Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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About this item
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
ASIN: 1491957662
VSKU: DBV.1491957662.A
Condition: Acceptable
Author/Artist:McKinney, Wes
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
ASIN: 1491957662
VSKU: DBV.1491957662.A
Condition: Acceptable
Author/Artist:McKinney, Wes
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: This copy has clearly been enjoyed—expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.
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