Manipulating Tables and Lookup Tables

This page covers the functors and techniques used to read, iterate over, and restructure Tables and Lookup Tables once they exist. See Table Type and Lookup Table Type for the general format of these two types — column naming, key marking, and type inference — which this page assumes as background.

A Tuple is a sequence of table cells, most often used to represent the key (or full set of keys) identifying a table row. Tuple elements are ordered by column index and have no names of their own — a Tuple by itself doesn't say which column each element belongs to; that mapping only exists in the context of the table it's being used against.

A Tuple literal uses bracket syntax — [2007] for a single-element Tuple, [2007, “Boston”] for several at once, the same list syntax a Table or Lookup Table literal uses. This bracket form is required when writing a Tuple as a literal constant: automatic conversion between types, such as a Real value becoming a one-element Tuple, only happens when a value is connected from one port to another — a variable reference, or a functor call inlined directly into the argument — never when a literal constant is parsed straight into a port (see Constants for this rule stated generally, beyond just Tuples). Each port's literal syntax is parsed by that type's own parser, which only accepts its own type's literal form; a bare 2007 fails wherever a Tuple is expected, even though a Real value, once connected, converts to a Tuple automatically. Binding a Tuple literal to a reusable name goes through the Tuple functor, the same way Table and Lookup Table wrap their own literals.

Add Tuple Value serves a different case: appending one element — often a connected variable, rather than a literal — to a Tuple that already exists. Every example below that builds a key from a variable, rather than writing every element as a literal, uses it for exactly this.

// A Tuple literal can hold more than one element directly, the same as a
// Table or Lookup Table literal; binding it to a name goes through Tuple.
fullKey := Tuple [2007, "Boston"];

// AddTupleValue reaches a one-element Tuple the same way, then appends to
// it — the pattern used throughout this page, typically with a connected
// variable rather than a second literal like the one shown here.
yearOnly := Tuple [2007];
alsoFullKey := AddTupleValue yearOnly "Boston";

Every functor below that identifies a row or sub-table by key takes that key as a Tuple.

Reads a single cell.

Reads an entire row — key and value columns together — for one key at once.

The Lookup Table equivalent of GetTableValue — no column argument, since a Lookup Table only ever has one.

Retrieves an entire column, across every row, as a new table containing just the keys and that one column — unlike the three functors above, which each read a single cell or a single row.

Example — Calc Areas returns a single output of type Table, with columns Category (key), Area_In_Cells, Area_In_Hectares, and Area_In_Square_Meters — every area measure for every category, in one call. A common mistake is to treat this output as if it were a single hectares figure; it's a table, and the column of interest has to be read out of it:

areaTable := CalcAreas landscape .no;
// the Area_In_Hectares column
hectaresColumn := GetTableColumn areaTable 3;

Example — a small constant table and lookup table, each read with the functor above that matches its shape:

myLookupTable := LookupTable [
    "Key" "Value",
    1 10,
    2 20,
    3 30
];
// lookedUpValue will be 20.
lookedUpValue := GetLookupTableValue myLookupTable 2;

myTable := Table [
    "CityId*", "Population", "Area",
    1, 667137, 125,
    2, 39398, 6
];
// population will be 667137.
population := GetTableValue myTable [1] "Population";

// wholeRow will be the three-element Tuple (2, 39398, 6) — the key (CityId)
// first, then Population, then Area.
wholeRow := GetTableRow [2] myTable;

A For Each loop run over a connected Lookup Table executes once per key. Inside the loop, a Step functor retrieves the current key — its input auto-binds to the container's current iteration value, the same mechanism described in Internal output ports, so it needs no explicit wiring. From there, the key can be used directly, or passed to Get Lookup Table Value to retrieve the corresponding value.

A loop like this can often run its iterations in parallel — see Basic Data Flow for the conditions that allow it.

Note: Every loop in the examples below meets those conditions — no mux, nothing consumed outside the loop, no submodel output port — so the engine is free to run iterations in any order, or concurrently. The printed lines can come out in a different order than the source table, and if two iterations genuinely run at the same time, their output can even interleave rather than appearing as clean, separate lines. Forcing a specific order means forcing sequential execution, which means introducing a mux — a plain MuxValue 0 0 is enough, at the cost of losing the parallelism these loops would otherwise qualify for; see The one exception: feedback in loops.

Example — printing every entry of a Lookup Table. Print is itself a container — its initialMessage is printed before whatever it contains runs, so an empty {{ }} block is enough when the only goal is to print something. Create String builds a message from a format string and the values connected inside it, referenced the same way a Code expression references a hook (see Verbose form) — by a numbered, type-prefixed tag (<v1> for the first connected value, and so on). This example also uses { initialMessage = message, logLevel = .result } nominal syntax for Print, and the _ discard convention for outputs that aren't needed:

Note: Dinamica prints every functor's own start and end at the Info level. Left unfiltered, a Print call's message would be buried in that noise. Wrapping the loop in Log Policy, with maximumLogLevel = .result, restricts what gets logged from inside it to Result and anything more severe — Unconditional, Error, Warning, Result; Info and beyond are suppressed, the same ordering documented on Calculate R Expression. Print's own logLevel = .result then places the custom message at exactly that threshold, so it stays visible. Every example on this page uses both together for that reason.

