Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas - Second Edition

By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

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: