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Mistake 4: Not testing the function Always test your function with a few inputs before applying it to hundreds of files. 🧪 Practical Exercise Create a simple function that calculates profit margin. Use: • Sales • Cost Formula: (Sales - Cost) / Sales For example: Sales = ₹10,000 Cost = ₹7,000 Profit Margin: (10000 - 7000) / 10000 = 0.30 = 30% Then test your function with different Sales and Cost values. After that, think about how you could use the same concept to transform multiple monthly files. 🎯 Key Takeaway Custom M Functions allow you to write transformation logic once and reuse it many times. They are particularly valuable when working with multiple files that require the same cleaning and transformation steps. Double Tap ❤️ For More ----- 1.42 ₽ · /balance_help

Now you can provide both the price and tax rate. For example: CalculatePrice(1000, 0.18) Result: 1180 And: CalculatePrice(1000, 0.05) Result: 1050 The function becomes more flexible. 6. Custom Functions with Tables Custom functions become especially powerful when working with tables. Imagine you receive these files: January.xlsx February.xlsx March.xlsx April.xlsx Each file contains raw sales data. You want every file to go through the same cleaning process. You can create a function such as: TransformSalesFile The function can: 1. Receive a file. 2. Extract the required table. 3. Remove unnecessary columns. 4. Rename columns. 5. Change data types. 6. Clean the data. 7. Return the transformed table. You can then apply the function to multiple files. 7. Custom Function Workflow A common workflow is: Files → Function → Cleaned Tables → Combined Dataset For example: Jan.xlsx Feb.xlsx Mar.xlsx Each file is passed through the same transformation function. The result is a consistent dataset ready for analysis. This is one of the most useful applications of custom functions in Power Query. 8. Creating a Function from an Existing Query Power Query can also help you create a function from an existing query. A common approach is: 1. Build the transformation for one sample file. 2. Turn the query into a function. 3. Define the input parameter. 4. Apply the function to other files. This is much easier than manually writing the entire function from scratch. 9. Function vs Parameter These two concepts can sometimes be confused. Parameter A parameter stores a value that can change. Example: MinimumSales = 5000 Function A function performs an operation using one or more inputs. Example: CalculateProfit(Sales, Cost) So: Parameter = value Function = reusable logic 10. Real-World Example Suppose your company receives employee files from different branches. Each file contains: Name Salary Department Rahul 50000 Sales Priya 60000 HR Every file needs the same cleaning: • Remove blank rows • Standardize department names • Change Salary to number • Trim employee names Instead of repeating these steps for every branch, create: CleanEmployeeFile Then apply that function to every branch file. If you later modify the cleaning logic, you can update the function rather than rebuilding the transformation for every file. 11. Important: Functions Need Consistent Inputs A custom function works best when the input structure is predictable. For example, if your function expects: • Name • Salary • Department but one file contains: • EmployeeName • Pay • Team the function may fail. Therefore, when working with multiple files, make sure the files have a consistent structure or build logic to handle differences. 12. Common Mistakes Mistake 1: Creating a function for everything Not every transformation needs a custom function. Use functions when the same logic needs to be reused. Mistake 2: Wrong input data type If your function expects a number but receives text, the function may produce an error. Mistake 3: Inconsistent file structures Different column names or missing columns can break a function.

📊 Power BI Learning Roadmap — Part 4 Topic 13: Custom M Functions After learning the basics of M language, the next important concept is Custom Functions. Custom functions allow you to write a transformation once and reuse it multiple times. This becomes very useful when you have many files or tables that need the same transformation logic. 1. What Is a Custom Function? A custom function is a reusable piece of M code that accepts an input and returns an output. For example, suppose you frequently need to calculate: Price + 18% GST Instead of writing the calculation separately for every query, you can create a function. Conceptually: Input: Price Function: Price × 1.18 Output: Price including GST If the input is ₹1,000: 1000 × 1.18 = 1180 2. Why Use Custom Functions? Imagine you receive 12 monthly files. Every file requires the same transformations: • Remove unnecessary columns • Change data types • Clean product names • Calculate revenue • Standardize dates You could repeat the same steps manually. But with a custom function, you can define the transformation once and apply it to each input. This saves time and makes your Power Query solution easier to maintain. 3. Basic Function Structure A simple M function can look like:

(price as number) as number =>
    price * 1.18

Here: price is the input. as number specifies the input data type. price * 1.18 is the calculation. The final as number specifies the output type. 4. Using the Function Suppose your function is called: AddGST You could use it like: AddGST(1000) Result: 1180 Another example: AddGST(5000) Result: 5900 The same logic can be reused with different values. 5. Functions Can Accept Multiple Inputs A function doesn't have to accept only one parameter. For example:

(price as number, taxRate as number) as number =>
    price * (1 + taxRate)

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These functions become much easier to understand once you recognize the pattern. 7. The each Keyword You will see each frequently in Power Query. For example: each [Sales] > 5000 It means the expression should be evaluated for each row. Another example: each [Quantity] * [Price] This calculates the result for every row. If: Quantity = 10 Price = 500 then: 10 × 500 = 5000 8. Column References In M, a column can commonly be referenced like: [Sales] For example: each [Sales] * 1.18 This calculates a value using the Sales column. For column names containing spaces or special characters, you may see syntax such as: [#"Sales Amount"] You don't need to memorize this immediately. Power Query usually generates the correct syntax for you. 9. M Is Case-Sensitive M language is case-sensitive. For example: Sales and sales are not necessarily treated as the same identifier. Similarly, function names need to be written correctly. This is one reason copying and modifying existing M code is often easier than writing everything from scratch. 10. Editing M Code You can modify M code directly using: View → Advanced Editor For example, you might change: [Sales] > 5000 to: [Sales] > 10000 This changes the filtering condition. However, don't edit M code blindly. If you change a step incorrectly, later steps may stop working. 11. Why Learn M If Power Query Has a GUI? The interface is enough for many common transformations. But M becomes useful when: • A transformation isn't easily available through the interface • You need dynamic logic • You want to reuse code • You need parameters • You want to create custom functions • You need more control over transformations • You want to troubleshoot query errors Think of the GUI as the easy way to build transformations. M gives you deeper control over those transformations. 12. M vs DAX This is an important distinction. Power Query M Used mainly for: • Extracting data • Cleaning data • Transforming data • Combining data • Preparing data before loading DAX Used mainly for: • Measures • Calculated columns • Calculations within the data model • Analysis and reporting For example: Removing unnecessary columns: Use Power Query. Creating: Total Sales = SUM(Sales[SalesAmount]) Use DAX. 🧪 Practical Exercise Take a simple Sales table containing: Product Quantity Price Laptop 2 50000 Mouse 5 1000 Keyboard 3 2000 In Power Query: 1. Change the data types. 2. Add a Revenue column. 3. Filter Revenue greater than 5000. 4. Rename the Revenue column. 5. Open the formula bar. 6. Look at the M code generated for each step. 7. Open Advanced Editor. 8. Identify the let and in sections. 🎯 Key Takeaway M is the language behind Power Query transformations. You don't need to learn all of M at once. Start by performing transformations through the interface, then study the M code Power Query generates. This approach makes M much easier to learn because you already understand what the code is supposed to accomplish. Double Tap ❤️ For More ----- 1.37 ₽ · /balance_help

📊 Power BI Learning Roadmap — Part 4 Topic 12: Power Query M Language Until now, we have mostly used the Power Query interface to clean and transform data. But behind almost every transformation you perform in Power Query, there is code. That language is called M. Understanding M doesn't mean you need to become a programmer. You just need to understand how Power Query represents the transformations you perform. 1. What is M Language? M is the language used by Power Query for data transformation. For example, when you: • Remove columns • Filter rows • Change data types • Rename columns • Add custom columns • Merge queries • Append queries • Group data Power Query creates M code for those actions. You can see this code in: View → Advanced Editor 2. A Simple M Example Suppose your table contains: Product Sales Laptop 10000 Mouse 3000 Keyboard 5000 If you filter Sales greater than 4000, Power Query generates an M expression similar to:

Table.SelectRows(Source, each [Sales] > 4000)

This means: Take the Source table and keep rows where Sales is greater than 4000. You don't have to memorize this immediately. The goal is to understand what the code is doing. 3. Power Query Steps Are M Code When you perform transformations, Power Query creates individual steps. For example: Source Changed Type Removed Columns Filtered Rows Renamed Columns Each step represents a transformation. The formula bar can show the M expression behind the selected step. This is one of the easiest ways to learn M. Instead of starting with programming syntax, perform a transformation and observe the code Power Query generates. 4. The let and in Structure A typical Power Query query looks like:

let
    Source = Excel.Workbook(File.Contents("Sales.xlsx")),
    SalesData = Source{[Item="Sales"]}[Data],
    ChangedType = Table.TransformColumnTypes(
        SalesData,
        {{"Sales", type number}}
    )
in
    ChangedType

Don't worry about every part yet. The important idea is:

let
    Step 1,
    Step 2,
    Step 3
in
    Final Step

Power Query performs the steps in sequence and returns the step specified after in. 5. Understanding a Step Consider:

FilteredRows =
    Table.SelectRows(Source, each [Sales] > 5000)

There are two important parts. Step name FilteredRows This is the name of the step. Transformation Table.SelectRows(Source, each [Sales] > 5000) This tells Power Query what transformation to perform. 6. Common M Functions You will frequently encounter functions such as: Table.SelectRows Used to filter rows.

Table.SelectRows(Source, each [Sales] > 5000)

Table.RemoveColumns Used to remove columns.

Table.RemoveColumns(Source, {"Region"})

Table.RenameColumns Used to rename columns.

Table.RenameColumns(Source, {{"Sales", "Revenue"}})

Table.AddColumn Used to create a new column.

Table.AddColumn(Source, "Profit", each [Sales] - [Cost])

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If you have two tables: January Sales and February Sales Use: Home → Append Queries → Append Queries as New Then select the two queries. 8. Append Three or More Queries You can also append more than two tables, for example January to June. All can be combined into one Sales table. This is useful when monthly data is stored separately. 9. What If Column Names Are Different? Suppose January contains Product Sales and February contains Product Revenue. Power Query treats Sales and Revenue as different columns, resulting in null values. So before appending, make sure equivalent columns have consistent names. 10. Merge vs Append Merge You have Sales (ProductID | Sales) and Products (ProductID | ProductName | Category). You want ProductID | Sales | ProductName | Category. You are adding columns. Use Merge. Append You have January Sales (Product | Sales) and February Sales (Product | Sales). You want one larger table containing all records. You are adding rows. Use Append. 11. Real-World Example Imagine you work for a bank. You receive transaction files every month: Jan_Transactions.csv, Feb_Transactions.csv, Mar_Transactions.csv. Each file contains Transaction ID, Account ID, Date, Amount, Currency. You can append all three files into one transaction table. Then you have a separate Account Master table with Account ID, Customer Name, Region, Account Type. You can merge the Account Master into the transaction table using Account ID. 12. Common Mistakes Mistake 1: Choosing the wrong key Using Customer Name instead of Customer ID can create unreliable matches. Prefer stable identifiers such as CustomerID, AccountID, ProductID. Mistake 2: Different data types Text and Number columns cannot reliably match. Check the data type before merging. Mistake 3: Duplicate keys Duplicate keys in the lookup table can multiply rows. Always investigate unexpected row-count increases. Mistake 4: Wrong Join Kind Choosing Inner instead of Left Outer can cause unmatched records to disappear. Mistake 5: Appending inconsistent columns If one file calls a column Sales and another calls it Revenue, Power Query treats them as separate columns. Standardize column names before appending. 🧪 Practical Exercise Create these three tables. Sales: P101 5000, P102 3000, P103 7000 Products: P101 Laptop Electronics, P102 Mouse Accessories, P103 Keyboard Accessories January: P101 5000, P102 3000 February: P101 6000, P102 3500 Try to: 1. Merge Sales with Products. 2. Bring ProductName and Category into Sales. 3. Append January and February. 4. Check the final row count. 5. Try a Left Anti join and identify products missing. 🎯 Double Tap ❤️ For More ----- 1.41 ₽ · /balance_help

