Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
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

Handling duplicate, missing, or invalid data

So far, we have discussed things we could change with the way the data was represented with zero ramifications. However, we didn't discuss a very important part of data cleaning: how to deal with data that appears to be duplicated, invalid, or missing. This is separated from the rest of the data cleaning discussion because it is an example where we will do some initial data cleaning, then reshape our data, and finally look to handle these potential issues; it is also a rather hefty topic.

We will be working in the 5-handling_data_issues.ipynb notebook and using the dirty_data.csv file. Let's start by importing pandas and reading in the data:

>>> import pandas as pd
>>> df = pd.read_csv('data/dirty_data.csv')

The dirty_data.csv file contains wide format data from the weather API that has been altered to introduce many common data issues that we will encounter in the wild. It contains the following...