我需要计算每天的colums持续时间和km的平均值 值== 1且值为0的行
df
Out[20]:
Date duration km value
0 2015-03-28 09:07:00.800001 0 0 0
1 2015-03-28 09:36:01.819998 1 2 1
2 2015-03-30 09:36:06.839997 1 3 1
3 2015-03-30 09:37:27.659997 nan 5 0
4 2015-04-22 09:51:40.440003 3 7 0
5 2015-04-23 10:15:25.080002 0 nan 1
如何修改此解决方案以获得持续时间duration_value0,duration_value1,km_value0和km_value1?
df = df.set_index('Date').groupby(pd.Grouper(freq='d')).mean().dropna(how='all')
print (df)
duration km
Date
2015-03-28 0.5 1.0
2015-03-30 1.5 4.0
2015-04-22 3.0 7.0
2015-04-23 0.0 0.0
答案 0 :(得分:2)
我认为按Date
以及value
进行分组应该这样做。
拨打dfGroupBy.mean
,然后拨打df.reset_index
以获得所需的输出:
In [713]: df.set_index('Date')\
.groupby([pd.Grouper(freq='d'), 'value'])\
.mean().reset_index(1, drop=True)
Out[713]:
duration km
Date
2015-03-28 0.0 0.0
2015-03-28 1.0 2.0
2015-03-30 NaN 5.0
2015-03-30 1.0 3.0
2015-04-22 3.0 7.0
2015-04-23 0.0 NaN
答案 1 :(得分:1)
我认为你正在寻找支点表,即
df.pivot_table(values=['duration','km'],columns=['value'],index=df['Date'].dt.date,aggfunc='mean')
输出:
duration km value 0 1 0 1 Date 2015-03-28 0.0 1.0 0.0 2.0 2015-03-30 NaN 1.0 5.0 3.0 2015-04-22 3.0 NaN 7.0 NaN 2015-04-23 NaN 0.0 NaN NaN In [24]:
如果你想要新的列名如distance0,distance1 ......你可以使用列表理解,即如果你将数据透视表存储在ndf
ndf.columns = [i[0]+str(i[1]) for i in ndf.columns]
输出:
duration0 duration1 km0 km1 Date 2015-03-28 0.0 1.0 0.0 2.0 2015-03-30 NaN 1.0 5.0 3.0 2015-04-22 3.0 NaN 7.0 NaN 2015-04-23 NaN 0.0 NaN NaN