Pdf Book Name: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
Publisher: O’Reilly Media
ISBN-10, 13: 1491957662,9781491957660
Pages: 544 / 541 Pages
File size: 13 MB
File format: PDF,EPUB
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Pdf Book Description:
This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While “data analysis” is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. For many people, the Python programming language has strong appeal. Since its first appearance in 1991, Python has become one of the most popular interpreted pro‐ gramming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular since 2005 or so for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages, as they can be used to quickly write small programs,or scripts to automate other tasks. I don’t like the term “scripting language,” as it carries a connotation that they cannot be used for building serious software. Among interpreted languages, for various historical and cultural reasons, Python has developed a large and active scientific computing and data analysis community. In the last 10 years, Python has gone from a bleeding edge or “at your own risk” scientific computing language to one of the most 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, and others. In recent years, Python’s improved support for libraries (such as pandas and scikit-learn) has made it a popular choice for data analysis tasks.
Combined with Python’s overall strength for general-purpose software engineering, it is an excellent option as a primary language for building data applications. Part of Python’s success in scientific computing is the ease of integrating C, C++, and FORTRAN code. Most modern computing environments share a similar set of legacy FORTRAN and C libraries for doing linear algebra, optimization, integration, fast Fourier transforms, and other such algorithms. The same story has held true for many companies and national labs that have used Python to glue together decades’ worth of legacy software. While Python is an excellent environment for building many kinds of analytical applications and general-purpose systems, there are a number of uses for which Python may be less suitable. As Python is an interpreted programming language, in general most Python code will run substantially slower than code written in a compiled language like Java or C++. As programmer time is often more valuable than CPU time, many are happy to make this trade-off. However, in an application with very low latency or demanding resource utilization requirements (e.g., a high-frequency trading system), the time spent programming in a lower-level (but also lower-productivity) language like C++ to achieve the maximum possible performance might be time well spent.
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