I have a string formatted date field which says "FY23Q1", now assuming the fiscal year starts in October and ends in September, I want to recode the string into 10/01/2022. ie the first date of the month
INPUT Expected output:
FY23Q1 10/01/2022
FY23Q2 01/01/2023
FY23Q3 04/01/2023
Add a new custom column in PQ and paste in the following code. Make sure [Column1] refers to your column with FY23Q1 date in it.
let
year = Number.From(Text.Middle([Column1], 2,2)),
quarter = Number.From(Text.Middle([Column1], 5,6)),
startDate = Date.AddQuarters( #date(year-1+2000, 10, 1), quarter - 1)
in startDate
Full working example:
let
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45Wcos0Mg40VIrVgTKNEExjBNMExjQBqY0FAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Column1 = _t]),
#"Added Custom" = Table.AddColumn(Source, "Custom", each let
year = Number.From(Text.Middle([Column1], 2,2)),
quarter = Number.From(Text.Middle([Column1], 5,6)),
startDate = Date.AddQuarters( #date(year-1+2000, 10, 1), quarter - 1)
in startDate)
in
#"Added Custom"
Related
I have a table with power plant capacities in different years. There are only entries when something changes in the capacities. In the years not listed, the last value applies.
Plant
Year
Capacity
Cottam
2003
800
Cottam
2009
600
Cottam
2015
800
Drax
2000
600
Drax
2005
1200
Drax
2010
1800
Drax
2013
1200
Drax
2020
0
Ironbridge
2007
500
Ironbridge
2015
0
Now I would like to transform the initial table, so that I also have values for all years in between and can display them in a stacked column chart, for example. The result should look like shown in the table below. Marked in yellow are the numbers from the initial table.
You can do this easily in the Query Editor in M code.
To reproduce, paste the code below into a blank query:
let
//change next line to reflect your actual data source
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45Wcs4vKUnMVdJRMjIwMAZSFgYGSrE6qOKWQMoMU9zQFEm9S1FiBUS1AZJqhChIraERurAhSLEhhhmGxlhVG4FUQ8Q8i/LzkooyU9JTIcabAylTA6xyYGcCZWIB", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Plant = _t, Year = _t, Capacity = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"Plant", type text}, {"Year", Int64.Type}, {"Capacity", Int64.Type}}),
//generate Table of all years
#"All Years" = Table.FromColumns(
{List.Numbers(List.Min(#"Changed Type"[Year]), List.Max(#"Changed Type"[Year])- List.Min(#"Changed Type"[Year]) + 1 )}),
//Group by Plant
// Aggregate by joining with the All Years table and "Fill Down" to replace blanks with previous year.
// then expand the grouped column
#"Group by Plant" = Table.Group(#"Changed Type","Plant",{
{"Joined", each Table.FillDown(Table.Join(#"All Years","Column1",_,"Year",JoinKind.FullOuter),{"Capacity"})}
}),
#"Expanded Joined" = Table.ExpandTableColumn(#"Group by Plant", "Joined", {"Column1", "Capacity"}, {"Column1", "Capacity"}),
//Replace nulls with zero's
#"Replaced Value" = Table.ReplaceValue(#"Expanded Joined",null,0,Replacer.ReplaceValue,{"Capacity"}),
//Pivot on year
// then set the data types
#"Pivoted Column" = Table.Pivot(Table.TransformColumnTypes(#"Replaced Value", {{"Column1", type text}}, "en-US"),
List.Distinct(Table.TransformColumnTypes(#"Replaced Value", {{"Column1", type text}}, "en-US")[Column1]), "Column1", "Capacity"),
//set data type
#"Changed Type1" = Table.TransformColumnTypes(#"Pivoted Column",
List.Transform(List.Sort(List.RemoveFirstN(Table.ColumnNames(#"Pivoted Column"),1), Order.Ascending), each {_, Int64.Type}))
in
#"Changed Type1"
Edit Note:
Actually, to create the graph in Power BI, you do NOT want to pivot the data, so the shorter code:
let
//change next line to reflect your actual data source
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45Wcs4vKUnMVdJRMjIwMAZSFgYGSrE6qOKWQMoMU9zQFEm9S1FiBUS1AZJqhChIraERurAhSLEhhhmGxlhVG4FUQ8Q8i/LzkooyU9JTIcabAylTA6xyYGcCZWIB", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Plant = _t, Year = _t, Capacity = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"Plant", type text}, {"Year", Int64.Type}, {"Capacity", Int64.Type}}),
//generate Table of all years
#"All Years" = Table.FromColumns(
{List.Numbers(List.Min(#"Changed Type"[Year]), List.Max(#"Changed Type"[Year])- List.Min(#"Changed Type"[Year]) + 1 )}),
//Group by Plant
// Aggregate by joining with the All Years table and "Fill Down" to replace blanks with previous year.
