For demonstrative purposes, in this chapter, we will utilize a dataset that we have created, so that we can showcase a variety of data levels and types. Let's set up our DataFrame and dive into our data.
We will use pandas to create the DataFrame we will work with, as this is the primary data structure in pandas. The advantage of a pandas DataFrame is that there are several attributes and methods available for us to perform on our data. This allows us to logically manipulate the data to develop a thorough understanding of what we are working with, and how best to structure our machine learning models:
- First, let's import
pandas:
# import pandas as pd
- Now, we can set up our
DataFrame X. To do this, we will utilize theDataFramemethod in pandas, which creates a tabular data structure (table with rows and columns). This method can take in a few types of data (NumPy arrays or dictionaries, to name a couple). Here, we will be passing-in a dictionary with keys as column headers...