Book Image

Python Machine Learning (Wiley)

By : Wei-Meng Lee
Book Image

Python Machine Learning (Wiley)

By: Wei-Meng Lee

Overview of this book

With computing power increasing exponentially and costs decreasing at the same time, this is the best time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. Python Machine Learning begins by covering some fundamental libraries used in Python that make machine learning possible. You'll learn how to manipulate arrays of numbers with NumPy and use pandas to deal with tabular data. Once you have a firm foundation in the basics, you'll explore machine learning using Python and the scikit-learn libraries. You'll learn how to visualize data by plotting different types of charts and graphs using the matplotlib library. You'll gain a solid understanding of how the various machine learning algorithms work behind the scenes. The later chapters explore the common machine learning algorithms, such as regression, clustering, and classification, and discuss how to deploy the models that you have built, so that they can be used by client applications running on mobile and desktop devices. By the end of the book, you'll have all the knowledge you need to begin machine learning using Python.
Table of Contents (16 chapters)
Free Chapter
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
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Pandas DataFrame

A Pandas DataFrame is a two‐dimensional NumPy‐like array. You can think of it as a table. Figure 3.2 shows the structure of a DataFrame in Pandas. It also shows you that an individual column in a DataFrame (together with the index) is a Series.

“Structure of a Pandas Dataframe depicting how data is stored in a spreadsheet comprising of columns and rows, which is useful for machine learning.”

Figure 3.2: A Pandas DataFrame

A DataFrame is very useful in the world of data science and machine learning, as it closely mirrors how data are stored in real‐life. Imagine the data stored in a spreadsheet, and you would have a very good visual impression of a DataFrame. A Pandas DataFrame is often used when representing data in machine learning. Hence, for the remaining sections in this chapter, we are going to invest significant time and effort in understanding how it works.

Creating a DataFrame

You can create a Pandas DataFrame using the DataFrame() class:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10,4),

In the preceding...