XML flattening (expanding) in Power Query

I have two XML files in folder (1). Their content is in the tables in column (2). Their content is extracted with "Xml.Document" function. If I click the Expand button (3) I could expand second column (2). After that I would have to expand the next column (4), and so on. I would end up flattening all my XML files. There are two problems with this approach. The first is that I don't want to click the expand button, I want to automatically expand all the columns (5). Another problem is that some columns may have a combination of tables and values in one column (6). In that case the expand button will not work. Let's solve this two problems.

funcIsOnlyTables

I need several helper functions to achieve my goal. funcIsOnlyTables is function which will tell me whether the only content of some column are nested tables. This function accepts two arguments – a table and a column position. This function will return TRUE if all the cells in that column have nested tables. Here, we are using function "Table.MatchesAnyRows" to see is there any cell in our column that has content that is not of type table.  If that is FALSE, then our function should return TRUE, because that mean that we only have nested tables in our column.

( TableToExpand as table, ColumnNumber as number) =>
let
    ColumnName = Table.ColumnNames( TableToExpand ){ColumnNumber}    
    , TemporaryRenaming = Table.RenameColumns( TableToExpand, { { ColumnName, "ŽŽŽ" } } )
    , HasSomethingElse = Table.MatchesAnyRows( TemporaryRenaming, each not Value.Is( [ŽŽŽ], type table ) )
    , HasOnlyTables = not HasSomethingElse
in
    HasOnlyTables

I had to rename my column because I had to reference it somehow in "Value.Is" function.

funcIsOnlyNonTables

funcIsOnlyNonTables is similar function. It works in the same way. The only difference is that we are here looking for cells that are of type table. If there are such cells, then our function will return FALSE, because that means that our column has some cells with nested tables.

( TableToExpand as table, ColumnNumber as number) =>
let
    ColumnName = Table.ColumnNames( TableToExpand ){ColumnNumber}    
    , PrivremenoPreimenovanje = Table.RenameColumns( TableToExpand, { { ColumnName, "ŽŽŽ" } } )
    , HasTables = Table.MatchesAnyRows( PrivremenoPreimenovanje, each Value.is( [ŽŽŽ], type table ) )    
    , IsOnlyNonTables = not HasTables
in
    IsOnlyNonTables

Residual logic

If both functions, "funcIsOnlyTables" and "funcIsOnlyNonTables" return FALSE then our column is of mixed type. It has both nested tables and values.

Now, we can discover nature of each column, so we can process each column differently based on its content.

funcExpandColumn

"funcExpandColumn" will expand only those columns that only have nested tables. First we have to create a list of all the names of columns in nested tables. On image, such list would be { "Quarter", "Quantity", "Revenue" } (1,2,3). We would prefix each subcolumn name with the name of original column ( "Quarters" + "." +  "Quantity" (4) ), so that each newly created column has unique name. Then, it is easy to expand our column with Table.ExpandTableColumn function.

( TableToExpand as table, ColumnNumber as number) =>
let
    ColumnName = Table.ColumnNames( TableToExpand ){ColumnNumber}    
    , TakeNamesOfSubColumns = List.Union( List.Transform( Table.Column( TableToExpand, ColumnName )
         , each Table.ColumnNames( _ ) ) ) 
    , NewNames = List.Transform( TakeNamesOfSubColumns, each ColumnName & "." & _ )
    , Expanding = Table.ExpandTableColumn( TableToExpand, ColumnName, TakeNamesOfSubColumns, NewNames )
in
    Expanding

funcTabelizeExpand

This function will solve the problem of columns with mixed content. We will transform each mixed column (A) into column where all the cells are tables (B). We will achieve that by wrapping each non table value with table that has one column and one row (1). Name of that new column (1) will be the same as the name of major column (B). Now that all the cells are filled with nested tables, we will just call function "funcExpandColumn" that we saw in the earlier step.

( TableToExpand as table, ColumnNumber as number) =>
let
    ColumnName = Table.ColumnNames( TableToExpand ){ColumnNumber}    
    , Tabelize = Table.TransformColumns( TableToExpand, { {   ColumnName, each if Value.Is( _, type table ) then _ 
          else #table( { ColumnName }, { { _ } } )    } } )
    , Expanding = funcExpandColumn( Tabelize, ColumnNumber )
in
    Expanding

Final logic

Now we have all the ingredients to flat our XML file. First we will prepare our input. Input is a table which have names of XML files, from some folder, in its first column "Name". Second column "Tabelix" has content of those files that is fetched with "Xml.Document" function.

