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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

By : Stefanie Molin
4.7 (11)
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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

4.7 (11)
By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
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1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Practicing working with data

Throughout this book, we have worked with various datasets from different sources with guided instructions. It doesn't have to stop here, though. This section is dedicated to some resources that can be used to continue with guided instruction and, eventually, work toward building a model for a predefined problem.

DataCamp (https://www.datacamp.com/), while not entirely free, provides various data science courses in Python. They include instructional videos and fill-in-the-blank coding practice problems so that you can build up your understanding of the topics.

Kaggle (https://www.kaggle.com/) offers content for learning data science, datasets for exploration that are shared by members of the community, and competitions that have been posted by companies—perhaps the Netflix recommendation contest sounds familiar (https://www.kaggle.com/netflix...

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