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


Practice building and evaluating machine learning models in scikit-learn with the following exercises:

  1. Build a clustering model to distinguish between red and white wine by their chemical properties:

    a) Combine the red and white wine datasets (data/winequality-red.csv and data/winequality-white.csv, respectively) and add a column for the kind of wine (red or white).

    b) Perform some initial EDA.

    c) Build and fit a pipeline that scales the data and then uses k-means clustering to make two clusters. Be sure not to use the quality column.

    d) Use the Fowlkes-Mallows Index (the fowlkes_mallows_score() function is in sklearn.metrics) to evaluate how well k-means is able to make the distinction between red and white wine.

    e) Find the center of each cluster.

  2. Predict star temperature:

    a) Using the data/stars.csv file, perform some initial EDA and then build a linear regression model of all the numeric columns to predict the temperature of the star.

    b) Train the model on 75% of...