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The Resource Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek

Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek

Label
Thinking in Pandas : how to use the Python data analysis library the right way
Title
Thinking in Pandas
Title remainder
how to use the Python data analysis library the right way
Statement of responsibility
Hannah Stepanek
Creator
Subject
Language
eng
Summary
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
Member of
Cataloging source
YDX
http://library.link/vocab/creatorName
Stepanek, Hannah
Dewey number
005.1
Index
index present
LC call number
QA76.76.A65
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Application program interfaces (Computer software)
  • Python (Computer program language)
  • Computer programming / software development
  • Machine learning
  • Databases
  • Programming & scripting languages: general
  • Computers
  • Computers
  • Computers
  • Computers
  • Application program interfaces (Computer software)
  • Python (Computer program language)
Label
Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek
Instantiates
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
1157934801
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9781484258392
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-1-4842-5
  • 10.1007/978-1-4842-5839-2.
http://library.link/vocab/ext/overdrive/overdriveId
cl0501000152
Specific material designation
remote
System control number
(OCoLC)1157934801
Label
Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
1157934801
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9781484258392
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-1-4842-5
  • 10.1007/978-1-4842-5839-2.
http://library.link/vocab/ext/overdrive/overdriveId
cl0501000152
Specific material designation
remote
System control number
(OCoLC)1157934801

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      38.946102 -92.330125
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