But Power BI GROUPBY() has a fundamentally different engine under the hood, and once you understand what makes it unique, you’ll know exactly when to reach for it — and how to combine it with FILTER() to solve problems that SUMMARIZE() simply cannot handle cleanly.

This tutorial covers how to use Power BI DAX GROUPBY with FILTER across real business scenarios, and gives you the full picture of where it fits in your DAX toolkit.

What Is GROUPBY() in Power BI DAX?

Power BI DAX GROUPBY() is a table function that groups a source table by one or more columns and returns a summary table, one row per unique group. Here’s the syntax:

GROUPBY(
    <table>,
    [<groupBy_column1>],
    [<groupBy_column2>],
    ...
    <name>, <expression>
)

So far, this sounds identical to SUMMARIZE(). The critical difference is in how aggregation expressions inside GROUPBY() work. GROUPBY() does not perform an implicit CALCULATE and does not transition row context into filter context the way SUMMARIZE() does. Instead, it gives you access to each group’s rows through a special function called CURRENTGROUP().

This distinction is everything. Let me show you what it means in practice.

Before writing any DAX, let’s import the Excel file into Power BI.

1. Open Power BI Desktop
2. Click Get Data
3. Choose Excel
4. Select the sample Excel file Load:
   • Sales Data
   • Customers table

Power BI CURRENTGROUP() — The Heart of GROUPBY()

CURRENTGROUP() is a function that works only within Power BI GROUPBY(). It returns the subset of rows from the source table that belong to the current group being iterated. You then pass CURRENTGROUP() into an iterator function SUMX(), AVERAGEX(), MAXX(), MINX(), and COUNTX() to compute your aggregation.

Regional Revenue = GROUPBY(
    'Sales Data',
    'Sales Data'[Region],
    "Total Revenue", SUMX(CURRENTGROUP(), 'Sales Data'[Revenue])
)

SUMX() here receives the current group’s rows via CURRENTGROUP() and sums their Revenue values. The result is a table with one row per Region and a Total Revenue column.

Compare this to SUMMARIZE() + ADDCOLUMNS():

-- SUMMARIZE approach
ADDCOLUMNS(
    SUMMARIZE(Sales, Sales[Region]),
    "Total Revenue", CALCULATE(SUM(Sales[Revenue]))
)

The SUMMARIZE approach uses CALCULATE(), which performs a context transition and evaluates SUM(Sales[Revenue]) in the filter context of the current Region. GROUPBY() avoids that context transition entirely. It works purely at the row level within each group in Power BI.

Why GROUPBY() Exists in Power BI: The Critical Use Case

Here’s where GROUPBY() becomes genuinely irreplaceable. When your source table is a virtual table with no column lineage — meaning a table created by DAX functions like ADDCOLUMNS(), SELECTCOLUMNS(), or UNION() that don’t have direct ties back to physical model columns — CALCULATE() doesn’t know how to establish filter context. Context transitions fail or produce wrong results.

GROUPBY() + CURRENTGROUP() doesn’t need column lineage or context transitions. It works with any table you hand it, making it the only reliable way to group and aggregate virtual tables.

This is the specific scenario Power BI GROUPBY() was designed for:

VAR TaggedCustomers =
    ADDCOLUMNS(
        VALUES(Customer[CustomerID]),
        "SpendCategory",
        IF([Total Sales] >= 1000, "High Value", "Standard")
    )
-- TaggedCustomers is a virtual table with no lineage on SpendCategory

VAR SpendSummary =
    GROUPBY(
        TaggedCustomers,
        [SpendCategory],
        "CustomerCount", COUNTX(CURRENTGROUP(), Customer[CustomerID])
    )
RETURN
SpendSummary

You cannot use SUMMARIZE() here because SpendCategory is a computed column in a virtual table — it has no lineage, no physical model backing. GROUPBY() handles it perfectly.

Power BI FILTER() + GROUPBY() — The Core Combination

Now let’s get into the main topic: combining Power BI FILTER() with GROUPBY(). There are two distinct ways to do this, and both are useful in different scenarios.

Method 1: FILTER the Input Table Before GROUPBY() in Power BI

The first approach is to filter your source data before it enters Power BI GROUPBY(). This is the more performance-friendly pattern — you reduce the number of rows being grouped before the grouping happens.

Revenue 2025 = 
GROUPBY(
    FILTER('Sales Data', 'Sales Data'[Year] = 2025),
    'Sales Data'[Region],
    "Revenue", SUMX(CURRENTGROUP(), 'Sales Data'[Revenue])
)

The inner FILTER() narrows Sales to 2025 rows only. GROUPBY() then groups the already-reduced dataset by Region. The result is a summary table of 2025 revenue per region.

Method 2: FILTER the Output of GROUPBY() in Power BI

The second approach builds the full summary first and then filters the resulting grouped table. Use this when your filter condition depends on an aggregated value — something that doesn’t exist at the row level.

High Revenue Regions = 
FILTER(
    GROUPBY(
        'Sales Data',
        'Sales Data'[Region],
        "Revenue", SUMX(CURRENTGROUP(), 'Sales Data'[Revenue])
    ),
    [Revenue] > 1000
)

Here, you can’t apply Revenue > 100000 before grouping — Revenue is only known after aggregation. So you let GROUPBY() build the summary first, then Power BI FILTER() keeps only the high-revenue regions.

Power BI DAX GROUPBY With FILTER

Now discuss some of the real world examples.

Example 1: Regional Revenue Analysis in Power BI

Business scenario: Your VP of Sales wants a count of how many regions exceeded $500 in revenue in 2025.

High Revenue Region Count = 
VAR RegionalRevenue =
    GROUPBY(
        FILTER(
            'Sales Data',
            YEAR('Sales Data'[OrderDate]) = 2025
        ),
        'Sales Data'[Region],
        "Revenue",
            SUMX(
                CURRENTGROUP(),
                'Sales Data'[Revenue]
            )
    )

VAR TopRegions =
    FILTER(
        RegionalRevenue,
        [Revenue] > 500
    )

RETURN
COUNTROWS(TopRegions)

The VAR structure makes the logic easy to follow. RegionalRevenue holds the summary. TopRegions filters it to the qualifying regions. COUNTROWS() delivers the final answer.

Example 2: Grouping a Virtual Table in Power BI (Where GROUPBY Shines)

This is the scenario where Power BI GROUPBY() is not just convenient — it’s the right tool and SUMMARIZE() would be unreliable.

Business scenario: You want to classify customers as “High Value” (total purchases > $500) or “Standard,” then count how many customers fall into each category.

High Revenue Region Count = 
VAR RegionalRevenue =
    GROUPBY(
        FILTER(
            'Sales Data',
            YEAR('Sales Data'[OrderDate]) = 2025
        ),
        'Sales Data'[Region],
        "Revenue",
            SUMX(
                CURRENTGROUP(),
                'Sales Data'[Revenue]
            )
    )

VAR TopRegions =
    FILTER(
        RegionalRevenue,
        [Revenue] > 500
    )

RETURN
COUNTROWS(TopRegions)

Because both TotalPurchases and Tier exist only as computed columns in virtual tables, GROUPBY() + CURRENTGROUP() is the only reliable aggregation approach here. SUMMARIZE() with Power BI ADDCOLUMNS() would work for simple cases, but GROUPBY() is more semantically correct and safer for virtual table grouping.

Example 3: Power BI Conditional Aggregation Inside GROUPBY()

One of the most powerful capabilities of CURRENTGROUP() is that you can apply conditions inside your aggregation expressions — effectively computing filtered aggregates per group.

Business scenario: For each product category, you want to know both the total revenue AND the revenue from orders above $300 only (your “premium order” threshold).

Category Revenue Breakdown =
GROUPBY(
    'Sales Data',
    'Sales Data'[Category],

    "Total Revenue",
        SUMX(
            CURRENTGROUP(),
            'Sales Data'[Revenue]
        ),

    "Premium Order Revenue",
        SUMX(
            CURRENTGROUP(),
            IF(
                'Sales Data'[Revenue] > 300,
                'Sales Data'[Revenue],
                0
            )
        ),

    "Premium Order Count",
        SUMX(
            CURRENTGROUP(),
            IF(
                'Sales Data'[Revenue] > 300,
                1,
                0
            )
        )
)

Notice what’s happening in “Premium Order Revenue”: FILTER(CURRENTGROUP(), Sales[Revenue] > 300) filters the current group’s rows to only those with Revenue above 300, and SUMX() aggregates that filtered subset. This is conditional aggregation within groups — all in a single GROUPBY() call.

This is genuinely difficult to achieve cleanly with SUMMARIZE() and would require multiple separate measures or multiple Power BI ADDCOLUMNS() calls.

Example 4: Multi-Column Grouping with Downstream FILTER() in Power BI

Business scenario: Your operations team needs to identify Region + Salesperson combinations where the average deal size dropped below $200, and they want the total revenue attributed to those underperforming combinations.

Underperforming Combo Revenue =
VAR RepRegionStats =
    GROUPBY(
        'Sales Data',
        'Sales Data'[Region],
        'Sales Data'[SalesRepName],
        "AvgDeal", AVERAGEX(CURRENTGROUP(), 'Sales Data'[Revenue]),
        "TotalRevenue", SUMX(CURRENTGROUP(), 'Sales Data'[Revenue])
    )

VAR UnderperformingCombos =
    FILTER(
        RepRegionStats,
        [AvgDeal] < 400
    )

RETURN
SUMX(
    UnderperformingCombos,
    [TotalRevenue]
)

The GROUPBY() creates a per-combination summary. FILTER() isolates underperforming combos. TREATAS() maps those combinations back to the physical Sales table for the final revenue calculation.

Example 5: Using GROUPBY to Tag and Re-Summarize

This is the classic GROUPBY() pattern from Power BI DAX Guide — tagging rows with a computed category and then re-grouping by that tag.

Business scenario: You want to know how many customers are “above average” buyers versus “below average,” based on total purchases compared to the company-wide average.

Category Revenue Breakdown = 
GROUPBY(
    'Sales Data',
    'Sales Data'[Category],

    "Total Revenue",
        SUMX(
            CURRENTGROUP(),
            'Sales Data'[Revenue]
        ),

    "Premium Order Revenue",
        SUMX(
            CURRENTGROUP(),
            IF(
                'Sales Data'[Revenue] > 300,
                'Sales Data'[Revenue],
                0
            )
        ),

    "Premium Order Count",
        SUMX(
            CURRENTGROUP(),
            IF(
                'Sales Data'[Revenue] > 300,
                1,
                0
            )
        )
)

This is a two-stage summarization — first by CustomerID to get individual totals, then by SpendCategory to get group-level counts. Power BI GROUPBY() in the second stage is essential because SpendCategory is a virtual column with no lineage.

Example 6: FILTER + GROUPBY for Time-Based Segmentation in Power BI

Business scenario: Your finance team wants to see, for each region, how many months in 2025 had revenue above $30,000 — essentially counting “good months” per region.

Good Month Count by Region =
VAR MonthlyRegional =
    GROUPBY(
        FILTER(
            'Sales Data',
            YEAR('Sales Data'[OrderDate]) = 2025
        ),
        'Sales Data'[Region],
        'Sales Data'[Month],
        "MonthlyRevenue",
            SUMX(CURRENTGROUP(), 'Sales Data'[Revenue])
    )

VAR GoodMonths =
    FILTER(
        MonthlyRegional,
        [MonthlyRevenue] > 600
    )

RETURN
GROUPBY(
    GoodMonths,
    'Sales Data'[Region],
    "GoodMonthCount",
        COUNTX(CURRENTGROUP(), 'Sales Data'[Month])
)

This uses GROUPBY() twice — first to summarize at the Region + Month level, then again after filtering to count qualifying months per region. This double-grouping pattern is exactly what GROUPBY() handles better than SUMMARIZE() because the second grouping operates on a virtual filtered table.

GROUPBY() vs. SUMMARIZE() vs. SUMMARIZECOLUMNS() — The Full Picture

After all these examples, here is an honest, experience-based comparison of all three:

The decision tree I use in practice:

• Physical table, simple grouping, need measures → SUMMARIZECOLUMNS()
• Physical table, need extension columns safely → SUMMARIZE() + ADDCOLUMNS()
• Virtual table or computed column grouping → GROUPBY()
• Need conditional aggregation within groups → GROUPBY() with FILTER(CURRENTGROUP(), …)

Important Restrictions of GROUPBY() You Must Know

GROUPBY() has some constraints that aren’t immediately obvious and can cause confusing errors:

1. You cannot use CALCULATE() inside GROUPBY() expressions

CALCULATE() performs a context transition which conflicts with GROUPBY()’s no-transition design. If you try, you’ll get an error:

-- This will ERROR
GROUPBY(
    Sales,
    Sales[Region],
    "Revenue", CALCULATE(SUM(Sales[Revenue]))  -- Not allowed
)

-- Use SUMX with CURRENTGROUP() instead
GROUPBY(
    Sales,
    Sales[Region],
    "Revenue", SUMX(CURRENTGROUP(), Sales[Revenue])  -- Correct
)

2. CURRENTGROUP() only works inside GROUPBY()

You cannot use CURRENTGROUP() inside SUMMARIZE(), ADDCOLUMNS(), or any other function. It’s exclusively a GROUPBY() companion.

3. Grouping columns must come from the source table or its related tables

GROUPBY() can group by columns that exist in the source table or columns from tables related to it in the “to-one” direction. It cannot group by arbitrary computed expressions — you need to add those as columns first using ADDCOLUMNS() before passing the table to GROUPBY().

4. GROUPBY() doesn’t automatically remove rows with all-BLANK measures

Unlike SUMMARIZECOLUMNS(), GROUPBY() may include rows where all computed values are BLANK. Add an explicit FILTER() to remove them if needed:

VAR Result =
    GROUPBY(Sales, Sales[Region], "Revenue", SUMX(CURRENTGROUP(), Sales[Revenue]))
VAR CleanResult =
    FILTER(Result, NOT ISBLANK([Revenue]))
RETURN
CleanResult

Common Mistakes Developers Make with GROUPBY()

Performance Best Practices

• Pre-filter before GROUPBY(), not after, wherever possible. Filtering the source table before grouping is almost always faster than filtering the grouped result — you’re reducing work, not just hiding results.
• Use Performance Analyzer to measure GROUPBY() measure execution time. Because GROUPBY() uses row-level iteration, it behaves differently from column-optimized functions and can surprise you on large datasets.
• Prefer SUMMARIZECOLUMNS() for physical tables. Reserve GROUPBY() for the specific scenarios where it’s genuinely needed — virtual tables, no-lineage computed columns, and conditional aggregation with CURRENTGROUP().
• Trim virtual tables before passing them to GROUPBY(). If your input table has many columns you don’t need for grouping or aggregation, use SELECTCOLUMNS() to keep only the relevant ones before passing to GROUPBY(). Less data in memory means faster execution.

Conclusion

GROUPBY() fills a very specific and important gap in the DAX function library. It’s not a general replacement for SUMMARIZE() or SUMMARIZECOLUMNS() — but when you’re working with virtual tables, computed columns with no lineage, or need conditional aggregation within groups using CURRENTGROUP(), it’s the cleanest and most reliable tool available.

The combination of FILTER() + GROUPBY() gives you a two-stage analysis capability — filter and group your source data, then filter the grouped result based on aggregated conditions that only exist at the summary level.

You may also like:

• Add Column Using Power BI Power Query Editor
• Select Multiple Values in Power BI Slicer
• Remove Leading Zeros in Power BI
• How to Change Data Type in Power BI

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

In this tutorial, I’ll walk you through every method — from the simplest one-click export in Power BI Desktop to the more advanced “Analyze in Excel” feature in Power BI Service. By the end, you’ll know exactly know how to export Power BI report to Excel.

Why Export Power BI Data to Excel?

Before we get into the steps, let me quickly explain why you’d even want to do this. Power BI is excellent for visuals and interactive dashboards, but Excel still wins when it comes to sharing data with people who don’t have Power BI access, doing ad-hoc analysis, creating custom pivot tables, or just sending a clean spreadsheet to your manager.

Here are some common scenarios:

• A stakeholder needs the raw numbers behind a chart
• You want to do further filtering or manipulation that’s easier in Excel
• Your team doesn’t have Power BI licenses but needs the data
• You need a snapshot of the data at a specific point in time

The screenshot below depicts the Power BI data after it has been exported to Excel.

Check out Power BI FREE vs PRO vs PREMIUM: Which License Do You Actually Need?

Method 1: Enable Export Options in Power BI Desktop (Do This First)

Before you can export anything from a Power BI Desktop report, you need to make sure the export option is switched on. This is a one-time setup step and it takes about 30 seconds.

Here’s how to do it:

1. Open Power BI Desktop and click the File tab at the top left.
2. Go to Options and settings → click Options.

3. Under the CURRENT FILE section on the left, click Report settings.
4. You’ll see an Export data section. Select the option: Allow end-users to export summarized data from the Power BI service or Power BI Report Server.
5. Click OK. Here is a screenshot for your reference.

If you want your users to also export underlying (raw) data, choose the third option instead: Allow end users to export data with current layout, summarized data, and underlying data from the service or Report Server.

This setting controls what your report consumers can export once you publish the report. If you skip this step and someone tries to export underlying data from the service, they’ll hit an error that says “The report author turned off this option.” That’s the fix.

Method 2: Export a Visual from Power BI Desktop

This is the most common method and works great when you want to export the data behind a specific chart or table.

I’ll use two examples here — a pie chart and a table visual — both of which are common in everyday Power BI reports.

Example 1 — Exporting a Pie Chart

Let’s say you have a sales report in Power BI Desktop with a pie chart breaking down revenue by region. Here’s how to export it:

1. Open your Power BI report in Power BI Desktop.
2. Click on the pie chart visual you want to export.
3. You’ll see three dots (…) at the top-right corner of the visual. Click them.
4. Select Export data from the dropdown.

5. A Save dialog box will appear. Choose your folder location, set the file type to CSV, and click Save.

6. Open the downloaded file in Excel — you’ll see all the data that was powering that pie chart. You can see the screenshot below for your reference.

That’s it. Three clicks and you have an Excel-ready file.

Example 2 — Exporting a Table Visual

Same idea, but with a Power BI table visual:

1. Click on your table visual in Power BI Desktop.
2. Click the three dots (…) at the top-right corner.
3. Select Export data.

1. Choose a location, pick CSV as the file format, and click Save.
2. Open the file in Excel — the table data will look exactly the same as it does in Power BI. You can see the exact output in the screenshot below:

One thing to note: the exported CSV will contain the data as it appears in the visual, so any filters or slicers applied to your report at the time of export will affect what shows up in the file. Keep that in mind if you’re getting fewer rows than expected.

Check out Merge Tables in Power BI

Method 3: Export Data from Power BI Service (Dashboard)

If you’re working in the Power BI Service (the web version at app.powerbi.com), the export process is slightly different and gives you a few more choices.

For this example, I’ll use a matrix visual on a published dashboard.

Step 1: Navigate to your dashboard

Go to the Power BI Service and open the dashboard that contains the visual you want to export. In my example, I’m working with a matrix visual.

Step 2: Open the Export menu

Click the three dots (…) at the top-right corner of the matrix visual and select Export data. You can see the screenshot below for your reference.

Step 3: Choose your export type

This is where it gets interesting. You’ll see three options:

• Data with current layout — Exports the data exactly as it appears in the visual, without the colors, icons, or conditional formatting. Great for a clean tabular view.
• Summarized data — Exports only the aggregated data used to build the visual (e.g., totals, averages). Best when you want a summary snapshot.
• Underlying data — Exports the raw, row-level data behind the visual. Most detailed, but requires permission from the report author.

Let’s go through each one.

Export with Current Layout

1. Click the … on your visual → select Export data.

2. Choose Data with current layout.

3. Click the Export button.
4. An .xlsx file downloads automatically to your system.
5. Open it in Excel — the data matches the structure you see in the visual.

This is perfect when you want a clean, presentation-friendly data snapshot without all the formatting baggage.

Export Summarized Data

1. Click the … → select Export data.

2. Choose Summarized data.

3. Select the file format — pick .xlsx if you want it to open directly in Excel, or .csv if you prefer a lighter file.
4. Click Export.
5. Open the file — you’ll see the aggregated numbers that Power BI used to build the chart.

Use summarized data when you’re sharing a high-level overview and don’t need granular row-level details. This way, you can export summarized data from the Power BI Dashboard to Excel.

Export Underlying Data

This one gives you the most detail — the raw, unaggregated rows powering the visual.

1. Select the visual -> click the three dots (…) -> select the Export data. After that, if you face an error like “The report author turned off this option.” Check the screenshot below.

2. To solve this error in the Power BI Desktop, go to the Report setting and select Allow end users to export data with current layout, summarized data, and underlying data from the service or Report Server under the Export data. Then click OK.

3. After that, republish your report or visual.

4. Go to the Power BI service and open the report you publish above. Then Select the visual -> click the three dots (…) -> select the Export data.

5. Next, select Underlying data. Then click the Export button.

6. The Excel file is downloaded to the local system. Then, open it in Excel. You can see the raw data in the Excel file.

You can export underlying data in Power BI service to Excel using this method.

Check out Card Visual in Power BI

Method 4: Copy a Table Directly into Excel

Here’s one that a lot of people don’t know about. If you just want to copy a table from Power BI Desktop and paste it into Excel without saving a file, this method is super quick.

1. In Power BI Desktop, switch to the Data view (the table icon on the left sidebar).
2. Find the table you want to copy.
3. Right-click on the table name.
4. Select Copy table.
5. Open Excel, click on a cell, and press Ctrl + V to paste.

The data pastes in perfectly with column headers. This is great for quick ad-hoc exports when you don’t want to go through the full export flow.

Method 5: Analyze in Excel (Power BI Service)

This is the most powerful method and the one most people haven’t tried yet. “Analyze in Excel” creates a live connection between your Power BI dataset and an Excel workbook. When the Power BI data refreshes, your Excel file can pull the latest data too.

Here’s how to use it:

1. Go to the Power BI Service and navigate to your workspace.
2. Find the dataset you want to connect to Excel.
3. Click the three dots (…) next to the dataset name.
4. Select Analyze in Excel.
5. An .xlsx file will download. Open it.
6. Enable editing and content when prompted.
7. You’ll see a pivot table pane in Excel linked to your Power BI data.

From here, you can build your own pivot tables and pivot charts using Power BI data, directly inside Excel. And when your data refreshes in Power BI, you can refresh the Excel file to get the latest numbers.

Alternatively, you can trigger this from inside a report: go to the report → click Export in the top menu → select Analyze in Excel.

Check out Concatenate with Space in Power BI

Power BI Export to Excel — Limitations to Know

Before you go and export everything in sight, here are a few things to keep in mind:

• Row limits: CSV exports are capped at 30,000 rows. Excel exports go up to 150,000 rows. If your dataset is larger, you’ll need a different approach (like Power Automate or direct API access).
• Conditional formatting doesn’t carry over: Any color scales, data bars, or icon sets you’ve set up in Power BI won’t appear in the Excel export.
• One visual at a time: You can’t export an entire report page in one go. You have to export each visual separately.
• Permissions matter: Exporting underlying data requires the report author to have enabled that option in Desktop settings.
• Calculated measures: Measures created in Power BI (like DAX formulas) may not behave exactly the same way once exported to Excel.
• Show items with no data: If this option is enabled in a visual, underlying data export won’t work for that visual.

Which Method Should You Use?

Here’s a quick way to decide which method to use.

• Just need a quick data dump from a visual? → Use Method 2 (Export from Desktop) or Method 3 (Export from Service).
• Need summarized numbers for a report? → Use Summarized Data export from Power BI Service.
• Need the raw row-level data? → Use Underlying Data export.
• Want a live, refreshable connection? → Use Analyze in Excel.
• Just need to paste a table quickly? → Use the Copy Table method.

Each method has its place. Most of the time, you’ll find yourself using the three-dot export from a visual — it handles 80% of everyday scenarios.

Conclusion

I hope you understand how to export data into Excel in both Power BI Desktop and Power BI Service.

To recap, here are the five methods we covered:

• Export from Power BI Desktop — right-click the visual and export as CSV or Excel
• Export from Power BI Service — use the three-dot menu on any visual
• Summarized Data export — great for high-level snapshots
• Underlying Data export — when you need every raw row
• Analyze in Excel — best for a live, refreshable connection

Start with the method that fits your immediate need. If you’re just sharing numbers with a colleague, a quick visual export does the job. If you need something more dynamic and refreshable, go with Analyze in Excel.

Got a question or ran into an issue with any of these steps? Drop it in the comments below — I’m happy to help.

You may also like:

• Add Column Using Power BI Power Query Editor
• Select Multiple Values in Power BI Slicer
• Remove Leading Zeros in Power BI
• How to Change Data Type in Power BI?

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

The honest answer is: it depends on what you’re trying to do. Are you a solo analyst playing around with data? A team manager who needs to share reports with colleagues? Or part of a large enterprise dealing with massive datasets and hundreds of users?

In this tutorial, I’m going to break down all three plans — Power BI Free, Power BI Pro, and Power BI Premium — in plain language, with real examples, so you can make a smart decision for yourself.

What Is Power BI (Quick Overview)

Power BI is Microsoft’s business intelligence tool that lets you connect to data sources, build reports, and share dashboards. It comes in different plans, and the one you choose determines what you can do — especially when it comes to sharing, collaboration, and data capacity.

There are three main tiers:

• Power BI Free — for individuals exploring BI on their own
• Power BI Pro — for teams that need to share and collaborate
• Power BI Premium — for large organizations that need scale, performance, and advanced features

Let’s walk through each one.

Power BI Free — Good for Getting Started

Power BI Free is exactly what it sounds like — it costs nothing. You can download Power BI Desktop, connect to your data, build reports, and publish them to the Power BI service. That’s a solid deal for zero dollars.

Here’s what you can do with the free plan:

• Connect to 70+ data sources (Excel, SQL, SharePoint, web, and more)
• Create reports and dashboards in Power BI Desktop
• Publish reports to the Power BI service (your personal workspace)
• Use basic AI-powered visuals like Q&A and key influencers
• Access the Power BI mobile app

But here’s the limitation that catches most people off guard: You can’t share your reports with other users. If you publish a report to your workspace, only you can see it. The only workaround is to use “Publish to Web,” which makes your report publicly accessible — meaning anyone with the link can see it. That’s fine for some public dashboards, but a big no for anything with sensitive business data.

Also, the Free tier gives you 1 GB of data storage per user and data refreshes are limited.

When does Free make sense?

• You’re learning Power BI for the first time
• You’re building personal reports for your own analysis
• You want to test Power BI before committing to a paid plan
• You’re a student or freelancer with no collaboration needs

Think of Power BI Free like Google Docs in “view only” mode — you get access to the tool, but you can’t really work with others.

Check out ISBLANK vs ISEMPTY in Power BI DAX

Power BI Pro — For Teams That Need to Collaborate

Power BI Pro is where things get serious. It’s the most popular paid plan for business users, and honestly, it covers the majority of team collaboration needs.

As of April 2025, Microsoft updated the pricing: Power BI Pro now costs $14 per user per month (previously $10). It’s the first price increase since Power BI launched nearly a decade ago.

Here’s what Pro adds on top of the Free plan:

• Share reports and dashboards with other Pro users inside your organization
• Publish content to shared workspaces (not just your personal workspace)
• 8 data refreshes per day — much better than Free’s daily refresh
• 10 GB storage per user
• Email subscriptions for reports and dashboards
• Export to CSV, Excel, and PowerPoint
• “Analyze in Excel” feature
• Full Power BI REST API access
• Embed reports in apps, websites, or SharePoint
• Advanced security with sensitivity labels and end-to-end encryption
• Real-time collaboration on dashboards

The important thing to note: Both the person sharing the report AND the person viewing it need a Pro license. If you send a report link to a colleague who only has a Free license, they won’t be able to open it. Everyone in the loop needs Pro.

A real-world example: Imagine you’re a sales manager and you want your entire sales team — say 20 people — to see live dashboards with weekly performance numbers.

With Pro, you build the report once, publish it to a shared workspace, and all 20 team members can view, filter, and interact with it. You can even set up automatic email reports every Monday morning. That’s Pro doing exactly what it’s built for.

When does Pro make sense?

• You work in a team and need to share reports securely
• Your organization has 10–200 users who need BI access
• You need more frequent data refreshes than Free allows
• You want to embed reports in internal SharePoint pages or apps
• Budget is a factor and capacity-based pricing feels excessive

Check out How to Filter Dates in Power BI DAX

Power BI Premium — For Enterprise Scale

Power BI Premium is a different beast altogether. It’s designed for large organizations that need dedicated computing resources, massive data capacity, and features that go beyond what Pro can offer.

Premium comes in two flavors:

Premium Per User (PPU)

This is the per-user version of Premium, priced at $24 per user per month (updated as of April 2025). Think of it as “Pro on steroids” — you get all Pro features plus everything Premium offers, but you’re still paying per person.

Premium Per Capacity

This is the traditional enterprise option, starting at $4,995 per month. Instead of paying per user, you’re paying for a block of dedicated computing resources (capacity). Here’s the big advantage: once you have a Premium capacity, Free users inside your organization can view reports without needing a Pro license. Only the people who create and publish content need Pro licenses.

What does Premium add that Pro doesn’t have?

• Up to 48 data refreshes per day (vs. 8 in Pro)
• 100 TB of storage for the organization (vs. 10 GB per Pro user)
• Dataset sizes up to 400 GB (vs. 1 GB in Pro)
• Paginated reports (pixel-perfect reports designed for printing, like invoices or statements)
• Power BI Report Server — for on-premises reporting when cloud isn’t an option
• Dedicated cloud capacity — your reports don’t compete with other customers’ workloads
• Advanced AI with Azure Cognitive Services (sentiment analysis, image recognition, NLP)
• AutoML for building machine learning models directly in Power BI
• XMLA endpoint read/write access for advanced development
• Deployment pipelines for managing dev/test/production environments
• Multi-geo support for data residency compliance
• Autoscale to handle unexpected spikes in demand
• Bring Your Own Key (BYOK) encryption for advanced security

A real-world example: A healthcare company has 5,000 employees. Instead of buying 5,000 Pro licenses (that would cost $70,000/month!), they buy Premium capacity. Now 50 report creators get Pro licenses, and all 5,000 employees can view reports through the Premium workspace — no individual license needed per viewer. The math works out hugely in their favor.

When does Premium make sense?

• You have a large number of report consumers (100+ users)
• You work with very large datasets or need fast, consistent refresh rates
• You need on-premises reporting via Power BI Report Server
• You need paginated reports for finance, compliance, or printing
• You want AI/ML capabilities built into your BI workflows
• You have strict data residency requirements (multi-geo)
• You’re managing multiple environments (dev, test, prod) with deployment pipelines

Check out Power BI DAX Functions with Examples

Power BI Free vs Pro vs Premium — Side-by-Side Comparison

Here’s a quick table to summarize the key differences between Power BI Free, Pro and Premium.

Which Plan Should You Choose?

Here’s my honest recommendation based on different scenarios:

Go with Free if:
You’re learning Power BI, building personal reports, or just exploring what it can do. Don’t spend money until you know you need the extra features.

Go with Pro if:
You’re on a team and need to share reports or collaborate. Most mid-sized businesses land here, and for a majority of use cases, Pro covers everything you need.

Go with Premium Per User (PPU) if:
You’re a power user or developer who needs advanced features like paginated reports, deployment pipelines, or AutoML — but you don’t have hundreds of report consumers. At $24/user, it’s a reasonable upgrade from Pro.

Go with Premium Per Capacity if:
You’re in a large enterprise with hundreds or thousands of report consumers. The math makes sense once your viewer count goes above 350–400 users (compared to buying individual Pro licenses for everyone).

Read How to Create Date Hierarchy in Power BI?

Common Questions People Ask

Wrapping Up

I hope now you understand when to choose between Power BI Free, Pro, and Premium. Start with Free to learn the tool. Move to Pro when your team needs to share reports. Step up to Premium when scale, performance, or advanced features become a business requirement.

The biggest mistake I see is organizations buying Premium capacity when Pro would do the job — or people sticking with Free and struggling to share anything. Match the plan to your actual needs, and you’ll get the most value for your money.

You may also like:

• Power BI Date Slicer
• Merge Columns in Power BI
• username() and userprincipalname() in Power BI
• Power BI Group by

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

But when I started using a date column in charts and filters, I noticed some issues. The dates were not showing correctly. In some visuals, Power BI treated the date column as text, and sorting by date was not working as expected.

After checking the source data, I found that the dates were coming in different formats. Some values were like 20240115, some were 15-01-2024, and some were normal text dates. Because of this, Power BI could not understand the dates correctly.

In this tutorial, I will explain how to change date formats in Power BI. I will cover three ways to format a date column: Power Query, DAX, and model formatting options.

Change Date Format in Power BI

There are three main ways to change or control date formats in Power BI:

1. Using Power Query Editor
2. Using DAX formulas
3. Using Column tools

Each method has its own use case, and choosing the right one depends on your requirements.

Method 1: Change Date Format Using Power Query Editor

In this method, I will use a simple sample dataset loaded into Power BI. The dataset contains a date column stored in different formats, such as numbers, text values, and mixed date formats. This helps explain how date issues usually appear in real projects.

For this method, I will cover three different examples, each using a slightly different date format. All transformations will be done in Power Query Editor, before the data is loaded into the Power BI model.

Example 1: Convert YYYYMMDD to Date in Power BI using Power Query Editor

In the above dataset, you can see the Order Date column contains values like 20250115 and 20250203. Power BI does not recognize this format as a date. In this example, I will show you how to convert this format into a proper date using Power Query Editor.

Steps to Convert YYYYMMDD to Date:

1. Open Power BI Desktop. Click on Transform data to open Power Query Editor.

2. Select the OrderDate column. Go to the Add Column tab. Click on Custom Column.

Note:

Make sure the column data type is Text. If it is not Text, change it to Text from the Data Type option.

3. In the Custom Column window, enter a column name like Formatted Date.

if Text.Length([Order Date]) = 8 then
    Date.From(
        Text.Start([Order Date], 4) & "-" &
        Text.Middle([Order Date], 4, 2) & "-" &
        Text.End([Order Date], 2)
    )
else
    [Order Date]

In my dataset, only two rows have dates in YYYYMMDD format, such as 20250115 and 20250203. All other rows have different date formats like 15-03-2025 or Apr 10 2025.

The YYYYMMDD format always contains 8 characters. Because of this, I used a condition to check the character length of the date value. Based on this condition, if the date contains 8 characters, the formula extracts the year, month, and day and converts them into a proper date. If the number of characters is not 8, the original date value is kept unchanged.

4. Then you see that a new column has been added.

This way, you can change the YYYYMMDD to a date in Power BI using Power Query Editor.

Example 2: Convert Text Date (DD-MM-YYYY) to Date using Power BI Power Query

In the previous example, we converted the YYYYMMDD format. Now, as you can see, the Formatted Date column is still in Text format for values like 15-03-2025 and 28-03-2025.

In this example, I will show you how to convert these DD-MM-YYYY text values into a proper Date format using Power Query Editor.

Follow the steps:

1. In the Power Query Editor, select the Formatted Date column. Confirm that the column data type is Text.

2. Go to the Transform tab. Click the Data Type Dropdown, then Select Date as the data type.

3. You will see that text values like 15-03-2025 and 28-03-2025 are now converted into a proper Date format.

This way, you can convert a text date (DD-MM-YYYY) to a date using Power BI Power Query.

Example 3: Format Date Using Power Query (Date Options)

In the previous examples, we converted different date formats into a proper Date data type. Now, in this example, I will show how to format the date using the built-in Date options in Power Query.

Once the column is converted to a Date type, Power Query provides several options to extract or format parts of the date, such as year, month, day, week, and more.

Steps to Format Date in Power Query:

1. Open Power BI Desktop. Click on Transform data to open Power Query Editor.
2. Select the Formatted Date column. Make sure the column data type is Date.

3. Go to the Add Column tab. Click on the Date dropdown option.
4. From the list, select the required format option:

5. From the drop-down, I select End of Month, and you can see that Power Query creates a new column.

Note:

Formatting options under Add Column -> Date work only when the column data type is Date. If the column is still in Text format, these options will be disabled.

This way, you can change the date format using Power Query Editor in Power BI.

Method 2: Change Date Format Using DAX in Power BI

In this method, I will use DAX (Data Analysis Expressions) to change and format dates in Power BI.

Note:

DAX formatting is mostly used for display purposes. Some DAX formulas return text instead of a date, so they should not be used for date calculations.

Example 1: Convert YYYYMMDD to Date Using DAX in Power BI

In this example, I use a format like “yyyymmdd” (e.g., 20240310 for March 10th, 2024). I want to convert these dates to a standard format to easily analyze delivery trends.

According to this scenario, we have an Excel file named Delivery Data with columns Delivery Date.

Follow the steps:

1. Open Power BI Desktop and load data using the Get data option. Then, you can see data in the Data pane.

2. Now Go to Table view -> Under the Table tools tab, click New column.

3. In the formula bar, put the following DAX expression. Then click the Commit button.

DeliveryDate =
DATE(
    VALUE(LEFT('DeliveryData'[Delivery Date], 4)),
    VALUE(MID('DeliveryData'[Delivery Date], 5, 2)),
    VALUE(RIGHT('DeliveryData'[Delivery Date], 2))
)

Where:

• DeliveryDate = This is the name we’re giving to the new column that will contain the date format.
• DATE = This function in DAX constructs a date value from the year, month, and day components.
• VALUE(LEFT(‘DeliveryData'[Delivery Date],4)) = This part extracts the leftmost 4 characters (representing the year) from the “Delivery Date” column in the “DeliveryData” table and converts them into a numeric value.
• VALUE(MID(‘DeliveryData'[Delivery Date],5,2)) = This section extracts the characters starting from the 5th position up to 2 characters long (representing the month) from the “Delivery Date” column and converts them into a numeric value.
• VALUE(RIGHT(‘DeliveryData'[Delivery Date],2)) = Here, it takes the rightmost 2 characters (representing the day) from the “Delivery Date” column and converts them into a numeric value.

4. You see the DeliveryDate created in the Table view.

This way, we can use DAX to convert “yyyymmdd” dates to a standard date format in Power BI. This lets you analyze delivery trends and make data-driven decisions to improve logistics operations.

Example 2: Format Date for Display Using DAX in Power BI

In this example, the date column is already in the correct date format. We will use DAX to change how the date is displayed in the report.

To do this, follow the steps below:

1. In Power BI Desktop. Go to Table view. Select the table that contains the date column (for example, DeliveryData).

2. Under the Table tools tab, click New column. In the formula bar, enter the following DAX formula:

Delivery Date Text = FORMAT([DeliveryDate], "dd-MMM-yyyy")

3. Press Enter. A new column is created with the formatted date.

This way, you can format the date using DAX in Power BI.

Method 3: Change Date Format from Column tools in Power BI

In this method, we will change the date format directly from the Column tools in Power BI. This method is useful when the date column is already in the correct Date data type, and you only want to change how the date is displayed in the report.

For this method, I am using the dataset below, where the date column is already in Date format.

I will change only the display format of the date using the Column tools in Power BI. This method does not change the actual data; it only changes the display format.

To do this, follow the steps below:

1. Open the Power BI Desktop and load the above data, then you can see the data in the Data section.

Note:

In the Data section, check whether the date column displays a calendar icon. If yes, then follow the below steps. If no, first change the date format in that column.

2. Click on the date column you want to format. On the top menu, go to the Column tools tab. In the Format dropdown, choose the required format, such as:

3. Now I want the dd mmmm yyyy format, so I selected the 3 options based on your requirement. The date format is updated immediately.
4. To check this, go to the table view, select the table, and you can see the date format has changed.

This way, you can change the date format from Column tools in Power BI.

Conclusion

In this tutorial, I explained how to change date formats in Power BI using different methods. I started by showing why date format issues happen when data comes from different sources.

First, I used Power Query Editor to convert dates like YYYYMMDD and DD-MM-YYYY into a proper date format. I also showed how to format dates using the built-in Date options in Power Query.

Next, I used DAX to convert dates and format them for display purposes. I explained that DAX is mainly used for formatting and that some DAX functions return text instead of a date.

Finally, I showed how to change the date format using Column tools in Power BI. This method is useful when the date column is already in the correct date format, and only the display format needs to be changed.

Additionally, you may like some more Power BI articles:

• Convert Date to Text in Power BI
• Convert Number to Text in Power BI
• Convert Hours to Minutes in Power BI

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

This can be done by adding a custom column in Power BI Power Query and using the following formula to generate today’s date dynamically:

Date.From(DateTime.LocalNow())

This formula returns the current date during each dataset refresh, making it ideal for applying daily filters and slicers in Power BI reports.

In this tutorial, you will learn how to add a today’s date column in Power BI Power Query using different approaches. Where I will be covered:

• Add today’s date column in Power BI Power Query
• How to add today’s date column using the Advanced Editor (M code) in Power BI
• Add today’s date with time using Power BI Power Query
• How to format the today’s date column in Power BI Power Query
• How to use today’s date column in existing date calculations

Add Today’s Date Column in Power BI Power Query

For this example, we will use a simple sales dataset that contains order details such as Order ID, Customer Name, Order Date, and Amount.

This dataset does not include today’s date, which is required to apply a dynamic daily filter in the Power BI report.

To add today’s date to the dataset, follow the steps below:

1. Open the Power Automate desktop and load your dataset where you want to add the today’s date column.

2. Then, under the Home tab, click Transform data to open the Power Query editor.

3. In the left pane, select the table where you want to add today’s date. Go to the Add Column tab, click on Custom Column.

4. The Custom Column dialog box will appear. In the New column name, enter Today’s Date, and in the Custom column formula, enter:

Date.From(DateTime.LocalNow())

5. After clicking OK, A new column named Today’s Date will be added to the table.

This way, you can add a today’s date column in Power BI Power Query.

Add Today’s Date Column using the Advanced Editor (M code) in Power BI

In some scenarios, you may prefer to use M code instead of the Power Query user interface, especially when you want more control over the query. Power BI allows you to add today’s date as a new column directly using the Advanced Editor in Power Query.

For this example, I will use the same dataset I uploaded to Power BI. Follow the steps below to add a column for today’s date using M code:

1. First, open Power BI Desktop and click on Home -> Transform data to open the Power Query Editor.
2. In the Power Query Editor, select the table where you want to add today’s date. Then, go to Home -> Advanced Editor.

3. In the Advanced Editor window, add the following line of M code to create a new column with today’s date:

,
    #"New Column" = Table.AddColumn( #"Added Custom","Today Date",each Date.From(DateTime.LocalNow()), type date
)

in
    #"New Column"

4. Click Done to save the changes. A new column named Today Date will be added to the table, displaying the current date.

Finally, click Home -> Close & Apply to load the updated dataset back into Power BI.

Advanced Editor is useful when you want to understand or modify the underlying M code and ensures that the Today’s Date column updates automatically every time the dataset is refreshed.

Add Today’s Date with Time using Power BI Power Query

In the above two examples, we learned how to add a column using two different methods in Power BI Power Query. However, you may notice that in those examples, the time value is not included.

If you want to add today’s date along with the current time, follow the steps below:

1. In the Power Query Editor, select the table where you want to add the date and time column. Go to the Add Column tab and click on Custom Column.

2. In the Custom Column dialog box, enter the New column name as Today DateTime. In the Custom column formula, enter the following formula:

DateTime.LocalNow()

3. Click OK to apply the formula. A new column will be added showing the current date and time for each row in the table.

4. If required, you can change the data type of the column by selecting it and choosing Date/Time from the Transform -> Data Type option.

Finally, click Home Close & Apply to save the changes.

This method ensures that the date and time values are updated automatically whenever the dataset is refreshed in Power BI.

Format the Today’s Date Column in Power BI Power Query

After adding today’s date as a column in Power BI Power Query, you may want to display it in a specific format such as dd/MM/yyyy HH:mm tt (for example, 12/01/2026 10:30 AM). Power Query allows you to format date and time values in different ways, depending on whether you want to keep the column as a Date/Time data type or convert it into text.

Method 1: Convert the Column to Date/Time Data Type

1. Open Power BI Desktop and select Home -> Transform data.
2. In Power Query Editor, select the Today’s Date column.

3. Go to the Transform tab. Click Data Type and choose Date/Time.

This ensures the column contains both date and time values and can be used in calculations and time-based analysis.

Note:

This method does not control the display format. The visual formatting can be adjusted later in the Power BI report view.

Method 2: Format Today’s Date Using Custom M Code (Text Format)

Now I want to extract the format dd/MM/yyyy HH:mm tt, convert the date-time value to text.

To do this, follow the steps below:

1. In Power Query Editor, click Add Column -> Custom Column.

2. Enter the following M code:

DateTime.ToText([Today DateTime], "dd/MM/yyyy HH:mm tt")

3. A new column will be created with today’s date and time formatted as text.

This is the way to format the today’s date column in Power BI Power Query.

How to use Today’s Date Column in Existing Date Calculations in Power BI Power Query

After adding Today’s Date as a column in Power BI Power Query, you can use it in various date calculations to make your reports dynamic and automated.

Our dataset contains the Order Date and Today’s Date columns. Now, I want to use these two columns to calculate the number of days between the order date and today.

Follow the steps below to calculate the days’ difference:

1. Open Power BI Desktop and go to Transform data.
2. In Power Query Editor, select Add Column, click Custom Column.

3. Enter the column name as Days Difference. Then use the following formula:

Duration.Days([Today Date] - [Order Date])

This creates a new column that shows how many days have passed since the order date.

Conclusion

In this tutorial, I covered how to add today’s date using a custom column, how to do the same using the Advanced Editor (M code), and how to add today’s date with time. I also showed how to format the date column for today’s date and how to use it in existing date calculations, such as finding the number of days between two dates.

• Filter Dates in Power BI DAX
• Add Column With a Fixed Value in Power BI
• Check If Text is Date in Power BI Power Query
• Add Column Using Power BI Power Query Editor
• Convert yyyymmdd to Date Format in Power BI

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

Both functions return TRUE when something is empty and FALSE when data exists. Because of this, many people get confused about when to use ISBLANK and when to use ISEMPTY.

In this tutorial, I will explain ISBLANK and ISEMPTY along with their syntax. I will show how to check whether a column value is missing using the ISBLANK DAX function, and how to check whether a table has no rows using the ISEMPTY function.

ISBLANK in Power BI DAX

The ISBLANK function in Power BI DAX is used to check whether a value is blank or missing. It works on single values, such as a column value, a calculated column, or a measure result.

If the value being checked is blank, ISBLANK returns TRUE. If the value contains any data, ISBLANK returns FALSE.

Syntax:

ISBLANK(<value>)

Here, <value> can be:

• Any DAX expression that returns a single value
• A column value
• A measure

Check Column Value Blank Using ISBLANK() in Power BI

In this example, I will use a sample dataset named Products Ordered. This dataset contains order-related information such as:

Now, I want to create a new column that checks whether the Ship to Zip Code column is blank or not. The new column will return TRUE if the value is blank and FALSE if the value is not blank.

Follow the steps below:

1. Open Power BI Desktop. Load the Products Ordered dataset.

2. Go to Table View and select New Column from the Table tools tab.

3. Enter the following DAX formula and click Enter:

Check Zip Code Blank = ISBLANK('Products Ordered'[Ship to Zip Code])

After this, a new column is added to the table. This column shows TRUE for rows where the Ship to Zip Code value is missing and FALSE where the value exists.

This way, you can check column value blank using ISBLANK() in Power BI.

Check Measure Result Blank Using ISBLANK() in Power BI

In this example, we will check whether a measure returns a blank value using the ISBLANK() function in Power BI.

Check the Excel below, which I will use in this example:

Follow the steps below:

1. Open Power BI Desktop and load the Products Ordered dataset.
2. Go to Table View and select the column. Click on New Measure from the Table tools tab.

3. Enter the following DAX formula and press Enter:

Total Sales = SUM('Products Ordered'[Sales Amount])

4. Click on New Measure. Enter the following DAX formula:

Check Measure Blank = ISBLANK([Total Sales])

5. To check the result, add a Table visual and add the Sales ID, Product Check Measure Blank, and Sales Amount column.

Then, the way you can check a measure result blank using ISBLANK() in Power BI.

Check DAX Expression Result Blank Using ISBLANK() in Power BI

In this example, we will check whether the result of a DAX expression is blank using the ISBLANK() function in Power BI.

For this example, I will use the same table:

Now follow the steps below:

1. Open Power BI Desktop and load the Data dataset.
2. Go to Table View. Select New column from the Table tools tab.
3. Create a measure to calculate the DAX expression:

Check Expression Blank = 
ISBLANK(
    DIVIDE(
        SUM('Products Ordered'[Sales Amount]),
        SUM('Products Ordered'[Quantity])
    )
)

After this, a new column is added to the table. This column shows TRUE for rows where the multiplication of sales amount and quantity value is missing and FALSE where the value exists.

This is how you can check whether the result of a DAX expression is blank using the ISBLANK() function in Power BI.

ISEMPTY Function in Power BI

The ISEMPTY function in Power BI DAX is used to check whether a table or table expression contains any rows.

• It returns TRUE if the table is empty (has no rows).
• It returns FALSE if the table contains at least one row.

Unlike ISBLANK, which works on single values, ISEMPTY works only with tables.

Syntax:

ISEMPTY(<table_expression>)

Here, <table_expression> Can be:

• A table name
• A filtered table
• Any DAX expression that returns a table

Check Table Name Empty Using the ISEMPTY Function in Power BI

In this example, we will check whether the Products Ordered table contains any rows.

1. Open Power BI Desktop
2. Load the Products Ordered dataset
3. Go to Table View
4. Click New Measure from the Table tools tab
5. Enter the following DAX formula:

Check Table Empty = ISEMPTY('Products Ordered')

To check the result, go to the report view and add a card visual.

This way, you can check whether the Products Ordered table contains any rows.

Check Filtered Table Empty Using ISEMPTY() in Power BI

In this example, we will check whether a filtered table returns any rows. I want to check whether there are any orders where the Quantity is greater than 1000.

1. Go to Table View
2. Click New Measure
3. Enter the following DAX formula:

Check Filtered Table Empty =
ISEMPTY(
    FILTER(
        'Products Ordered',
        'Products Ordered'[Quantity] > 1000
    )
)

Now, to check the result, go to the Report view, add a card visual, then add the above measure to it.

Check DAX Table Expression Empty Using ISEMPTY() in Power BI

In this example, we will check whether a DAX table expression returns any rows.

So here we will check whether the summarized Products Ordered table returns data.

To do this, follow the steps below:

1. Go to Table View
2. Click New Measure
3. Enter the following DAX formula:

Check Table Expression Empty =
ISEMPTY(
    SUMMARIZE(
        'Products Ordered',
        'Products Ordered'[Product],
        "Total Sales", SUM('Products Ordered'[Sales Amount])
    )
)

To check the result, go to the report view and add a card visual.

This is how to check whether a DAX table expression returns any rows.

ISBLANK() vs ISEMPTY()

ISBLANK() is used in Power BI DAX to check whether a single value is blank or missing. It works with column values, measures, and DAX expressions that return one value. ISBLANK returns TRUE when the value is blank and FALSE when data exists. It is commonly used to detect missing data in columns, handle empty measure results, or control calculations when a value is not available.

ISEMPTY(), on the other hand, is used to check whether a table or table expression has any rows. It returns TRUE when the table contains no rows and FALSE when at least one row exists. ISEMPTY is mostly used with filtered tables, summarized tables, or virtual tables created using DAX functions like FILTER or SUMMARIZE. Unlike ISBLANK, it does not work on single values.

Conclusion

In this tutorial, I explained the difference between ISBLANK and ISEMPTY in Power BI DAX. Also, I showed how ISBLANK() is used to check missing or empty values in columns, measures, and DAX expressions with real examples.

I also explained how ISEMPTY() is used to check whether a table contains no rows, using examples with a table name, a filtered table, and a DAX table expression.

You may also like:

• Select Multiple Values in Power BI Slicer
• Check If Text is Date in Power BI Power Query
• Add Column Using Power BI Power Query Editor
• Extract Text in Power BI Power Query Using Add Column

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

Power BI provides multiple DAX functions, such as ISBLANK, COALESCE, FIRSTNONBLANK, and LASTNONBLANK, to identify, remove, or replace blank values from the dataset.

Using these DAX functions, we can easily exclude blank values and work only with valid data in Power BI reports.

In this Power BI tutorial, I will explain how to filter blank and non-blank values using different DAX functions. You will learn how to use ISBLANK, COALESCE, FIRSTNONBLANK, and LASTNONBLANK to remove empty values and work only with valid data in your Power BI reports.