"funcExpandAll" is the last function. This function will use all of the previous functions. This function is based on all powerfull "List.Generate" function. We will not use recursion, because "List.Generate" is faster. Logic of "List.Generate" function is similar to recursion. We have three steps:
1) First step is to establish initial conditions. TableToExpand is the table from the image above. ColumnNumber is a column of that table that will be first process, so it will be number zero.

2) Second step is condition. We will end processing of columns when we reach the last column. Note that number of columns in our table will increase with each "Table.ExpandTableColumn". This means that we don't have only two columns from the table from the image above ("Name" and "Tabelix"). As we expand columns, number of columns will increase (like on the first image in this blog post).

3) Third step is processing.  In first cycle, processing is done on initial arguments TableToExpand and ColumnNumber. After first cycle, new values will be created and those new values will be input arguments for the nest cycle. Just like in recursion.
a) Here we use IF logic to decide how to process current column. If we have normal column, we want change anything. If we have column with nested tables, we will apply expansion with "funcExpandColumn". In all other cases we have mixed column. For mixed columns we will call function "funcTabelizeExpand" which will first make sure that all the values are tables and then it will call "funcExpandColumn" to finish the job. Result of the first cycle will be initial table unchanged, or it will be initial table with one column expanded.
b) We also have to change the column that will be processed in the next cycle. If we didn't work on column that had only "non table" values, then that column is just expanded and newly created column on that position could also contain nested tables. We will have to cycle through column on that position again. If our column had only normal values (numbers, strings, dates) then we can jump to the next column.

( TableToExpand as table ) =>
let
    ExpandAll = List.Generate( 
        ()=>[ TemporaryResult = TableToExpand, ColumnNumber = 0 ]
        , each [ColumnNumber] < Table.ColumnCount( [TemporaryResult] )
        , each [ TemporaryResult = if funcIsOnlyNonTables( [TemporaryResult], [ColumnNumber] ) then [TemporaryResult] 
             else if funcIsOnlyTables( [TemporaryResult], [ColumnNumber] ) 
                 then funcExpandColumn( [TemporaryResult], [ColumnNumber] )
             else funcTabelizeExpand( [TemporaryResult], [ColumnNumber] )
        , ColumnNumber = Iif not funcIsOnlyNonTables( [TemporaryResult], [ColumnNumber] ) then [ColumnNumber] 
             else [ColumnNumber]  + 1  ]               
        , each [TemporaryResult]
    )
    , OnlyLastElement = List.LastN( ExpandAll, 1 ){0}
in
    OnlyLastElement

"List.Generate" will return all the interim results as a list. That is why, we want to fetch only the last one.

Sample file can be downloaded from here. Just go to "XMLsInput" query to change the folder where XML files are placed on your hard disk.

Three interesting cases in Power Query

Code for all three examples can be found in sample file that can be downloaded below.

Uncomplete Pivot

After cleaning some data from XML file, I got all the values presented vertically (1). My goal was to transform this table (1) into normal horizontal table (2). This can be done with Table.Pivot function.

Problem is that, above, in the left table we only have 2 columns. That is enough to write down our Table.Pivot function, but our result will not be correct. We will get an error.

Table.Pivot( FillDownID, { "ID", "OrderDate", "Item", "Units" }, "Name", "Value" )

The problem stems from the fact that there are no other columns to make up the rows of our pivot tables. So, we have to generate one more column. That column should uniquely define each row of a new table. We have to use ID values to create our new column.

First, we will use IF expression to create new column (1). After this, we can fill down values in that column, to fill empty spaces (2).

That is all. Now, we can use our Table.Pivot function to create final result, because our source table now has three needed columns.

Concatenated Values

I got an Excel file with values concatenated, as in (1). As always, we want our data presented as a regular table (2).

First we need to untangle concatented values that are tied to the names of contintents. Because we have two columns with such data (Stock and Sale), our first step is to unpivot table (1). Only then can we fix concatenated values.

We will then remove prefix "Continent-" from "Attribute" column (1->3). After that, we will split column "Value" into to two columns (2->4) by using function Table.SplitColumn. Next, we can have our final result by applying Table.Pivot function on the table with corrected columns.

Table.Pivot( SplitValueColumn, { "Sale", "Stock" }, "Attribute", "Value" )

Direct Join

We have two tables (1,2). We want to join them into one table (3). First row should be joined with the first row, second row with the second row, etc. Problem is that the first table contains only dimensions, and second table contains only values, there is no column to base our join on. We can not use Table.Join function.

