pandas groupby 熊猫中的错误
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Error in groupby pandas
提问by user1345283
I am running a script in windows and I get the following error:
我在 Windows 中运行脚本，但出现以下错误：
Traceback (most recent call last):
File "C:\Users\esalazar\Desktop\datos\stat_cea_2011\emas\amealco\promedios-emas.py", line 64, in <module>
g=index.groupby(level=0)
AttributeError: 'NoneType' object has no attribute 'groupby'
I have installed pandas, scipy and numpy. I need to install some other library? How do I fix this error?
我已经安装了 pandas、scipy 和 numpy。我需要安装其他一些库吗？我该如何解决这个错误？
This is part of my code:
这是我的代码的一部分：
data = pd.read_csv('C:/Users/esalazar/Desktop/datos/stat_cea_2011/emas/amealco/enero.csv',skiprows=1,names=['Fecha','Hora','C','D','E','Temperatura','TempRocio','DirViento','I','MagViento','K','Humedad','Presion','N','PreciAcu','P','Q','R','S'],header=0)
Uviento=[]
Vviento=[]
for i in range(0,len(data['MagViento'])):
Uviento.append((data['MagViento'][i]*sin((data['DirViento'][i]+180)*(pi/180.0))))
Vviento.append((data['MagViento'][i]*cos((data['DirViento'][i]+180)*(pi/180.0))))
data['PromeU']=Uviento
data['PromeV']=Vviento
data
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4463 entries, 0 to 4462
Data columns (total 19 columns):
Fecha 4463 non-null values
Hora 4463 non-null values
C 4463 non-null values
D 4463 non-null values
E 4463 non-null values
Temperatura 4463 non-null values
TempRocio 4463 non-null values
DirViento 4463 non-null values
I 4463 non-null values
MagViento 4463 non-null values
K 4463 non-null values
Humedad 4463 non-null values
Presion 4463 non-null values
N 4463 non-null values
PreciAcu 4463 non-null values
P 4463 non-null values
Q 4463 non-null values
R 4463 non-null values
S 4463 non-null values
dtypes: float64(8), int64(4), object(7)
df = data.set_index(['Fecha','Hora'],inplace=True)
df
i get this error
我收到这个错误
TypeError: NoneType object has no atribute '--getitem--'
therefore,
所以，
grouped = df.groupby(level=0)
is error too.
也是错误。
回答by sashkello
data.set_index(['Fecha','Hora'], inplace=True)modifies your DataFrame in place (see docs); this is what inplace=Truespecifies. That is, it doesn't create a new object but rather modifies datadirectly. You can do either
data.set_index(['Fecha','Hora'], inplace=True)就地修改您的 DataFrame（参见文档）；这就是inplace=True指定的内容。也就是说，它不会创建新对象，而是data直接修改。你可以这样做
df = data.set_index(['Fecha','Hora'])
grouped = df.groupby(level=0)
or
或者
data.set_index(['Fecha','Hora'], inplace=True)
grouped = data.groupby(level=0)
