Synopsis
Learn all about manipulating, cleaning, processing, and preparing records with Python: Updated to Python 3.6, this consistently practice-based book shows you how to effectively solve a variety of typical data analysis problems using concrete case examples. At the same time, you will get to know the latest versions of Pandas, NumPy, IPython and Jupyter.
Written by Wes McKinney, the founder of the pandas project, data analysis with Python provides a practical introduction to Python's data science tools. The book is suitable for both data analysts for whom Python is new territory and Python programmers who want to get involved in data science and scientific computing. Data and related material of the book are available on GitHub.
From the contents:
- Use the IPython shell and Jupyter notebook for exploratory computing
- Learn basic features and advanced features of NumPy
- Use the data analysis tools of the Pandas library
- Use flexible tools to load, clean, transform, merge and reshape data
- Create interformative visualizations with matplotlib
- Apply pandas' GroupBy mechanisms to trim, redesign and summarize records
- Analyze and manipulate a wide range of time series data
For this updated 2. Edition, all code has been adapted to Python 3.6 and the latest versions of the Pandas library. New in this edition: information on advanced pandas tools as well as a brief introduction to statsmodels and scikit-learn.
"Already a classic in the Python ecosystem, this reissue has been updated in all areas, from Python 3.6 to the latest features of pandas. By accurately explaining Python's data tools, this book helps readers use them effectively in new and creative ways. The book is an essential part of any modern library on data-intensive computing."
― Fernando Pérez, Assistant Professor of Statistics at UC Berkeley, founder of IPython and co-founder of Project Jupyter
"synopsis" may belong to another edition of this title.
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Datenanalyse mit Python: Auswertung von Daten mit Pandas, NumPy und IPython (Animals)
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Datenanalyse mit Python: Auswertung von Daten mit Pandas, NumPy und IPython (Animals)
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