Solution is to flip our columns and rows with Table.Transpose. We'll get transposed tables (1). Each table will have exactly 7 columns because the original tables had 7 rows. Now we can create their union (2) to get table (3). Table (3) has two problems. First problem is that columns and rows are switched, second problem is that we have lost column names. By transposing table (3) we can solve first problem.

Second problem should be solved by taking original column names from tables (1,2). Then, those column names must  be applied to the final table (4).

Sample file can be downloaded from here:

Literals in Power Query (M language)

Table

We can create empty table by specifying number of columns to create. For values we just have to provide empty list.
#table(4,{})
This is how we create regular table. All the columns will have unspecified type.
#table( {"A", "B"}, { {1, 2}, {3, 4} } )
We can assign type to table columns.
#table(type table [Digit = number, Name = text], {{1,"one"}, {2,"two"}, {3,"three"}} )

Some information about Tables

It is possible to make an union of several tables with "&" operator. Orphan columns will be filled with nulls.
#table({"A","B"}, {{1,2}}) & #table({"B","C"}, {{3,4}})

Tables are considered the same if for each column in one table there is equivalent column in another table. Order of columns is not important.

#table({"A","B"},{{1,2}}) = #table({"A","B"},{{1,2}}) // true
#table({"A","B"},{{1,2}}) = #table({"X","Y"},{{1,2}}) // false
#table({"A","B"},{{1,2}}) = #table({"B","A"},{{2,1}}) // true

Number

There are three ways to write literal numbers.
Regular = -1.5
Scientific = 2.3e-5
Hexadecimal = 0xff
There are also two special symbols for infinity and for "not a number". Infinity is something we get when we try to divide one and zero. If we try to divide zero and zero, we will get "NaN". "NaN" is the only value that is not equal to itself.
Infinity = #infinity
NaN = #nan

Type

With "type" keyword we can create literal types.
ListOfNumbers = type { number }
TwoNumberFieldsRecordType = type [ X = number, Y = number ]
DateTimeType = type datetime
TextType = type text

Logic, text and null

Logic literals are TRUE and FALSE. Text literals are wraped with quotes. Null is a special literal which symbolizes missing data.
TRUESymbol = true
FALSESymbol = false
TextLiteral = "word"
NullSymbol = null

Record

This is how we can write record literal.
Record = [ A = 1, B = 2 ]

Some information about records

Records are equal if for each field in one record ther is an equal field in another record. Order of fields is not important.

[ A = 1, B = 2 ] = [ A = 1, B = 2 ]        // true 
[ B = 2, A = 1 ] = [ A = 1, B = 2 ]        // true

We can make a union of several records. If we have one field in more than one record, then the value from the last record with that field will be the final one. In our example x will be assigned the value 3, and not the value 1.

[ x = 1, y = 2 ] & [ x = 3, z = 4 ]  // [ x = 3, y = 2, z = 4 ] 

List

List is created by placing comma separated list inside of curly brackets.
List = {1, 2, 3}

Some information about lists

Two lists are equal if the have the same elements and position of those elements is the same.

{1, 2} = {1, 2} // true 
{2, 1} = {1, 2} // false

We can concatenate several lists with & operators.

{1, 4} & {2, 3} // list concatenation: {1, 4, 2, 3} 

Special Signs

Special signs, that are invisible, can be written by their symbolic presentation.
TAB = #(cr)
LineFeed =  #(lf)
CariageReturn = #(tab)

I used some unusual unicode brackets in the "Expression" column to prevent typed text to be identify as a special sign. In the "Result" column, we can see that both "cr" and "lf" signs are causing line break.

If we want to type two characters "#(", we have to write them like "#(#)(", because those characters has to be escaped. Two signs "#(cr)#(lf)", when consecutive, can be typed as "#(cr,lf)", the result would be the same.

Unicode Signs

We can enter any Unicode symbol in Power Query by using their codes.
YenSymbol = "#(00A5)"
CopyRightSymbol = "#(00A9)"
CapitalTheta =  "#(0398)"

Binary – list of bytes

Binary data is actually list of bytes. We can present those bytes with letters, hexadecimal numbers or ordinary numbers.
BinaryByLetters = #binary("AQID")
BinaryByHexidecimalNumbers = #binary( {0x00, 0x01, 0x02, 0x03} )
BinaryByRegularNumbers = #binary({65, 66, 67})

Date and time

Date and time can be expressed with these literals.

