DataFrame in Expression Language

DataFrame represents a two dimensional array of data with one-to-many columns and zero-to-many rows, like a relational database table, an Excel sheet or a CSV data file. Each column in the DataFrame has a name, and there must not be more than one column with the same name. DataFrame is the generic data structure used to manage all kinds data in QPR ProcessAnalyzer expression engine that run in-memory. DataFrames as linked to other entities in QPR ProcessAnalyzer as follows:

• Datatable contents is fetched into the memory as a DataFrame object
• DataFrame can be stored (persisted) to a Datatable
• ETL operations, such as joining, unions, filtering and grouping are based on the DataFrames
• Data extracted from an external data source, e.g. using ODBC, is fetched to the in-memory calculation as a DataFrame.
• When using a loading scripts, cases and events data is fed to the model using the DataFrame.

Extract Data to DataFrame

DataFrame functions	Parameters	Description
ImportOdbc (DataFrame)	connection string (string) query (string)	Runs given query in given ODBC datasource and returns data as DataFrame. AllowExternalDatasources setting needs to be true to use the ImportODBC function. Note also that the ODBC connection requires an ODBC driver to the datasource to be installed in the QPR ProcessAnalyzer Server machine. Examples: ImportOdbc("Driver={SQL Server};Server=localhost;DataBase=QPR_PA1;Trusted_Connection=yes", "SELECT * FROM OdbcTest") Returns: Contents of OdbcTest table in given ODBC data source inside a DataFrame.
ImportOdbcSecure (DataFrame)	project id (Integer) connection string key (string) query (string)	Similar command as ImportOdbc, except instead of the plaintext connection string, a secure string key is provided. Also a project id, from where to fetch the connection string key, needs to be provided. Examples: ImportOdbcSecure(12, "MySecureConnectionString", "SELECT * FROM OdbcTest") Returns: Contents of OdbcTest table in given ODBC data source inside a DataFrame.

DataFrame Properties

DataFrame properties	Description
Columns (String*)	DataFrame columns names as an array in the order the columns are in the DataFrame.
ColumnNames (Dictionary*)	DataFrame columns metadata (names and data types) in an array of dictionaries, where each dictionary has the Name and Datatype properties. For in-memory DataFrames, the precise data types are available only if the dataframe originates directly from a datatable. In other cases, for example in DataFrames originating from modification operations, the data type Any is returns for all columns. Example: 3rd column data type for a variable stored DataFrame: myDataframe.Columns[2].Datatype
Rows (Object**)	Returns the data content of the DataFrame as a two-dimensional array (matrix). The column names are not part of the data content. Examples: DatatableById(5).DataFrame.Rows[0][0] Returns: the value in the first row and first column in a datatable with id 5.
<column name> (Object*)	Returns an array of values of given column in the datatable. If the column name contains spaces, the Column function needs to be used to refer to a column. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right Returns: [zero, two, three] ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right[2] Returns: three

DataFrame Functions

DataFrame functions	Parameters	Description
Aggregate (DataFrame)	Aggregated columns (string array, or key-value pairs) Aggregation methods (string array)	Create a new DataFrame from a GroupedDataFrame by performing aggregations to all groups separately and returning one row for each group. Also returns all the columns used in the grouping of the values. Parameters: columns: Key-value pairs where each mapping describes the column name in the original DataFrame name (key) and the name of the created column (value). Columns having null value in the dictionary are not renamed. aggregation method: Array of string values describing aggregation method for each of the aggregation. The length of the array must be equal to the length of columns array. Supported aggregations are: Average: Average value of the specified column. Count: Count of rows in this group. DateTimeRange: Duration in seconds between the minimum and the maximum values of the DateTimes of the specified column. Max: Maximum value of the specified column. Median: Median value of the specified column. Min: Minimum value of the specified column. Sum: Sum of the specified column. List: Combines several string values into one string. Optionally a separator character and sorting order for the list can be defined as follows (see example below): #{"Function": "List", "Ordering": ["<ColumnToSort>"], "Separator": ","} Examples: ToDataFrame([[0, "zero"], [10, "zero"], [2, "two"], [12, "two"], [22, "two"], [3, "three"]], ["id", "text"]) .GroupBy(["text"]) .Aggregate(["ids": "id"], ["sum"]).ToCsv() Returns string: text;id three;3 two;36 zero;10 ToDataFrame([[0, "zero"], [10, "zero"], [2, "two"], [12, "two"], [22, "two"], [3, "three"]], ["id", "text"]) .GroupBy(["text"]) .Aggregate( ["average": "id", "sum": "id", "min": "id", "max": "id", "median": "id"], ["average", "sum", "min", "max", "median"] ).ToCsv() Returns string: text;average;sum;min;max;median three;3;3;3;3;3 two;12;36;2;22;12 zero;5;10;0;10;5 ToDataFrame([[0, DateTime(2020, 1)], [0, DateTime(2020, 4)], [0, DateTime(2020, 2)], [1, DateTime(2019, 1)], [1, DateTime(2009, 1)]], ["id", "timestamp"]) .GroupBy(["id"]) .Aggregate( ["duration": "timestamp", "count": "id"], ["DateTimeRange", "Count"] ).ToCsv() Returns string: id;duration;count 0;7862400;3 1;315532800;2 ToDataFrame([[1, "zero"], [1, "one"], [1, "two"], [2, "two"], [3, "two"], [3, "three"]], ["id", "text"]) .GroupBy(["id"]) .Aggregate( ["text"], [#{"Function": "List", "Ordering": ["text"], "Separator": ", "}] ).ToCsv() Returns: id;text 1;one, two, zero 2;two 3;three, two
Append (DataFrame)	DataFrame to append	Creates a new DataFrame that has the contents of given DataFrame added to the end of this DataFrame. When the data is combined, the order of columns matters, not the names of the columns. The resulting DataFrame gets column names from this DataFrame. If the number of columns is different between this DataFrame and the other DataFrame, an exception is thrown. Examples: let dataframe1 = ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]); let dataframe2 = ToDataFrame([[1, "one"], [4, "four"]], ["id", "text"]); dataframe1.Append(dataframe2); Returns: id;text 0;zero 2;two 3;three 1;one 4;four
Clone (DataFrame)	DataFrame to clone	Returns a new DataFrame that is an exact copy of the data frame this method was called for. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Clone() Returns:A copy of the original DataFrame object.
ColumnIndexes (Integer*)	Column names (String*)	Convert DataFrame column names into column indexes. The indexes are starting from zero. If a column is not found, an exception is given. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ColumnIndexes(["right", "id"]) Returns: [1, 0]
Column (Object*)	Column name	Returns an array of values of given column in the order rows are in the datatable. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Column("right") Returns: [zero, two, three]
Columns (DataFrame)	Array of column names	Creates a new DataFrame having only the defined columns of the original DataFrame. Note that Columns function is different than Columns property (difference is that the function has parameters). Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Columns(["right"]).ToCsv() Returns: right zero two three
ExcludeValues (DataFrame)	Column name (string) Value (single item) or values (array) to exclude	Creates a new DataFrame having only rows for which given column does not have any of the specified values. Examples: ToDataFrame([[0, "zero"], [1, "one"], [2, "two"]], ["id", "left"]).ExcludeValues("id", 1).ToCsv() Returns: id;left 0;zero 2;two ToDataFrame([[0, "zero"], [1, "one"], [2, "two"]], ["id", "left"]).ExcludeValues("left", ["one", "two", "three"]).ToCsv() Returns: id;left 0;zero
Head (DataFrame)	Number of top rows	Creates a new DataFrame that only contains the defined top number of rows of this DataFrame. If the DataFrame has less than the defined top rows, all rows are returned. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Head(2).ToCsv() Results string: id;right 0;zero 2;two
