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Python for Data Analysis By Wes McKinney

This book cares with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. This Book is to supply a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools which will equip you to become an efficient data analyst. While “data analysis” is within the title of the book, the main target is specifically on Python programming, libraries, and tools as against data analysis methodology. this is often the Python programming you would like for data analysis.0

Why Python for Data Analysis?

For many people, the Python programming language has strong appeal. Since its introduction in 1991, Python has become one among the foremost popular interpreted programming languages, along side Perl, Ruby, etc. . Python and Ruby became especially popular since 2005 approximately for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages, as they will be wont to quickly write small programs, or scripts to automate other tasks. I don’t just like the term “scripting language,” because it carries a connotation that they can't be used for building serious software. Among interpreted languages, for various historical and cultural reasons, Python has developed an outsizes and active scientific computing and data analysis community. within the last 10 years, Python has gone from a bleeding-edge or “at your own risk” scientific computing language to at least one of the foremost important languages for data science, machine learning, and general software development in academia and industry. For data analysis and interactive computing and data visualization, Python will inevitably draw comparisons with other open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, et al. . In recent years, Python’s improved support for libraries (such as pandas and scikit-learn) has made it a well-liked choice for data analysis tasks. Combined with Python’s overall strength for general-purpose software engineering, it's a superb option as a primary language for building data applications.

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