Python: Real-World Data Science

Advanced uses of pandas for data analysis

In this section we will consider some advanced pandas use cases.

Hierarchical indexing

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() ...