// then expand the grouped column
#"Group by Plant" = Table.Group(#"Changed Type","Plant",{
{"Joined", each Table.FillDown(Table.Join(#"All Years","Column1",_,"Year",JoinKind.FullOuter),{"Capacity"})}
}),
#"Expanded Joined" = Table.ExpandTableColumn(#"Group by Plant", "Joined", {"Column1", "Capacity"}, {"Year", "Capacity"}),
//Replace nulls with zero's
#"Replaced Value" = Table.ReplaceValue(#"Expanded Joined",null,0,Replacer.ReplaceValue,{"Capacity"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Replaced Value",{{"Year", Int64.Type}, {"Capacity", Int64.Type}})
in
#"Changed Type1"
Then, in Power BI, you can generate this:
Note:
The code below presents the Table FillDown / Table Join sequence from the first code using variables and more comments. Should be easier to understand (might be less efficient, though)
...
{"Joined", each
let
//join the subtable with the All Years table
#"Joined Table" = Table.Join(#"All Years", "Column1", _, "Year", JoinKind.FullOuter),
//Fill down the Capacity column so as to fill with the "last year" data
// since that column will contain a null after the Table.Join for years with no data
#"Fill Down" = Table.FillDown(#"Joined Table",{"Capacity"})
in
#"Fill Down"
}
...
Here's how to solve this (more easily) in DAX:
Prerequisite is separate Calendar table with a 1:many relation on the year
Calendar =
SELECTCOLUMNS(
GENERATESERIES(
MIN(Plants[Year]),
MAX(Plants[Year])
),
"Year", [Value]
)
Next calculate the Last Given Capacity per year
Last Given Capacity =
VAR current_year =
MAX(Calendar[Year])
VAR last_capacity_year =
CALCULATE(
MAX(Plants[Year]),
'Calendar'[Year] <= current_year
)
RETURN
CALCULATE(
MAX(Plants[Capacity]),
Calendar[Year] = last_capacity_year
)
Finally put it all together in a Stacked Column Chart with
X-axis: 'Calendar'[Year]
Y-axis: [Last Given Capacity]
Legend: 'Plants'[Plant]
I have a table with received time like the below:
As you see, the frequency of received data in some rows is different and they are 1000ms, 1001ms, 998ms.
How can I calculate the average frequency of received time in ms?
I suggest using Power Query to
add a column that is the original date_time column offset by 1
Then add another column showing the difference between current row and previous row
This method, at least in M Code, is faster than using an Index column to refer to previous row
Then you can do your mathematical analyses.
let
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("dc27DcAgDAXAVRA1An9iO2EVxP5rgEQZXn3FjZGjsTUh4cRdqctTwy3P8heD4lv8KgHl3RJX+dCjBIXRo3JkLg==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [date_time = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"date_time", type datetime}}),
//add column containing previous rows data
//usually faster than using and INDEX column
prevList = {null} & List.RemoveLastN(#"Changed Type"[date_time]),
tbl1 = Table.FromColumns(
{#"Changed Type"[date_time],prevList},
{"date_time","Prev Row"}
),
#"Added Custom" = Table.AddColumn(tbl1, "Difference", each /*if [Prev Row] = null then 0
else*/ Duration.TotalSeconds([date_time] - [Prev Row])),
#"Changed Type1" = Table.TransformColumnTypes(#"Added Custom",{
{"date_time", type datetime}, {"Prev Row", type datetime}, {"Difference", type number}})
in
#"Changed Type1"
In PowerQuery add an Index column to your data so that you can address singe rows. Then with DAX add a calulated column to the table:
Milliseconds =
VAR NextIndex = 'Table'[Index] + 1
VAR MaxIndex = MAX(Table[Index])
VAR Difference =
IF(
'Table'[Index] = MaxIndex,
BLANK(),
'Table'[date_time] -
CALCULATE(
VALUES('Table'[date_time]),
Filter(
ALL('Table'),
'Table'[Index] = NextIndex
)
)
)
RETURN
CONVERT(ABS(Difference * 3600 * 24 * 1000), INTEGER)
Now you are looking for AVERAGE( Milliseconds ).
Note: Things would have been easier if you provided copyable data instead of a screenshot.
