我有一个时间序列df：
Menge Dates Time month
19.5 2018-01-01 00:00:00 Jan
19.0 2018-01-01 00:15:00 Jan
19.5 2018-01-01 00:30:00 Jan
19.5 2018-01-01 00:45:00 Jan
21.0 2018-01-01 01:00:00 Jan
19.5 2018-01-01 01:15:00 Jan
20.0 2018-01-01 01:30:00 Jan
23.0 2018-01-01 01:45:00 Jan
20.5 2018-01-01 02:00:00 Jan
84.5 2018-01-02 02:00:00 Jan
80.0 2018-01-02 02:15:00 Jan
75.5 2018-01-02 02:30:00 Jan
72.0 2018-01-02 02:45:00 Jan
70.0 2018-01-02 03:00:00 Jan
69.0 2018-01-02 03:15:00 Jan
67.5 2018-01-02 03:30:00 Jan
67.0 2018-01-02 03:45:00 Jan
66.0 2018-01-02 04:00:00 Jan
189.5 2018-02-06 07:00:00 Feb
188.0 2018-02-06 07:15:00 Feb
190.5 2018-02-06 07:30:00 Feb
192.0 2018-02-06 07:45:00 Feb
185.5 2018-02-06 08:00:00 Feb
182.5 2018-02-06 08:15:00 Feb
178.0 2018-02-06 08:30:00 Feb
189.5 2018-02-06 08:45:00 Feb
181.0 2018-02-06 09:00:00 Feb
161.0 2018-02-06 21:00:00 Feb
159.0 2018-02-06 21:15:00 Feb
163.5 2018-02-06 21:30:00 Feb
162.5 2018-02-06 21:45:00 Feb
163.0 2018-02-06 22:00:00 Feb
162.5 2018-02-06 22:15:00 Feb
162.5 2018-02-06 22:30:00 Feb
162.0 2018-02-06 22:45:00 Feb
158.5 2018-02-06 23:00:00 Feb
我正在尝试计算相同的每小时，每天和每月平均值。
我已经完成：
data['month'] = [d.strftime('%b') for d in data.Dates]
获取month列以及我正在做的事情：
data_nan_dropped = data.dropna(axis = 0)
data_nan_dropped.Dates = pd.to_datetime(data_nan_dropped.Dates)
data_nan_dropped.Time = pd.to_datetime(data_nan_dropped.Time, format='%H:%M:%S')
hourly_mean = data_nan_dropped.groupby([data_nan_dropped.Dates, data_nan_dropped.Time.dt.hour]).mean()
monthly_mean = data_nan_dropped.groupby(data_nan_dropped.month).mean()
daily_mean = data_nan_dropped.groupby([data_nan_dropped.Dates]).mean()
此代码绝对可以正常工作，但是我想要的是将这些每小时，每月，每天的均值列添加到我的数据框data_nan_dropped中，为此，我尝试将上述代码编辑为：
data_nan_dropped['hourly_mean'] = data_nan_dropped.groupby([data_nan_dropped.Dates, data_nan_dropped.Time.dt.hour]).transform('mean')
data_nan_dropped['monthly_mean'] = data_nan_dropped.groupby(data_nan_dropped.month).transform('mean')
data_nan_dropped['daily_mean'] = data_nan_dropped.groupby([data_nan_dropped.Dates]).transform('mean')
data_nan_dropped['hourly_mean']可以正常工作，并且在我的数据框中创建了一个名为hourly_mean的新列。
但是对于monthly_mean和daily_mean，出现以下错误：
Traceback (most recent call last):
File "<ipython-input-5-159d11ea8819>", line 1, in <module>
data_nan_dropped['daily_mean'] = data_nan_dropped.groupby([data_nan_dropped.Dates]).transform('mean')
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\frame.py", line 3370, in __setitem__
self._set_item(key, value)
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\frame.py", line 3446, in _set_item
NDFrame._set_item(self, key, value)
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\generic.py", line 3172, in _set_item
self._data.set(key, value)
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\internals\managers.py", line 1056, in set
self.insert(len(self.items), item, value)
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\internals\managers.py", line 1158, in insert
placement=slice(loc, loc + 1))
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\internals\blocks.py", line 3095, in make_block
return klass(values, ndim=ndim, placement=placement)
File "C:\Users\kashy\Anaconda3\envs\py36\lib\site-packages\pandas\core\internals\blocks.py", line 87, in __init__
'{mgr}'.format(val=len(self.values), mgr=len(self.mgr_locs)))
ValueError: Wrong number of items passed 2, placement implies 1
要克服此错误必须更改什么？
谢谢！
答案 0 :(得分：1)
鉴于您的“日期”和“时间”列均为datetime64 [ns]，则：
df = data.dropna(axis = 0)
df['Dates'] = pd.to_datetime(df['Dates'])
df['Time'] = pd.to_datetime(df['Time'])
df['month_mean'] = df.groupby(df['Dates'].dt.month).transform('mean')
df['hourbyday_mean'] =df.groupby([df['Dates'].dt.day, df['Time'].dt.hour]).transform('mean')
df['day_mean'] =df.groupby(df['Dates'].dt.day).transform('mean')
将为您提供三个新列，其各自的均值。
答案 1 :(得分：0)
这里的问题是您应该指出获取mean的列，这里我假设它是Menge
data_nan_dropped['hourly_mean'] = data_nan_dropped.groupby([data_nan_dropped.Dates, data_nan_dropped.Time.dt.hour])['Menge'].transform('mean')
答案 2 :(得分：0)
我再次使用该代码，下面的代码为我提供了正确的答案：
data_nan_dropped.Dates = pd.to_datetime(data_nan_dropped.Dates)
data_nan_dropped.Time = pd.to_datetime(data_nan_dropped.Time, format='%H:%M:%S')
data_nan_dropped['monthly_mean'] = data_nan_dropped.groupby(data_nan_dropped['month'])['Menge'].transform('mean')
data_nan_dropped['hourly_mean'] = data_nan_dropped.groupby([data_nan_dropped['Dates'], data_nan_dropped['Time'].dt.hour])['Menge'].transform('mean')
data_nan_dropped['daily_mean'] =data_nan_dropped.groupby(data_nan_dropped['Dates'])['Menge'].transform('mean')