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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas
2. Chapter 1: Introduction to Data Analysis FREE CHAPTER 3. Chapter 2: Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Chapter 3: Data Wrangling with Pandas 6. Chapter 4: Aggregating Pandas DataFrames 7. Chapter 5: Visualizing Data with Pandas and Matplotlib 8. Chapter 6: Plotting with Seaborn and Customization Techniques 9. Section 3: Applications – Real-World Analyses Using Pandas
10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market 11. Chapter 8: Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Chapter 9: Getting Started with Machine Learning in Python 14. Chapter 10: Making Better Predictions – Optimizing Models 15. Chapter 11: Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. Chapter 12: The Road Ahead 18. Solutions
19. Other Books You May Enjoy Appendix

Inspecting a DataFrame object

The first thing we should do when we read in our data is inspect it; we want to make sure that our dataframe isn't empty and that the rows look as we would expect. Our main goal is to verify that it was read in properly and that all the data is there; however, this initial inspection will also give us ideas with regard to where we should direct our data wrangling efforts. In this section, we will explore ways in which we can inspect our dataframes in the 4-inspecting_dataframes.ipynb notebook.

Since this is a new notebook, we must once again handle our setup. This time, we need to import pandas and numpy, as well as read in the CSV file with the earthquake data:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.read_csv('data/earthquakes.csv')

Examining the data

First, we want to make sure that we actually have data in our dataframe. We can check the empty attribute to find out:

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