IncludeOnlyValues	Column name (string) Value (single item) or values (array) to include	Create a new DataFrame containing only those rows for which the given column has any of the given values. Values can be provided as a single object (if there is only one value) or an array of objects (if multiple values). Examples: ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]).IncludeOnlyValues("id", 1).ToCsv() Returns: id;left 1;one ToDataFrame([[0, "zero"], [1, "one"], [3, "three"]], ["id", "left"]).IncludeOnlyValues("id", [0, 1, 2]).ToCsv() Returns: id;left 0;zero 1;one ToDataFrame([[0, "zero"], [1, "one"], [3, "three"]], ["id", "left"]).IncludeOnlyValues("left", ["zero", "three"]).ToCsv() Returns: id;left 0;zero 3;three df.IncludeOnlyValues("EventType", ["start", "end"]) Returns a data frame containing all the rows in DataFrame df that have either "start" or "end" value in the column "EventType".
Join (DataFrame)	Other DataFrame Columns to join join type	See Joining DataFrames.
GroupBy (GroupedDataFrame)	Grouped columns (string array)	Creates a GroupedDataFrame object based based on given columns. Takes as a parameter an array of column names, based on which to group the rows. For examples, see the Aggregate function.
GroupBy (DataFrame)	Array of columns to group Array of grouping expressions	Creates a new DataFrame based on the current DataFrame. The resulting DataFrame has rows grouped by given columns and values aggregated using given functions. In the resulting DataFrame one row in the end result corresponds with one group. Parameters: Columns: Columns to group identified by column names. Aggregation expressions: Array containing the column name as a key and the aggregation expression as a value. Examples: ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id"], [ "ids": () => Sum(id), "texts": () => StringJoin(",", text), "constant": 123 ]).ToCsv() Returns: ids;texts;constant 0;zero,zero2;123 6;two,two,two3;123 3;three;123 ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id", "text"], [ "ids": () => Sum(id), "texts": () => StringJoin(",", text), "constant": 123 ]).ToCsv() Returns: ids;texts;constant 0;zero;123 0;zero2;123 4;two,two;123 2;two3;123 3;three;123 Analysis("OperationLog") .GroupBy( ["User Name"], [ "User Name": () => Column("User Name")[0], "Count": () => CountTop(Rows), "Avg. Duration": () => Average(Duration), "Max. Duration": () => Max(Duration) ] ).ToCsv() Returns (similar to this): User Name;Count;Avg. Duration;Max. Duration ;207;0.617434782608696;20.556 Administrator;665;16.3750631578947;4225.497 qpr;128;2.158765625;20.346
Merge (DataFrame)		See Merging DataFrames.
OrderBy (DataFrame)	primary ordering expression secondary ordering expression ...	Creates a new DataFrame having rows ordered in an ascending order using the given expression(s) evaluated on each row. Rows that have same ordering value in the primary ordering exprssion, are sorted based on the secondary ordering expression. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).OrderBy(text).ToCsv() Returns: id;right 3;three 2;two 0;zero Analysis("OperationLog").OrderBy(Duration).Head(1) Results string: id;right 0;zero 2;two 3;three el.Analysis("EventTypes").OrderBy(Count, Name) Returns event types in eventlog ordered primarily by Count and secondarily by Name. If there is a need to sort by some column ascending and some column descending, the sortings can be chained. Example: el.Analysis("EventTypes").OrderByDescending(Name).OrderBy(Count) Returns event types in eventlog ordered primarily by Count ascending and secondarily by Name descending.
OrderByColumns (DataFrame)	Columns to be ordered (String array) Sorting order (boolean array)	Creates a new DataFrame having rows ordered based on given columns in given directions. Parameters: columns: Column to be sorted. sort order: Array of boolean values indicating whether to sort the columns in ascending (true) or descending (false) direction. The length of the array must be equal to the length of columns array. Null values are always first in the order (both in ascending and descending order). Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).OrderByColumns(["id"], [false]).ToCsv() Returns string: id;text 3;three 2;two 0;zero ToDataFrame([[0, "zero"], [0, "nolla"], [2, "two"], [3, "three"]], ["id", "text"]).OrderByColumns(["id", "text"], [true, false]).ToCsv() Returns string: id;text 0;zero 0;nolla 2;two 3;three
OrderByDescending (DataFrame)	primary ordering expression secondary ordering expression ...	Creates a new DataFrame having rows ordered in an descending order using the given expression(s) evaluated on each row. See OrderBy above for examples.
Persist (DataTable)	Datatable name Additional parameters	Writes a DataFrame into a datatable. If a datatable with that name does not exist in the project, a new datatable is created. If a datatable with that name already exists, the DataFrame will be stored into that DataTable. The function returns the written datatable object. The following parameters are supported: ProjectName: Name of the project where the datatable is created. ProjectId: Id of project where the datatable is created. Append, ImportExistingColumnOnly and MatchByColumns: See the import function for details. Examples: let right = ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]); right.Persist("RightDataTable", ["ProjectName": "TestData"]) Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, its contents will be overwritten by the new content. let newData = ToDataFrame([[4, "four"]], ["id", "right"]); newData.Persist("RightDataTable", ["ProjectName": "TestData", "Append": true]) Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, new content will be appended into the end of the table. The following script reads data from a datatable (MyProject -> MyDatatable) convert one column (MyColumn) into floats and writes the data back to the same datatable. let project = (Projects.Where(Name=="MyProject"))[0]; let datatable = (project.Datatables.Where(Name=="MyDatatable"))[0]; DatatableById(datatable.Id).DataFrame .SetColumns([ "MyColumn": () => ToFloat(Column("MyColumn")) ]) .Persist(datatable.Name, ["ProjectId": project.Id, "Append": false]);
RemoveColumns (DataFrame)	Column names (string array)	Creates new DataFrame where the defined columns have been removed. Throws an exception if the original DataFrame doesn't contain any of the defined columns. Examples: ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]).RemoveColumns(["id"]).ToCsv() Returns string: left zero one
RenameColumns (DataFrame)	Key-value pairs of name mappings	Renames DataFrame columns. Takes a parameter of key-value pairs describing how old names (value) are changed to new names (key). Throws an exception if any of the (old) column names don't exist. Renaming DataFrame columns doesn't copy the data, so it's a quick operation for even large datasets. Examples: ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]).RenameColumns(["newId": "id", "newLeft": "left"]).ToCsv() Returns string: newId;newLeft 0;zero 1;one
Select	Column names (string array, or key-value pairs)	Creates a new DataFrame where only the selected columns are included. Allows also to change column names when the parameter contains key-value pairs where the original column names are as values and new columns names as keys (see the examples). Throws an exception if any of the columns specified does not exist. Examples: ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]).Select(["left"]).ToCsv() Returns string: left zero one ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]).Select(["newLeft": "left"]).ToCsv() Returns string: newLeft zero one ToDataFrame([[0, "zero", "nolla"], [1, "one", "yksi"]], ["id", "left", "right"]).Select(["left": "newLeft", "right"]).ToCsv() Returns string: newLeft;right zero;nolla one;yksi
SetColumns (DataFrame)	New/modified columns as array	Creates a new DataFrame based on the current DataFrame, where new columns have been created and/or existing columns have been modified. New and modified columns are defined using an array, where the column name is as a key and as a value there is the expression to calculate the new or modified column. When specifying a column that already exists, the column values are modified. When specifying a new column name, that column is created as a new column to the resulting DataFrame. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns([ "both": () => text + "=" + id ]).ToCsv() Returns: id;text;both 0;zero;zero=0 2;two;two=2 3;three;three=3 ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns([ "text": () => text + "=" + id ]).ToCsv() Returns: id;text 0;zero=0 2;two=2 3;three=3 ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns([ "both": () => text + "=" + id, "both+1": () => both + 1, "text": () => "Done: " + Column("both+1"), "constant": 1234 ]).ToCsv() Returns: id;text;both;both+1;constant 0;Done: zero=01;zero=0;zero=01;1234 2;Done: two=21;two=2;two=21;1234 3;Done: three=31;three=3;three=31;1234
Skip (DataFrame)	Number of rows to skip	Return DataFrame where the defined number of rows is removed ("skipped"). Returns an empty DataFrame if skipping more rows than there are rows in the DataFrame. Examples: ToDataFrame([[0, "zero"], [1, "one"], [2, "two"], [3, "three"]], ["id", "right"]).Skip(1).ToCsv() Results string: id;right 1;one 2;two 3;three
Tail (DataFrame)	Number of rows	Creates a new DataFrame that has only the bottom number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned. Example: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Tail(2).ToCsv() Results string: id;right 2;two 3;three
ToCsv (String)	includeHeaders (boolean)	Converts a DataFrame into a CSV data (i.e. string). The CSV data has the following formatting: Column separator: semicolon (;) Decimal separator in numeric fields: period (.) Quotation character for text fields: double quotes (") (used when the textual value contains semicolon, double quotes, linebreak or tabulator) Escape character: Double quotes in textual fields are escaped with two double quotes ("") Date format for date fields: yyyy-MM-dd HH:mm:ss,fff Timespan (duration) format: dd.hh:mm:ss First line contains column headers Parameter includeHeaders defines whether the header (column names) is returned (true, default) or not (false) as the first row. Example: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ToCsv() Returns: id;right 0;zero 2;two 3;three
