Python 如何克服'；ValueError：传递的项目数错误2，放置意味着1'；错误？
我有一个时间序列df：Python 如何克服'；ValueError：传递的项目数错误2，放置意味着1'；错误？,python,python-3.x,pandas,dataframe,pandas-groupby,Python,Python 3.x,Pandas,Dataframe,Pandas Groupby,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
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
data['month'] = [d.strftime('%b') for d in data.Dates]
monthdata_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()
data\u nan\u dropdata_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\u nan\u drop['hourly\u mean']hourly\u mean月平均值日平均值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
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
数据删除['daily_mean']=数据删除.groupby（[data_nan_Drop.Dates]）。转换（'mean'））
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\frame.py”，第3370行，在uu setitem中__
自我设置项目（键、值）
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\frame.py”，第3446行，在集合项中
NDFrame.\u设置\u项（自身、键、值）
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\generic.py”，第3172行，在集合项中
self.\u数据集（键、值）
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\internals\managers.py”，第1056行，在集合中
self.insert（len（self.items）、item、value）
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\internals\managers.py”，第1158行，插入
位置=切片（loc，loc+1））
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\internals\blocks.py”，第3095行，在make\u block中
返回klass（值，ndim=ndim，placement=placement）
文件“C:\Users\kashy\Anaconda3\envs\py36\lib\site packages\pandas\core\internals\blocks.py”，第87行，在uu init中__
“{mgr}.”格式（val=len（self.values），mgr=len（self.mgr_locs）））
ValueError:传递的项目数错误2，放置意味着1
谢谢 如果您的日期和时间列都是datetime64[ns]，那么：
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及其各自的平均值。这里的问题是您应该指出哪些列可以获得
平均值，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')
我又在玩代码了，下面的代码给出了正确的答案：
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')
我在上面添加了3行。将您的代码替换为df=data.dropna（axis=0）
中的“我的代码”，以确保所有内容都相同。你的时间将是错误的格式，但你可以在完成所有转换后更改它。当我运行df['time']
code时，它会给我以下错误：TypeError:不能转换为datetime
嘿，当我这样做时，只有“monthly\u mean”给我正确的答案，但是每日的意思和每小时的意思给了我错误的答案