问题
Since .ix has been deprecated as of Pandas 0.20, I wonder what is the proper way to mix lable-based, boolean-based and position-based indexing in Pandas? I need to assign values to a slice of dataframe that can be best referenced with label or boolean on the index and position on the columns. For example (using .loc as placeholder for the desired slicing method):
df.loc[df['a'] == 'x', -12:-1] = 3
obviously this doesn't work, with which I get:
TypeError: cannot do slice indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [-12] of <class 'int'>
If I use .iloc, I get:
NotImplementedError: iLocation based boolean indexing on an integer type is not available
So how do I do it, without chaining, obviously to avoid chained assignment problem.
回答1:
Let's use .loc with the boolean indexing, and accessing the column labels via the dataframe column index with index slicing:
df.loc[df['a'] == 'x',df.columns[-12:-1]] = 3
来源：https://stackoverflow.com/questions/44764042/pandas-mix-position-and-label-indexing-without-chaining