2011-01-01 05:00:00  51.0
2011-01-01 06:00:00  55.0
2011-01-01 07:00:00  65.0
2011-01-01 08:00:00  55.0
2011-01-01 09:00:00  55.0

我有另一个数据框 df 作为:

df[['Start Time', 'End Time']].ix[1:10]
                           Start Time              End Time
DateTime                                                   
2011-01-23 05:00:00 2011-01-01 05:00:00 2011-01-01 06:11:00
2011-01-25 04:00:00 2011-01-25 04:51:00 2011-01-26 00:19:00
2011-01-26 04:00:00 2011-01-26 04:29:00 2011-01-26 23:13:00
2011-02-03 07:00:00 2011-02-03 07:56:00 2011-02-03 08:11:00
2011-02-12 19:00:00 2011-02-12 19:52:00 2011-02-13 12:14:00
2011-02-15 14:00:00 2011-02-15 14:09:00 2011-02-15 14:22:00
2011-02-22 05:00:00 2011-02-22 05:47:00 2011-02-22 05:55:00
2011-02-26 06:00:00 2011-02-26 06:47:00 2011-02-26 07:25:00
2011-03-01 00:00:00 2011-03-01 00:44:00 2011-03-02 00:11:00

对于 df 的每一行,我想 select 来自 temp 的最大值,其中 temp 我提取 Start Time 之间的所有值,包括 Start Time ] 和 End Time.

因此,对于 df 的第一行,我的答案将是:

df[['Start Time', 'End Time']].ix[1:10]
                           Start Time              End Time   Max Temp
DateTime                                                   
2011-01-23 05:00:00 2011-01-01 05:00:00 2011-01-01 06:11:00   55

除了循环遍历 df 的每一行之外,我不确定如何进行此操作,这可能不是一种有趣的方法。

我试过:

[np.max(temp[(temp.index >= x[0]) & (temp.index <= x[1])])['KCRP] for x in
                      zip(df['Start Time'], df['End Time'])]

一个简单的方法是使用 apply:

def get_max_temp(row):
    return max(temp[(temp['DateTime'] >= row['Start_Time']) & (temp['DateTime'] <= row['End_Time'])]['KCRP'])

df['Max_Temp'] = df.apply(get_max_temp, axis=1)

您也可以使用向量化函数以获得更好的性能,但显式迭代数据帧中的行几乎总是最后的选择。

更新:

矢量版本:

def get_max_temp(start, end):
    return max(temp[(temp['DateTime'] >= start) & (temp['DateTime'] <= end)]['KCRP'])

get_max_temp = np.vectorize(get_max_temp)
df['Max_Temp'] = get_max_temp(df['Start_Time'], df['End_Time'])