Quick Tips: Renaming All Tables’ Columns in One Go in Power Query

Renaming All Tables' Columns in One Go in Power Query

I previously wrote a blog post explaining how to rename all columns in a table in one go with Power Query. One of my visitors raised a question in the comments about the possibility to rename all columns from all tables in one go. Interestingly enough, one of my customers had a similar requirement. So I thought it is good to write a Quick Tip explaining how to meet the requirement.

The Problem

You are connecting to the data sources from Power BI Desktop (or Excel or Data Flows). The columns of the source tables are not user friendly, so you require to rename all columns. You already know how to rename all columns of a table in one go but you’d like to apply the renaming columns patterns to all tables.

The Solution

The solution is quite simple. We require to connect to the source, but we do not navigate to any tables straight away. In my case, my source table is an on-premises SQL Server. So I connect to the SQL Server instance using the Sql.Database(Server, DB) function in Power Query where the Server and the DB are query parameters. Read more about query parameters here. The results would like the following image:

The Results of Sql.Database() Function in Power Query
The results of running the Sql.Database(Server, DB) function

As you see in the above image, the results include Tables, Views and Functions. We are not interested in Functions therefore we just filter them out. The following image shows the results after applying the filter:

Filtering out SQL Server Functions After Connecting from Power Query
Filtering out SQL Server Functions

If we look closer to the Data column, we see that the column is indeed a Structured Column. The structured values of the Data column are Table values. If we click on a cell (not on the Table value of the cell), we can see the actual underlying data, as shown in the following image:

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A Power Query Custom Function to Rename all Columns at Once in a Table

A Power Query Custom Function to Rename all Columns at Once in a Table

I am involved with a Power BI development in the past few days. I got some data exported from various systems in different formats, including Excel, CSV and OData. The CSV files are data export dumps from an ERP system. Working with ERP systems can be very time consuming, especially when you don’t have access to the data model, and you get the data in raw format in CSV files. It is challenging, as in the ERP systems, the table names and column names are not user friendly at all, which makes sense. The ERP systems are being used in various environments for many different customers with different requirements. So if we can get our hands to the underlying data model, we see configuration tables keeping column names. Some of the columns are custom built to cover specific needs. The tables may have many columns that are not necessarily useful for analytical purposes. So it is quite critical to have a good understanding of the underlying entity model. Anyhow, I don’t want to go off-topic.

The Problem

So, here is my scenario. I received about 10 files, including 15 tables. Some tables are quite small, so I didn’t bother. But some of them are really wide like having between 150 to 208 columns. Nice!

Looking at the column names, they cannot be more difficult to read than they are, and I have multiple tables like that. So I have to rename those columns to something more readable, more on this side of the story later.

Background

I emailed back to my customer, asking for their help. Luckily they have a very nice data expert who also understands their ERP system as well as the underlying entity model. I emailed him all the current column names and asked if he can provide more user-friendly names. He replied me back with a mapping table in Excel. Here is an example to show the Column Names Mapping table:

Column Names Mapping

I was quite happy with the mapping table. Now, the next step is to rename all columns is based on the mapping table. Ouch! I have almost 800 columns to rename. That is literally a pain in the neck, and it doesn’t sound quite right to burn the project time to rename 800 columns.

But wait, what about writing automating the rename process? Like writing a custom function to rename all columns at once? I recall I read an excellent blog post about renaming multiple columns in Power Query that Gilbert Quevauvilliers wrote in 2018. I definitely recommend looking at his blog post. So I must do something similar to what Gilbert did; creating a custom function that gets the original columns names and brings back the new names. Then I use the custom function in each table to rename the columns. Easy!

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Quick Tips: OData Feed Analyser Custom Function in Power Query

OData Feed Analyser Custom Function in Power Query for Power BI and Excel

It’s been a while that I am working with OData data source in Power BI. One challenge that I almost always do not have a good understanding of the underlying data model. It can be really hard and time consuming if there is no one in the business that understands the underlying data model. I know, we can use $metadata to get the metadata schema from the OData feed, but let’s not go there. I am not an OData expert but here is the thing for someone like me, I work with various data sources which I am not necessarily an expert in, but I need to understand what the entities are, how they are connected etc… then what if I do not have access any SMEs (Subject Matter Expert) who can help me with that?

So getting involved with more OData options, let’s get into it.

The custom function below accepts an OData URL then it discovers all tables, their column count, their row count (more on this later), number and list of related tables, number and list of columns of type text, type number and Decimal.Type.