#time(hour, minute, second)
#date(year, month, day)
#datetime(year, month, day, hour, minute, second) 
#datetimezone( year, month, day, hour, minute, second, offset-hours, offset-minutes) 
#duration(days as number, hours as number, minutes as number, seconds as number)
For each argument there is a limit on what values that argument could have.
1 ≤ year ≤ 9999
1 ≤ month ≤ 12
1 ≤ day ≤ 31
0 ≤ hour ≤ 23
0 ≤ minute ≤ 59
0 ≤ second ≤ 59
-14 ≤ offset-hours ≤ 14
-59 ≤ offset-minutes ≤ 59

Some information about dates and times

We can concatenate date and time.#date(2013,02,26) & #time(09,17,00) // #datetime(2013,02,26,09,17,00)
We can add duration to date or to time.#datetime(2010,05,20,0,0,0) + #duration( 8, 0, 0 ) //#datetime(2010,5,20,8,0,0)
#time(8,0,0)+#duration(30,5,0,0)     //#time(13,0,0)
Duration can be multiplied.#duration(2,1,0,15.1) * 2      // #duration(4, 2, 0, 30.2)
Durations can be divided.#duration(2,0,0,0) / #duration(0,2,0,0)        //24
Dates and times can be converted to numbers and from.Number.From(#datetime(2020, 3, 20, 6, 0, 0)) // 43910.25
Date.From(43910) // #date(2020,3,20)
Time.From(0.7575) // #time(18,10,48)
Duration.From(2.525) // #duration(2,12,36,0)

Sample file can be downloaded from here:

Descriptive Statistics in SPSS

We saw in one of earlier posts what are descriptive statistics. Let's see how to calculate those statistics in SPSS.

Descriptive Statistics Dialogs

SPSS has two similar dialogs for creation of descriptive statistics. Both dialogs can be called from menu Analyze > Descriptive Statistics (0). There we can choose option Frequencies (1) to get dialog (2), or we can choose Descriptives (3) to get dialog (4). Dialogs (2,4) are similar. Dialog Frequencies gives us more options than dialog Descriptives.

First step is to select columns for which we will calculate descriptive statistics. By using mouse, and keys Ctrl or Shift, we should select some of the columns from the first pane (1). Then we will click on button (2) so that columns are moved to second pane (3). These are the columns for which we will get our descriptive statistics.

If we had made a mistake, it is possible to move columns from the right pane to the left pane, by using the same process (4,5,6). It is also possible to move selected columns to opposite pane by dragging and dropping with mouse (7).

Analyze > Descriptive Statistics > Frequencies

In Frequencies dialog, we should click on "Statistics" button (0). This will open new dialog where we can choose what descriptive statistics should be calculated. We can choose Percentiles (1), Central Tendency statistics (2), Measures of Variability (3), and Distribution (4).

Option (5) "Values are group midpoints" is used when our source data is grouped and each group is presented by one value. For example, all people in their thirties are coded as value 35. In that case this option "Values are group midpoints" will estimate Median and Percentiles for original, ungrouped data.

Custom percentiles are added by typing them into textbox (1) and then we click on "Add" (2). This will add that percentile ("24") to the pane bellow. If we click on "Change" (3) then the new value ("24") will replace currently selected old value ("15"). If one of the values (4) is selected then we can remove that value with button "Remove" (5).

Now we can click Continue and OK to close all dialogs and SPSS will calculate our results. All the results will be presented in one tall table (1). On the top we can see valid and missing values (2). Missing values are nulls. Bellow are all the others statistics that we are already familiar with (3).

At the bottom of the table, we have percentiles. All the percentiles are presented together (4,5,6). I've color-coded the percentiles here to help us understand the "Percentile Values" options. If we check option "Quartiles" (4), we will get percentiles 25, 50, 75. Option (5) allow us to divide 100% to several groups of equal size in %. If we want to get 5 groups, then 100% will be divided by using 4 cut points. Those cut points are 20, 40, 60 and 80 . At last, we will see all custom percentiles we have entered (6).

For several selected variables result would be presented as new columns in the result table. Order of columns will be the same as order of selected variables.

Analyze > Descriptive Statistics > Descriptives

If we use Descriptives dialog, process is similar. First, we select our columns (3) and then we click on button (1). This will open dialog (2) where we can select what descriptive statistics should be calculated.

Final result will be presented in one table where each variable will have its results presented in one row. Order of rows can be controlled by options (4). Default is to use "Variable list" as order of columns (5).

OCR a PDF Document With Python and Tesseract

We have a pdf document that is like a picture. We want to turn it into searchable document. We will use python Pytesseract module to do the transformation.

WinPython

For this demonstration I will use WinPython distribution. This distribution has size of 813 MB for download. When decompressed this archive will become a 4 GB folder. Choose wisely where to place this folder because latter moving or deleting this folder could take some time.

I am using WinPython distribution because it has many modules already preinstalled, and this will save us some effort. It will also keep us from dependency hell problems.