WithDenseRankNumberColumn (DataFrame)	New column name (String) Order by columns (String array) Partition by columns (String array)	Similar to the WithRankColumn function, except rank numbers doesn't contain gaps when there are rows with same rank values. let data = ToDataFrame([ ["A", "Dallas", 8], ["B", "Dallas", 5], ["C", "Dallas", 5], ["D", "Dallas", 4], ["B", "New York", 6], ["C", "New York", 2] ], ["Customer", "Region", "Revenue"]); data.WithDenseRankColumn("Rank", ["Revenue"]).OrderByColumns(["Revenue"], [true]).ToCsv(); Returns: Customer;Region;Revenue;Rank C;New York;2;1 D;Dallas;4;2 B;Dallas;5;3 C;Dallas;5;3 B;New York;6;4 A;Dallas;8;5
WithColumn (DataFrame)	New column name (String) Calculation expression	Creates new DataFrame with new column having value evaluated using given expression. If the column name already exist in the original DataFrame, it gets replaced with the evaluated expression. ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).WithColumn("combinedtext", text + "=" + (id * 2)).ToCsv() Returns: id;text;combinedtext 0;zero;zero=0 2;two;two=4 3;three;three=6
WithRankColumn (DataFrame)	New column name (String) Order by columns (String array) Partition by columns (String array)	Similar to the WithRowNumberColumn function, except the produced numbering is based on the ranking logic. The difference to the row number is that the rows with an equal sorting value, gets the same rank number. The numbering continues being same as the row number, i.e. there are gaps in the assigned ranks if there are rows same rank values (see the example). let data = ToDataFrame([ ["A", "Dallas", 8], ["B", "Dallas", 5], ["C", "Dallas", 5], ["D", "Dallas", 4], ["B", "New York", 6], ["C", "New York", 2] ], ["Customer", "Region", "Revenue"]); data.WithRankColumn("Rank", ["Revenue"]).OrderByColumns(["Revenue"], [true]).ToCsv(); Returns: Customer;Region;Revenue;Rank C;New York;2;1 D;Dallas;4;2 B;Dallas;5;3 C;Dallas;5;3 B;New York;6;5 A;Dallas;8;6
WithRowNumberColumn (DataFrame)	New column name (String) Order by columns (String array) Partition by columns (String array)	Creates new DataFrame with a new column containing row numbers based on defined partitions and ordering. Partitions are defined as one or several columns, and each partition will contain own row numbering starting from one. Rows are ordered within each partition and the row numbering is based in the ordering (which is separate than the ordering of the result data). Parameters: New column name: Name of the new column containing the row number. Order by columns: Array of column(s) to order the rows where the row number is based on. Partition by columns: Array of column(s) to partition the rows by. Each partition will have own row numbering starting from one. Partition parameter can be omitted, which will treat entire data as one partition. Examples: let data = ToDataFrame([ ["A", "Dallas", 8], ["B", "Dallas", 5], ["C", "Dallas", 4], ["B", "New York", 6], ["C", "New York", 2] ], ["Customer", "Region", "Revenue"]); data.WithRowNumberColumn("Order", ["Revenue"]).OrderByColumns(["Revenue"], [true]).ToCsv(); Returns: Customer;Region;Revenue;Order C;New York;2;1 C;Dallas;4;2 B;Dallas;5;3 B;New York;6;4 A;Dallas;8;5 data.WithRowNumberColumn("Order", ["Revenue"], ["Region"]).OrderByColumns(["Region", "Revenue"], [true, true]).ToCsv() Returns: Customer;Region;Revenue;Order C;Dallas;4;1 B;Dallas;5;2 A;Dallas;8;3 C;New York;2;1 B;New York;6;2
Where (DataFrame)	Condition expression	Creates a new DataFrame having only rows for which the given condition expression returns true. The condition expression can refer to the columns of the DataFrame (see the example below). Examples: let df = ToDataFrame([[0, "zero"], [2, "two", true], [3, "three"]], ["id", "string"]); All the following expression return the same: df.Where(id < 3); df.Where(Column("id") < 3); df.Where(_[0] < 3); Returns: id;string 0;zero 2;two
Zip (DataFrame)	DataFrame	Creates a new DataFrame that has the contents of given DataFrame appended as new columns into the end of this DataFrame. Returns a new DataFrame that has the colums from both the data frames so that the columns from the other DataFrame are appended to the end of the columns of this DataFrame. If the number of rows is different between this DataFrame and the other DataFrame, an exception is thrown. There must not be duplicate column names in the DataFrames - otherwise an exception is thrown. Examples: let df1 = ToDataFrame([[0, "zero"], [1, "one"], [4, "four"]], ["id", "text"]); let df2 = ToDataFrame([[1, "one"], [2, "two"], [3, "three"]], ["id2", "text2"]); df1.Zip(df2).ToCsv(); Returns: id;text;id2;text2 0;zero;1;one 1;one;2;two 4;four;3;three

