Extending Excel with Python and R: unlock the potential of analytics languages for advanced data manipulation and visualization
Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insight...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
April 2024
|
| Ausgabe: | 1st edition |
| Schlagworte: | |
| Online-Zugang: | DE-1050 DE-706 Volltext |
| Zusammenfassung: | Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description For businesses, data analysis and visualization are crucial for informed decision-making; however, Excel's limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. |
| Beschreibung: | 1 Online-Ressource (xvii, 325 Seiten) |
| ISBN: | 9781804615546 |
Internformat
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| 505 | 8 | |a Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics -- Reading and Writing Excel Files from R and Python -- Chapter 1: Reading Excel Spreadsheets -- Technical requirements -- Working with R packages for Excel manipulation -- Reading Excel files to R -- Installing and loading libraries -- Reading multiple sheets with readxl and a custom function -- Python packages for Excel manipulation -- Python packages for Excel manipulation -- Considerations -- Opening an Excel sheet from Python and reading the data -- Using pandas | |
| 505 | 8 | |a Using openpyxl -- Reading in multiple sheets with Python (openpyxl and custom functions) -- The importance of reading multiple sheets -- Using openpyxl to access sheets -- Reading data from each sheet -- Retrieving sheet data with openpyxl -- Combining data from multiple sheets -- Custom function for reading multiple sheets -- Customizing the code -- Summary -- Chapter 2: Writing Excel Spreadsheets -- Technical requirements -- Packages to write into Excel files -- writexl -- openxlsx -- xlsx -- A comprehensive recap and insights -- Creating and manipulating Excel sheets using Python | |
| 505 | 8 | |a Why export data to Excel? -- Keeping it simple -- exporting data to Excel with pandas -- Advanced mode -- openpyxl for Excel manipulation -- Creating a new workbook -- Adding sheets to the workbook -- Deleting a sheet -- Manipulating an existing workbook -- Choosing between openpyxl and pandas -- Other alternatives -- Summary -- Chapter 3: Executing VBA Code from R and Python -- Technical requirements -- Installing and explaining the RDCOMClient R library -- Installing RDCOMClient -- Executing sample VBA with RDCOMClient -- Integrating VBA with Python using pywin32 | |
| 505 | 8 | |a Why execute VBA code from Python? -- Setting up the environment -- Error handling with the environment setup -- Writing and executing VBA code -- Automating Excel tasks -- Pros and cons of executing VBA from Python -- Summary -- Chapter 4: Automating Further -- Task Scheduling and Email -- Technical requirements -- Installing and understanding the tasksheduleR library -- Creating sample scripts -- RDCOMClient for Outlook -- Using the Microsoft365R and blastula packages -- Microsoft365R -- The blastula package -- Scheduling Python scripts -- Introduction to Python script scheduling | |
| 505 | 8 | |a Built-in scheduling options -- Third-party scheduling libraries -- Best practices and considerations for robust automation -- Email notifications and automation with Python -- Introduction to email notifications in Python -- Setting up email services -- Sending basic emails -- Sending email notifications for script status -- Summary -- Part 2: Making It Pretty -- Formatting, Graphs, and More -- Chapter 5: Formatting Your Excel Sheet -- Technical requirements -- Installing and using styledTables in R -- Installing and using basictabler in R -- Advanced options for formatting with Python | |
| 520 | |a Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description For businesses, data analysis and visualization are crucial for informed decision-making; however, Excel's limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. | ||
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Datensatz im Suchindex
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| author | Sanderson, Steven Kun, David |
| author_facet | Sanderson, Steven Kun, David |
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| author_variant | s s ss d k dk |
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| contents | Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics -- Reading and Writing Excel Files from R and Python -- Chapter 1: Reading Excel Spreadsheets -- Technical requirements -- Working with R packages for Excel manipulation -- Reading Excel files to R -- Installing and loading libraries -- Reading multiple sheets with readxl and a custom function -- Python packages for Excel manipulation -- Python packages for Excel manipulation -- Considerations -- Opening an Excel sheet from Python and reading the data -- Using pandas Using openpyxl -- Reading in multiple sheets with Python (openpyxl and custom functions) -- The importance of reading multiple sheets -- Using openpyxl to access sheets -- Reading data from each sheet -- Retrieving sheet data with openpyxl -- Combining data from multiple sheets -- Custom function for reading multiple sheets -- Customizing the code -- Summary -- Chapter 2: Writing Excel Spreadsheets -- Technical requirements -- Packages to write into Excel files -- writexl -- openxlsx -- xlsx -- A comprehensive recap and insights -- Creating and manipulating Excel sheets using Python Why export data to Excel? -- Keeping it simple -- exporting data to Excel with pandas -- Advanced mode -- openpyxl for Excel manipulation -- Creating a new workbook -- Adding sheets to the workbook -- Deleting a sheet -- Manipulating an existing workbook -- Choosing between openpyxl and pandas -- Other alternatives -- Summary -- Chapter 3: Executing VBA Code from R and Python -- Technical requirements -- Installing and explaining the RDCOMClient R library -- Installing RDCOMClient -- Executing sample VBA with RDCOMClient -- Integrating VBA with Python using pywin32 Why execute VBA code from Python? -- Setting up the environment -- Error handling with the environment setup -- Writing and executing VBA code -- Automating Excel tasks -- Pros and cons of executing VBA from Python -- Summary -- Chapter 4: Automating Further -- Task Scheduling and Email -- Technical requirements -- Installing and understanding the tasksheduleR library -- Creating sample scripts -- RDCOMClient for Outlook -- Using the Microsoft365R and blastula packages -- Microsoft365R -- The blastula package -- Scheduling Python scripts -- Introduction to Python script scheduling Built-in scheduling options -- Third-party scheduling libraries -- Best practices and considerations for robust automation -- Email notifications and automation with Python -- Introduction to email notifications in Python -- Setting up email services -- Sending basic emails -- Sending email notifications for script status -- Summary -- Part 2: Making It Pretty -- Formatting, Graphs, and More -- Chapter 5: Formatting Your Excel Sheet -- Technical requirements -- Installing and using styledTables in R -- Installing and using basictabler in R -- Advanced options for formatting with Python |
| ctrlnum | (ZDB-221-PDA)9781804615546 (OCoLC)1450742768 (DE-599)BVBBV049792849 |
| edition | 1st edition |
| format | Electronic eBook |
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| id | DE-604.BV049792849 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-04T13:01:45Z |
| institution | BVB |
| isbn | 9781804615546 |
| language | English |
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| oclc_num | 1450742768 |
| open_access_boolean | |
| owner | DE-1050 DE-706 |
| owner_facet | DE-1050 DE-706 |
| physical | 1 Online-Ressource (xvii, 325 Seiten) |
| psigel | ZDB-30-PQE ZDB-221-PDA ZDB-30-PQE FHD01_PQE_Kauf |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Sanderson, Steven Verfasser aut Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization Steven Sanderson, David Kun 1st edition Birmingham, UK Packt Publishing April 2024 1 Online-Ressource (xvii, 325 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics -- Reading and Writing Excel Files from R and Python -- Chapter 1: Reading Excel Spreadsheets -- Technical requirements -- Working with R packages for Excel manipulation -- Reading Excel files to R -- Installing and loading libraries -- Reading multiple sheets with readxl and a custom function -- Python packages for Excel manipulation -- Python packages for Excel manipulation -- Considerations -- Opening an Excel sheet from Python and reading the data -- Using pandas Using openpyxl -- Reading in multiple sheets with Python (openpyxl and custom functions) -- The importance of reading multiple sheets -- Using openpyxl to access sheets -- Reading data from each sheet -- Retrieving sheet data with openpyxl -- Combining data from multiple sheets -- Custom function for reading multiple sheets -- Customizing the code -- Summary -- Chapter 2: Writing Excel Spreadsheets -- Technical requirements -- Packages to write into Excel files -- writexl -- openxlsx -- xlsx -- A comprehensive recap and insights -- Creating and manipulating Excel sheets using Python Why export data to Excel? -- Keeping it simple -- exporting data to Excel with pandas -- Advanced mode -- openpyxl for Excel manipulation -- Creating a new workbook -- Adding sheets to the workbook -- Deleting a sheet -- Manipulating an existing workbook -- Choosing between openpyxl and pandas -- Other alternatives -- Summary -- Chapter 3: Executing VBA Code from R and Python -- Technical requirements -- Installing and explaining the RDCOMClient R library -- Installing RDCOMClient -- Executing sample VBA with RDCOMClient -- Integrating VBA with Python using pywin32 Why execute VBA code from Python? -- Setting up the environment -- Error handling with the environment setup -- Writing and executing VBA code -- Automating Excel tasks -- Pros and cons of executing VBA from Python -- Summary -- Chapter 4: Automating Further -- Task Scheduling and Email -- Technical requirements -- Installing and understanding the tasksheduleR library -- Creating sample scripts -- RDCOMClient for Outlook -- Using the Microsoft365R and blastula packages -- Microsoft365R -- The blastula package -- Scheduling Python scripts -- Introduction to Python script