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Thinking in Pandas : how to use the Python data analysis library the right way /
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of b...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[United States] :
Apress,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Thinking in Pandas : |b how to use the Python data analysis library the right way / |c Hannah Stepanek. |
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| 505 | 0 | |a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Chapter 1: Introduction -- About pandas -- How pandas helped build an image of a black hole -- How pandas helps financial institutions make more informed predictions about the future market -- How pandas helps improve discoverability of content -- Chapter 2: Basic Data Access and Merging -- DataFrame creation and access -- The iloc method -- The loc method -- Combining DataFrames using the merge method -- Combining DataFrames using the join method -- Combining DataFrames using the concat method | |
| 505 | 8 | |a Chapter 3: How pandas Works Under the Hood -- Python data structures -- The performance of the CPython interpreter, Python, and NumPy -- An introduction to pandas performance -- Choosing the right DataFrame -- Chapter 4: Loading and Normalizing Data -- pd.read_csv -- pd.read_json -- pd.read_sql, pd.read_sql_table, and pd.read_sql_query -- Chapter 5: Basic Data Transformation in pandas -- Pivot and pivot table -- Stack and unstack -- Melt -- Transpose -- Chapter 6: The apply Method -- When not to use apply -- When to use apply -- Improving performance of apply using Cython -- Chapter 7: Groupby | |
| 505 | 8 | |a Using groupby correctly -- Indexing -- Avoiding groupby -- Chapter 8: Performance Improvements Beyond pandas -- Computer architecture -- How NumExpr improves performance -- BLAS and LAPACK -- Chapter 9: The Future of pandas -- pandas 1.0 -- Conclusion -- Appendix: Useful Reference Tables -- Index | |
| 500 | |a Includes index. | ||
| 520 | |a Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered. By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas--the right way. You will: Understand the underlying data structure of pandas and why it performs the way it does under certain circumstances Discover how to use pandas to extract, transform, and load data correctly with an emphasis on performance Choose the right DataFrame so that the data analysis is simple and efficient. Improve performance of pandas operations with other Python libraries. | ||
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