Select named index level from pandas DataFrame MultiIndex

Question

I created a dataframe as :

df1 = pandas.read_csv(ifile_name,  header=None,  sep=r"\s+",  usecols=[0,1,2,3,4],
                              index_col=[0,1,2], names=["year", "month", "day", "something1", "something2"])

now I would like to create another dataframe where year>2008. Hence I tried :

df2 = df1[df1.year>2008]

But getting error :

AttributeError: 'DataFrame' object has no attribute 'year'

I guess, it is not seeing the "year" among the columns because I defined it within index. But how can I get data based on year>2008 in that case?

Answer 1

Get the level by name using MultiIndex.get_level_values and create a boolean mask for row selection:

df2 = df1[df1.index.get_level_values('year') > 2008]

If you plan to make modifications, create a copy of df1 so as to not operate on the view.

df2 = df1[df1.index.get_level_values('year') > 2008].copy()