Extending Power BI with Python and R: perform advanced analysis using the power of analytical languages
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 2nd edition |
| Schlagwörter: | |
| Links: | https://portal.igpublish.com/iglibrary/search/PACKT0007111.html https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31233987 https://portal.igpublish.com/iglibrary/search/PACKT0007111.html https://portal.igpublish.com/iglibrary/search/PACKT0007111.html https://portal.igpublish.com/iglibrary/search/PACKT0007111.html |
| Umfang: | 1 Online-Ressource (xxviii, 799 Seiten) |
| ISBN: | 9781837635863 |
Internformat
MARC
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Datensatz im Suchindex
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| author | Zavarella, Luca Talwar, Rajat |
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| contents | Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Where and How to Use R and Python Scripts in Power BI -- Technical requirements -- Injecting R or Python scripts into Power BI -- Data loading -- Data transformation -- Data visualization -- Using R and Python to interact with your data -- Python and R compatibility across Power BI products -- Summary -- Test your knowledge -- Chapter 2: Configuring R with Power BI -- Technical requirements -- The available R engines -- The CRAN R distribution -- The Microsoft R Open distribution and MRAN -- Multi-threading in MRO -- Choosing an R engine to install -- The R engines used by Power BI -- Installing the suggested R engines -- The R engine for data transformation -- The R engine for R script visuals on the Power BI service -- What to do when the Power BI service upgrades the R engine -- Installing an IDE for R development -- Installing RStudio -- Installing RTools -- Linking Intel's MKL to R -- Configuring Power BI Desktop to work with R -- Debugging an R script visual -- Configuring the Power BI service to work with R -- Installing the on-premises data gateway in personal mode -- Sharing reports that use R scripts in the Power BI service -- R script visuals limitations -- Summary -- Test your knowledge -- Chapter 3: Configuring Python with Power BI -- Technical requirements -- The available Python engines -- Choosing a Python engine to install -- The Python engines used by Power BI -- Installing the suggested Python engines -- The Python engine for data transformation -- Creating an environment for data transformations using pip -- Creating an optimized environment for data transformations using conda -- Creating an environment for Python script visuals on the Power BI service -- What to do when the Power BI service upgrades the Python engine Installing an IDE for Python development -- Configuring Python with RStudio -- Configuring Python with Visual Studio Code -- Working with the Python Interactive window in Visual Studio Code -- Configuring Power BI Desktop to work with Python -- Configuring the Power BI service to work with Python -- Sharing reports that use Python scripts in the Power BI service -- Limitations of Python visuals -- Summary -- Test your knowledge -- Chapter 4: Solving Common Issues When Using Python and R in Power BI -- Technical requirements -- Avoiding the ADO.NET error when running a Python script in Power BI -- The real cause of the problem -- A practical solution to the problem -- Avoiding the Formula.Firewall error -- Incompatible privacy levels -- Indirect access to a data source -- The easy way -- Combining queries and/or transformations -- Encapsulating queries into functions -- Using multiple datasets in Python and R script steps -- Applying a full join with Merge -- Using arguments of the Python.Execute function -- Dealing with dates/times in Python and R script steps -- Summary -- Test your knowledge -- Chapter 5: Importing Unhandled Data Objects -- Technical requirements -- Importing RDS files in R -- A brief introduction to Tidyverse -- Creating a serialized R object -- Configuring the environment and installing Tidyverse -- Creating the RDS files -- Using an RDS file in Power BI -- Importing an RDS file into the Power Query Editor -- Importing an RDS file in an R script visual -- Importing PKL files in Python -- A very short introduction to the PyData world -- Creating a serialized Python object -- Configuring the environment and installing the PyData packages -- Creating the PKL files -- Using a PKL file in Power BI -- Importing a PKL file into the Power Query Editor -- Importing a PKL file in a Python script visual -- Summary -- References Test your knowledge -- Chapter 6: Using Regular Expressions in Power BI -- Technical requirements -- A brief introduction to regexes -- The basics of regexes -- Literal characters -- Special characters in regex -- The and anchors -- OR operators -- Negated character classes -- Shorthand character classes -- Quantifiers -- The dot -- Greedy and lazy matches -- Checking the validity of email addresses -- Checking the validity of dates -- Validating data using regex in Power BI -- Using regex in Power BI to validate emails with Python -- Using regex in Power BI to validate emails with R -- Using regex in Power BI to validate dates with Python -- Using regex in Power BI to validate dates with R -- Loading complex log files using regex in Power BI -- Apache access logs -- Importing Apache access logs in Power BI with Python -- Importing Apache access logs in Power BI with R -- Extracting values from text using regex in Power BI -- One regex to rule them all -- Using regex in Power BI to extract values with Python -- Using regex in Power BI to extract values with R -- Summary -- References -- Test your knowledge -- Chapter 7: Anonymizing and Pseudonymizing Your Data in Power BI -- Technical requirements -- De-identifying data -- De-identification techniques -- Information removal -- Data masking -- Data swapping -- Generalization -- Data perturbation -- Tokenization -- Hashing -- Encryption -- Understanding pseudonymization -- What is anonymization? -- Anonymizing data in Power BI -- Anonymizing data using Python -- Anonymizing data using R -- Pseudonymizing data in Power BI -- Pseudonymizing data using Python -- Pseudonymizing data using R -- Summary -- References -- Test your knowledge -- Chapter 8: Logging Data from Power BI to External Sources -- Technical requirements -- Logging to CSV files -- Logging to CSV files with Python Using the pandas module -- Logging emails to CSV files in Power BI with Python -- Logging to CSV files with R -- Using Tidyverse functions -- Logging dates to CSV files in Power BI with R -- Logging to Excel files -- Logging to Excel files with Python -- Using the pandas module -- Logging emails and dates to Excel files in Power BI with Python -- Logging to Excel files with R -- Using the readxl and openxlsx packages -- Logging emails and dates to Excel in Power BI with R -- Logging to (Azure) SQL Server -- Installing SQL Server Express -- Creating an Azure SQL Database -- Logging to an (Azure) SQL server with Python -- Using the pyodbc module -- Logging emails and dates to an Azure SQL Database in Power BI with Python -- Logging to an (Azure) SQL Server with R -- Using the DBI and odbc packages -- Logging emails and dates to an Azure SQL Database in Power BI with R -- Managing credentials in the code -- Creating environment variables -- Using environment variables in Python -- Using environment variables in R -- Summary -- References -- Test your knowledge -- Chapter 9: Loading Large Datasets Beyond the Available RAM in Power BI -- Technical requirements -- A typical analytic scenario using large datasets -- Importing large datasets with Python -- Installing Dask on your laptop -- Creating a Dask DataFrame -- Extracting information from a Dask DataFrame -- Importing a large dataset in Power BI with Python -- Importing large datasets with R -- Introducing Apache Arrow -- Installing arrow on your laptop -- Creating and extracting information from an Arrow Dataset object -- Importing a large dataset in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 10: Boosting Data Loading Speed in Power BI with Parquet Format -- Technical requirements -- From CSV to the Parquet file format Limitations of using Parquet files natively in Power BI -- Using Parquet files with Python -- Analyzing Parquet data with Dask -- Analyzing Parquet data with PyArrow -- Performance differences between Dask and PyArrow -- Using Parquet files with R -- Analyzing Parquet data with Arrow for R -- Using the Parquet format to speed up a Power BI report -- Transforming historical data in Parquet -- Appending new data to and analyzing the Parquet dataset -- Analyzing Parquet data in Power BI with Python -- Analyzing Parquet data in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 11: Calling External APIs to EnrichYour Data -- Technical requirements -- What is a web service? -- Registering for Bing Maps web services -- Geocoding addresses using Python -- Using an explicit GET request -- Using an explicit GET request in parallel -- Using the Geocoder library in parallel -- Geocoding addresses using R -- Using an explicit GET request -- Using an explicit GET request in parallel -- Using the tidygeocoder package in parallel -- Accessing web services using Power BI -- Geocoding addresses in Power BI with Python -- Geocoding addresses in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 12: Calculating Columns Using Complex Algorithms: Distances -- Technical requirements -- What is a distance? -- The distance between two geographic locations -- Some theory first -- Spherical trigonometry -- The law of Cosines distance -- The law of Haversines distance -- Vincenty's distance -- What kind of distance to use and when -- Implementing distances using Python -- Calculating distances with Python -- Calculating distances in Power BI with Python -- Implementing distances using R -- Calculating distances with R -- Calculating distances in Power BI with R -- The distance between two strings -- Some theory first The Hamming distance |
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| dewey-full | 001.42260285 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 001 - Knowledge |
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| discipline | Allgemeines |
| edition | 2nd edition |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2025-01-29T15:02:15Z |
| institution | BVB |
| isbn | 9781837635863 |
| language | English |
