Handson Data Analysis With Pandas Second Edition A Python Data Science Handbook For Data Collection Wrangling Analysis And Visualization 2nd Edition Stefanie Molin by Stefanie Molin 9781800563452, 1800563450 instant download after payment.

Get to grips with pandas - a fast, versatile, and high-performance Python library for data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features: Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Become well-versed in using pandas as an effective data exploration tool Book Description: Data analysis has become an essential 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 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. What You Will Learn: 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 algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets S