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
Resource Information
The item Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- 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
- Language
- eng
- Extent
- 1 online resource
- Note
- Includes index
- 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
- Isbn
- 9781484258392
- 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
- Subject
-
- Application program interfaces (Computer software)
- Application program interfaces (Computer software)
- Computer programming / software development
- Computers -- Database Management | General
- Computers -- Intelligence (AI) & Semantics
- Computers -- Programming Languages | Python
- Databases
- Machine learning
- Programming & scripting languages: general
- Python (Computer program language)
- Python (Computer program language)
- Computers -- Programming | Open Source
- 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
- 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
- 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
- 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
Subject
- Application program interfaces (Computer software)
- Application program interfaces (Computer software)
- Computer programming / software development
- Computers -- Database Management | General
- Computers -- Intelligence (AI) & Semantics
- Computers -- Programming Languages | Python
- Databases
- Machine learning
- Programming & scripting languages: general
- Python (Computer program language)
- Python (Computer program language)
- Computers -- Programming | Open Source
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Thinking-in-Pandas--how-to-use-the-Python-data/I6ob-QYdrI8/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Thinking-in-Pandas--how-to-use-the-Python-data/I6ob-QYdrI8/">Thinking in Pandas : how to use the Python data analysis library the right way, Hannah Stepanek</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
