In this section we will consider some advanced Pandas use cases.
Hierarchical indexing provides us with a way to work with higher dimensional data in a lower dimension by structuring the data object into multiple index levels on an axis:
>>> s8 = pd.Series(np.random.rand(8), index=[['a','a','b','b','c','c', 'd','d'], [0, 1, 0, 1, 0,1, 0, 1, ]]) >>> s8 a 0 0.721652 1 0.297784 b 0 0.271995 1 0.125342 c 0 0.444074 1 0.948363 d 0 0.197565 1 0.883776 dtype: float64
In the preceding example, we have a Series object that has two index levels. The object can be rearranged into a DataFrame using the unstack function. In an inverse situation, the stack function can be used:
>>> s8.unstack() 0 1 a 0.549211 0.420874 b 0.051516 0.715021 c 0.503072 0.720772 d 0.373037 0.207026
We can also create a DataFrame to have a hierarchical index in both axes...