Filter Blank Values using ISBLANK in Power BI

The ISBLANK function in Power BI DAX is used to check whether a column value is blank or not. It returns TRUE if the value is blank and FALSE if the value contains data.

Syntax:

ISBLANK(<value>)

In this example, I am using a Sales Data table that contains some blank values in the Sales Amount column.

I want to filter out rows where the Sales Amount is blank and work only with valid data.

To do this, follow the steps below:

1. Open Power BI Desktop and load your dataset. In my case, I loaded the dataset shown above.
2. Go to the Table view from the left navigation pane and select the required table.

3. Under Table tools, click New column.

4. Now, enter the DAX expression below to filter blank values using the ISBLANK function:

Is Sales Blank = ISBLANK('Sales'[Sales Amount])

Where:

• Sales: Table Name
• Sales Amount: Column Name

After creating the column, Power BI adds a new column that returns TRUE for blank Sales Amount values and FALSE for non-blank values.

This way, you can use the ISBLANK function to Filter Blank Values in Power BI Desktop.

Filter Blank Values using COALESCE in Power BI

The COALESCE function in Power BI DAX is used to return the first non-blank value from a list of expressions. If a column contains blank values, COALESCE helps replace those blanks with a default value.

Syntax:

COALESCE(<expression1>, <expression2>, ...)

In this example, I will use the same dataset and check the Sales Amount column. If the Sales Amount is blank, I will replace it with 0.

Follow the steps below:

1. Open Power BI Desktop and load the dataset. Go to the Table view and select the table.
2. Under Table tools, click New column.

3. Now enter the DAX expression below and click Enter:

Sales Amount (No Blank) = COALESCE('Sales'[Sales Amount], 0)

After applying this formula, Power BI creates a new column where Blank values are replaced with zero, and Non-blank values remain unchanged.

This way, you can work only with valid values and avoid blank-related issues in calculations and visuals.

Filter Blank Values using FIRSTNONBLANK in Power BI

The FIRSTNONBLANK function returns the first non-blank value in a column, based on the evaluation of another column or expression.

Syntax:

FIRSTNONBLANK(<column>, <expression>)

• column – The column you want to evaluate.
• expression – Any expression that is evaluated for each row (commonly 1 or another column).

In this example, I want to return the first non-blank Sales Amount.

1. Open Power BI Desktop and load the dataset.
2. Go to Table view, select the table, then, under Table tools, click New measure.

3. Enter the following DAX formula:

First Non Blank Sales = 
FIRSTNONBLANK(
    'Sales'[Sales Amount],
    1
)

4. Now go to the report view and add a Card visual, then add the above measure to ot you can see it will 12k because it is the first non-blank sales amount.

This way, you can filter blank values using FIRSTNONBLANK in Power BI.

Filter Blank Values using LASTNONBLANK in Power BI

The LASTNONBLANK function in Power BI returns the last non-blank value in a column, based on the evaluation of another column or expression.

It is useful when your data contains blank values, and you want to retrieve the last available value while ignoring blanks.

Syntax:

LASTNONBLANK(<column>, <expression>)

• column – The column you want to evaluate
• expression – Any expression evaluated for each row (commonly 1 or another column)

In this example, we want to return the last non-blank Sales Amount from the dataset.

To do this, follow the steps below:

1. Open Power BI Desktop and load the dataset. Go to Table view.
2. Select the table. Under Table tools, click New measure.
3. Enter the following DAX formula:

Last Non Blank Sales =
LASTNONBLANK(
    'Sales'[Sales Amount],
    1
)

4. Go to Report view. Add a Card visual. Drag the Last Non Blank Sales measure into the card.

You will see the value 45000, because it is the last non-blank Sales Amount available in the dataset.

In this tutorial, I covered how to handle blank values in Power BI using different DAX functions. I explained how to identify blank values with ISBLANK, replace empty values with COALESCE, and retrieve the first and last non-blank values using FIRSTNONBLANK and LASTNONBLANK.

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Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

Column = CONCATENATE(names[First Name] & " " & names[Last Name])

However, this caused an error because the CONCATENATE function in DAX only accepts two arguments in the format CONCATENATE(column1, column2). Since a space also needs to be added, the formula must be written differently.

To solve this, the space has to be concatenated separately, like this:

CONCATENATE(column1, CONCATENATE(" ", column2))

Using this approach, we can successfully join two columns with a space between them.

In this Power BI tutorial, I will show you how to concatenate columns with spaces. Where I will cover the following topics: 

• Concatenate with a space using a calculated column in Power BI
• Concatenate two columns with a space using Merge Columns in Power BI Power Query
• Concatenate two columns with a space using a Custom Column in Power BI Power Query

Concatenate with a Space using a Calculated Column in Power BI

In this example, I am using a Products Orders table. This table contains sales and shipping-related information. The columns used in this dataset are:

• Ship to Zip Code – Shipping zip code (Number)
• Product Ordered – Product name (Text)
• Sales – Sales amount (Number)
• Ordered Date – Date when the product was ordered (Date)
• Ship to City – Shipping city (Text)
• Ship to State – Shipping state code (Text)

Here, I want to join Product Ordered and Ship to City to create a single column with readable values.

To do this, follow the steps below:

1. Open Power BI Desktop and load the Products Orders table. Go to the Table view from the left navigation pane.

2. Select the Products Orders table. Click New column from the Table tools tab.

3. Enter the following DAX formula to concatenate the columns with a space:

Product City =
CONCATENATE(
    'Products Orders'[Product Ordered],
    CONCATENATE(" ", 'Products Orders'[Ship to City])
)

Where:

• Product City: New Column Name
• CONCATENATE(): joins two text values.
• Products Orders'[Product Ordered]: This is the product name
• CONCATENATE(” “, ‘Products Orders'[Ship to City]): Adds a space between the product name and city.

After applying this formula, Power BI creates a new column that combines the Product Ordered and Ship to City values with a space between them.

This is how to concatenate with a space using a Calculated Column in Power BI.

Concatenate Two Columns with a Space using Merge Columns in Power BI Power Query

In this example, I will show how to concatenate two columns with a space using the Merge Columns option in Power BI Power Query. This method is practical when you want to combine columns during data transformation, before loading the data into the report.

Here, I am using the same Products Orders table. I will merge the Product Ordered and Ship to City columns with a space between them.

Follow the steps below:

1. Open Power BI Desktop and load the dataset. Go to the Home tab and click Transform data to open the Power Query Editor.

2. In the Power Query Editor, select the Product Ordered column. Hold the Ctrl key and select the Ship to City column.

3. Go to the Transform tab and click Merge Columns. In the Merge Columns window:
   • Choose Space as the separator
   • Enter a new column name (for example: Product City)

After this, Power Query creates a new column that combines the Product Ordered and Ship to City values with a space between them.

This is how to concatenate two columns with a space using Merge Columns in Power BI Power Query.

Concatenate Two Columns with a Space using a Custom Column in Power BI Power Query

In this example, I will show how to concatenate two columns with a space using a Custom Column in Power BI Power Query. This approach gives more control because you can write your own formula.

I am using the same Products Orders table and will concatenate the Product Ordered and Ship to City columns with a space between them.

To do this, follow the steps below:

1. Open Power BI Desktop and load the dataset. Go to the Home tab and click Transform data to open the Power Query Editor.
2. In the Power Query Editor, go to the Add Column tab. Click Custom Column.

3. In the Custom Column window:
   • Enter a new column name (for example: Product City)
   • Add the following formula:

[Product Ordered] & " " & [Ship to City]

4. Click OK.

After clicking OK, a new column is created that combines the Product Ordered and Ship to City values with a space between them.

This is how to concatenate two columns with a space using a Custom Column in Power BI Power Query.

Conclusion

In this Power BI tutorial, I explained different ways to concatenate two columns with a space. First, I showed how to use a calculated column with DAX to join values by handling the CONCATENATE function correctly.

Then, I explained how to combine columns using Merge Columns in Power BI Power Query. Finally, I showed how to concatenate columns using a Custom Column in Power Query for more control.

You may also like:

• Select Multiple Values in Power BI Slicer
• Check If Text is Date in Power BI Power Query
• Add Column Using Power BI Power Query Editor
• Extract Text in Power BI Power Query Using Add Column

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

The issue was that the column contained a mix of valid dates and text values. Because of this, the report was failing whenever date functions or filters were applied.

To solve this problem, I used Power Query to check whether each value is a valid date before converting it. By using a custom column, we can easily identify date values and handle text values without breaking the report.

In this Power BI article, I will show how to check if text is a date using Power BI Power Query.

Check If Text is Date in Power BI Power Query

Now, I will show how to check if text is a date using Power BI Power Query with two simple examples.

In the first example, we will check whether a text value can be converted into a date and return TRUE or FALSE based on the result.

In the second example, we will check if the value is already a date and return the date value; otherwise, it will return null. This approach is useful when you want to keep only valid date values and ignore incorrect text data.

