Power Query M Language with Specialize in Web Scraping

Master Power Query M language to transform and clean data, overcome graphical user interface limitations, and implement functions for analytics and web scraping.

Learn the power query m language basics in the power query editor, using the formula bar, let keywords, and variables to build transformations and steps like source and promoted headers.

Rename multiple columns in a Power Query M table in one step and sort a column in ascending or descending order, using lists to map names and define order.

Master power query list functions by creating lists from a column, applying distinct items, using list.dot tools, and deriving min and max values to analyze data.

Learn to perform group by aggregation in Power Query M to sum total revenue by distributor and channel, using custom columns, lists, and table functions.

Sort total revenue list in descending order with List.Sort and extract the top N with List.Range. Convert to a table, then join datasets and group by channel to compute averages.

Master the difference between lists and records in Power Query M language, build and access records, convert lists to tables, and fetch specific fields like consultant and wholesale.

Learn to scrape city populations from the web with Power Query, using a custom function to fetch data for India, USA, and Pakistan, then combine results into a single table.

Learn to perform web scraping with Power Query M by building a function that averages city populations per country, using lists and invoked functions for multiple countries.

Power Query M language table creation uses two methods: manually building a hash table with explicit headers and records, and defining typed headers to auto-create a table.

Explore nested let in power query m language and understand local versus global scope, defining and referencing variables in the advanced editor for complex transformations.

Learn to create and invoke Power Query M functions, define parameters and types, and apply custom functions and invoked columns to transform data.

Learn to use the Power Query index column in M language to fetch the previous row's value, build custom columns, and demonstrate error handling.

Learn to repeat index values within each country by grouping data in Power Query M, applying a zero-based index per group, and creating a per-group index with a custom column.

Learn to use Power Query M language to join product category and product subcategory tables and transpose rows to columns with the table.transpose function, showcasing a practical data transformation workflow.

Create a Power Query M language project that produces a three-column table: product category, a comma-separated subcategory list, and the total product count per category using joins and group by.

Use Power Query M to compute the top two subcategories per category by total products, by joining tables, counting products, sorting in descending order, and extracting the top records.

Learn Power Query input parameters—any value, list, and dynamic—and apply them to filter data by category and list price, then save as a template for parameter-driven queries.

Explore a live ali baba web scraping case study using Power Query M language to extract product, price, seller, and rating data from web tables, then clean and parameterize it.

Master Power Query M Language to automate web scraping of related searches on ecommerce sites, collect table data, and merge it into a complete dataset for analysis.

Learn to convert comma-delimited product lists in Power Query into rows, then count distinct products per warehouse using delimiter split and distinct count.