The following functions can be used to initialize DataFrame objects:

Function	Parameters	Description
ToDataFrame	data as 2-dimensional array column names (array of strings) or column metadata (array of dictionaries)	Creates a DataFrame object containing the given data (in two dimensional array) and the array of column names. Number of column names must be the same as the number of columns in the two dimensional array. To be able to define data types for the columns, the second parameter can also be an array of dictionaries (one for each column), where each dictionary contains Name and DataType properties (see an example below). Available data types are String, Integer, Float, DateTime, Boolean, Duration (Timespan) and Any (can contain any type of data). The first parameter can also be an existing DataFrame which will create a copy of the DataFrame. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ToCsv() Returns: A string containing: id;right 0;zero 2;two 3;three ToDataFrame( [[0, "zero"], [2, "two"], [3, "three"]], [ #{"Name": "id", "DataType": "Integer"}, #{"Name": "right", "DataType": "String"} ] ).ToCsv(); Returns: id;right 0;zero 2;two 3;three

Joining DataFrames

Performs a joining operation between two DataFrames.

Parameters:

1. DataFrame: The other DataFrame to join.
2. Columns to match: Columns which the joining is based on, can be defined as follows:
   • If joining using one column having the same name in both DataFrames, the column name is specified as as string.
   • If joining using several columns having the same names in both DataFrames, the column names are specified as a string array.
   • If joining using columns having different names between the DataFrames, columns are specified as an array of key-value pairs, where the key is the column name in the left side DataFrame, and value is the column name in the right side DataFrame.
3. Join type which can be
   • inner (default): row is generated if both DataFrames have the key.
   • leftouter: at least one row is generated for each left side DataFrame row, even if there is no matching other row (in that case null is given as value for the other columns).
   • rightouter: at least one row is generated for each right side DataFrame row, even if there is no matching other row (in that case null is given as value for the other columns).
   • outer: at least one row is generated both for the left and right side DataFrames even if there is no matching other row (in that case null is given as value for the other columns).

Examples:

let left = ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]);
let right = ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]);
left.join(right, "id").ToCsv()
Returns:
id;left;right
0;zero;zero

let left = ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"]);
let right = ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]);
left.join(right, "id", "leftouter").ToCsv()
Returns:
id;left;right
0;zero;zero
1;one;

let left = ToDataFrame([[0, 0, "zerozeroleft"], [0, 1, "zeroleft"], [1, 2, "oneleft"]], ["idleft1", "idleft2", "left"]);
let right = ToDataFrame([[0, 0, "zerozeroright"], [0, 1, "zeroright"], [2, 3, "tworight"], [3, 4, "threeright"]], ["idright1", "idright2", "right"]);
left.join(right, ["idleft1": "idright1"], "inner");
Returns:
idleft1;idleft2;left;idright2;right
0;0;zerozeroleft;0;zerozeroright
0;0;zerozeroleft;1;zeroright
0;1;zeroleft;0;zerozeroright
0;1;zeroleft;1;zeroright

let left = ToDataFrame([[0, 0, "zerozeroleft"], [0, 1, "zeroleft"], [1, 2, "oneleft"]], ["idleft1", "idleft2", "left"]);
let right = ToDataFrame([[0, 0, "zerozeroright"], [0, 1, "zeroright"], [2, 3, "tworight"], [3, 4, "threeright"]], ["idright1", "idright2", "right"]);
left.join(right, ["idleft1": "idright1", "idleft2": "idright2"], "inner");
Returns:
idleft1;idleft2;left;right
0;0;zerozeroleft;zerozeroright
0;1;zeroleft;zeroright

Merging DataFrames

Creates a new DataFrame that has the contents of a DataFrame (target) merged with another DataFrame (source). The merging works in the same principle as in the SQL language. Note that the merging does not create new columns but the result DataFrame has the same columns as the target DataFrame. Merging has the following principle:

Parameters:

1. Source DataFrame: DataFrame to be merged with the target DataFrame.
2. Columns to used for matching: Columns used to match the source and target DataFrames. This parameter has similar syntax as the 2. parameter in the join function.
3. Columns to UPDATE for matching rows: Columns to update from the source DataFrame to the target DataFrame for the rows that match between the DataFrames. Note that the matching columns cannot be updated because they always have same values due to the matching logic. This parameter has similar syntax as the 2. parameter in the join function. Following special values can also be used:
   • If the value is _remove, all matching rows are be deleted from the resulting DataFrame.
   • If the value is [], no columns are updated to the resulting DataFrame.
   • If the value is null (default), all columns are copied from the source to target (the ones having matching names).
4. Columns to CREATE for non-match rows in source: Columns to create to the result DataFrame, if a matching row in the source DataFrame is not found in the target DataFrame. This parameter has similar syntax as the 2. parameter in the join function. Following special values can also be used:
   • If the value is _remove, no new rows are created to the resulting DataFrame.
   • If the value is [], new rows are created to the resulting DataFrame but all columns of the created rows get empty values.
   • If the value is null (default), when creating rows, all columns are copied from the source to target (the ones having matching names).
5. Keep or DELETE non-matching rows in target: Boolean value that defines whether those rows in the target DataFrame where no matching row in the source DataFrame is found, are included in the result DataFrame (true) or deleted (false). The default value is true.

Examples:

Update case attribute values for some cases and create non-existing:

let CaseData = ToDataFrame([
	["0", "New York", 6],
	["1", "Dallas", 3],
	["2", "Dallas", 8],
	["3", "Chicago", 4],
	["4", "New York", 2]
], ["Case id", "Region", "Cost"]);
let UpdatedData = ToDataFrame([
	["0", 7],
	["1", 5],
	["4", 2],
	["5", 2]
], ["Case id", "Cost"]);
CaseData.Merge(UpdatedData, "Case id").toCsv();

Returns:
Case id;Region;Cost
0;New York;7
1;Dallas;5
2;Dallas;8
3;Chicago;4
4;New York;2
5;;2

Update case attribute values for some cases and don't create non-existing:

let CaseData = ToDataFrame([
	["0", "New York", 6],
	["1", "Dallas", 3],
	["2", "Dallas", 8],
	["3", "Chicago", 4],
	["4", "New York", 2]
], ["Case id", "Region", "Cost"]);
let UpdatedData = ToDataFrame([
	["0", 7],
	["1", 5],
	["4", 2],
	["5", 2]
], ["Case id", "Cost"]);
CaseData.Merge(UpdatedData, "Case id", null, _remove).toCsv();

Returns:
Case id;Region;Cost
0;New York;7
1;Dallas;5
2;Dallas;8
3;Chicago;4
4;New York;2

Update case attribute values for some cases (create non-existing) where columns to match have different names in the source and target DataFrames ("Case id" in target and "Case" in source are used to match, and "Cost" in target is updated from "Variable Cost" in source):

let CaseData = ToDataFrame([
	["0", "New York", 6],
	["1", "Dallas", 3],
	["2", "Dallas", 8],
	["3", "Chicago", 4],
	["4", "New York", 2]
], ["Case id", "Region", "Cost"]);
let UpdatedData = ToDataFrame([
	["0", 7, 4],
	["1", 5, 2],
	["4", 2, 0],
	["5", 2, 1]
], ["Case", "Cost", "Variable Cost"]);
CaseData.Merge(UpdatedData, ["Case id": "Case"], ["Cost": "Variable Cost"]).toCsv();

Returns:
Case id;Region;Cost
0;New York;4
1;Dallas;2
2;Dallas;8
3;Chicago;4
4;New York;0
5;;1

Delete matching cases (don't create non-matching):

let CaseData = ToDataFrame([
	["0", "New York", 6],
	["1", "Dallas", 3],
	["2", "Dallas", 8],
	["3", "Chicago", 4],
	["4", "New York", 2]
], ["Case id", "Region", "Cost"]);
let UpdatedData = ToDataFrame([
	["0"],
	["1"],
	["4"],
	["5"]
], ["Case id"]);
CaseData.Merge(UpdatedData, "Case id", _remove, _remove).toCsv();

Returns:
Case id;Region;Cost
2;Dallas;8
3;Chicago;4

Update matching cases, create non-matching by source as new, and delete non-matching by target:

let CaseData = ToDataFrame([
	["0", "New York", 6],
	["1", "Dallas", 3],
	["2", "Dallas", 8],
	["3", "Chicago", 4],
	["4", "New York", 2]
], ["Case id", "Region", "Cost"]);
let UpdatedData = ToDataFrame([
	["0", 7],
	["1", 5],
	["4", 2],
	["5", 2]
], ["Case id", "Cost"]);
CaseData.Merge(UpdatedData, "Case id", null, null, false).toCsv();

Returns:
Case id;Region;Cost
0;New York;7
1;Dallas;5
4;New York;2
5;;2

let target = ToDataFrame([[0, "zero", "target"], [1, "", "target"]], ["id", "text", "frame"]);
let source = ToDataFrame([[1, "one", "source"], [2, "two", "source"], [3, "three", "source"]], ["id", "text", "frame"]);

target.Merge(source, "id").ToCsv()
Returns (one key, default parameters, identical dataframe columns):
id;text;frame
0;zero;target
1;one;source
2;two;source
3;three;source

target.Merge(source, "id", ["text"]).ToCsv()
Returns (one key, default parameters, identical dataframe columns, copy only text column from source):
id;text;frame
0;zero;target
1;one;target
2;two;
3;three;

target.Merge(source, "id", ["text"], _remove).ToCsv()
Returns (one key, default parameters, identical dataframe columns, copy only text column from source, remove rows found only in source):
id;text;frame
0;zero;target
1;one;target

target.Merge(source, "id", ["text"], _remove, false).ToCsv()
Returns (one key, identical dataframe columns, copy only text column from source, remove rows found only in source or only in target):
id;text;frame
1;one;target

target.Merge(source, "id", _remove, _remove, false).ToCsv()
Returns (one key, identical dataframe columns, remove all rows):
id;text;frame


let target = ToDataFrame([[0, 0, "zerozeroleft", "target"], [0, 1, "zeroleft", "target"], [1, 2, "left", "target"], [4, 5, "fourleft", "target"]], ["idleft1", "idleft2", "textleft", "frame"]);
let source = ToDataFrame([[0, 0, "zerozeroright", "source"], [0, 1, "zeroright", "target"], [1, 2, "oneright", "source"], [2, 3, "tworight", "source"], [3, 4, "threeright", "source"]], ["idright1", "idright2", "textright", "frame"]);

target.Merge(source, ["idleft1": "idright1"]).ToCsv()
Returns (one key, default parameters, different dataframe columns, copy all matching columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;0;zerozeroleft;target
0;1;zeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;;;source
3;;;source

target.Merge(source, ["idleft1": "idright1"], []).ToCsv()
Returns (one key, default parameters, different dataframe columns, copy only key column):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;target
0;0;zerozeroleft;target
0;1;zeroleft;target
0;1;zeroleft;target
1;2;left;target
4;5;fourleft;target
2;;;
3;;;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy all matching columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;3;;source
3;4;;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy only textright -column):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright", "frame"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy textright and frame -columns):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;source
0;1;zeroright;target
1;2;oneright;source
4;5;fourleft;target
2;3;tworight;source
3;4;threeright;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], _remove).ToCsv()
Returns (two keys, default parameters, different dataframe columns, remove all matching rows, copy only matching columns):
idleft1;idleft2;textleft;frame
4;5;fourleft;target
2;3;;
3;4;;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id columns and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
4;5;fourleft;target
2;3;tworight;source
3;4;threeright;source

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], null, ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv()
Returns (two keys, default parameters, different dataframe columns, don't copy any columns from source for matching columns, copy id columns and textright-column for rows not found in target):
idleft1;idleft2;textleft;frame
0;0;zerozeroleft;source
0;1;zeroleft;target
1;2;left;source
4;5;fourleft;target
2;3;tworight;
3;4;threeright;

target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"], false).ToCsv()
Returns (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target, remove all rows not found in source):
idleft1;idleft2;textleft;frame
0;0;zerozeroright;target
0;1;zeroright;target
1;2;oneright;target
2;3;tworight;source
3;4;threeright;source