We can download WinPython from this page "https://sourceforge.net/projects/winpython/files/". On that page click on the green button (1) and download EXE file on your computer (2). This file is self-extracting archive (3). Choose where do you want this file to be extracted. You will get a folder (4). In the subfolder "python-3.10.5.amd64" (5), of that folder, there is "python.exe" file.

PATH Environment Variable

Windows has a small key-value "database", where each key-value pair is one environment variable. We can open dialog with those variables by going to Run dialog (shortcut Win+R), and typing "sysdm.cpl" (1). In the new dialog we will go to Advanced tab (2), we will click on "Environment variables" button (3), and then we will see dialog with a list of all the key-values pairs (4,5) for environment variables. Some variables are available only for the logged user (6), and system variables are available for all the users (7).

For example, there is key-value pair TEMP = "C: \Windows \TEMP". This allows every program to easily find where Windows temporary folder resides. If Microsoft change the location of this folder, all other programs will still work correctly because they will read that new location from the environment variable. Name of a key is stable, only value is changing, so environment variables are used for communication between programs.

There is an environment variable named PATH. We will change value of this variable so we could easily use our WinPython. In user variables (1) find a variable named "Path" (2). Click on Edit button (3), and then, in a new dialog, click on button "New" (4). This will create a place where we can paste our python folder (5). Python folder is a folder where "python.exe" file resides.

If we don't have Path variable, then we click on "New" button (6) and we fill new dialog with the variable name and variable value (7). Now, python location is added to path variable.

Tesseract

Tesseract is an open-source optical character recognition software. Python is using this software for OCR. First, we go to address https://digi.bib.uni-mannheim.de/tesseract/, and from there we download this software. We will download latest version (1). After EXE file is downloaded to our computer (2), we will start installation. During the installation we can choose (3) additional scripts (Latin, Cyrillic…) and languages (Serbian, Romanian…). Selected scripts and languages will be downloaded during installation (4).

Folder with "tesseract.exe" file should be added to our path user environment variable ( "C:\Program Files\Tesseract-OCR" ). Now that both python and Tesseract are on the path, we can check whether they are correctly installed. Go to windows command prompt and type commands "python –version" and "tesseract -v". We will now see what versions are installed on our computer.

There is no need to change working directory inside command prompt. Windows will find both python and Tesseract because they are on the path. This is why we placed them in this environment variable in the first place. Now it is much easier to find these two programs.

Pytesseract

Pytesseract is python module which communicate with Tesseract software. We will use "pip" module for its installation. Go to command prompt and type command "python -m pip install pytesseract" (1). After successful installation we can check our installed modul with command "python -m pip show pytesseract" (2).

Poppler

Poppler is open-source library for PDF rendering. Go to web site https://blog.alivate.com.au/poppler-windows/ and download latest poppler (1). Downloaded file will be "7z" archive. Extract files from that archive into folder of your choice. Poppler's folder has subfolder "bin" (2). We should add this "bin" folder to path user environment variable.

Pdf2image

Pdf2image is python module for interacting with poppler program. We will install it in the same way as pytesseract module. We'll use "python -m pip install pdf2image" (1) and "python -m pip show pdf2image" (2).

Python IDE

We finally have all the ingredients for our project. Now, we will open our favorite python IDE or text editor. If you don't have one, you can go to WinPython folder. Inside it, there is a file "Spyder.exe". Click on this file and you will get Spyder IDE where we can test our code.

Before running the script, we will first tell Spyder what python.exe to use. Go to Tools > Preferences (1). In the new dialog, type location of our WinPython python.exe file in "Python interpreter > Use the following Python interpreter" (2).

Now we can paste our python script in the pane (3) of the Spyder, and click the Run button (4). OCR-ed text will appear in the console (5) and will also appear as a TXT file on our desktop (6).

Python Script

This is code we are using for our OCR. First, we transform our PDF to sequence of images, using "pdf2image" module. For each page in that sequence, we apply tessarect image_to_string method. This will create our final result. At the end, we write our result in console and in TXT file.

image = pdf2image.convert_from_path('C:\\Users\\Sima\\Desktop\\OCRfile.pdf')
with open('C:\\Users\\Sima\\Desktop\\OCRtext.txt', 'w') as file:
    for page number, page in enumerate(image):
        detected_text = pytesseract.image_to_string(page)
        print( detected_text )
        file.write( detected_text )

Bellow, we can see how our pdf document looks like as an original (1), in console (2) or in TXT file (3).

Here is the sample file. It contains python script, sample PDF file and poppler software.