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,我对观察结果进行分组的方式并不直接!查看 Value1
,我想将它们分组为 非零
值。例如,索引 1,2,3
将在一组中。一组中的发病率 7,8,9
,另一组是 12,13,14
。应避免使用 value1==0
的行,零仅作为组之间的分隔符。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,然后根据那。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。
最佳答案
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对这个组进行聚合 http://pandas.pydata.org/pandas-docs/stable/groupby.html
对于您特别希望作为输出获得的内容,也请查看此答案:Python Pandas: Multiple aggregations of the same column
df2 = gb.agg({
'ActivityDateTime': ['first', 'last'],
'Value1': 'mean',
'Value2': 'mean'})
关于python - 选定行的 Pandas 数据框聚合,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/37220693/