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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Stepanek, Hannah
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [United States] : Apress, 2020.
Temas:
Application program interfaces (Computer software)
Python (Computer program language)
Interfaces de programmation d'applications.
Python (Langage de programmation)
APIs (interfaces)
Computer programming > software development.
Machine learning.
Databases.
Programming & scripting languages: general.
Computers > Programming > Open Source.
Computers > Intelligence (AI) & Semantics.
Computers > Database Management > General.
Computers > Programming Languages > Python.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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

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