无法使用这些索引器对 <class 'pandas.core.indexes.period.PeriodIndex'> 进行切片索引
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/49281713/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
cannot do slice indexing on <class 'pandas.core.indexes.period.PeriodIndex'> with these indexers
提问by Gaurav
file_location3 = "F:/python/course1_downloads/City_Zhvi_AllHomes.csv"
housing = pd.read_csv(file_location3)
housing.set_index(['State','RegionName'],inplace=True)
housing = housing.iloc[:, 49:]
housing = housing.groupby(pd.PeriodIndex(housing.columns,freq='Q'),axis=1).mean()
data = housing
data = data.iloc[:,'2008q3' : '2009q2']
The error that I am getting is:
我得到的错误是：
cannot do slice indexing on
'<class 'pandas.core.indexes.period.PeriodIndex'>with these indexers[2008q3]of<'class 'str'>
不能做切片索引对
'<class 'pandas.core.indexes.period.PeriodIndex'>这些索引[2008q3]的<'class 'str'>
Now I'm getting another error
现在我收到另一个错误
def price_ratio(row):
return (row['2008q3'] - row['2009q2']) / row['2008q3']
data['up&down'] = data.apply(price_ratio, axis=1)
This gives me error: KeyError: ('2008q3', 'occurred at index 0')
这给了我错误： KeyError: ('2008q3', 'occurred at index 0')
回答by Scott Boston
Try:
尝试：
data.loc[:,'2008q3':'2009q2']
回答by Gaurav
Thanks @Scott for helping me out, After like trying a lot, i got it to work now.
感谢@Scott 帮助我，在尝试了很多之后，我现在可以使用它了。
I converted the data i had to DataFrame and then performed the above operation, it worked then.
我将我拥有的数据转换为DataFrame，然后执行上述操作，然后就可以了。
data = pd.DataFrame(housing)
data = data.loc[:,'2008q3':'2009q2']
data = data.reset_index()
data.columns = ['State', 'RegionName', '2008Q3', '2008Q4', '2009Q1', '2009Q2']
def price_ratio(difference):
return difference['2008Q3'] - difference['2009Q2']
data['Diff'] = data.apply(price_ratio,axis=1)
回答by Alex W
Transforming the columns like so:
像这样转换列：
data.columns = data.columns.astype(str)
will fix the issue. You can visualize the problem:
将解决问题。您可以将问题形象化：
With PeriodIndex:
使用周期索引：
>>> print(data.columns)
Index([2000Q1, 2000Q2, 2000Q3, 2000Q4, 2001Q1, 2001Q2, 2001Q3, 2001Q4, 2002Q1,
2002Q2, 2002Q3, 2002Q4, 2003Q1, 2003Q2, 2003Q3, 2003Q4, 2004Q1, 2004Q2,
2004Q3, 2004Q4, 2005Q1, 2005Q2, 2005Q3, 2005Q4, 2006Q1, 2006Q2, 2006Q3,
2006Q4, 2007Q1, 2007Q2, 2007Q3, 2007Q4, 2008Q1, 2008Q2, 2008Q3, 2008Q4,
2009Q1, 2009Q2, 2009Q3, 2009Q4, 2010Q1, 2010Q2, 2010Q3, 2010Q4, 2011Q1,
2011Q2, 2011Q3, 2011Q4, 2012Q1, 2012Q2, 2012Q3, 2012Q4, 2013Q1, 2013Q2,
2013Q3, 2013Q4, 2014Q1, 2014Q2, 2014Q3, 2014Q4, 2015Q1, 2015Q2, 2015Q3,
2015Q4, 2016Q1, 2016Q2, 2016Q3],
dtype='object')
After setting data.columns = data.columns.astype(str)
设置后 data.columns = data.columns.astype(str)
>>> print(data.columns)
Index(['2000Q1', '2000Q2', '2000Q3', '2000Q4', '2001Q1', '2001Q2', '2001Q3',
'2001Q4', '2002Q1', '2002Q2', '2002Q3', '2002Q4', '2003Q1', '2003Q2',
'2003Q3', '2003Q4', '2004Q1', '2004Q2', '2004Q3', '2004Q4', '2005Q1',
'2005Q2', '2005Q3', '2005Q4', '2006Q1', '2006Q2', '2006Q3', '2006Q4',
'2007Q1', '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',
'2008Q4', '2009Q1', '2009Q2', '2009Q3', '2009Q4', '2010Q1', '2010Q2',
'2010Q3', '2010Q4', '2011Q1', '2011Q2', '2011Q3', '2011Q4', '2012Q1',
'2012Q2', '2012Q3', '2012Q4', '2013Q1', '2013Q2', '2013Q3', '2013Q4',
'2014Q1', '2014Q2', '2014Q3', '2014Q4', '2015Q1', '2015Q2', '2015Q3',
'2015Q4', '2016Q1', '2016Q2', '2016Q3'],
dtype='object')
You'll know it worked because data.loc['Texas'].loc['Austin'].loc['2002Q3']will work, instead of having to use data.loc['Texas'].loc['Austin'].loc[pd.Period('2002Q3')]
你会知道它有效，因为data.loc['Texas'].loc['Austin'].loc['2002Q3']会有效，而不是必须使用data.loc['Texas'].loc['Austin'].loc[pd.Period('2002Q3')]
if you need them in lowercase, e.g. 2001q3instead of 2001Q3:
如果您需要小写，例如2001q3而不是2001Q3：
data.columns = list(map(str.lower, data.columns.astype(str)))
