pd.Grouper allows you to specify a “groupby instruction for a target object”. In particular, you can use it to group by dates even if df.index is not a DatetimeIndex: df.groupby(pd.Grouper(freq=’2D’, level=-1)) The level=-1 tells pd.Grouper to look for the dates in the last level of the MultiIndex. Moreover, you can use this in conjunction with … Read more
There is a get_level_values method that you can use in conjunction with boolean indexing to get the the intended result. In [13]: df = pd.DataFrame(np.random.random((4,4))) df.columns = pd.MultiIndex.from_product([[1,2],[‘A’,’B’]]) print df 1 2 A B A B 0 0.543980 0.628078 0.756941 0.698824 1 0.633005 0.089604 0.198510 0.783556 2 0.662391 0.541182 0.544060 0.059381 3 0.841242 0.634603 0.815334 … Read more
Hierarchical indexing (also referred to as “multi-level” indexing) was introduced in the pandas 0.4 release. This opens the door to some quite sophisticated data analysis and manipulation, especially for working with higher dimensional data. In essence, it enables you to effectively store and manipulate arbitrarily high dimension data in a 2-dimensional tabular structure (DataFrame), for … Read more
The most straightforward way is with .loc: >>> data.loc[:, ([‘one’, ‘two’], [‘a’, ‘b’])] one two a b a b 0 0.4 -0.6 -0.7 0.9 1 0.1 0.4 0.5 -0.3 2 0.7 -1.6 0.7 -0.8 3 -0.9 2.6 1.9 0.6 Remember that [] and () have special meaning when dealing with a MultiIndex object: (…) a … Read more
To query the df by the MultiIndex values, for example where (A > 1.7) and (B < 666): In [536]: result_df = df.loc[(df.index.get_level_values(‘A’) > 1.7) & (df.index.get_level_values(‘B’) < 666)] In [537]: result_df Out[537]: C A B 3.3 222 43 333 59 5.5 333 56 Hence, to get for example the ‘A’ index values, if still … Read more
It’s actually pretty simple (FWIW, I originally thought to do it your way): df[‘bar’, ‘three’] = [0, 1, 2] df = df.sort_index(axis=1) print(df) bar baz one two three one two A -0.212901 0.503615 0 -1.660945 0.446778 B -0.803926 -0.417570 1 -0.336827 0.989343 C 3.400885 -0.214245 2 0.895745 1.011671
I think the easiest way to do this would be to set the columns to the top level: df.columns = df.columns.get_level_values(0) Note: if the to level has a name you can also access it by this, rather than 0. . If you want to combine/join your MultiIndex into one Index (assuming you have just string … Read more
One way is to use the get_level_values Index method: In [11]: df Out[11]: 0 A B 1 4 1 2 5 2 3 6 3 In [12]: df.iloc[df.index.get_level_values(‘A’) == 1] Out[12]: 0 A B 1 4 1 In 0.13 you’ll be able to use xs with drop_level argument: df.xs(1, level=”A”, drop_level=False) # axis=1 if columns … Read more
Pandas wants the MultiIndex values as tuples, not nested dicts. The simplest thing is to convert your dictionary to the right format before trying to pass it to DataFrame: >>> reform = {(outerKey, innerKey): values for outerKey, innerDict in dictionary.iteritems() for innerKey, values in innerDict.iteritems()} >>> reform {(‘A’, ‘a’): [1, 2, 3, 4, 5], (‘A’, … Read more
Method #1: reset_index() >>> g uses books sum sum token year xanthos 1830 3 3 1840 3 3 1868 2 2 1875 1 1 [4 rows x 2 columns] >>> g = g.reset_index() >>> g token year uses books sum sum 0 xanthos 1830 3 3 1 xanthos 1840 3 3 2 xanthos 1868 2 … Read more