myLookupTable := LookupTable [
    "Key" "Value",
    1 10,
    2 20,
    3 30
];

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach myLookupTable {{
        key := Step;
        value := GetLookupTableValue myLookupTable key;

        message := CreateString "(Key: <v1>, Value: <v2>)" {{
            NumberValue key 1;
            NumberValue value 2;
        }};

        Print { initialMessage = message, logLevel = .result } {{ }};
    }};
}};

Table rows are iterated the same way, but need one extra functor: Get Table Keys returns a table mapping unique indices to the keys of the input table's first key column. When the key column is already Real-typed, those indices and the keys are the same numbers, so the mapping is trivial. When the key column is String-typed, the indices are distinct numeric placeholders, and a GetTableValue call inside the loop is needed to map the current index back to its actual String key — this indirection is why String-typed key columns are usually avoided when a table will be iterated (see Table Type).

A table with more than one key column can only be iterated one key column at a time this way. To examine every key column, nest loops — see Sub-Tables below.

Example — printing every entry of a Table with a Real-typed key column. General tables are wrapped in Table rather than LookupTable:

myTable := Table [
    "CityId*", "Population",
    1, 667137,
    2, 39398
];

allKeys := GetTableKeys myTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach allKeys {{
        key := Step;
        value := GetTableValue myTable key "Population";

        message := CreateString "(Key: <v1>, Value: <v2>)" {{
            NumberValue key 1;
            NumberValue value 2;
        }};

        Print { initialMessage = message, logLevel = .result } {{ }};
    }};
}};

Example — the same, but with a String-typed key column. This is the extra-indirection case described above: GetTableKeys returns indices, not the original keys, so an extra GetTableValue inside the loop maps each index back to its String key before it can be used:

myStringKeyedTable := Table [
    "CityName*#string", "Population",
    "Boston", 667137,
    "Chelsea", 39398
];

indexToKey := GetTableKeys myStringKeyedTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach indexToKey {{
        index := Step;
        key := GetTableValue indexToKey index 2;
        value := GetTableValue myStringKeyedTable key "Population";

        message := CreateString "(Key: <s1>, Value: <v1>)" {{
            NumberString key 1;
            NumberValue value 1;
        }};

        Print { initialMessage = message, logLevel = .result } {{ }};
    }};
}};

Example — a table with more than one value column. Nothing new is needed: call GetTableValue once per value column and combine the results:

myTable := Table [
    "CityId*", "Population", "Area",
    1, 667137, 125,
    2, 39398, 6
];

allKeys := GetTableKeys myTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach allKeys {{
        key := Step;
        population := GetTableValue myTable key "Population";
        area := GetTableValue myTable key "Area";

        message := CreateString "(CityId: <v1>, Population: <v2>, Area: <v3>)" {{
            NumberValue key 1;
            NumberValue population 2;
            NumberValue area 3;
        }};

        Print { initialMessage = message, logLevel = .result } {{ }};
    }};
}};

A sub-table is a table containing only the rows matching one specific value of a key, with that key column removed from the result. Sub-tables are how Dinamica handles tables with more than one key column: peel off one key at a time, working with what remains.

Retrieves the rows matching the given key(s), with those key columns dropped from the result.

Inserts or replaces the rows matching the given key(s).

Both Get Table From Key and Set Table By Key only operate on the table's leftmost key column(s) — they can't reach into the middle of a composite key. If the key you need isn't leftmost, reorder the columns first with Reorder Table Column, which moves one column (key or value) to a new index; key and value columns can't be moved across each other.

Worked example. Take a table of commodity prices, keyed by Year and City:

Retrieving the sub-table for Year 2007 — a single key, since Year is leftmost — leaves City/Price pairs:

To instead retrieve by City first, Year would need to be reordered ahead of it, since GetTableFromKey can only key off the leftmost column(s). With three key columns, the same idea nests: peel off the leftmost key to get a sub-table, then peel off the next leftmost key of that sub-table, and so on — which is exactly how nested ForEach loops iterate a multi-key table one column at a time; see Container functors for how nesting containers this way affects execution order.

Example — building the table above, retrieving the 2007 sub-table, updating one of its prices with Set Table Cell Value, writing it back, and finally reordering City ahead of Year to key by City instead:

priceTable := Table [
    "Year*", "City*", "Price",
    2004, "Boston", 1200,
    2004, "Chelsea", 1453,
    2007, "Boston", 4332,
    2007, "Chelsea", 233
];

// Retrieve the City*/Price sub-table for Year 2007.
pricesIn2007 := GetTableFromKey priceTable [2007];

// Replace the price for Chelsea within that sub-table.
updatedPricesIn2007 := SetTableCellValue pricesIn2007 "Price" ["Chelsea"] 999;

// Write the modified sub-table back under the same key.
priceTable2 := SetTableByKey priceTable [2007] updatedPricesIn2007;

// Move City (currently index 2) ahead of Year (index 1), so sub-tables can
// instead be retrieved by City first.
priceTableByCity := ReorderTableColumn priceTable2 "City" 1;
// citySubTable will be the Year*/Price pairs for Boston.
citySubTable := GetTableFromKey priceTableByCity ["Boston"];

