Update app.py

app.py

@@ -600,7 +600,7 @@ Time Intelligence and the Date dimension play a crucial role in Power BI analysi
 
 Custom columns in Power Query enable advanced data transformations through various built-in functions. The Index Column feature adds row numbers useful for creating unique identifiers. Column from Selection splits existing columns based on delimiters or specific positions. The Extract feature pulls out specific parts of text, dates, or numbers. For example, you can extract email domains, split addresses into components, or create date parts like month names from full dates. Duplicate Column creates a copy of existing columns, useful when you need to maintain the original data while creating transformed versions. The Statistics feature adds columns with running totals, running averages, or row differences, essential for financial and analytical reporting.
 
-Data integration in Power BI offers two main methods: Merge and Append. Merge combines tables horizontally based on matching columns, similar to joining tables in Excel. For example, when you merge a sales table with a customer table, you can combine customer details with their sales information. Different merge types include Left Outer which keeps all rows from the first table and matching rows from the second, Right Outer which keeps all rows from the second table and matching rows from the first, Inner which keeps only matching rows from both tables, and Full Outer which keeps all rows from both tables. The Append operation adds rows from one table to another, like stacking sheets in Excel. This is useful when combining monthly sales data or reports from different regions that share the same column structure.
+Data integration in Power BI offers two main methods: Append and Merge. When datasets are appended, additional rows are added to the attribute table. However, when datasets are merged or joined, additional columns are added. For example, appending is useful when combining monthly sales data where each month's data is in the same format - the months stack on top of each other adding more rows. Merging is useful when you want to add more information by bringing in columns from another table - like adding customer details (name, address, phone) to a sales table using a customer ID to match the records.
 
 Advanced time intelligence calculations in DAX require careful consideration of filter context and date relationships. The CALCULATE function, combined with time intelligence functions, allows for sophisticated period comparisons. For example, to calculate the growth percentage between this year and last year, you might create a measure like: Growth % = DIVIDE(([Current Year Sales] - [Last Year Sales]), [Last Year Sales]). Rolling calculations, like 3-month moving averages, can be created using functions like DATESINPERIOD or DATEADD. These functions work in conjunction with your date dimension to provide dynamic date ranges based on the current filter context. Time intelligence functions also support custom calendar scenarios, such as 445 calendars or fiscal years that don't align with calendar years, through proper configuration of the date dimension and appropriate DAX formulas.