Operations with petl: Advanced Extractions & Transformations
Operations with petl: Advanced Extractions & Transformations
- discover the key concepts covered in this course
- access regular expression patterns in data tables
- implement regular expression searches on petl data tables
- implement split operations on data stored within petl data tables
- unpack nested fields when creating data tables
- use mapping to perform various operations on columns and fields in data tables
- transform data by rows using rowmap() and rowmapmany() functions
- perform sort operations on data stored within a data table
- implement SQL-like joins on data from multiple petl tables
- summarize the key concepts covered in this course
Petl facilitates and streamlines tasks related to data extraction and manipulation, often required by software developers to make data fit for actionable business intelligence (BI).
In this course, you'll work with complex operations in petl and outline how to extract data from a source and convert it to a format that complies with your requirements.
You'll begin by investigating the use of regular expressions to analyze, search, and extract specific rows and columns in a petl table. You'll then create transform functions and apply them to your data. These include operations on numeric as well as string fields.
Moving on, you'll implement sort operations to organize data in a petl table and arrange it in a sequence that suits your purposes. Finally, you'll investigate how to perform joins and set operations on data tables and meaningfully reduce the data in them using aggregation functions.
Operations with petl: Basic Data Transformations
Operations with petl: Basic Data Transformations
- discover the key concepts covered in this course
- create a petl data table from Python-based data structures, such as NumPy arrays and Pandas DataFrames
- perform slicing, dicing, and merging operations on petl data tables
- combine data from multiple tables into one table
- insert as well as edit rows and columns in petl data tables
- perform various replace and type change operations on data
- find and replace specific values in a field
- implement SQL-like query operations on petl data tables
- filter data based on single as well as a combination of conditions
- use petl's facet() function to define filters for specific fields in a table
- summarize the key concepts covered in this course
Software development often requires manipulation of data that has been extracted from different data sources to make it compatible with the user's specifications and requirements. petl's data transformation features can help achieve this. In this course, you'll investigate fundamental data transformations that can be performed using the petl library. You'll demonstrate how to load data into a petl table, filter columns, and combine multiple tables using different forms of concatenation operations. Next, you'll outline how to convert data in a petl table into a form that is compatible with your requirements. This includes transforming strings to numbers, applying calculations to numeric fields, and replacing specific values in the table. Lastly, you'll explore ways to filter content in petl tables using the facet() function and different select operations.
Operations with petl: Introduction
Operations with petl: Introduction
- discover the key concepts covered in this course
- install petl and create a basic petl table from a toy dataset
- import data from a CSV file and extract it to a petl table
- perform various import and export operations on CSV, TSV, and TXT files
- export data from petl using a template, epilogue, and prologue
- perform lookups on data imported from pickle files
- import data from XML files and perform lookups on it
- implement read operations on data and export it in HTML format
- read JSON data and perform lookup operations on it
- export data stored in petl data tables to a persistent file format
- import data from Microsoft Excel and perform basic operations on it
- view summary statistics of data in petl and export it to Microsoft Excel
- create a table in SQLite and import it to petl using SQLAlchemy and SQLite3
- slice and dice data stored as records within a petl data table
- summarize the key concepts covered in this course
Extract, Transform, and Load (ETL) tasks help in collecting and manipulating data from diverse sources to fit the user's requirements. In this course, you'll explore different interfaces available in the petl library and perform basic ETL tasks using petl.
You will begin by examining how to import data from various data sources, including delimited text files, Microsoft Excel, and structured JSON data. You'll also recognize how to load and save data in these formats.
Next, you'll outline how to integrate petl with a relational database using SQLAlchemy and SQLite3. Finally, you'll perform transform operations on data using different petl features to filter specific data needed by you.
Once you have completed this course, you'll have a clear understanding of the role played by petl in simplifying ETL tasks.