Example — printing every entry of a table with more than one key column. GetTableKeys only ever iterates the first key column, so a table with several needs one nested loop per extra key: peel off a sub-table for each Year, then iterate the City key within it:

priceTable := Table [
    "Year*", "City*", "Price",
    2004, "Boston", 1200,
    2004, "Chelsea", 1453,
    2007, "Boston", 4332,
    2007, "Chelsea", 233
];

yearKeys := GetTableKeys priceTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach yearKeys {{
        year := Step;
        pricesInYear := GetTableFromKey priceTable year;

        cityKeys := GetTableKeys pricesInYear;

        _ := ForEach cityKeys {{
            cityIndex := Step;
            // City is String-typed, so cityIndex is a placeholder, not the real
            // city — map it back the same way as any String-typed key column.
            city := GetTableValue cityKeys cityIndex 2;
            price := GetTableValue pricesInYear city "Price";

            message := CreateString "(Year: <v1>, City: <s1>, Price: <v2>)" {{
                NumberValue year 1;
                NumberString city 1;
                NumberValue price 2;
            }};

            Print { initialMessage = message, logLevel = .result } {{ }};
        }};
    }};
}};

A modified version of the same example: instead of reading Price from the City-only sub-table, build the full composite key — both Year and City together, as a Tuple — and read directly from the original table:

priceTable := Table [
    "Year*", "City*", "Price",
    2004, "Boston", 1200,
    2004, "Chelsea", 1453,
    2007, "Boston", 4332,
    2007, "Chelsea", 233
];

yearKeys := GetTableKeys priceTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach yearKeys {{
        year := Step;
        pricesInYear := GetTableFromKey priceTable year;

        cityKeys := GetTableKeys pricesInYear;

        _ := ForEach cityKeys {{
            cityIndex := Step;
            city := GetTableValue cityKeys cityIndex 2;

            // The composite key, built by appending City onto Year.
            fullKey := AddTupleValue year city;

            // Reads the full row from the original table, as a Tuple: the
            // composite key first (Year, City), then Price — the table's
            // only value column.
            row := GetTableRow fullKey priceTable;

            price := GetTableValue priceTable fullKey "Price";

            message := CreateString "(Year: <v1>, City: <s1>, Price: <v2>)" {{
                NumberValue year 1;
                NumberString city 1;
                NumberValue price 2;
            }};

            Print { initialMessage = message, logLevel = .result } {{ }};
        }};
    }};
}};

GetTableFromKey is still used to enumerate which City keys exist for a given Year — nothing else on this page discovers a table's keys without it. What changes is the read itself: GetTableRow and GetTableValue are both called against priceTable with the full (Year, City) Tuple, not against pricesInYear with City alone.

A Mux Table or Mux Lookup Table can carry a Table or Lookup Table across the iterations of a loop, the same way Mux Value carries a single value — see Carrying and selecting values across iterations. Each iteration reads the mux's current output, adds a row to it with AddTableRow, and feeds the result back in as the mux's feedback input for the next iteration, building up a result table one row at a time.

The accumulated table is read after the loop the same way any container's internal result is read from outside it: a functor after the loop simply takes the accumulator's feedback variable as an input. There's nothing special about this — the loop is guaranteed to finish before anything depending on it runs, per Basic Data Flow.

sourceLookup := LookupTable [
    "Key" "Value",
    1 667137,
    2 39398,
    3 181045
];

emptyResults := Table [
    "CityId*#real", "DoubledPopulation#real"
];

_ := ForEach sourceLookup {{
    accumulated := MuxTable emptyResults nextAccumulated;

    key := Step;
    population := GetLookupTableValue sourceLookup key;
    doubled := $ [ $population * 2 ];

    newRow := AddTupleValue key doubled;
    nextAccumulated := AddTableRow accumulated newRow;
}};

// finalResults holds every row added across all iterations. Use nextAccumulated,
// not accumulated (the mux's own output), which lags one iteration behind.
// The Table carrier passes it through, since := always binds a functor call.
finalResults := Table nextAccumulated;

Note: This is also a reason to avoid reading accumulated anywhere else in the same iteration, beyond its use in AddTableRow. The engine schedules operations to minimize copying: when a value only needs to be read before it's destructively updated, the reads are scheduled first and the update last, avoiding a copy entirely. But if two or more functors both need to destructively update the same value, no scheduling avoids a copy for all of them — only one destructive update can safely be the last one to run. See Basic Data Flow for this same rule stated generally, beyond just tables.

Why this isn't the best approach. The mux above disqualifies this loop from the parallel execution described earlier — it's what ties every iteration to the one before it, forcing the loop to run sequentially even though the per-iteration work is otherwise completely independent (each row's value depends only on that row's own key).

When the transformation is this simple — one output row per input row, computed independently — the calculator shorthand already covered in Lookup Table Operators produces the same result without a loop, a mux, or the sequential cost that comes with one:

doubledResults := % [ %sourceLookup[line] * 2 ] "CityId" "DoubledPopulation" sourceLookup;

Every example so far assumes the reader already knows a table's shape — its column names and their types — since GetTableValue and CreateString's hooks are each wired to one specific name and type at authoring time. Printing a table generically, with any number of columns of any mix of Real and String, needs a different approach: read each row as a Tuple, discover how many elements it has, and determine each element's type at runtime rather than assuming it.