Given a "start date" column and an "end date" column, how can I create a calculated column that returns "TRUE" if October 1st falls between those two dates?
Assuming that start date is always before end date and that dates can span more than 1 year, I would do it like this in m:
let
Src = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("PczRCcBACAPQXfxWSJTS3iyH+69RW23Bn/Bi9haHw7DMD9EOdAtK6hiL/bcKMcbr+Qu8xmVg3WfnzGgXeyTzBg==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [#"Date from" = _t, #"Date to" = _t]),
#"Date type" = Table.TransformColumnTypes(Src,{{"Date from", type date}, {"Date to", type date}}),
#"Which 1st october" = Table.AddColumn(#"Date type", "First october to look for", each if #date(Date.Year([Date from]), 10, 1) > [Date from] then #date(Date.Year([Date from]), 10, 1) else #date(Date.Year([Date from]) + 1, 10, 1)),
#"Is 1st Oct inbetween" = Table.AddColumn(#"Which 1st october", "1st October inbetween", each if [Date to] > [First october to look for] then true else false, type logical
),
#"Remove temp col" = Table.RemoveColumns(#"Is 1st Oct inbetween",{"First october to look for"})
in
#"Remove temp col"
This produces the following result:
How to get Dates for N last full months. I want the last month to be determined by Sales amount.
This is the whole table for the example.
The expected result is a calculated table of Date column from 2020-05-01 to 2020-07-31. Looks like this:
Date
2020-05-01
2020-05-02
2020-05-03
…
2020-07-29
2020-07-30
2020-07-31
What have I tried? First, a measure to get the last date with sales:
MaxDate =
CALCULATE(
EOMONTH( MAX( T[Date] ), 0),
ALL( T ),
T[Amount] > 0
)
And the calculated table:
T_Range =
var a = [MaxDate]
var b = DATESINPERIOD( T[Date] , a, -3, MONTH )
return
b
But it returns only 3 days, not the whole range from 2020-05-01 to 2020-07-31.
The table to reproduce the problem:
let
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("ZcrLCQAgDATRXnJWSNZ/LWL/bShIQFyY02PmFCg0qp0kiMkKTmBKTJmpROCjyldj6pceGb+YkhgJXNYG", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [Date = _t, Amount = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"Date", type date}, {"Amount", Int64.Type}})
in
#"Changed Type"
Please check if this helps -
Created a new column with 'Max date' measure you provided.
Then crated min date column with below DAX.
Min Date = EDATE([MaxDate],-3)+1
Created new table using below.
T_Range = CALENDAR(MAX(T[Min Date]),MAX(T[Max Date]))
In PowerBI, I have a simple table with 3 columns:
let
Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("TYzBCcAwDAN38dugyk5bdxaT/deICzXN77hDyhSKCkHYwafwbJyaYiUc9I4XaH/1MgHeXQO+bcf7ux0XW3x5Lg==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [id = _t, start = _t, end = _t]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"id", Int64.Type}, {"start", type date}, {"end", type date}})
in
#"Changed Type"
From which I can create the following visual
My challenge is to calculate the total duration in days of the times series based on filter selected above. Any help would be appreciated.
I have tried the following DAX formula but it gives me crazy results as shown above.
YTDDuration =
var start_Date=FIRSTDATE(ALLSELECTED('CALENDAR'[Date]))
var end_Date=LASTDATE(ALLSELECTED('CALENDAR'[Date]))
var current_Start=MAX(Table2[start])
var current_end=MAX(Table2[end])
var bigif=IF(current_end>start_Date&¤t_Start<end_Date,DATEDIFF(MAX(start_Date,current_Start),MIN(end_Date,current_end),DAY),0)
RETURN
CALCULATE(SUMX(Table2, bigif),FILTER(ALL(Table2), Table2[start] <= max(Table2[end])))
Expected output would be:
The key here is to account for gaps in dates and consolidate overlapping dates.
You can iterate through your calendar table and count the number of days where that day falls into one of the id time periods.
YTDDuration =
var current_Start = CALCULATE(MIN(Table2[start]), ALL(Table2))
var current_end = MAX(Table2[end])
RETURN
SUMX(
FILTER('CALENDAR', 'CALENDAR'[Date] <= current_end),
MAXX(ALL(Table2), IF([Date] > [start] && [Date] <= [end], 1, 0))
)
This starts at the minimal start date and adds 1 for each day where that Date is between start and end for some row in Table2. If none of the rows contains that Date, the max is over just zeros and returns zero. If one or more matches, you get a max of one and we count that day.
YTD Duration by end: