尝试访问索引时，Python Pandas键错误 [英] Python Pandas Key Error When Trying to Access Index

问题描述

我在列中有以下股票数据集，在行的下方(使用彭博(Bloomberg)的Python API下载-请忽略事实，它们都是'NaN'-仅用于数据的这一部分):

我正在尝试从索引中提取月份和年份，以便稍后进行枢轴操作:

values['month'] = values['date'].apply(lambda x: x.month)

其中值是上述DataFrame的名称.

但是这会导致错误:'KeyError'date'

运行:

values.index

看起来不错:

DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-23',
           '2010-01-24', '2010-01-29', '2010-01-30', '2010-01-31',
           '2010-02-13', '2010-02-14',
           ...
           '2017-08-12', '2017-08-27', '2017-08-31', '2017-09-01',
           '2017-09-03', '2017-09-09', '2017-09-24', '2017-09-29',
           '2017-09-30', '2017-10-01'],
          dtype='datetime64[ns]', name='date', length=593, freq=None)

所以我只是想知道问题出在哪里，为什么我似乎无法在此处访问实际的索引?

解决方案

第一列称为index，date为index.name.

您可以通过以下方式进行检查:

print (df.index.name)

因此，您需要 DatetimeIndex.month 和 DatetimeIndex.year :

values.index.month

对于自定义字符串格式，日期用作 strftime :

values['name'] = values.index.strftime('%B - %Y')

I have the following data set of stocks along the columns, dates down the rows(downloaded using Bloomberg's Python API - please ignore fact that they are all 'NaN' - this is just for this portion of the data):

I am trying to extract the Month and Years from the Index in order to later do a pivot:

values['month'] = values['date'].apply(lambda x: x.month)

Where values is the name of the above DataFrame.

However this gives an error: 'KeyError 'date'

Running:

values.index

Looks fine:

DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-23',
           '2010-01-24', '2010-01-29', '2010-01-30', '2010-01-31',
           '2010-02-13', '2010-02-14',
           ...
           '2017-08-12', '2017-08-27', '2017-08-31', '2017-09-01',
           '2017-09-03', '2017-09-09', '2017-09-24', '2017-09-29',
           '2017-09-30', '2017-10-01'],
          dtype='datetime64[ns]', name='date', length=593, freq=None)

So I am just wondering what is going wrong and why I don't seem able to access the actual index here?

解决方案

First columns is called index and date is index.name.

You can check it by:

print (df.index.name)

So you need DatetimeIndex.month and DatetimeIndex.year:

values.index.month

EDIT:

For custom string format dates are used as strftime:

values['name'] = values.index.strftime('%B - %Y')

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