问题：
I load some machine learning data from a CSV file.我从 CSV 文件加载了一些机器学习数据。 The first 2 columns are observations and the remaining columns are features.前两列是观测值，其余列是特征。
Currently, I do the following:目前，我执行以下操作：
data = pandas.read_csv('mydata.csv')
which gives something like:这给出了类似的东西：
data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde'))
I'd like to slice this dataframe in two dataframes: one containing the columns a and b and one containing the columns c , d and e .我想将此数据帧分成两个数据帧：一个包含列a和b ，另一个包含列c 、 d和e 。
It is not possible to write something like不可能写出类似的东西
observations = data[:'c']
features = data['c':]
I'm not sure what the best method is.我不确定最好的方法是什么。 Do I need a pd.Panel ?我需要一个pd.Panel吗？
By the way, I find dataframe indexing pretty inconsistent: data['a'] is permitted, but data[0] is not.顺便说一下，我发现数据帧索引非常不一致：允许使用data['a'] ，但不允许使用data[0] 。 On the other side, data['a':] is not permitted but data[0:] is.另一方面，不允许使用data['a':]但允许使用data[0:] 。 Is there a practical reason for this?这有实际的原因吗？ This is really confusing if columns are indexed by Int, given that data[0] != data[0:1]考虑到data[0] != data[0:1] ，如果列由 Int 索引，这真的很令人困惑
解决方案：
参考一： https://en.stackoom.com/question/ikgT参考二： https://stackoom.com/question/ikgT