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


Complete the following exercises for some practice with the machine learning workflow and exposure to some additional anomaly detection strategies:

  1. A one-class SVM is another model that can be used for unsupervised outlier detection. Build a one-class SVM with the default parameters, using a pipeline with a StandardScaler object followed by a OneClassSVM object. Train the model on the January 2018 data, just as we did for the isolation forest. Make predictions on that same data. Count the number of inliers and outliers this model identifies.
  2. Using the 2018 minutely data, build a k-means model with two clusters after standardizing the data with a StandardScaler object. With the labeled data in the attacks table in the SQLite database (logs/logs.db), see whether this model gets a good Fowlkes-Mallows score (use the fowlkes_mallows_score() function in sklearn.metrics).
  3. Evaluate the performance of a random forest classifier for supervised anomaly detection. Set...