对选定行的 Pandas 数据框进行聚合

Question

我有一个像这样的pandas 排序数据框（基于时间）：

from datetime import datetime
df = pd.DataFrame({ 'ActivityDateTime' : [datetime(2016,5,13,6,14),datetime(2016,5,13,6,16),
                                 datetime(2016,5,13,6,20),datetime(2016,5,13,6,27),datetime(2016,5,13,6,31),
                                 datetime(2016,5,13,6,32),
                                datetime(2016,5,13,17,34),datetime(2016,5,13,17,36),
                                 datetime(2016,5,13,17,38),datetime(2016,5,13,17,45),datetime(2016,5,13,17,47),
                                datetime(2016,5,16,13,3),datetime(2016,5,16,13,6),
                                 datetime(2016,5,16,13,10),datetime(2016,5,16,13,14),datetime(2016,5,16,13,16)],
              'Value1' : [0.0,2.0,3.0,4.0,0.0,0.0,0.0,7.0,8.0,4.0,0.0,0.0,3.0,9.0,1.0,0.0],
               'Value2' : [0.0,2.0,3.0,4.0,0.0,0.0,0.0,7.0,8.0,4.0,0.0,0.0,3.0,9.0,1.0,0.0]
        })

结果是这样的：

ActivityDateTime    Value1  Value2
0   2016-05-13 06:14:00 0.0 0.0
1   2016-05-13 06:16:00 2.0 2.0
2   2016-05-13 06:20:00 3.0 3.0
3   2016-05-13 06:27:00 4.0 4.0
4   2016-05-13 06:31:00 0.0 0.0
5   2016-05-13 06:32:00 0.0 0.0
6   2016-05-13 17:34:00 0.0 0.0
7   2016-05-13 17:36:00 7.0 7.0
8   2016-05-13 17:38:00 8.0 8.0
9   2016-05-13 17:45:00 4.0 4.0
10  2016-05-13 17:47:00 0.0 0.0
11  2016-05-16 13:03:00 0.0 0.0
12  2016-05-16 13:06:00 3.0 3.0
13  2016-05-16 13:10:00 9.0 9.0
14  2016-05-16 13:14:00 1.0 1.0
15  2016-05-16 13:16:00 0.0 0.0

我想在没有 for 循环的情况下聚合数据（平均）。但是，我将观察结果分组的方式并不简单！查看Value1，我想将它们组合为non-zero 值。例如，索引1,2,3 将在一个组中。 Incidies 7,8,9 在一组中，另一组是12,13,14。应避免使用value1==0 所在的行，而零仅充当组之间的分隔符。最终我想得到这样的东西：

Activity_end    Activity_start  Value1  Value2  num_observations
0   2016-05-13 06:27:00 2016-05-13 06:16:00 4.50    4.50    3
1   2016-05-13 17:45:00 2016-05-13 17:36:00 6.33    6.33    3
2   2016-05-16 13:14:00 2016-05-16 13:06:00 4.33    4.33    3

目前，我在想我应该以某种方式将数字 1、2 和 3 分配给一个新列，然后基于此聚合它们。我不确定如何在没有 for 循环的情况下制作该列！请注意Value1 和Value2 不一定相同。

Answer 1

一种方法是创建一些临时列

# First create a new series, which is true whenever the value changes from a zero value to a non-zero value (which will be at the start of each group)
nonzero = (df['Value1'] > 0) & (df['Value1'].shift(1) == 0)
# Take a cumulative sum. This means each group will have it's own number.
df['group'] = df['nonzero'].cumsum()
# Group by the group column
gb = df[df['Value1'] > 0].groupby('group')

然后您可以使用聚合函数 http://pandas.pydata.org/pandas-docs/stable/groupby.html 对该组进行聚合

对于您特别希望获得的输出，也请查看以下答案：Python Pandas: Multiple aggregations of the same column

df2 = gb.agg({
    'ActivityDateTime': ['first', 'last'],
    'Value1': 'mean',
    'Value2': 'mean'})