📊 Power BI Learning Roadmap — Part 4 Topic 10: Merge Queries and Append Queries in Power Query When working with real-world data, you rarely get everything in one table. You may have: • Sales data in one table • Product information in another • Customer information in another • Monthly sales in separate files Power Query provides two important features to combine these datasets: 1. Merge Queries 2. Append Queries The key difference is simple: • Merge Queries = add columns from another table • Append Queries = add rows from another table 1. Merge Queries Merge Queries is used when you want to combine two tables based on a common column. It is similar to a JOIN in SQL or a lookup such as XLOOKUP in Excel. Example Sales table: ProductID Quantity Sales P101 10 5000 P102 5 3000 P103 8 6400 Products table: ProductID ProductName Category P101 Laptop Electronics P102 Mouse Accessories P103 Keyboard Accessories Both tables contain ProductID. We can merge them using ProductID. After merging and expanding: ProductID Quantity Sales ProductName Category P101 10 5000 Laptop Electronics P102 5 3000 Mouse Accessories P103 8 6400 Keyboard Accessories 2. How to Merge Queries In Power Query: 1. Select the first table. 2. Go to Home. 3. Select Merge Queries. 4. Select the second table. 5. Select the matching column in both tables. 6. Choose the Join Kind. 7. Click OK. 8. A new column containing a table will appear. 9. Click the Expand button. 10. Select the columns you want to bring into the first table. 3. Join Keys The column used to match two tables is called a key. For example: ProductID Both tables should have compatible data types. If one table has ProductID = Text and the other has ProductID = Number, the merge may not work as expected. Always check the data type before merging. 4. Types of Joins Left Outer Keeps every row from the first table and brings matching rows from the second table. This is one of the most commonly used joins. Inner Keeps only rows that have a match in both tables. Right Outer Keeps all rows from the second table and matching rows from the first table. Full Outer Keeps all rows from both tables. Matching rows are combined, while unmatched rows are also retained. Left Anti Returns rows from the first table that do NOT have a match in the second table. This is extremely useful for finding missing records. Example: If Sales has P101, P102, P103, P104 and Products has P101, P102, P103, Left Anti identifies P104. Right Anti The opposite of Left Anti. It identifies records that exist in the second table but not in the first. 5. A Very Important Problem: Duplicate Keys Suppose your Products table contains: P101 Laptop P101 Laptop - New And your Sales table contains P101 5000 If you merge these tables, Power Query may produce multiple rows for P101. This can unintentionally increase your row count. So before merging, understand whether your key is unique. 6. Append Queries Append is completely different from Merge. Append combines tables by stacking rows. Example: January Sales: Laptop 5000, Mouse 2000 February Sales: Laptop 6000, Mouse 2500 After Append: All rows stacked vertically. 7. Append Two Queries

