The perfect guide for intermediate programmers, Python for Data Science shows you the best ways to leverage Python for data-driven applications. Loaded with practical examples, the book provides a wide-ranging tour of Python's abilities to obtain, transform, and analyze data. You'll discover Python's rich built-in data structures and its robust ecosystem of open source data science libraries, including NumPy, pandas, scikit-learn, and Matplotlib. You'll practice loading data in various formats; streamlining, grouping, and aggregating datasets; and creating visualizations like charts and maps, in-depth examples show you how to build real-world data applications, including a taxi service powered by location data, an association rule analysis of transaction data to identify items commonly purchased together, and a machine learning model to predict stock trends. Exercises throughout the book encourage you to try techniques out for yourself. You'll learn how to: Efficiently manipulate data structures such as lists, dictionaries, NumPy arrays, and pandas DataFrames, Move data to and from both relational and NoSQL databases, Gain actionable insights about your data through aggregation, visualization, and other analysis methods, Work with plaintext, GPS data, time series data, JSON and CSV files, and many other types and formats of data, Use machine learning for natural language processing tasks such as sentiment analysis, In Python for Data Science, you'll practice modern data processing techniques for immediate use in fields like business management, marketing, and finance. Discover the power of the Python programming language. Book jacket.