// fnODataFeedAnalyser
(ODataFeed as text) => 
  let
    Source = OData.Feed(ODataFeed),
    SourceToTable = Table.RenameColumns(
        Table.DemoteHeaders(Table.FromValue(Source)), 
        {{"Column1", "Name"}, {"Column2", "Data"}}
      ),
    FilterTables = Table.SelectRows(
        SourceToTable, 
        each Type.Is(Value.Type([Data]), Table.Type) = true
      ),
    SchemaAdded = Table.AddColumn(FilterTables, "Schema", each Table.Schema([Data])),
    TableColumnCountAdded = Table.AddColumn(
        SchemaAdded, 
        "Table Column Count", 
        each Table.ColumnCount([Data]), 
        Int64.Type
      ),
    TableCountRowsAdded = Table.AddColumn(
        TableColumnCountAdded, 
        "Table Row Count", 
        each Table.RowCount([Data]), 
        Int64.Type
      ),
    NumberOfRelatedTablesAdded = Table.AddColumn(
        TableCountRowsAdded, 
        "Number of Related Tables", 
        each List.Count(Table.ColumnsOfType([Data], {Table.Type}))
      ),
    ListOfRelatedTables = Table.AddColumn(
        NumberOfRelatedTablesAdded, 
        "List of Related Tables", 
        each 
          if [Number of Related Tables] = 0 then 
            null
          else 
            Table.ColumnsOfType([Data], {Table.Type}), 
        List.Type
      ),
    NumberOfTextColumnsAdded = Table.AddColumn(
        ListOfRelatedTables, 
        "Number of Text Columns", 
        each List.Count(Table.SelectRows([Schema], each Text.Contains([Kind], "text"))[Name]), 
        Int64.Type
      ),
    ListOfTextColunmsAdded = Table.AddColumn(
        NumberOfTextColumnsAdded, 
        "List of Text Columns", 
        each 
          if [Number of Text Columns] = 0 then 
            null
          else 
            Table.SelectRows([Schema], each Text.Contains([Kind], "text"))[Name]
      ),
    NumberOfNumericColumnsAdded = Table.AddColumn(
        ListOfTextColunmsAdded, 
        "Number of Numeric Columns", 
        each List.Count(Table.SelectRows([Schema], each Text.Contains([Kind], "number"))[Name]), 
        Int64.Type
      ),
    ListOfNumericColunmsAdded = Table.AddColumn(
        NumberOfNumericColumnsAdded, 
        "List of Numeric Columns", 
        each 
          if [Number of Numeric Columns] = 0 then 
            null
          else 
            Table.SelectRows([Schema], each Text.Contains([Kind], "number"))[Name]
      ),
    NumberOfDecimalColumnsAdded = Table.AddColumn(
        ListOfNumericColunmsAdded, 
        "Number of Decimal Columns", 
        each List.Count(
            Table.SelectRows([Schema], each Text.Contains([TypeName], "Decimal.Type"))[Name]
          ), 
        Int64.Type
      ),
    ListOfDcimalColunmsAdded = Table.AddColumn(
        NumberOfDecimalColumnsAdded, 
        "List of Decimal Columns", 
        each 
          if [Number of Decimal Columns] = 0 then 
            null
          else 
            Table.SelectRows([Schema], each Text.Contains([TypeName], "Decimal.Type"))[Name]
      ),
    #"Removed Other Columns" = Table.SelectColumns(
        ListOfDcimalColunmsAdded, 
        {
          "Name", 
          "Table Column Count", 
          "Table Row Count", 
          "Number of Related Tables", 
          "List of Related Tables", 
          "Number of Text Columns", 
          "List of Text Columns", 
          "Number of Numeric Columns", 
          "List of Numeric Columns", 
          "Number of Decimal Columns", 
          "List of Decimal Columns"
        }
      )
  in
    #"Removed Other Columns"
Continue reading “Quick Tips: OData Feed Analyser Custom Function in Power Query”

Finding Minimum Date and Maximum Date Across All Tables in Power Query in Power BI and Excel

Finding Minimum Date and Maximum Date Across All Tables in Power Query in Power BI and Excel

When we talk about data analysis in Power BI, creating a Date table is inevitable. There are different methods to create a Date table either in DAX or in Power Query. In DAX you my use either CALENDAR() function or CALENDARAUTO() function to create the Date table. In Power Query you may use a combination of List.Dates()#date() and #duration() functions. Either way, there is one point that is always challenging and it is how to find out a proper date range, starting from a date in the past and ending with a date in the future, that covers all relevant dates within the data model. One simple answer is, we can ask the business. The SMEs know what the valid date range is..

While this is a correct argument it is not always the case. Especially with the Start Date which is a date in the past. In many cases the business says:

Lets’s have a look at the data to find out.

That is also a correct point, we can always a look at the data, find all columns with either Date or DateTime datatypes then sort the data in ascending or descending order to get the results. But what if there many of them? Then this process can be very time consuming.

Many of you may already thought that we can use CALENDARAUTO() in DAX and we are good to go. Well, that’s not quite right. In many cases there are some Date or DateTime columns that must not be considered in our Date dimension. Like Birth Date or Deceased Date. More on this later in this post.

In this post I share a piece of code I wrote for myself. I was in a situation to identify the Start Date and the End Date of the date dimension many times, so I thought it might help you as well.

How it works?

The Power Query expressions I share in this post starts with getting all existing queries using:

  • #sections intrinsic variable
  • Filtering out the current query name, which is GetMinMaxAllDates in my sample, to avoid getting the following error:

Expression.Error: A cyclic reference was encountered during evaluation.

Expression.Error: A cyclic reference was encountered during evaluation.
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