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Python for Data Analysisby Wes Mckinney
Synopses & Reviews
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries youll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. Its ideal for analysts new to Python and for Python programmers new to scientific computing.
Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the field—Python.
Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community.
About the Author
Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as aquantitative analyst at AQR Capital Management and Python consultantbefore founding DataPad, a data analytics company, in 2013. Hegraduated from MIT with an S.B. in Mathematics.
Table of Contents
PrefaceChapter 1: PreliminariesChapter 2: Introductory ExamplesChapter 3: IPython: An Interactive Computing and Development EnvironmentChapter 4: NumPy Basics: Arrays and Vectorized ComputationChapter 5: Getting Started with pandasChapter 6: Data Loading, Storage, and File FormatsChapter 7: Data Wrangling: Clean, Transform, Merge, ReshapeChapter 8: Plotting and VisualizationChapter 9: Data Aggregation and Group OperationsChapter 10: Time SeriesChapter 11: Financial and Economic Data ApplicationsChapter 12: Advanced NumPyPython Language EssentialsColophon
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