The Pandas data structure

Let's first get acquainted with two of Pandas' primary data structures: the Series and the DataFrame. They can handle the majority of use cases in finance, statistic, social science, and many areas of engineering.

Series

A Series is a one-dimensional object similar to an array, list, or column in table. Each item in a Series is assigned to an entry in an index:

>>> s1 = pd.Series(np.random.rand(4),
                   index=['a', 'b', 'c', 'd'])
>>> s1
a    0.6122
b    0.98096
c    0.3350
d    0.7221
dtype: float64

By default, if no index is passed, it will be created to have values ranging from 0 to N-1, where N is the length of the Series:

>>> s2 = pd.Series(np.random.rand(4))
>>> s2
0    0.6913
1    0.8487
2    0.8627
3    0.7286
dtype: float64

We can access the value of a Series by using the index:

>>> s1['c']
0.3350
>>>s1['c'] = 3.14
>&gt...