I need to access the previous record of the DTH_REFER_PEDID column to make the IF comparison (DTH_REFER_PEDID-1 <> "A").
That is, I'm reading the index X, I need to compare with the index X-1
Addition_Stats = VAR Atendido_OV = PR_HIST_MOVIM_PEDID[OVITEM_Hist]
VAR linha_anterior2 = CALCULATE(values(PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL]);filter(PR_HIST_MOVIM_PEDID;EARLIER(PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID])))
Return
if(PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Month]<PR_HIST_MOVIM_PEDID[DAT_MAIOR_PLANE].[Month];"Atraso mês ant";
if(PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL] = "A" && PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Day]<=PR_HIST_MOVIM_PEDID[DAT_MAIOR_PLANE].[Day];"Atendido no Prazo";
if((PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL]="P"||PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL]="L") && PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Day]<= PR_HIST_MOVIM_PEDID[DAT_MAIOR_PLANE].[Day];"Planejado no prazo";
if(PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL]<>"A" && PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Day]>PR_HIST_MOVIM_PEDID[DAT_MAIOR_PLANE].[Day];"Em atraso";
if(PR_HIST_MOVIM_PEDID[STA_ITEM_PEDCL] = "A"
&& linha_anterior2 <>"A"
&& PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Day]>PR_HIST_MOVIM_PEDID[DAT_MAIOR_PLANE].[Day];"Atend fora Prazo"
;IF((PR_HIST_MOVIM_PEDID[OVITEM_Hist]=Atendido_OV)&&(PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID]>FIRSTDATE(PR_HIST_MOVIM_PEDID[DTH_REFER_PEDID].[Date]));"A retido";"NA")
)
)
)
)
)
//)
The error displayed is: A circular dependency has been detected: PR_HIST_MOVIM_PEDID [Addition_Stats].
How do I compare DTH_REFER_PEDID-1 <> "A"?
An easy way to work with previous or next records is:
Make sure your data is in a table with a primary key (=ID)
Make a query with all the fields as in your table and add one colum with ID+1. (or ID-1)
Make another query with the table and the query mentioned above and make a join between ID and ID+1 (or ID-1). Place all the fields of the table and the 1st query and you end up with all the values in 1 record. This way you can work with the previous or next values.
Related
I´ve been struggling with this:
My table shows 3 records but when expanding there are like 100 columns. I used this code:
#"Expanded Data" = Table.ExpandTableColumn(#"Source", "Document", List.Union(List.Transform(#"Source"[Document]), each Table.ColumnNames(_))),
but it's not working. How can I expand simultaneously all columns? Also, inside those columns there are even more, for example I expand the first time end then those new columns have more records inside.
What could I do? Thanks in advance!
Try this ExpandAllRecords function - it recursively expands every Record-type column:
https://gist.github.com/Mike-Honey/0a252edf66c3c486b69b
This should work for Records Columns.
let
ExpandIt = (TableToExpand as table, optional ColumnName as text) =>
let
ListAllColumns = Table.ColumnNames(TableToExpand),
ColumnsTotal = Table.ColumnCount(TableToExpand),
CurrentColumnIndex = if (ColumnName = null) then 0 else List.PositionOf(ListAllColumns, ColumnName),
CurrentColumnName = ListAllColumns{CurrentColumnIndex},
CurrentColumnContent = Table.Column(TableToExpand, CurrentColumnName),
IsExpandable = if List.IsEmpty(List.Distinct(List.Select(CurrentColumnContent, each _ is record))) then false else true,
FieldsToExpand = if IsExpandable then Record.FieldNames(List.First(List.Select(CurrentColumnContent, each _ is record))) else {},
ColumnNewNames = List.Transform(FieldsToExpand, each CurrentColumnName &"."& _),
ExpandedTable = if IsExpandable then Table.ExpandRecordColumn(TableToExpand, CurrentColumnName, FieldsToExpand, ColumnNewNames) else TableToExpand,
NextColumnIndex = CurrentColumnIndex+1,
NextColumnName = ListAllColumns{NextColumnIndex},
OutputTable = if NextColumnIndex > ColumnsTotal-1 then ExpandedTable else #fx_ExpandIt(ExpandedTable, NextColumnName)
in
OutputTable
in
ExpandIt
This basically takes Table to Transform as the main argument,and then one by one checks if the Column Record is expandable (if column has "records" in it, it will expand it, otherwise move to next column and checks it again).
Then it returns the Output table once everything is expanded.
This function is calling the function from inside for each iteration.
I need a custom function that takes two parameters, Column1 and Column2, so:
For each Row, return the value of Column1 but only if exists a Value in the Column2 else return null
I have tried this:
let ColumnsFilter = (Tabla,C1,C2)=>
Table.AddColumn(Tabla, "Custom", each if [C2] <> null then [C1] else null)
in
ColumnsFilter
And calling the function:
#"Previous Step" = .....
#"P" = ColumnsFilter(#"Previous Step","Column1","Column2")
in
P
And is not working. clearly I am not using the syntax properly.