Example :1 Check If Text Is a Date Using a Custom Column

In this example, I am using a Vehicles table as the data source. The table contains a Released Date column where some values are stored as text, and some are stored as date values.

Now follow the steps below:

1. Open Power BI Desktop and load the dataset using the Get Data option. After the data is loaded, go to the Home tab and click Transform data. This will open the Power Query Editor.

2. In the Power Query Editor, select the column where you want to check whether the value is a date. In my case, this column is the Release Date.

3. Then go to the Add Column tab and click Custom Column to check whether the value in the column is a date or not.

4. In the Custom Column window, enter a suitable column name, add the required formula to check if the value is a date, and then click OK to create the new column.

=try Value.Is(Date.From([Released Date]),type date) otherwise false

After clicking OK, Power Query adds a new custom column. This column shows TRUE for values that can be converted to a date and FALSE for values that are not valid dates.

To confirm that the custom column is working correctly, change the column data type from Text to Date by selecting Home -> Data type -> Date. After this change, the values that are valid dates will be converted successfully, while the text values will show an error.

At the same time, check the custom column results. You will see TRUE for rows where the value is a valid date and FALSE for rows where the value is text. This makes it easy to verify that the custom column is correctly identifying date and non-date values.

Example:2 Check If Value Is Date or Text in Power BI

In this example, I am using a Bikes table to show a slightly different scenario. In this table, the Released Date column contains both date values and text values. You can refer to the screenshot below to see how the data looks before applying any transformation.

Here, I want to return the date value if it is a valid date; otherwise, return null.

To do this, follow the steps below:

1. Load the data source into Power BI Desktop, then go to the Home tab and click Transform data. This will open the Power Query Editor, where you can start working with the data.
2. Now, in the Power Query Editor, you can see that the Released Date column contains a mix of Text and Date values, as shown in the screenshot below.

3. Select the Add Column tab and click Custom Column to check whether the value in the column is a date or not, as shown below.

4. In the Custom Column pop-up window, enter a new column name, paste the formula shown below, and then click OK to apply it.

if([Released Date] is date) then [Released Date] else null

Then, you can see that the custom column displays null values for the text data type. Check the screenshot below.

Now, change the column data type from Text to Date by selecting Home -> Data type -> Date. After changing the data type, you will notice that valid date values are converted and displayed correctly, while non-date text values return null.

Similarly, the custom column also reflects this logic by showing the date value for valid dates and null for text values, based on the condition applied.

This is how to check if the text is a date using the Power Query editor in Power BI.

In this article, I covered how to check whether a text value is a date in Power BI Power Query. I covered two examples: one shows how to return TRUE or FALSE when a text value can be converted to a date, and the other shows how to return the date value or NULL based on the data type.

You may like the following Power BI tutorials:

• Select Multiple Values in Power BI Slicer
• Add Column Using Power BI Power Query Editor
• Extract Text in Power BI Power Query Using Add Column
• Best Way to Count Rows and Distinct Values in Power BI Power Query

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com

To handle these scenarios, I used the Add Column feature in Power Query. It provides multiple options to create new columns without modifying the original data.

In this tutorial, I will explain how to add columns using Power BI Power Query. Throughout this guide, I will cover:

• Add Column Using Custom Column in Power BI Power Query
• Create a Conditional Column in Power BI Power Query
• Add a Date-Based Column in Power BI Power Query
• Use Index Column to Add a New Column in Power BI Power Query
• Add Column Using Advanced Editor in Power BI Power Query

Add Column Using Custom Column in Power BI Power Query

For this example, I am using a simple sales dataset that contains order-level details such as product name, sales amount, and cost.

In this dataset, there is no Profit column. To analyze profitability in Power BI, we need to create a new column using existing columns such as Sales and Cost.

To do this, follow the steps below:

1. Open Power BI Desktop and load the dataset using the Get Data option.

2. After the data is loaded, go to the Home tab and click Transform data. This will open the Power Query Editor, where we can clean and transform our data.
   

3. In the Power Query Editor, make sure the table containing Sales and Cost columns is selected. Go to the Add Column tab in the top menu and click Custom Column.

4. In the Custom Column dialog box, enter Profit as the new column name. In the formula box, enter the calculation:

[Sales] - [Cost]

5. Click OK to apply the formula.

Power Query will create a new column named Profit, showing the calculated profit for each order. If required, change the data type of the new column to Decimal Number or Whole Number.

Now, you can use the Profit column in visuals such as tables, charts, and KPIs.

This is how you can add a new column using Custom Column in Power BI Power Query to create calculated values without modifying the original source data.

Create a Conditional Column in Power BI Power Query

For this example, I will use the same sales dataset that contains Product Name, Sales, Cost, and the Profit column we created earlier.

Here, I want to classify orders based on profit value. If the profit is greater than 0, it should be marked as Profit; otherwise, Loss. This helps in quickly identifying profitable and non-profitable orders in Power BI reports.

Follow the Steps to Create a Conditional Column in Power BI Power Query:

1. Open Power BI Desktop and go to Transform data to open the Power Query Editor. Select the table that contains the Profit column.
2. Go to the Add Column tab and click Conditional Column.

3. In the Conditional Column window:
   • Enter Profit Status as the column name.
   • Set the condition as:
     • If Profit is greater than 0, then output Profit
     • Else output Loss
4. Click OK to apply the condition.

Power Query will create a new column named Profit Status, showing whether each order is a profit or a loss. Review the values and adjust the data type if needed.

Now, you can use the Profit Status column in slicers, tables, or charts to analyze profitable versus loss-making orders.

Add a Date-Based Column in Power BI Power Query

For this example, I am using the Super Store dataset, which contains the Ship Date column.

In this scenario, I will use the Ship Date column to extract the Month Name.

To do this, follow the steps below:

1. Open Power BI Desktop and click Transform data to open the Power Query Editor.
2. Select the table that contains the Ship Date column. Ensure the Ship Date column data type is set to Date.

3. Go to the Add Column tab. Expand Date, then select Name of Month.

Power Query will create a new column displaying the month name (January, February, etc.) based on the Ship Date. Rename the new column to Ship Month.

Now we can easily analyze sales data by shipping month using slicers and visuals in Power BI.

Use Index Column to Add a New Column in Power BI Power Query

While working on reports, there are situations where I need a row number, serial number, or a unique sequence to track records. For example, I want to display a serial number in a table visual or use an index value for sorting and reference purposes.

In this example, I will add an Index Column to the sales dataset to generate a sequential number for each row.

Follow the steps below:

1. Open Power BI Desktop and load your data using the Get Data option.
2. Go to the Home tab and click Transform data to open Power Query Editor.

3. In Power Query Editor, select the table. Go to the Add Column tab. Click the Index Column.
4. Choose from 1 (or from 0, based on your requirement).

A new column will be created with sequential numbers for each row. You can rename the column to Index or Serial Number.

This is how you can use the Index Column option to add a new column in Power BI Power Query.

Add Column Using Advanced Editor in Power BI Power Query

While working on Power BI reports, there are situations where the standard options, like Custom Column, are not enough. In such cases, you can use the Advanced Editor in Power Query to add a new column using M logic.

In this example, I will add a Profit column using the Advanced Editor in Power Query based on the existing Sales and Cost columns.

Follow the steps below:

1. Open Power BI Desktop and load the data using Get Data. Go to Home, click Transform data to open Power Query Editor.
2. In Power Query Editor, go to Home, click Advanced Editor.

3. In the Advanced Editor window, locate the last step of your query. Add a new step to create the Profit column, for example:

#"Added Profit Column" = Table.AddColumn(
    #"Previous Step",
    "Profit",
    each [Sales] - [Cost],
    Int64.Type
)

4. Make sure the new step name is used in the final line of the query.
5. Click Done to apply the changes.

Power Query will add a new Profit column calculated from the Sales and Cost columns.

This is how you can add a column using the Advanced Editor in Power Query for advanced data transformations in Power BI.

Conclusion

In this tutorial, I explained how to add new columns in Power BI Power Query using different methods. I covered how to create a column using Custom Column for calculations, how to use a Conditional Column to apply simple logic, how to add a date-based column from a date field, how to create an Index Column for row numbers, and how to add a column using the Advanced Editor for more control.

You may like the following Power BI tutorials:

• Merge Tables in Power BI
• Remove Leading Zeros in Power BI
• Convert Number to Text in Power BI
• Count Rows and Distinct Values in Power BI Power Query

Bijay Kumar

Hey! I’m Bijay Kumar, founder of SPGuides.com and a Microsoft Business Applications MVP (Power Automate, Power Apps). I launched this site in 2020 because I truly enjoy working with SharePoint, Power Platform, and SharePoint Framework (SPFx), and wanted to share that passion through step-by-step tutorials, guides, and training videos. My mission is to help you learn these technologies so you can utilize SharePoint, enhance productivity, and potentially build business solutions along the way.

www.enjoysharepoint.com