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Transpose changes the physical orientation of the table. 7. Pivot vs Unpivot vs Transpose Pivot: Turns values into columns. Rows → Columns Unpivot: Turns columns into rows. Columns → Rows Transpose: Flips the orientation of the entire table. Rows ↔ Columns 🎯 Real-World Example Imagine an organization sends you this monthly sales report: Region | Jan | Feb | Mar | Apr West | 80 | 90 | 95 | 100 South | 60 | 65 | 70 | 75 North | 50 | 55 | 58 | 62 You could unpivot the month columns: Region | Month | Sales West | Jan | 80 West | Feb | 90 West | Mar | 95 West | Apr | 100 South | Jan | 60 South | Feb | 65 Now you can easily analyze: Monthly trends, Regional performance, Month-over-month changes, Yearly comparisons. ⚠️ Common Mistake Don't assume that every dataset should be unpivoted. The correct transformation depends on: What the data represents, How the source is structured, How the data will be analyzed, Whether the columns represent actual attributes or repeated categories. 🎯 Practical Exercise Take a dataset structured like: Product | Jan | Feb | Mar | Apr Laptop | 100 | 120 | 140 | 150 Monitor | 80 | 90 | 110 | 125 Keyboard | 50 | 60 | 70 | 75 Try the following in Power Query: 1. Unpivot the month columns. 2. Rename Attribute to Month. 3. Rename Value to Sales. 4. Observe the resulting structure. 5. Try pivoting the data back. 6. Create a small table and experiment with Transpose. 💡 Key takeaway: Pivot, Unpivot, and Transpose aren't just buttons to memorize. They are tools for changing the structure of your data so that it better matches the way you need to analyze it. Double Tap ❤️ For More ----- 1.28 ₽ · /balance_help

📊 Power BI Learning Roadmap — Part 4 Topic 9: Pivot, Unpivot & Transpose in Power Query These three transformations are extremely useful when your source data is arranged in a way that isn't ideal for analysis. A common Power BI problem isn't that the data is incorrect — it's that the structure of the data is not suitable for a data model. 1. Pivot Columns Pivoting converts values from rows into separate columns. Suppose your data looks like this: Region | Month | Sales West | Jan | 50,000 West | Feb | 60,000 South | Jan | 40,000 South | Feb | 45,000 You could pivot the Month column to create: Region | Jan | Feb West | 50,000 | 60,000 South | 40,000 | 45,000 Now the values from the Month column have become separate columns. When is Pivot useful? Pivot can be useful when the business specifically requires a wide format. For example: Region | Jan | Feb | Mar | Apr might be useful for a particular reporting output. However, a wide structure isn't always the best structure for a Power BI data model. 2. Unpivot Columns Unpivot does the opposite. It converts multiple columns into rows. Suppose your source looks like this: Region | Jan | Feb | Mar West | 50,000 | 60,000 | 55,000 South | 40,000 | 45,000 | 48,000 You can unpivot the month columns to create: Region | Month | Sales West | Jan | 50,000 West | Feb | 60,000 West | Mar | 55,000 South | Jan | 40,000 South | Feb | 45,000 South | Mar | 48,000 Now Month is a column containing values rather than separate columns. 3. Why Unpivot Is So Important Imagine a company provides a monthly report like this: Employee | Jan | Feb | Mar | Apr | May Rahul | 20 | 22 | 18 | 25 | 21 Priya | 18 | 20 | 24 | 23 | 26 Next month, they add: Jun The source structure keeps changing because every month becomes another column. After unpivoting, the structure becomes: Employee | Month | Hours Rahul | Jan | 20 Rahul | Feb | 22 Rahul | Mar | 18 Priya | Jan | 18 Priya | Feb | 20 Priya | Mar | 24 When June arrives, June simply becomes another value in the Month column. 4. Unpivot Other Columns Suppose your table contains: Employee | Department | Jan | Feb | Mar Rahul | Finance | 20 | 22 | 18 Priya | HR | 18 | 20 | 24 You want to preserve Employee and Department and convert the remaining columns into rows. Unpivot Other Columns is particularly useful here. You select the columns you want to keep, and Power Query unpivots the remaining columns. 5. Attribute and Value Columns After unpivoting, Power Query generally creates two important columns. Attribute: Contains the names of the columns that were unpivoted. Example: Jan, Feb, Mar Value: Contains the corresponding values. Example: 20, 22, 18 You can rename these to something more meaningful. For example: Attribute → Month, Value → Hours. 6. Transpose Transpose is different from Pivot and Unpivot. Transpose essentially turns rows into columns and columns into rows. Suppose your data is: Name | Rahul | Priya | Amit Sales | 80,000 | 70,000 | 60,000 Region | West | South | North After transposing, the orientation changes.

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