All the required information to build a model doesn't always come from a single table or data source. In many cases, two datasets need to be joined/merged to get more information (read new column/variable). Sometimes, small datasets need to be appended together to make a big dataset which contains the complete picture. Thus, merging and appending are important components of an analyst's armor.
Let's learn each of these methods one by one. For illustrating these methods, we will be using a lot of new interesting datasets. The one we are going to use first is a dataset about the mineral contents of wine; we will have separate datasets for red and white wine. Each sample represents a different sample of red or white wine.
Let us import this dataset and have a look at it. The delimiter for this dataset is ; (a semi-colon), which needs to be taken care of:
import pandas as pd
data1=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Merge and...
