Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
McKinney, Wes
ISBN 10: 109810403X ISBN 13: 9781098104030
Published by O'Reilly Media (edition 3), 2022
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Synopsis:
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third 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, and Jupyter in the process. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing.
About the Author: Wes McKinney is an open source software developer focusing on data analysis tools. He created the Python pandas project and is a co-creator of Apache Arrow, his current development focus. He authored 2 editions of the reference book Python for Data Analysis. Wes is a Member of The Apache Software Foundation and also a PMC member for Apache Parquet. He is the director of Ursa Labs, an not-for-profit development group focused on data science tools for Python and R powered by Apache Arrow, built in partnership with RStudio. Previously, he worked for Two Sigma, Cloudera, and AQR Capital Management, and he was co-founder and CEO of the startup DataPad.
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Bibliographic Details
Title: Python for Data Analysis: Data Wrangling ...
Publisher: O'Reilly Media (edition 3)
Publication Date: 2022
Binding: Paperback
Condition: Very Good
Edition: 3.
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Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
Published by
O'Reilly Media (edition 3), 2022
ISBN 10: 109810403X
ISBN 13: 9781098104030
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Paperback
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Condition: New The 3rd edition of "Python for Data Analysis" by Wes McKinney (the creator of the pandas library) is the definitive, hands-on guide for manipulating, processing, cleaning, and crunching datasets in Python. It serves as a practical, modern introduction to the data science tools in Python. The primary focus is on the nuts and bolts of using Python programming and its data-oriented ecosystem (particularly the pandas, NumPy, and Jupyter libraries) to solve a broad set of data analysis problems. The book is an ideal resource for: Analysts new to Python. Python programmers new to data science and scientific computing. Key Topics The book provides thorough coverage of the entire data analysis pipeline using Python's core libraries, including: pandas: Introduction to Series and DataFrame objects, essential functionality (indexing, sorting, alignment), data loading, cleaning, and preparation (handling missing data, transformation). NumPy: Basics of the ndarray (multidimensional array object) and vectorized computation. Data Wrangling: Techniques for combining, merging, joining, and reshaping datasets. Data Aggregation and Group Operations: Using the groupby facility to slice, dice, and summarize data. Time Series: Analyzing and manipulating regular and irregular time series data. Visualization: Creating informative plots using matplotlib and seaborn. IPython and Jupyter: Using these tools for exploratory computing. The 3rd edition is specifically updated for Python 3.10 and pandas 2.0.0. It uses practical case studies and detailed examples to help readers effectively apply these tools to real-world data challenges. Seller Inventory # ABE-1760020267218
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