Get Table Row, Get Tuple Size, and Get Tuple Value make the column-count side of this possible: a row read as a Tuple can be measured with GetTupleSize, and each of its elements retrieved by position, rather than by column name, with GetTupleValue. Each retrieved element has type TableValue — a generic type able to hold either a Real or a String.

String and Real Value each convert a TableValue the same conditional way: the conversion only succeeds if the TableValue actually holds that type, and raises an error otherwise — converting an already-typed, known RealValue or String, by contrast, never fails. Attempting the String conversion wrapped in Skip On Error turns that failure into a type test: SkipOnError catches the error rather than aborting the model, and its executionCompletedSucessfully output reports whether the TableValue really was a String. If Then and If Not Then, branching on that result, take the matching path — use the converted String directly, or, having ruled out String, extract it as a RealValue instead (safe now, since only Real remains possible) and convert that known value to a string — and a String Junction recombines the two branches into one result, since only one of them ever actually runs.

myTable := Table [
    "CityId*", "CityName", "Population",
    1, "Boston", 667137,
    2, "Chelsea", 39398
];

allKeys := GetTableKeys myTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach allKeys {{
        key := Step;
        row := GetTableRow key myTable;
        rowSize := GetTupleSize row;

        // row[1] is the key itself; the actual value columns start at
        // index 2, so the loop skips index 1.
        _ := For 2 rowSize {{
            columnIndex := Step;
            cellValue := GetTupleValue columnIndex row;
            // Displayed as a 1-based value-column number, not the raw
            // Tuple index, which is offset by the key at position 1.
            displayColumn := $ [ $columnIndex - 1 ];

            // Attempt to read the cell as a String; SkipOnError catches the
            // failure if it's actually a Real, rather than aborting.
            attempt := SkipOnError .yes {{
                asString := String cellValue;
            }};

            _ := IfThen attempt {{
                stringDisplay := String asString;
            }};

            _ := IfNotThen attempt {{
                // cellValue was a Real instead; RealValue extracts it, and
                // String — safe here, since converting a known Real never
                // fails — turns it into a display string.
                asReal := RealValue cellValue;
                realDisplay := String asReal;
            }};

            // Only one of stringDisplay/realDisplay was actually produced;
            // StringJunction picks whichever one has a value.
            cellDisplay := StringJunction stringDisplay realDisplay;

            message := CreateString "(Key: <v1>, Column: <v2>, Value: <s1>)" {{
                NumberValue key 1;
                NumberValue displayColumn 2;
                NumberString cellDisplay 1;
            }};

            Print { initialMessage = message, logLevel = .result } {{ }};
        }};
    }};
}};

This generalizes the value side of printing — any number of columns, any mix of Real and String — but not the key side: key := Step used directly as myTable's row key still assumes a single, Real-typed key column, the same assumption the basic iteration example earlier makes. A table with a known, fixed number of key columns can still use the techniques already covered in Iterating over Table rows and Sub-Tables — mapping a String key's placeholder index back, or nesting one loop per key column. When the number of key columns isn't known in advance, see Printing a table with any number of key columns below.

A new column can be derived from an existing table's own data, one metric at a time. Calculate Lookup Table Values evaluates an expression once per row of a base table, producing a new Lookup Table keyed the same way as the source — so each derived metric comes out as its own separate result — and a later expression can reference an earlier result directly, the same way it references any other connected table. Add Table Column then merges each of those results back into the original table as a new column, one call per column, since each call only ever adds one.

stationData := Table [
    "StationId*", "Jan_mm", "Feb_mm", "Mar_mm", "Apr_mm", "May_mm", "Jun_mm", "Jul_mm", "Aug_mm", "Sep_mm", "Oct_mm", "Nov_mm", "Dec_mm",
    1,  85,  75,  95, 100,  90,  85,  80,  90,  95, 105, 110,  95,
    2,  90,  80, 100, 105,  95, 100,  95, 100, 105, 110, 115, 100,
    3, 100,  90, 105,  95,  85,  75,  70,  85,  95, 110, 120, 105,
    4,  75,  70,  90,  95, 100, 105, 100,  95,  90,  95,  90,  80,
    5,  95,  85, 100, 100,  90,  85,  85,  90,  90, 100, 105, 100,
    6, 110, 100, 105, 100,  90,  80,  75,  85,  90, 105, 115, 110
];

averages := % [
    ( %stationData[[line]["Jan_mm"]] + %stationData[[line]["Feb_mm"]] + %stationData[[line]["Mar_mm"]] +
      %stationData[[line]["Apr_mm"]] + %stationData[[line]["May_mm"]] + %stationData[[line]["Jun_mm"]] +
      %stationData[[line]["Jul_mm"]] + %stationData[[line]["Aug_mm"]] + %stationData[[line]["Sep_mm"]] +
      %stationData[[line]["Oct_mm"]] + %stationData[[line]["Nov_mm"]] + %stationData[[line]["Dec_mm"]] ) / 12
] "StationId" "Average_mm" stationData;

stdDevs := % [
    sqrt( (
        (%stationData[[line]["Jan_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Feb_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Mar_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Apr_mm"]] - %averages[line])^2 +
        (%stationData[[line]["May_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Jun_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Jul_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Aug_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Sep_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Oct_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Nov_mm"]] - %averages[line])^2 +
        (%stationData[[line]["Dec_mm"]] - %averages[line])^2
    ) / 12 )
] "StationId" "StdDev_mm" stationData;

withAverage := AddTableColumn { table = stationData, columnName = "Average_mm", columnType = .real, columnValues = averages };
enrichedStationData := AddTableColumn { table = withAverage, columnName = "StdDev_mm", columnType = .real, columnValues = stdDevs };

stdDevs demonstrates the chaining directly: rather than re-deriving the twelve-term average a second time inside the deviation formula, %averages[line] looks up the already-computed mean for the current row, the same way %stationData[[line]["Jan_mm"]] looks up a raw monthly value — both are just references to a connected table, one newly derived and one original.

Each AddTableColumn call only adds a single column, so producing two new columns takes two calls, the second operating on the first's result — withAverage is a genuine intermediate table, not a byproduct to discard. columnValues expects a table with the same key column(s) as the target and exactly one data column; a Lookup Table's *#real, #real shape already matches that, converting the same way documented in Automatic Conversions on Table Type.

The previous section names every column explicitly in the expression — workable for twelve months, but it doesn't scale to a table with, say, one column per day of the year. When the number of data columns isn't known in advance, a loop reading each row generically — the same GetTupleSize/GetTupleValue technique already used for printing — can compute a per-row result instead. Four ways to do this follow, roughly from most to least manual: looping row by row, looping column by column, aggregating the other way — across rows instead of columns — and, best of all when it's available, reorganizing the data so no loop is needed at all.

Imagine a table shaped like the station data from before, but with one column per day instead of one per month:

GetTableRow reads an entire row as a Tuple — the key first, then every data column in order (see GetTableRow above) — and GetTupleSize reports how many elements it has, so the loop bound adapts to however many day columns the table actually has.

allStationIds := GetTableKeys dailyStationData;

emptyAverages := LookupTable [ "Key" "Value" ];
emptyStdDevs := LookupTable [ "Key" "Value" ];

_ := ForEach allStationIds {{
    stationId := Step;
    row := GetTableRow stationId dailyStationData;
    rowSize := GetTupleSize row;
    // row[1] is the key itself; the actual day values start at index 2.
    dayCount := $ [ $rowSize - 1 ];

    // First pass: sum every day's value to compute the average.
    _ := For 2 rowSize {{
        dayIndex := Step;
        cellValue := GetTupleValue dayIndex row;
        dailyValue := RealValue cellValue;

        runningSum := MuxValue 0 nextRunningSum;
        nextRunningSum := $ [ $runningSum + $dailyValue ];
    }};

    average := $ [ $nextRunningSum / $dayCount ];

    // Second pass: sum the squared deviations from that average.
    _ := For 2 rowSize {{
        devDayIndex := Step;
        devCellValue := GetTupleValue devDayIndex row;
        devDailyValue := RealValue devCellValue;

        runningSquaredDevSum := MuxValue 0 nextRunningSquaredDevSum;
        nextRunningSquaredDevSum := $ [ $runningSquaredDevSum + ($devDailyValue - $average)^2 ];
    }};

    stdDev := $ [ sqrt( $nextRunningSquaredDevSum / $dayCount ) ];

    // Accumulate (StationId, average) and (StationId, stdDev) pairs into
    // two growing result tables, the same accumulator pattern as before.
    averagesAccum := MuxLookupTable emptyAverages nextAveragesAccum;
    nextAveragesAccum := SetLookupTableValue averagesAccum stationId average;

    stdDevsAccum := MuxLookupTable emptyStdDevs nextStdDevsAccum;
    nextStdDevsAccum := SetLookupTableValue stdDevsAccum stationId stdDev;
}};

averages := LookupTable nextAveragesAccum;
stdDevs := LookupTable nextStdDevsAccum;

Three levels of accumulation are threaded through this loop: two separate MuxValue passes sum a station's own daily values — one for the average, a second for the squared deviations from it, since the standard deviation formula needs the average already known before it can compute those deviations — and MuxLookupTable carries each growing result across stations, in the outer loop, one accumulator per statistic. Set Lookup Table Value is what actually inserts each computed value into its growing table — the write counterpart to GetLookupTableValue. RealValue cellValue is used directly here, without the SkipOnError type test from the generic printer — this example assumes every data column is genuinely numeric, which is reasonable for daily measurements, unlike the printer, which had to handle either type. The second pass's variable names are all prefixed (devDayIndex, devCellValue, devDailyValue) to keep them clearly distinct from the first pass's, even though each For loop is its own separate container.

Three mux levels disqualify this loop from parallel execution — see The one exception: feedback in loops — consistent with every mux-based accumulator already documented on this page.

The row-by-row approach above isn't the only way to handle an arbitrary number of data columns. GetTableColumn inverts the iteration: instead of looping over stations and, within each one, over days, this loops over days, and for each one pulls that entire day's values — every station at once — as a table shaped (StationId → that day's value). % then folds each day into a running per-station total, one call per day, rather than a mux-driven inner sum.

allStationIds := GetTableKeys dailyStationData;
firstStationId := GetTableValue allStationIds [1] 2;
sampleRow := GetTableRow firstStationId dailyStationData;
sampleRowSize := GetTupleSize sampleRow;
dayCount := $ [ $sampleRowSize - 1 ];

initialSums := % [ 0 ] "StationId" "Sum" dailyStationData;

// First pass: sum every day's column, per station, to compute the average.
_ := For 1 dayCount {{
    dayNumber := Step;
    // dailyStationData's columns are StationId (index 1), then each
    // day in order, so day N sits at raw column index N + 1.
    rawColumnIndex := $ [ $dayNumber + 1 ];
    dayColumn := GetTableColumn dailyStationData rawColumnIndex;

    runningSums := MuxLookupTable initialSums nextRunningSums;
    // Adds this day's value for each station to that station's
    // running total, computed for every station in one call.
    nextRunningSums := % [ column + %dayColumn[[line][2]] ] "StationId" "Sum" runningSums;
}};

sums := LookupTable nextRunningSums;
averages := % [ %sums[line] / $dayCount ] "StationId" "Average_mm" sums;

initialSquaredDevSums := % [ 0 ] "StationId" "SquaredDevSum" dailyStationData;

// Second pass: sum the squared deviations from each station's own average,
// now that it's known.
_ := For 1 dayCount {{
    devDayNumber := Step;
    devRawColumnIndex := $ [ $devDayNumber + 1 ];
    devDayColumn := GetTableColumn dailyStationData devRawColumnIndex;

    runningSquaredDevSums := MuxLookupTable initialSquaredDevSums nextRunningSquaredDevSums;
    nextRunningSquaredDevSums := % [ column + (%devDayColumn[[line][2]] - %averages[line])^2 ] "StationId" "SquaredDevSum" runningSquaredDevSums;
}};

squaredDevSums := LookupTable nextRunningSquaredDevSums;
stdDevs := % [ sqrt( %squaredDevSums[line] / $dayCount ) ] "StationId" "StdDev_mm" squaredDevSums;

firstStationId picks an arbitrary station just to measure the row shape once, the same GetTableRow/GetTupleSize technique as above, minus 1 for the key — every row has the same number of columns, so any one station works. initialSums starts every station at zero via a single-call %, evaluated once per row of dailyStationData with no other operand referenced. Inside the first loop, %dayColumn[[line][2]] reads the current day's value for the row currently being computed — the multi-column operator, since dayColumn is a Table, not a Lookup Table — while column reads that same row's running total from runningSums, the base table the whole expression is evaluated against.

The second loop repeats this shape for the sum of squared deviations, referencing %averages[line] — the already-computed per-station mean from the first loop, looked up by key — the same chaining technique already established in Computing new columns for a table. Its variables are all prefixed (devDayNumber, devRawColumnIndex, devDayColumn) to keep them clearly distinct from the first loop's, even though each For loop is its own separate container. Two mux levels thread through this version now instead of one, MuxLookupTable carrying the running sums, then the running squared-deviation sums, day to day; there's still no inner mux within either pass, since % handles summing across all stations for a given day in one call rather than needing an accumulator of its own.

The two techniques above both compute one result per station, aggregating across days. The opposite direction — one result per day, aggregating across stations — needs a different pair of functors: Get Table Column still extracts one day's values, but instead of folding them together with %, Extract Lookup Table Attributes computes summary statistics over an entire Lookup Table directly, including meanValue — the mean of the values themselves, exactly the average across stations this needs.

Its result is retrieved with the tX[“NAME”] operator (see Lookup Table Operators on Calculate Functors), built specifically for tables produced this way — it reads directly from the Table that ExtractLookupTableAttributes returns, with no conversion needed. Its own key column is a String (the attribute's name), which rules out converting it to a Lookup Table anyway, since those only ever take Real keys.

Example — the average and standard deviation across all stations for a single day:

oneDayColumn := GetTableColumn dailyStationData 2;

attrs := ExtractLookupTableAttributes { table = oneDayColumn, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
averageAcrossStations := $ [ %attrs["meanValue"] ];
stdDevAcrossStations := $ [ %attrs["valueStd"] ];

message := CreateString "(Average across stations for Day 1: <v1>, StdDev: <v2>)" {{
    NumberValue averageAcrossStations 1;
    NumberValue stdDevAcrossStations 2;
}};

LogPolicy { maximumLogLevel = .result } {{
    Print { initialMessage = message, logLevel = .result } {{ }};
}};

Example — the same computation repeated for every day, building a (Day → average across stations) result:

allStationIds := GetTableKeys dailyStationData;
firstStationId := GetTableValue allStationIds [1] 2;
sampleRow := GetTableRow firstStationId dailyStationData;
sampleRowSize := GetTupleSize sampleRow;
dayCount := $ [ $sampleRowSize - 1 ];

emptyDayAverages := LookupTable [ "Key" "Value" ];
emptyDayStdDevs := LookupTable [ "Key" "Value" ];

_ := For 1 dayCount {{
    dayNumber := Step;
    // dailyStationData's columns are StationId (index 1), then each
    // day in order, so day N sits at raw column index N + 1.
    rawColumnIndex := $ [ $dayNumber + 1 ];
    dayColumn := GetTableColumn dailyStationData rawColumnIndex;

    attrs := ExtractLookupTableAttributes { table = dayColumn, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
    averageAcrossStations := $ [ %attrs["meanValue"] ];
    stdDevAcrossStations := $ [ %attrs["valueStd"] ];

    dayAveragesAccum := MuxLookupTable emptyDayAverages nextDayAveragesAccum;
    nextDayAveragesAccum := SetLookupTableValue dayAveragesAccum dayNumber averageAcrossStations;

    dayStdDevsAccum := MuxLookupTable emptyDayStdDevs nextDayStdDevsAccum;
    nextDayStdDevsAccum := SetLookupTableValue dayStdDevsAccum dayNumber stdDevAcrossStations;
}};

dayAverages := LookupTable nextDayAveragesAccum;
dayStdDevs := LookupTable nextDayStdDevsAccum;

ExtractLookupTableAttributes only needs extractStatisticalValueAttributes here — the other two attribute groups are switched off, since both meanValue and valueStd come from that one group. Two accumulators thread through the loop instead of one, MuxLookupTable carrying each day-by-day result separately — the average across stations and its standard deviation both still come from the same single ExtractLookupTableAttributes call, though, not a second pass over the data.

The wide format used throughout the examples above — one row per station, one column per day — isn't the only way to organize this data. A long format instead uses one row per (station, day) combination, with two key columns instead of one:

This trades a wide table for a longer, denser one — but it also means every aggregate computed with a loop above can instead be reached directly with GetTableFromKey and ExtractLookupTableAttributes, no loop at all: peeling off one key, the same technique from Sub-Tables, leaves a sub-table already shaped like a Lookup Table, ready for a single ExtractLookupTableAttributes call.

Example — the average and standard deviation across days for a single station:

stationSubTable := GetTableFromKey longFormatData [1];

attrs := ExtractLookupTableAttributes { table = stationSubTable, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
averageForStation := $ [ %attrs["meanValue"] ];
stdDevForStation := $ [ %attrs["valueStd"] ];

message := CreateString "(Average across days for Station 1: <v1>, StdDev: <v2>)" {{
    NumberValue averageForStation 1;
    NumberValue stdDevForStation 2;
}};

LogPolicy { maximumLogLevel = .result } {{
    Print { initialMessage = message, logLevel = .result } {{ }};
}};

Example — the average across stations for a single day. Day isn't leftmost, so ReorderTableColumn moves it there first, the same technique from Sub-Tables:

longFormatDataByDay := ReorderTableColumn longFormatData "Day" 1;

daySubTable := GetTableFromKey longFormatDataByDay [1];

attrs := ExtractLookupTableAttributes { table = daySubTable, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
averageForDay := $ [ %attrs["meanValue"] ];
stdDevForDay := $ [ %attrs["valueStd"] ];

message := CreateString "(Average across stations for Day 1: <v1>, StdDev: <v2>)" {{
    NumberValue averageForDay 1;
    NumberValue stdDevForDay 2;
}};

LogPolicy { maximumLogLevel = .result } {{
    Print { initialMessage = message, logLevel = .result } {{ }};
}};

Unlike every example in the previous section, neither of these needs a loop, a mux, or the parallelism cost that comes with one — GetTableFromKey does the equivalent of what a whole For loop with GetTableColumn did before, in a single call. Neither stationSubTable/daySubTable going into ExtractLookupTableAttributes, nor attrs going into the tX[“NAME”] operator right after, needs an explicit LookupTable carrier — the first is a direct connection between two ports, and the second reads straight from the Table type that operator is built for.

This “no loop” advantage is specific to querying a single, known key. Computing the same statistic for every station, or every day, still needs a loop over that set of keys — though only one level of it, since each pass resolves its whole aggregate in a single ExtractLookupTableAttributes call, with no inner accumulator the way the wide-format loops needed one.

Example — the average across days, for every station:

allStationIds := GetTableKeys longFormatData;

emptyStationAverages := LookupTable [ "Key" "Value" ];
emptyStationStdDevs := LookupTable [ "Key" "Value" ];

_ := ForEach allStationIds {{
    stationId := Step;
    stationSubTable := GetTableFromKey longFormatData stationId;

    attrs := ExtractLookupTableAttributes { table = stationSubTable, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
    averageForStation := $ [ %attrs["meanValue"] ];
    stdDevForStation := $ [ %attrs["valueStd"] ];

    stationAveragesAccum := MuxLookupTable emptyStationAverages nextStationAveragesAccum;
    nextStationAveragesAccum := SetLookupTableValue stationAveragesAccum stationId averageForStation;

    stationStdDevsAccum := MuxLookupTable emptyStationStdDevs nextStationStdDevsAccum;
    nextStationStdDevsAccum := SetLookupTableValue stationStdDevsAccum stationId stdDevForStation;
}};

stationAverages := LookupTable nextStationAveragesAccum;
stationStdDevs := LookupTable nextStationStdDevsAccum;

Example — the average across stations, for every day:

longFormatDataByDay := ReorderTableColumn longFormatData "Day" 1;
allDayIds := GetTableKeys longFormatDataByDay;

emptyDayAverages := LookupTable [ "Key" "Value" ];
emptyDayStdDevs := LookupTable [ "Key" "Value" ];

_ := ForEach allDayIds {{
    day := Step;
    daySubTable := GetTableFromKey longFormatDataByDay day;

    attrs := ExtractLookupTableAttributes { table = daySubTable, extractStatisticalKeyAttributes = .no, extractDynamicKeyValueAttributes = .no };
    averageForDay := $ [ %attrs["meanValue"] ];
    stdDevForDay := $ [ %attrs["valueStd"] ];

    dayAveragesAccum := MuxLookupTable emptyDayAverages nextDayAveragesAccum;
    nextDayAveragesAccum := SetLookupTableValue dayAveragesAccum day averageForDay;

    dayStdDevsAccum := MuxLookupTable emptyDayStdDevs nextDayStdDevsAccum;
    nextDayStdDevsAccum := SetLookupTableValue dayStdDevsAccum day stdDevForDay;
}};

dayAverages := LookupTable nextDayAveragesAccum;
dayStdDevs := LookupTable nextDayStdDevsAccum;

Both follow the same shape as the single-key examples above, just wrapped in a ForEach over every key and accumulated with MuxLookupTable — one accumulator per statistic — the same accumulator pattern used throughout this page, disqualifying these loops from parallel execution the same way.

Long format, as above, is a good fit for computing a statistic — one row per key naturally supports “summarize everything for this key.” It's a poor fit for a different goal: printing or otherwise working with a table's rows exactly as given, preserving its actual key structure, rather than reshaping it into something new. That's a harder problem: printing every row means iterating the first key's values, and for each of those, the second key's values, and so on, with a nesting depth that has to match however many key columns the table actually has. A model's graph is fixed before it runs, so there's no direct way to wire up “however many nested loops turn out to be needed” for an arbitrary table.

The practical way to solve this different goal is to reshape the table's keys instead of its rows, using a tool built for arbitrary shapes to begin with. Calculate Python Expression can read a table with any number of key columns as a plain list of lists — see Expression inputs — regardless of how many of its columns are keys, since Python doesn't need to know that shape in advance the way EGO Script's connections do. Turning every original key column into a plain data column, and generating a single new sequential key to replace them, collapses the table down to the simple single-key shape the earlier examples on this page already handle.

myTable := Table [
    "Year*", "City*", "Product*", "Price",
    2004, "Boston", "Widget", 1200,
    2004, "Boston", "Gadget", 300,
    2004, "Chelsea", "Widget", 1453,
    2007, "Boston", "Widget", 4332,
    2007, "Chelsea", "Widget", 233,
    2007, "Chelsea", "Gadget", 87
];

result := CalculatePythonExpression (String $"(
inputTable = dinamica.inputs['t1']
header = inputTable[0]
rows = inputTable[1:]

# Every original column, key or not, becomes a plain data column; a single
# sequential integer becomes the new (and only) key.
newHeader = ['Id*'] + [name.rstrip('*') for name in header]
newRows = [[i + 1] + list(row) for i, row in enumerate(rows)]
newTable = [newHeader] + newRows

dinamica.outputs['reshapedTable'] = dinamica.prepareTable(newTable, 1)
)") {{
    NumberTable myTable 1;
}};

reshapedTable := ExtractStructTable result "reshapedTable";

allIds := GetTableKeys reshapedTable;

LogPolicy { maximumLogLevel = .result } {{
    _ := ForEach allIds {{
        id := Step;
        row := GetTableRow id reshapedTable;
        rowSize := GetTupleSize row;

        // row[1] is the key itself; the actual value columns start at
        // index 2, so the loop skips index 1.
        _ := For 2 rowSize {{
            columnIndex := Step;
            cellValue := GetTupleValue columnIndex row;
            displayColumn := $ [ $columnIndex - 1 ];

            // Same generic Real/String printing technique as the earlier
            // example: attempt String first, SkipOnError catches the
            // failure if it's actually a Real, RealValue then converts it.
            attempt := SkipOnError .yes {{
                asString := String cellValue;
            }};

            _ := IfThen attempt {{
                stringDisplay := String asString;
            }};

            _ := IfNotThen attempt {{
                asReal := RealValue cellValue;
                realDisplay := String asReal;
            }};

            cellDisplay := StringJunction stringDisplay realDisplay;

            message := CreateString "(Key: <v1>, Column: <v2>, Value: <s1>)" {{
                NumberValue id 1;
                NumberValue displayColumn 2;
                NumberString cellDisplay 1;
            }};

            Print { initialMessage = message, logLevel = .result } {{ }};
        }};
    }};
}};

The original key columns' names lose their * marker on the way in (.rstrip('*')), since they're plain data now; the new Id column carries it instead, as the only key. Everything after the Python call is the same single-key iteration and generic value-printing already established — nothing about it needs to know how many keys the original table had.

A Lookup Table's fixed shape — one Real key, one Real value — makes it faster to query and iterate than a general Table, and it supports proximity-based lookups (nearest key, linear interpolation) that Tables don't. Prefer a Lookup Table whenever the data actually fits that shape, including inside map/value expressions, where Lookup Table operands add less evaluation overhead than Table operands — see Lookup Table Operators and Multi-Column Table Operators for how each is queried from within an expression.

Reach for a general Table instead when a single value per key isn't enough — when a row needs more than one associated value, or a String value, or when rows need to be indexed by more than one key at once.