In summary I need a table as input and a table as output adding custom columns.
How can I write this?
(Please don't tell me to use the assisted of Power Query, I need to write similar functions manually)
Since you're passing column names as text and individual rows are a record type, you have to use Record.Field to pull the right column (field) from the current row (record).
let
ColumnsFilter = (Tabla as table, C1 as text, C2 as text) as table =>
Table.AddColumn(Tabla, "Custom",
each if Record.Field(_, C2) <> null then Record.Field(_, C1) else null
)
in
ColumnsFilter
i have 2 tables and, i would like to check if table 1 (Type_Sorting) == (CCSClassCode_Type) is matched with table 2 (_Type Sorting) == (_CCS Class Type):
for example, you can see vi got the wrong value in table 1 (CCSClassCode_Type)
and, the right value is XLBas you can see in table 2 (_CCS Class Type) not ULM,
the idea of table 2 to check if people type the right values, Please not that table 2 (_CCS Class Type) have duplicate values
thank you in advance :)
You can calculate this like that:
Table 2 =
Var trt =
SELECTCOLUMNS(Table_2, "XX"
, COMBINEVALUES(",",Table_2[_CCS Class Type],Table_2[_Type Sorting]))
return
SUMMARIZECOLUMNS(Table_1[Column1]
, Table_1[CCSClassCode_Type]
, Table_1[Type_Sorting]
, FILTER(ALL(Table_1[CCSClassCode_Type],Table_1[Type_Sorting]), not( COMBINEVALUES(",",Table_1[CCSClassCode_Type],Table_1[Type_Sorting])
in trt )
))
I need to add a column that Sums the value column of all columns that have a common id. However, any id = null is not summed, but equal to the value column.
The above example should result in:
TopPaymendId JournalLineNetAmount TopAmount
fcbcd407-ca26-4ea0-839a-c39767d05403 -3623.98 -7061.23
fcbcd407-ca26-4ea0-839a-c39767d05403 -3437.25 -7061.23
ce77faac-1638-40e9-ad62-be1813ce9031 -88.68 -88.68
531d9bde-3f52-47f3-a9cf-6f3566733af2 -152.23 -152.23
8266dfef-dd14-4654-a6d2-091729defde7 229.42 229.42
f8b97a47-15ef-427d-95e0-ce23cc8efb1f -777 -777
null -3.01 -3.01
null -2.94 -2.94
null 3312.5 3312.5
This code should work:
let
Source = Excel.CurrentWorkbook(){[Name="Data"]}[Content],
group = Table.Group(Source, {"TopPaymendId"}, {"TopAmount", each List.Sum([JournalLineNetAmount])}),
join = Table.Join(Source,{"TopPaymendId"},group,{"TopPaymendId"}),
replace = Table.ReplaceValue(join,each [TopAmount],each if [TopPaymendId] = null
then [JournalLineNetAmount] else [TopAmount],Replacer.ReplaceValue,{"TopAmount"})
in
replace
I have time series data in two separate DataFrame columns which refer to the same parameter but are of differing lengths.
On dates where data only exist in one column, I'd like this value to be placed in my new column. On dates where there are entries for both columns, I'd like to have the mean value. (I'd like to join using the index, which is a datetime value)
Could somebody suggest a way that I could combine my two columns? Thanks.
Edit2: I written some code which should merge the data from both of my column, but I get a KeyError when I try to set the new values using my index generated from rows where my first df has values but my second df doesn't. Here's the code:
def merge_func(df):
null_index = df[(df['DOC_mg/L'].isnull() == False) & (df['TOC_mg/L'].isnull() == True)].index
df['TOC_mg/L'][null_index] = df[null_index]['DOC_mg/L']
notnull_index = df[(df['DOC_mg/L'].isnull() == True) & (df['TOC_mg/L'].isnull() == False)].index
df['DOC_mg/L'][notnull_index] = df[notnull_index]['TOC_mg/L']
df.insert(len(df.columns), 'Mean_mg/L', 0.0)
df['Mean_mg/L'] = (df['DOC_mg/L'] + df['TOC_mg/L']) / 2
return df
merge_func(sve)
And here's the error:
KeyError: "['2004-01-14T01:00:00.000000000+0100' '2004-03-04T01:00:00.000000000+0100'\n '2004-03-30T02:00:00.000000000+0200' '2004-04-12T02:00:00.000000000+0200'\n '2004-04-15T02:00:00.000000000+0200' '2004-04-17T02:00:00.000000000+0200'\n '2004-04-19T02:00:00.000000000+0200' '2004-04-20T02:00:00.000000000+0200'\n '2004-04-22T02:00:00.000000000+0200' '2004-04-26T02:00:00.000000000+0200'\n '2004-04-28T02:00:00.000000000+0200' '2004-04-30T02:00:00.000000000+0200'\n '2004-05-05T02:00:00.000000000+0200' '2004-05-07T02:00:00.000000000+0200'\n '2004-05-10T02:00:00.000000000+0200' '2004-05-13T02:00:00.000000000+0200'\n '2004-05-17T02:00:00.000000000+0200' '2004-05-20T02:00:00.000000000+0200'\n '2004-05-24T02:00:00.000000000+0200' '2004-05-28T02:00:00.000000000+0200'\n '2004-06-04T02:00:00.000000000+0200' '2004-06-10T02:00:00.000000000+0200'\n '2004-08-27T02:00:00.000000000+0200' '2004-10-06T02:00:00.000000000+0200'\n '2004-11-02T01:00:00.000000000+0100' '2004-12-08T01:00:00.000000000+0100'\n '2011-02-21T01:00:00.000000000+0100' '2011-03-21T01:00:00.000000000+0100'\n '2011-04-04T02:00:00.000000000+0200' '2011-04-11T02:00:00.000000000+0200'\n '2011-04-14T02:00:00.000000000+0200' '2011-04-18T02:00:00.000000000+0200'\n '2011-04-21T02:00:00.000000000+0200' '2011-04-25T02:00:00.000000000+0200'\n '2011-05-02T02:00:00.000000000+0200' '2011-05-09T02:00:00.000000000+0200'\n '2011-05-23T02:00:00.000000000+0200' '2011-06-07T02:00:00.000000000+0200'\n '2011-06-21T02:00:00.000000000+0200' '2011-07-04T02:00:00.000000000+0200'\n '2011-07-18T02:00:00.000000000+0200' '2011-08-31T02:00:00.000000000+0200'\n '2011-09-13T02:00:00.000000000+0200' '2011-09-28T02:00:00.000000000+0200'\n '2011-10-10T02:00:00.000000000+0200' '2011-10-25T02:00:00.000000000+0200'\n '2011-11-08T01:00:00.000000000+0100' '2011-11-28T01:00:00.000000000+0100'\n '2011-12-20T01:00:00.000000000+0100' '2012-01-19T01:00:00.000000000+0100'\n '2012-02-14T01:00:00.000000000+0100' '2012-03-13T01:00:00.000000000+0100'\n '2012-03-27T02:00:00.000000000+0200' '2012-04-02T02:00:00.000000000+0200'\n '2012-04-10T02:00:00.000000000+0200' '2012-04-17T02:00:00.000000000+0200'\n '2012-04-26T02:00:00.000000000+0200' '2012-04-30T02:00:00.000000000+0200'\n '2012-05-03T02:00:00.000000000+0200' '2012-05-07T02:00:00.000000000+0200'\n '2012-05-10T02:00:00.000000000+0200' '2012-05-14T02:00:00.000000000+0200'\n '2012-05-22T02:00:00.000000000+0200' '2012-06-05T02:00:00.000000000+0200'\n '2012-06-19T02:00:00.000000000+0200' '2012-07-03T02:00:00.000000000+0200'\n '2012-07-17T02:00:00.000000000+0200' '2012-07-31T02:00:00.000000000+0200'\n '2012-08-14T02:00:00.000000000+0200' '2012-08-28T02:00:00.000000000+0200'\n '2012-09-11T02:00:00.000000000+0200' '2012-09-25T02:00:00.000000000+0200'\n '2012-10-10T02:00:00.000000000+0200' '2012-10-24T02:00:00.000000000+0200'\n '2012-11-21T01:00:00.000000000+0100' '2012-12-18T01:00:00.000000000+0100'] not in index"
You are close, but you actually don't need to iterate over the rows when using the isnull() functions. by default
df[(df['DOC_mg/L'].isnull() == False) & (df['TOC_mg/L'].isnull() == True)].index
Will return just the index of the rows where DOC_mg/L is not null and TOC_mg/L is null.
Now you can do something like this to set the values for TOC_mg/L:
null_index = df[(df['DOC_mg/L'].isnull() == False) & \
(df['TOC_mg/L'].isnull() == True)].index
df['TOC_mg/L'][null_index] = df['DOC_mg/L'][null_index] # EDIT To switch the index position.
This will use the index of the rows where TOC_mg/L is null and DOC_mg/L is not null, and set the values for TOC_mg/L to the those found in DOC_mg/L in the same rows.
Note: This is not the accepted way for setting values using an index, but it is how I've been doing it for some time. Just make sure that when setting values, the left side of the equation is df['col_name'][index]. If col_name and index are switched you will set the values to a copy which is never set back to the original.
Now to set the mean, you can create a new column, we'll call this Mean_mg/L and set the value = 0.0. Then set this new column to the mean of both columns:
# Insert a new col at the end of the dataframe columns name 'Mean_mg/L'
# with default value 0.0
df.insert(len(df.columns), 'Mean_mg/L', 0.0)
# Set this columns value to the average of DOC_mg/L and TOC_mg/L
df['Mean_mg/L'] = (df['DOC_mg/L'] + df['TOC_mg/L']) / 2
In the columns where we filled null values with the corresponding column value, the average will be the same as the values.