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| spellingShingle | Zavarella, Luca Talwar, Rajat Extending Power BI with Python and R perform advanced analysis using the power of analytical languages Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Where and How to Use R and Python Scripts in Power BI -- Technical requirements -- Injecting R or Python scripts into Power BI -- Data loading -- Data transformation -- Data visualization -- Using R and Python to interact with your data -- Python and R compatibility across Power BI products -- Summary -- Test your knowledge -- Chapter 2: Configuring R with Power BI -- Technical requirements -- The available R engines -- The CRAN R distribution -- The Microsoft R Open distribution and MRAN -- Multi-threading in MRO -- Choosing an R engine to install -- The R engines used by Power BI -- Installing the suggested R engines -- The R engine for data transformation -- The R engine for R script visuals on the Power BI service -- What to do when the Power BI service upgrades the R engine -- Installing an IDE for R development -- Installing RStudio -- Installing RTools -- Linking Intel's MKL to R -- Configuring Power BI Desktop to work with R -- Debugging an R script visual -- Configuring the Power BI service to work with R -- Installing the on-premises data gateway in personal mode -- Sharing reports that use R scripts in the Power BI service -- R script visuals limitations -- Summary -- Test your knowledge -- Chapter 3: Configuring Python with Power BI -- Technical requirements -- The available Python engines -- Choosing a Python engine to install -- The Python engines used by Power BI -- Installing the suggested Python engines -- The Python engine for data transformation -- Creating an environment for data transformations using pip -- Creating an optimized environment for data transformations using conda -- Creating an environment for Python script visuals on the Power BI service -- What to do when the Power BI service upgrades the Python engine Installing an IDE for Python development -- Configuring Python with RStudio -- Configuring Python with Visual Studio Code -- Working with the Python Interactive window in Visual Studio Code -- Configuring Power BI Desktop to work with Python -- Configuring the Power BI service to work with Python -- Sharing reports that use Python scripts in the Power BI service -- Limitations of Python visuals -- Summary -- Test your knowledge -- Chapter 4: Solving Common Issues When Using Python and R in Power BI -- Technical requirements -- Avoiding the ADO.NET error when running a Python script in Power BI -- The real cause of the problem -- A practical solution to the problem -- Avoiding the Formula.Firewall error -- Incompatible privacy levels -- Indirect access to a data source -- The easy way -- Combining queries and/or transformations -- Encapsulating queries into functions -- Using multiple datasets in Python and R script steps -- Applying a full join with Merge -- Using arguments of the Python.Execute function -- Dealing with dates/times in Python and R script steps -- Summary -- Test your knowledge -- Chapter 5: Importing Unhandled Data Objects -- Technical requirements -- Importing RDS files in R -- A brief introduction to Tidyverse -- Creating a serialized R object -- Configuring the environment and installing Tidyverse -- Creating the RDS files -- Using an RDS file in Power BI -- Importing an RDS file into the Power Query Editor -- Importing an RDS file in an R script visual -- Importing PKL files in Python -- A very short introduction to the PyData world -- Creating a serialized Python object -- Configuring the environment and installing the PyData packages -- Creating the PKL files -- Using a PKL file in Power BI -- Importing a PKL file into the Power Query Editor -- Importing a PKL file in a Python script visual -- Summary -- References Test your knowledge -- Chapter 6: Using Regular Expressions in Power BI -- Technical requirements -- A brief introduction to regexes -- The basics of regexes -- Literal characters -- Special characters in regex -- The and anchors -- OR operators -- Negated character classes -- Shorthand character classes -- Quantifiers -- The dot -- Greedy and lazy matches -- Checking the validity of email addresses -- Checking the validity of dates -- Validating data using regex in Power BI -- Using regex in Power BI to validate emails with Python -- Using regex in Power BI to validate emails with R -- Using regex in Power BI to validate dates with Python -- Using regex in Power BI to validate dates with R -- Loading complex log files using regex in Power BI -- Apache access logs -- Importing Apache access logs in Power BI with Python -- Importing Apache access logs in Power BI with R -- Extracting values from text using regex in Power BI -- One regex to rule them all -- Using regex in Power BI to extract values with Python -- Using regex in Power BI to extract values with R -- Summary -- References -- Test your knowledge -- Chapter 7: Anonymizing and Pseudonymizing Your Data in Power BI -- Technical requirements -- De-identifying data -- De-identification techniques -- Information removal -- Data masking -- Data swapping -- Generalization -- Data perturbation -- Tokenization -- Hashing -- Encryption -- Understanding pseudonymization -- What is anonymization? -- Anonymizing data in Power BI -- Anonymizing data using Python -- Anonymizing data using R -- Pseudonymizing data in Power BI -- Pseudonymizing data using Python -- Pseudonymizing data using