scheduling Built-in scheduling options -- Third-party scheduling libraries -- Best practices and considerations for robust automation -- Email notifications and automation with Python -- Introduction to email notifications in Python -- Setting up email services -- Sending basic emails -- Sending email notifications for script status -- Summary -- Part 2: Making It Pretty -- Formatting, Graphs, and More -- Chapter 5: Formatting Your Excel Sheet -- Technical requirements -- Installing and using styledTables in R -- Installing and using basictabler in R -- Advanced options for formatting with Python Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description For businesses, data analysis and visualization are crucial for informed decision-making; however, Excel's limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. Data mining Data mining / Computer programs Kun, David Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-80461-069-5 https://portal.igpublish.com/iglibrary/search/PACKT0007149.html Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Sanderson, Steven Kun, David Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics -- Reading and Writing Excel Files from R and Python -- Chapter 1: Reading Excel Spreadsheets -- Technical requirements -- Working with R packages for Excel manipulation -- Reading Excel files to R -- Installing and loading libraries -- Reading multiple sheets with readxl and a custom function -- Python packages for Excel manipulation -- Python packages for Excel manipulation -- Considerations -- Opening an Excel sheet from Python and reading the data -- Using pandas Using openpyxl -- Reading in multiple sheets with Python (openpyxl and custom functions) -- The importance of reading multiple sheets -- Using openpyxl to access sheets -- Reading data from each sheet -- Retrieving sheet data with openpyxl -- Combining data from multiple sheets -- Custom function for reading multiple sheets -- Customizing the code -- Summary -- Chapter 2: Writing Excel Spreadsheets -- Technical requirements -- Packages to write into Excel files -- writexl -- openxlsx -- xlsx -- A comprehensive recap and insights -- Creating and manipulating Excel sheets using Python Why export data to Excel? -- Keeping it simple -- exporting data to Excel with pandas -- Advanced mode -- openpyxl for Excel manipulation -- Creating a new workbook -- Adding sheets to the workbook -- Deleting a sheet -- Manipulating an existing workbook -- Choosing between openpyxl and pandas -- Other alternatives -- Summary -- Chapter 3: Executing VBA Code from R and Python -- Technical requirements -- Installing and explaining the RDCOMClient R library -- Installing RDCOMClient -- Executing sample VBA with RDCOMClient -- Integrating VBA with Python using pywin32 Why execute VBA code from Python? -- Setting up the environment -- Error handling with the environment setup -- Writing and executing VBA code -- Automating Excel tasks -- Pros and cons of executing VBA from Python -- Summary -- Chapter 4: Automating Further -- Task Scheduling and Email -- Technical requirements -- Installing and understanding the tasksheduleR library -- Creating sample scripts -- RDCOMClient for Outlook -- Using the Microsoft365R and blastula packages -- Microsoft365R -- The blastula package -- Scheduling Python scripts -- Introduction to Python script scheduling Built-in scheduling options -- Third-party scheduling libraries -- Best practices and considerations for robust automation -- Email notifications and automation with Python -- Introduction to email notifications in Python -- Setting up email services -- Sending basic emails -- Sending email notifications for script status -- Summary -- Part 2: Making It Pretty -- Formatting, Graphs, and More -- Chapter 5: Formatting Your Excel Sheet -- Technical requirements -- Installing and using styledTables in R -- Installing and using basictabler in R -- Advanced options for formatting with Python Data mining Data mining / Computer programs |
| title | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization |
| title_auth | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization |
| title_exact_search | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization |
| title_full | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization Steven Sanderson, David Kun |
| title_fullStr | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization Steven Sanderson, David Kun |
| title_full_unstemmed | Extending Excel with Python and R unlock the potential of analytics languages for advanced data manipulation and visualization Steven Sanderson, David Kun |
| title_short | Extending Excel with Python and R |
| title_sort | extending excel with python and r unlock the potential of analytics languages for advanced data manipulation and visualization |
| title_sub | unlock the potential of analytics languages for advanced data manipulation and visualization |
| topic | Data mining Data mining / Computer programs |
| topic_facet | Data mining Data mining / Computer programs |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007149.html |
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