Book Image

The Pandas Workshop

By : Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So
5 (1)
Book Image

The Pandas Workshop

5 (1)
By: Blaine Bateman, Saikat Basak, Thomas V. Joseph, William So

Overview of this book

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.
Table of Contents (21 chapters)
1
Part 1 – Introduction to pandas
6
Part 2 – Working with Data
11
Part 3 – Data Modeling
15
Part 4 – Additional Use Cases for pandas

Exploring matplotlib

Matplotlib is one of the most frequently used Python libraries. It can generate plotting diagrams with great flexibility. The pandas plot() function is a wrapper on top of matplotlib with some bare minimum functionality. While it does simplify the syntax, it also restrains the numerous possibilities of matplotlib. If you want to build complex visualizations, then matplotlib will be your best choice, as it allows controls over all kinds of properties, such as the size, the type of figures and markers, the line width, the colors, and the styles. We will see some of the customizations that can be easily done with matplotlib compared to pandas:

  1. Let's start with an example. Consider the following snippet:
    # Importing libraries
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
     
    # Defining a DataFrame
    data_frame = pd.DataFrame({
    'Year':['2010','2011','2012','2013','2014',&apos...