Now let's examine using the Series
and DataFrame
objects, building up an understanding of their capabilities that will assist us in working with financial data.
A Series
can be created by passing a scalar value, a NumPy array, or a Python dictionary/list to the constructor of the Series
object. The following command creates a Series
from 100
normally distributed random numbers:
In [2]: np.random.seed(1) s = pd.Series(np.random.randn(100)) s Out[2]: 0 1.624345 1 -0.611756 2 -0.528172 3 -1.072969 ... 96 -0.343854 97 0.043597 98 -0.620001 99 0.698032 Length: 100, dtype: float64
Individual elements of a Series
can be retrieved using the []
operator of the Series
object. The item with the index label 2
can be retrieved using the following code:
In [3]: s[2] Out[3]: -0.528171752263
Multiple values can be retrieved using an array...