R -- Summary -- References -- Test your knowledge -- Chapter 8: Logging Data from Power BI to External Sources -- Technical requirements -- Logging to CSV files -- Logging to CSV files with Python Using the pandas module -- Logging emails to CSV files in Power BI with Python -- Logging to CSV files with R -- Using Tidyverse functions -- Logging dates to CSV files in Power BI with R -- Logging to Excel files -- Logging to Excel files with Python -- Using the pandas module -- Logging emails and dates to Excel files in Power BI with Python -- Logging to Excel files with R -- Using the readxl and openxlsx packages -- Logging emails and dates to Excel in Power BI with R -- Logging to (Azure) SQL Server -- Installing SQL Server Express -- Creating an Azure SQL Database -- Logging to an (Azure) SQL server with Python -- Using the pyodbc module -- Logging emails and dates to an Azure SQL Database in Power BI with Python -- Logging to an (Azure) SQL Server with R -- Using the DBI and odbc packages -- Logging emails and dates to an Azure SQL Database in Power BI with R -- Managing credentials in the code -- Creating environment variables -- Using environment variables in Python -- Using environment variables in R -- Summary -- References -- Test your knowledge -- Chapter 9: Loading Large Datasets Beyond the Available RAM in Power BI -- Technical requirements -- A typical analytic scenario using large datasets -- Importing large datasets with Python -- Installing Dask on your laptop -- Creating a Dask DataFrame -- Extracting information from a Dask DataFrame -- Importing a large dataset in Power BI with Python -- Importing large datasets with R -- Introducing Apache Arrow -- Installing arrow on your laptop -- Creating and extracting information from an Arrow Dataset object -- Importing a large dataset in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 10: Boosting Data Loading Speed in Power BI with Parquet Format -- Technical requirements -- From CSV to the Parquet file format Limitations of using Parquet files natively in Power BI -- Using Parquet files with Python -- Analyzing Parquet data with Dask -- Analyzing Parquet data with PyArrow -- Performance differences between Dask and PyArrow -- Using Parquet files with R -- Analyzing Parquet data with Arrow for R -- Using the Parquet format to speed up a Power BI report -- Transforming historical data in Parquet -- Appending new data to and analyzing the Parquet dataset -- Analyzing Parquet data in Power BI with Python -- Analyzing Parquet data in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 11: Calling External APIs to EnrichYour Data -- Technical requirements -- What is a web service? -- Registering for Bing Maps web services -- Geocoding addresses using Python -- Using an explicit GET request -- Using an explicit GET request in parallel -- Using the Geocoder library in parallel -- Geocoding addresses using R -- Using an explicit GET request -- Using an explicit GET request in parallel -- Using the tidygeocoder package in parallel -- Accessing web services using Power BI -- Geocoding addresses in Power BI with Python -- Geocoding addresses in Power BI with R -- Summary -- References -- Test your knowledge -- Chapter 12: Calculating Columns Using Complex Algorithms: Distances -- Technical requirements -- What is a distance? -- The distance between two geographic locations -- Some theory first -- Spherical trigonometry -- The law of Cosines distance -- The law of Haversines distance -- Vincenty's distance -- What kind of distance to use and when -- Implementing distances using Python -- Calculating distances with Python -- Calculating distances in Power BI with Python -- Implementing distances using R -- Calculating distances with R -- Calculating distances in Power BI with R -- The distance between two strings -- Some theory first The Hamming distance Business intelligence-Computer programs Data mining-Computer programs Information visualization-Computer programs Python (Computer program language) |
| title | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_auth | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_exact_search | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_full | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_fullStr | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_full_unstemmed | Extending Power BI with Python and R perform advanced analysis using the power of analytical languages |
| title_short | Extending Power BI with Python and R |
| title_sort | extending power bi with python and r perform advanced analysis using the power of analytical languages |
| title_sub | perform advanced analysis using the power of analytical languages |
| topic | Business intelligence-Computer programs Data mining-Computer programs Information visualization-Computer programs Python (Computer program language) |
| topic_facet | Business intelligence-Computer programs Data mining-Computer programs Information visualization-Computer programs Python (Computer program language) |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007111.html |
| work_keys_str_mv | AT zavarellaluca extendingpowerbiwithpythonandrperformadvancedanalysisusingthepowerofanalyticallanguages AT talwarrajat extendingpowerbiwithpythonandrperformadvancedanalysisusingthepowerofanalyticallanguages |