- Understand how data analysts and scientists gather and analyze data
- Perform data analysis and data wrangling using Python
- Combine, group, and aggregate data from multiple sources
- Create data visualizations with pandas, matplotlib, and seaborn
- Apply machine learning (ML) algorithms to identify patterns and make predictions
- Use Python data science libraries to analyze real-world datasets
- Use pandas to solve common data representation and analysis problems
- Build Python scripts, modules, and packages for reusable analysis code
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.
Features
- Perform efficient data analysis and manipulation tasks using pandas
- Apply pandas to different real-world domains with the help of step-by-step demonstrations
- Get accustomed to using pandas as an effective data exploration tool.
