Python for Finance - Second Edition

How to merge different datasets

It is a common task to merge different datasets, such as merging index data with stock data and the like. Thus, it is quite important to understand the mechanism of merging different datasets. Here, the pandas.merge() function is discussed:

import pandas as pd
import scipy as s
x= pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                 'A': ['A0', 'A1', 'A2', 'A3'],
                 'B': ['B0', 'B1', 'B2', 'B3']})
y = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K6'],
                 'C': ['C0', 'C1', 'C2', 'C3'],
                 'D': ['D0', 'D1', 'D2', 'D3']})

The sizes of both x and y are 4 by 3, that is, four rows and three columns; see the following code:

print(sp.shape(x))
print(x)

The output is shown here:

print(sp.shape(y))
print(y)

Assume that we intend to merge them based ...