Min or Early Start Date

hi AllanBerces ,

You can write a calculated table like:

Table = 
ADDCOLUMNS(
    VALUES(Table1[Job]),
    "Early Start",
    CALCULATE(MIN(Table1[Date]))
)

it works like:

If you have a big table, it would be more advisible to do it with Power Query, like:

M code:

let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("ldMxC8IwEAXg/9K5pLlccklnLVSKqLUiQZxEUBRHf78RkRRuyWV5yzc83pHTqdodO0ivqqtjt59SrK+XWwpAFSiladrGaEPVuS7BoEUasu7GyTpylrgnpV1KbJyMU+b9FJdMph5eBn0pDBke4hinVBR5U6sssNmKPAi9EXoUepv9Ko49GLSZL+7v+/PrQBl2mRLuZXw2/hA78sER561CNn2BBpE2Io0ibUXaiTSJ9Pw8w+b1COS5JgW/Ty/Ts1tuh+1mrUOrOUflfxxl3Mr4f8TzBw==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Job = _t, Group = _t, Trade = _t, #"Hours Per day" = _t, Date = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Job", type text}, {"Group", type text}, {"Trade", type text}, {"Hours Per day", type number}, {"Date", type date}}),
    #"Grouped Rows" = Table.Group(#"Changed Type", {"Job"}, {{"EarlyStart", each List.Min([Date]), type nullable date}})
in
    #"Grouped Rows"

HI,

This M code in Power Query works

let
    Source = Excel.CurrentWorkbook(){[Name="Data"]}[Content],
    #"Grouped Rows" = Table.Group(Source, {"Job"}, {{"Count", each Table.Min(_,"Date")}}),
    #"Expanded Count" = Table.ExpandRecordColumn(#"Grouped Rows", "Count", {"Group", "Trade", "Hours Per day", "Date"}, {"Group", "Trade", "Hours Per day", "Date"}),
    #"Changed Type1" = Table.TransformColumnTypes(#"Expanded Count",{{"Job", type text}, {"Group", type text}, {"Trade", type text}, {"Hours Per day", type number}, {"Date", type date}})
in
    #"Changed Type1"

Hope this helps.

In Power Query:

1. Transform data → Power Query
2. Select your table
3. Keep only Job and Date 
4. Home ➜ Group By

• Group by: Job
• New column name: Start Date
• Operation: Min

However, if your table is big as you say I would suggest make these aggregations upstream like in SQL as it would be more effective in terms of performans and capacity usage.