VAL_TG_CG.ipynb

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   "source": [
    "# Create Target/Control Group Split with Teradata VAL  \\\n",
    "**author:** Bazman  \n",
    "**place/date:** wfh, sep-2021"
   ]
  },
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   "execution_count": 412,
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      "text/markdown": [
       "#### %addconnect\n",
       "Add a Teradata Vantage database connection specification.\n",
       "\n",
       "%addconnect name=&lt;Connection Name&gt;[, user=&lt;User Name&gt;], host=&lt;Database Server Name or IP Address&gt; [, database=\"&lt;Default Database&gt;\"] [, props=\"&lt;DB Properties&gt;\"] [, dbviews=\"ViewsV\"]\n",
       " \n",
       "If the **user** parameter is not provided with the %addconnect, you must provide the **&lt;User Name&gt;** value with the %connect command.\n",
       "\n",
       "**database=\"&lt;Default Database&gt;\"** will set the default database for the connection. Executing the DATABASE SQL command statement will take precedence over the default database connection setting.\n",
       "\n",
       "**%help currentdb** command will display the current database value.\n",
       "\n",
       "**props=\"&lt;DB Properties&gt;\"** is used to set certain supported connection properties. The supported properties are:\n",
       "\n",
       "| Property        | Default | Description\n",
       "|:----------------|:--------|:----------------------------------------------------------------\n",
       "| **account**     |         | Specifies the Teradata Database account.\n",
       "| **cop**         | true    | Specifies whether COP Discovery is performed.\n",
       "| **coplast**     | false   | Specifies how COP Discovery determines the last COP hostname.\n",
       "| **dbs_port**    | 1025    | Specifies the Teradata Database port number.\n",
       "| **encryptdata** | true    | Controls encryption of data to database.  It is not recommended to set this to false.\n",
       "| **log**         | 0       | Controls driver debug logging.\n",
       "| **logdata**     |         | Specifies extra data for the chosen logon authentication method.\n",
       "| **logmech**     | TD2     | Specifies the logon authentication method. Supported values are **TD2**,**LDAP**,**TDNEGO**.\n",
       "| **sip_support** | true    | Controls whether StatementInfo parcel is used.\n",
       "| **tmode**       | DEFAULT | Specifies the transaction mode. Possible values are **DEFAULT**, **ANSI**, or **TERA** (must be uppercase).\n",
       "\n",
       "For more detailed information on the supported properties see\n",
       "https://github.com/Teradata/jupyterextensions/blob/master/docs/CONNECTIONPARAMETERS.md\n",
       "\n",
       "* Multiple DB properties can be passed in a comma-separated list, e.g. props=\"encryptdata=false, tmode=ANSI\".\n",
       "* Use of any unsupported properties will be ignored or errors will be reported as appropriate.\n",
       "\n",
       "**dbviews=\"ViewsV\"** is used to set the data dictionary query to use the **V** views instead of the **VX** views.\n",
       "  * By default, the VX views are used, which limits the display of database objects to only those for which \n",
       "the connected user has been granted permission.\n",
       "\n",
       "The connection specification will be saved and can be used in the **%connect**, **%disconnect** and **%rmconnect** commands.\n",
       "\n",
       "The **Connection Manager** can also be used to add Teradata connections. The Connection Manager can be\n",
       "accessed from the left-hand, vertical toolbar, by selecting the Manage Teradata Connections icon. The Connection Manager will\n",
       "display the list of connections, with options to **test**, **edit**, **copy/clone**, or **remove** the connection. If you hover over\n",
       "a connection, the details for that connection are provided in a pop-up message. Options are also provided in the Connection Manager toolbar to\n",
       "**refresh** the list of connections and **add** a new connection. </br>\n",
       "\n",
       "---\n",
       "#### %connect\n",
       "Connect to a Vantage system and/or make the connection active.\n",
       "\n",
       "%connect | %%connect [name=]&lt;Connection Name&gt; [, user=&lt;User Name&gt;] [, database=&lt;Default Database&gt;] [, rowcount=&lt;Row Count&gt;] [, props=\"&lt;DB Properties&gt;\"]\n",
       "\n",
       "* **%connect** will make the named connection the active connection.\n",
       "* **%%connect** is a cell magic command and can be followed on a new line in the same cell by a SQL statement.\n",
       "  * The SQL statement must not be on the same line as the magic and it's parameters.\n",
       "  * This will set the connection used for this SQL statement but will not change the active connection.\n",
       "* **user** can be specified on the connect. It will override the **user** parameter on the %addconnect if one was specified. \n",
       " * If the **user** has not been specified in the %addconnect, nor %connect, a prompt will be displayed and the **&lt;User Name&gt;** can be entered in the text field.\n",
       " * A password is required only if not already connected. A prompt will be displayed and the password can be entered in a hidden text field.\n",
       "* **database=\"&lt;Default Database&gt;\"** will set the default database for the connection. This will override the default database setting stored for the connection.\n",
       " * Executing the DATABASE SQL command statement will take precedence over the default database connection setting.\n",
       "* **%help currentdb** command will display the current database value.\n",
       "* **rowcount** limits the number of rows displayed in the result set (e.g. rowcount=100).\n",
       "  * If **rowcount** is provided with the cell magic, it will only be applied to the result set returned for the cell SQL statement.\n",
       "* **%help rowcount** command will display the current rowcount value.\n",
       "* **props=\"&lt;DB Properties&gt;\"** is used to override supported connection properties. The supported properties are:\n",
       "\n",
       "| Property        | Default | Description\n",
       "|:----------------|:--------|:----------------------------------------------------------------\n",
       "| **account**     |         | Specifies the Teradata Database account.\n",
       "| **cop**         | true    | Specifies whether COP Discovery is performed.\n",
       "| **coplast**     | false   | Specifies how COP Discovery determines the last COP hostname.\n",
       "| **dbs_port**    | 1025    | Specifies the Teradata Database port number.\n",
       "| **encryptdata** | true    | Controls encryption of data to database.  It is not recommended to set this to false.\n",
       "| **log**         | 0       | Controls driver debug logging.\n",
       "| **logdata**     |         | Specifies extra data for the chosen logon authentication method.\n",
       "| **logmech**     | TD2     | Specifies the logon authentication method. Supported values are **TD2**,**LDAP**,**TDNEGO**.\n",
       "| **sip_support** | true    | Controls whether StatementInfo parcel is used.\n",
       "| **tmode**       | DEFAULT | Specifies the transaction mode. Possible values are **DEFAULT**, **ANSI**, or **TERA** (must be uppercase).\n",
       "\n",
       "For more detailed information on the supported properties see\n",
       "https://github.com/Teradata/jupyterextensions/blob/master/docs/CONNECTIONPARAMETERS.md\n",
       "\n",
       "* Multiple DB properties can be passed in a comma-separated list, e.g. props=\"encryptdata=false, tmode=ANSI\".\n",
       "* Use of any unsupported properties will be ignored or errors will be reported as appropriate.\n",
       "\n",
       "The **Connection Manager** can also be used to manage Teradata connections. The Connection Manager can be\n",
       "accessed from the left-hand, vertical toolbar, by selecting the Manage Teradata Connections icon. The Connection Manager will\n",
       "display the list of connections, with options to **test**, **edit**, **copy/clone**, or **remove** the connection. If you hover over\n",
       "a connection, the details for that connection are provided in a pop-up message. Options are also provided in the Connection Manager toolbar to\n",
       "**refresh** the list of connections and **add** a new connection.\n",
       "\n",
       "---\n",
       "#### %disconnect\n",
       "Disconnect from a Vantage system.\n",
       "\n",
       "%disconnect &lt;Connection Name&gt;\n",
       "\n",
       "---\n",
       "#### %rmconnect\n",
       "Remove a connection specification.\n",
       "\n",
       "%rmconnect &lt;Connection Name&gt;\n",
       "\n",
       "The **Connection Manager** can also be used to remove a Teradata connection specification. The Connection Manager can be\n",
       "accessed from the left-hand, vertical toolbar, by selecting the Manage Teradata Connections icon. The Connection Manager will\n",
       "display the list of connections, with options to **test**, **edit**, **copy/clone**, or **remove** the connection. If you hover over\n",
       "a connection, the details for that connection are provided in a pop-up message. Options are also provided in the Connection Manager toolbar to\n",
       "**refresh** the list of connections and **add** a new connection.\n",
       "\n",
       "---\n",
       "#### %chconnect\n",
       "Change a connection specification.\n",
       "\n",
       "%chconnect name=&lt;Connection Name&gt; [, user=&lt;New User Name&gt;] [, host=&lt;New Database Server Name or IP Address&gt;] [, database=\"&lt;Default Database&gt;\"] [, props=\"&lt;New DB Properties&gt;\"] [, dbviews=\"ViewsV\"]\n",
       "\t\t\n",
       "* Enter the new values for the connection specification parameters, or enter an empty string to remove the parameter altogether.\n",
       "* The **name** value cannot be changed.\n",
       "* The **host** value can be changed but cannot be removed, as it is a required parameter for the connection specification.\n",
       "* **database=\"&lt;Default Database&gt;\"** will change the default database setting stored for the connection. \n",
       " * Executing the DATABASE SQL command statement will take precedence over the default database connection setting.\n",
       "* **%help currentdb** command will display the current database value.\n",
       "* If the connection is currently **connected**, you will need to disconnect and re-connect for the changes to take effect.\n",
       "* **props=\"&lt;New DB Properties&gt;\"** is used to change supported connection properties. The supported properties are:\n",
       "\n",
       "| Property        | Default | Description\n",
       "|:----------------|:--------|:----------------------------------------------------------------\n",
       "| **account**     |         | Specifies the Teradata Database account.\n",
       "| **cop**         | true    | Specifies whether COP Discovery is performed.\n",
       "| **coplast**     | false   | Specifies how COP Discovery determines the last COP hostname.\n",
       "| **dbs_port**    | 1025    | Specifies the Teradata Database port number.\n",
       "| **encryptdata** | true    | Controls encryption of data to database.  It is not recommended to set this to false.\n",
       "| **log**         | 0       | Controls driver debug logging.\n",
       "| **logdata**     |         | Specifies extra data for the chosen logon authentication method.\n",
       "| **logmech**     | TD2     | Specifies the logon authentication method. Supported values are **TD2**,**LDAP**,**TDNEGO**.\n",
       "| **sip_support** | true    | Controls whether StatementInfo parcel is used.\n",
       "| **tmode**       | DEFAULT | Specifies the transaction mode. Possible values are **DEFAULT**, **ANSI**, or **TERA** (must be uppercase).\n",
       "\n",
       "For more detailed information on the supported properties see\n",
       "https://github.com/Teradata/jupyterextensions/blob/master/docs/CONNECTIONPARAMETERS.md\n",
       "\n",
       "* Multiple DB properties can be passed in a comma-separated list, e.g. props=\"encryptdata=false, tmode=ANSI\".\n",
       "* Use of any unsupported properties will be ignored or errors will be reported as appropriate.\n",
       "\n",
       "The **Connection Manager** can also be used to edit a Teradata connection. The Connection Manager can be\n",
       "accessed from the left-hand, vertical toolbar, by selecting the Manage Teradata Connections icon. The Connection Manager will\n",
       "display the list of connections, with options to **test**, **edit**, **copy/clone**, or **remove** the connection. If you hover over\n",
       "a connection, the details for that connection are provided in a pop-up message. Options are also provided in the Connection Manager toolbar to\n",
       "**refresh** the list of connections and **add** a new connection.\n",
       "\n",
       "---\n",
       "#### %lsconnect\n",
       "List the known connections.\n",
       "\n",
       "%lsconnect\n",
       "\n",
       "* The active connection is marked with a *.\n",
       "\n",
       "---\n",
       "#### %chart\n",
       "Display a Vega-Lite visualization of a query result set or an existing Vega or VegaLite chart.\n",
       "\n",
       "%chart x=&lt;XColumnName&gt;, y=&lt;YColumnName&gt;,</br>\n",
       "[title=&lt;ChartTitle&gt;,] [id=&lt;ResultSetID&gt;|&lt;HistoryID&gt;,] [part=&lt;PartNum&gt;]</br>\n",
       "[labelx=&lt;XLabel&gt;,] [labely=&lt;YLabel&gt;,]</br>\n",
       "[gridx=&lt;ttrue|false&gt;,] [gridy=&lt;true|false&gt;,]</br>\n",
       "[typex=&lt;q|n|o|t&gt;,] [typey=&lt;q|n|o|t&gt;,]</br>\n",
       "[mark=&lt;area|bar|circle|line|point|rect|rule|square|text|tick&gt;,]</br>\n",
       "[color=&lt;CSSColorSpec&gt;,] [height=&lt;ChartHeight&gt;,] [width=&lt;ChartWidth&gt;]</br>\n",
       "[spec=&lt;ChartSpec&gt;, [data=&lt;ChartData&gt;]]\n",
       "\n",
       "* **typex** and **typey** values: q=quantitative n=nominal o=ordinal t=temporal\n",
       "  * (defaults based on column type: numeric types -> quantitative, time times -> temporal, other types -> nominal)\n",
       "* **&lt;ResultSetID&gt;** is the timestamp/directory name of the result set.\n",
       "* **&lt;HistoryID&gt;** is the id of the history item (see **%history** command).\n",
       "* If the **id** is not specified, the most recently accessed result set is displayed.\n",
       "* The **spec** parameter is used to display an existing chart spec.\n",
       "* If **spec** is used, only **data**, **id**, and **part** parameters are supported.\n",
       "\n",
       "---\n",
       "#### %table\n",
       "Display a previous result set as a table.\n",
       "\n",
       "%table [[id=]&lt;ResultSetID&gt;|&lt;HistoryID&gt;]\n",
       "\n",
       "* **&lt;ResultSetID&gt;** is the timestamp/directory name of the result set.\n",
       "* **&lt;HistoryID&gt;** is the id of the history item (see **%history** command).\n",
       "* If the **id** is not specified, the most recently accessed result set is displayed.\n",
       "\n",
       "---\n",
       "#### %meta\n",
       "Display metadata of a previous result set.\n",
       "\n",
       "%meta [[id=]&lt;ResultSetID&gt;|&lt;HistoryID&gt;]\n",
       "\n",
       "* **&lt;ResultSetID&gt;** is the timestamp/directory name of the result set.\n",
       "* **&lt;HistoryID&gt;** is the id of the history item (see **%history** command).\n",
       "* If the **id** is not specified, the metadata of the most recently accessed result set is displayed.\n",
       "\n",
       "---\n",
       "#### %dataload\n",
       "Load a CSV file into a database table.\n",
       "\n",
       "%dataload database=&lt;DatabaseName&gt;, table=&lt;TableName&gt;, filepath=&lt;Path to CSV file&gt; [, batchsize=&lt;Rows per Batch&gt;]\n",
       "\n",
       "* The **filepath** is relative to the JupyterLab workspace (where JupyterLab was launched).\n",
       "* You can specify an absolute file path by starting the path with the OS specific directory notation.\n",
       "* **batchsize** specifies the number of rows per batch.\n",
       "  * Valid values are between 1 and 16383. The default value is 5000.\n",
       "* The maximum number of rows that the **%dataload** magic will load is currently set to 100,000 rows.  Loading will stop when the maximum is reached.\n",
       "* The data must be in the proper format to insert into the columns of your table. Specifically, data types must match and any data constraints must be met. \n",
       "No pre-processing of the data file is performed.\n",
       "* If an error occurs while attempting to load your data, the entire load operation will be rolled back. \n",
       "* The status of your dataload is stored in the Teradata SQL History.\n",
       "\n",
       "---\n",
       "#### %history\n",
       "Display a table or the details of previously executed commands.\n",
       "\t\tIf a single history item is specified, the details for that item will be displayed.\n",
       "\n",
       "%history [all] | [id=HistoryID] | [&lt;Limit&gt;[,&lt;StartID&gt;]]\n",
       "\n",
       "* **all** display all history items.\n",
       "* **&lt;HistoryID&gt;** the id of the history item to display (will display history details rather than a table).\n",
       "* **&lt;Limit&gt;** is the number of history items to display. The default limit is 20.\n",
       "* **&lt;StartID&gt;** is the starting position to list. The default is to display the **&lt;Limit&gt;** most recent items.\n",
       "\n",
       "---\n",
       "#### %rmhistory\n",
       "Remove one or more history items and associated result set(s).\n",
       "\n",
       "%rmhistory &lt;HistoryID&gt; | [&lt;StartID&gt;-&lt;EndID&gt;]]\n",
       "\n",
       "Remove a single history item **&lt;HistoryID&gt;** or a range of history items **&lt;StartID&gt;-&lt;EndID&gt;**.\n",
       "\n",
       "* **&lt;HistoryID&gt;** is the single history item to remove.\n",
       "* **&lt;StartID&gt;** is the low end (inclusive) of the range of history items to remove.\n",
       "* **&lt;EndID&gt;** is the high end (inclusive) of the range of history items to remove.\n",
       "* The **%rmhistory** command will prompt for verification before removing history items.\n",
       "* The result set that is associated with the history item will also be removed.\n",
       "\n",
       "---\n",
       "#### %var\n",
       "Define one or more variables for use in SQL statements and magic commands.\n",
       "\n",
       "%var &lt;name&gt;=&lt;value&gt; [, &lt;name&gt;=&lt;value&gt;]...\n",
       "\n",
       "* Variables can be used in SQL statements with the notation **\\$\\{name\\}**.\n",
       "\t* e.g.\n",
       "\t\t\t%var db=mydb, table=mytable\n",
       "\t\t\tselect * from ${db}.${table}\n",
       "\t\twill result in the following SQL being run\n",
       "\t\t\tselect * from mydb.mytable\n",
       "\t\tand\n",
       "\t\t\t%var col1=c1, col2=c2\n",
       "\t\t\tselect ${col1}, ${col2} from ${db}.${table}\n",
       "\t\twill result in\n",
       "\t\t\tselect c1, c2 from mydb.mytable\n",
       "\n",
       "\n",
       "* Variables can also be used in magic commands with the same notation **\\$\\{name\\}**.\n",
       "\t* e.g.\n",
       "\t\t\t%var conName=peach, conUser=user1, conHost=host1\n",
       "\t\t\t%connect name=${conname}, user=${conuser}, host=${conhost}\n",
       "\t\twill result in the following magic command being run\n",
       "\t\t\t%connect name=peach, user=user1, host=host1\n",
       "\n",
       "\n",
       "* Values do not need to be quoted unless they include the characters space, equal, double-quote, comma, or newline.\n",
       "\t* Values can be quoted using double quotes to include these special characters.\n",
       "\t* Single quotes can be included in values but have no special meaning.\n",
       "\t* e.g.\n",
       "\t\t\t%var db=\"mydb . \", table=mytable\n",
       "\t\t\tselect * from ${db}${table}\n",
       "\t\twill result in the following SQL being run\n",
       "\t\t\tselect * from mydb . mytable\n",
       "\n",
       "\n",
       "* Values are substitued exactly as defined regardless of SQL statement quoting or other context.\n",
       "\t* e.g.\n",
       "\t\t\t%var db=mydb, table=mytable, whereval='abc'\n",
       "\t\t\tselect * from \"${db}\".\"${table}\" where f1 = ${whereval}\n",
       "\t\twill result in (note the single quotes are from the variable value)\n",
       "\t\t\tselect * from \"mydb\".\"mytable\" where f1 = 'abc'\n",
       "\n",
       "\n",
       "* To include a double-quote character (\"), the value should be quoted and the included quote should be doubled.\n",
       "\t* e.g.\n",
       "\t\t\t%var str=\"this string contains \"\"embedded\"\" quotes\"\n",
       "\n",
       "\n",
       "* Variable names must begin with an alphabetic character (a-z and A-Z) and can only include alphnumeric charaters (a-z, A-Z, and 0-9).\n",
       "\t* e.g. Abc=xyz and a1=abc are OK, but 1=abc, 1a=abc, and a_b=abc are not allowed\n",
       "\n",
       "\n",
       "* Variable names are case sensitive (e.g. Name1 and name1 are different variables).\n",
       "\n",
       "* If a variable is already defined, the new value will replace the existing value.\n",
       "\n",
       "* If a variable is not defined and is used in a SQL or magic command, no substitution will be done.\n",
       "\n",
       "* Each notebook has a unique set of variables.\n",
       "\n",
       "* Variables are not persisted.\n",
       "\n",
       "---\n",
       "#### %lsvar\n",
       "List one or more variables. If no names are provided, list all defined variables.\n",
       "\n",
       "%lsvar [&lt;name&gt;][, &lt;name&gt;]...\n",
       "\t\t\n",
       "* Variable names are case sensitive (e.g. Name1 and name1 are different variables).\n",
       "\n",
       "---\n",
       "#### %rmvar\n",
       "Remove one or more defined variables.\n",
       "\n",
       "%rmvar &lt;name&gt; [, &lt;name&gt;]...\n",
       "\t\t\n",
       "* Variable names are case sensitive (e.g. Name1 and name1 are different variables).\n",
       "\n",
       "\n",
       "---\n",
       "#### %pyinfo\n",
       "Display information about the version of Python installed on the Vantage system.\n",
       "\n",
       "%pyinfo [module=&lt;ModuleName&gt;]\n",
       "\n",
       "* If **module** parameter is provided, reports information about any installed modules that begin with the given value.\n",
       "\n",
       "---\n",
       "#### %rinfo\n",
       "Display information about the version of R installed on the Vantage system.\n",
       "\n",
       "%rinfo [module=&lt;ModuleName&gt;]\n",
       "\n",
       "* If **module** parameter is provided, reports information about any installed modules that begin with the given value.\n",
       "\n",
       "---\n",
       "#### %runsto\n",
       "Run a Script Table Operator.\n",
       "\n",
       "%runsto | %%runsto\n",
       "\n",
       "* **%%runsto** is a cell magic command and the script that is to be run follows the parameters to the command.\n",
       "* **%runsto** is a line magic command which runs the script that is already installed on the Vantage system.\n",
       "\n",
       "The parameters that are used by both the **%runsto** and **%%runsto** variants are:</br></br>\n",
       "filename=&lt;Name of script file to run (and install for %%runsto).&gt;,</br>\n",
       "on=&lt;Input to the script, table/view name or query SQL.&gt;,</br>\n",
       "returns=&lt;Names and data types of values returned by the script.&gt;</br>\n",
       "[, consumesql=&lt;SQL that consumes the output of the script.&gt;]</br>\n",
       "[, onoptions=&lt;Options to the ON clause, AS, Hash/Partition/Order.&gt;]</br>\n",
       "[, delimiter=&lt;Character that delimits fields in input and output.&gt;]</br>\n",
       "[, charset=&lt;LATIN|UTF-16 (Encoding of data passed to and from script).&gt;]</br>\n",
       "[, quotechar=&lt;Quotes the input and output data of script.&gt;]</br>\n",
       "[, auth=&lt;Binds an operating system user to script via authorization objects.&gt;]</br>\n",
       "[, as=&lt;Alias of output table and, optionally, its columns.&gt;]</br>\n",
       "[, command=&lt;Linux command to be run with \"%s\" indicating where the script's name is to be inserted.&gt;]</br></br>\n",
       "Additional parameters used by **%%runsto**:</br></br>\n",
       "regname=&lt;Registered name of installed file&gt;</br>\n",
       "[, replacefile=&lt;true|false&gt;]</br>\n",
       "[, forcereplace=&lt;true|false&gt;]</br>\n",
       "script body follows the newline following the parameters.\n",
       "\n",
       "Default values for optional parameters:</br>\n",
       "* consumesql – SELECT * FROM\n",
       "* onoptions – no ON options clause\n",
       "* delimiter – no DELIMITER clause, database default, tab character, used\n",
       "* charset – no CHARSET clause, database default LATIN used\n",
       "* quotechar – no QUOTECHAR clause, database default of not quoting fields\n",
       "* auth – no AUTH clause\n",
       "* as – no AS clause\n",
       "* command – python %s\n",
       "* replacefile – false\n",
       "* forcereplace – false\n",
       "\n",
       "---\n",
       "#### %installfile\n",
       "Install a file onto the Vantage system.\n",
       "\n",
       "%installfile regname=&lt;Registered name of installed file&gt;,</br>filename=&lt;Name of file in Teradata&gt;,</br>localfile=&lt;Name of the file to be installed&gt;</br>[, replace=&lt;true|false&gt;]</br>[, forcereplace=&lt;true|false&gt;]</br>[, text=&lt;true|false&gt;]\n",
       "\n",
       "* The **regname** is used when replacing, removing and showing the file.\n",
       "* The **filename** is used by scripts that reference the file.\n",
       "* The **localfile** is the name of the file on your file system that is to be installed.\n",
       "* The **replace** parameter indicates whether the file is being initially installed (replace=false) or being replaced (replace=true).\n",
       "* The **forcereplace** parameter indicates whether the file is to be replaced even if it is currently being used.\n",
       "* The **text** parameter indicates if the file is a text file (true, the default value) or a binary file (false).\n",
       "\n",
       "---\n",
       "#### %rmfile\n",
       "Remove a file that has been installed on the Vantage system.\n",
       "\n",
       "%rmfile regname=&lt;Registered name of installed file&gt;</br>\n",
       "[, forceremove=&lt;true|false&gt;]</br>\n",
       "[, prompt=&lt;true|false&gt;]</br>\n",
       "\n",
       "* The **regname** is the name specified when installing or replacing the file.\n",
       "* The **forceremove** parameter indicates whether the file is to be removed even if it is currently being used.\n",
       "* If the **prompt** parameter is not specified, you will be prompted to confirm the request.\n",
       "\n",
       "---\n",
       "#### %showfile\n",
       "Display the contents of a file that has been installed on the Vantage system.\n",
       "\n",
       "%showfile regname=&lt;Registered name of installed file&gt; [,linenums=&lt;true|false&gt;]\n",
       "\n",
       "* Line numbers will be included if linenums is true or omitted.\n",
       "* Contents of binary files are not shown.\n",
       "\n",
       "---\n",
       "#### %loglevel\n",
       "Set the logging level for the Teradata SQL kernel.\n",
       "\n",
       "%loglevel [&lt;value&gt;]\n",
       "\n",
       "* The **value** must be one of the supported logging\n",
       "levels: **debug**, **info**, **warn**, **error**, **fatal**, or **panic**.\n",
       "* If no **value** is provided, the current logging level is returned.\n",
       "  * The default value for the logging level is **info**.\n",
       "\n",
       "---\n",
       "#### %lsmagic\n",
       "List the supported magic commands.\n",
       "\n",
       "%lsmagic\n",
       "\n",
       "---\n",
       "#### %quickref\n",
       "Display help for magic commands.\n",
       "\n",
       "%help | %quickref [command | all | snippets | rowcount]\n",
       "\t\n",
       "* %quickref with no parameters displays the list of magic commands and summaries.\n",
       "* %quickref **command** (without %) shows help for the specified magic command.\n",
       "* %quickref **all** displays help for all magic commands.\n",
       "* %quickref **snippets** displays the list of SQL command Snippets.\n",
       "* %quickref **rowcount** displays the current rowcount value.\n",
       "* %quickref **currentdb** displays the current database for the user.\n",
       "\n",
       "---\n",
       "#### %help\n",
       "Display help for magic commands.\n",
       "\n",
       "%help | %quickref [command | all | snippets | rowcount | currentdb]\n",
       "\n",
       "* %help with no parameters displays the list of magic commands and summaries.\n",
       "* %help **command** (without %) shows help for the specified magic command.\n",
       "* %help **all** displays help for all magic commands.\n",
       "* %help **snippets** displays the list of SQL command Snippets.\n",
       "* %help **rowcount** displays the current rowcount value.\n",
       "* %help **currentdb** displays the current database for the user.\n",
       "\n",
       "---\n"
      ]
     },
     "execution_count": 412,
     "metadata": {
      "application/vnd.teradata.resultset": "helpdata"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%help all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Password: ·········\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success: 'demo_user' connection established and activated for user 'demo_user', with default database 'demo_db'\n"
     ]
    }
   ],
   "source": [
    "%connect  demo_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"ColumnName\"\n\"ca_resident_ind\"\n\"il_resident_ind\"\n\"az_resident_ind\"\n\"oh_resident_ind\"\n\"cc_acct_ind\"\n\"ck_avg_bal\"\n\"sv_avg_bal\"\n\"ck_avg_tran_amt\"\n\"sv_avg_tran_amt\"\n\"cc_avg_tran_amt\"\n\"q1_trans_cnt\"\n\"q3_trans_cnt\"\n\"q4_trans_cnt\"\n\"ck_acct_ind\"\n\"married_ind\"\n\"tot_cust_years\"\n\"tot_income\"\n\"cust_id\"\n\"cc_avg_bal\"\n\"tx_resident_ind\"\n\"female_ind\"\n\"tot_children\"\n\"tot_age\"\n\"q2_trans_cnt\"\n\"sv_acct_ind\"\n\"ny_resident_ind\"\n\"separated_ind\"\n\"single_ind\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory133823\"><style>\n",
       "      table.tdhistory133823 { display: block !important; min-height: 588px !important; overflow: auto !important; height: 588px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory133823 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory133823:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory133823 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory133823 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory133823 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory133823\"></th><th class=\"tdhistory133823\">ColumnName</th></tr></thead><tbody class=\"tdhistory133823 resultset-body\"><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory133823\">ca_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory133823\">il_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory133823\">az_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory133823\">oh_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory133823\">cc_acct_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory133823\">ck_avg_bal</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory133823\">sv_avg_bal</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory133823\">ck_avg_tran_amt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory133823\">sv_avg_tran_amt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory133823\">cc_avg_tran_amt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">11</th><td class=\"tdhistory133823\">q1_trans_cnt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">12</th><td class=\"tdhistory133823\">q3_trans_cnt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">13</th><td class=\"tdhistory133823\">q4_trans_cnt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">14</th><td class=\"tdhistory133823\">ck_acct_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">15</th><td class=\"tdhistory133823\">married_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">16</th><td class=\"tdhistory133823\">tot_cust_years</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">17</th><td class=\"tdhistory133823\">tot_income</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">18</th><td class=\"tdhistory133823\">cust_id</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">19</th><td class=\"tdhistory133823\">cc_avg_bal</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">20</th><td class=\"tdhistory133823\">tx_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">21</th><td class=\"tdhistory133823\">female_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">22</th><td class=\"tdhistory133823\">tot_children</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">23</th><td class=\"tdhistory133823\">tot_age</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">24</th><td class=\"tdhistory133823\">q2_trans_cnt</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">25</th><td class=\"tdhistory133823\">sv_acct_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">26</th><td class=\"tdhistory133823\">ny_resident_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">27</th><td class=\"tdhistory133823\">separated_ind</td></tr><tr><th class=\"tdhistory133823\" style= \"background: rgba(243, 243, 243, 0.75)\">28</th><td class=\"tdhistory133823\">single_ind</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 413,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.08_11.31.25.330_EDT\",\"historyID\":722,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":28,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"ColumnName\",\"columnTypeName\":\"VARCHAR\",\"length\":128,\"scale\":128}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- select table fields\n",
    "-- snippet for future work with dynamic sql\n",
    "SELECT ColumnName \n",
    "FROM dbc.columnsV\n",
    "WHERE DatabaseName = 'TRNG_XSP' and\n",
    "TableName = 'TWM_ADS';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"Request Text\"\n\"CREATE SET TABLE TRNG_XSP.TWM_ADS ,FALLBACK ,\r     NO BEFORE JOURNAL,\r     NO AFTER JOURNAL,\r     CHECKSUM = DEFAULT,\r     DEFAULT MERGEBLOCKRATIO,\r     MAP = TD_MAP1\r     (\r      cust_id INTEGER,\r      tot_income DECIMAL(15,1),\r      tot_age INTEGER,\r      tot_cust_years INTEGER,\r      tot_children INTEGER,\r      single_ind BYTEINT,\r      female_ind BYTEINT,\r      married_ind BYTEINT,\r      separated_ind BYTEINT,\r      ca_resident_ind BYTEINT,\r      ny_resident_ind BYTEINT,\r      tx_resident_ind BYTEINT,\r      il_resident_ind BYTEINT,\r      az_resident_ind BYTEINT,\r      oh_resident_ind BYTEINT,\r      ck_acct_ind BYTEINT,\r      sv_acct_ind BYTEINT,\r      cc_acct_ind BYTEINT,\r      ck_avg_bal FLOAT,\r      sv_avg_bal FLOAT,\r      cc_avg_bal FLOAT,\r      ck_avg_tran_amt FLOAT,\r      sv_avg_tran_amt FLOAT,\r      cc_avg_tran_amt FLOAT,\r      q1_trans_cnt INTEGER,\r      q2_trans_cnt INTEGER,\r      q3_trans_cnt INTEGER,\r      q4_trans_cnt INTEGER)\rUNIQUE PRIMARY INDEX ( cust_id );\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory186637\"><style>\n",
       "      table.tdhistory186637 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory186637 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory186637:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory186637 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory186637 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory186637 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory186637\"></th><th class=\"tdhistory186637\">Request Text</th></tr></thead><tbody class=\"tdhistory186637 resultset-body\"><tr><th class=\"tdhistory186637\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory186637\">CREATE SET TABLE TRNG_XSP.TWM_ADS ,FALLBACK ,\r",
       "     NO BEFORE JOURNAL,\r",
       "     NO AFTER JOURNAL,\r",
       "     CHECKSUM = DEFAULT,\r",
       "     DEFAULT MERGEBLOCKRATIO,\r",
       "     MAP = TD_MAP1\r",
       "     (\r",
       "      cust_id INTEGER,\r",
       "      tot_income DECIMAL(15,1),\r",
       "      tot_age INTEGER,\r",
       "      tot_cust_years INTEGER,\r",
       "      tot_children INTEGER,\r",
       "      single_ind BYTEINT,\r",
       "      female_ind BYTEINT,\r",
       "      married_ind BYTEINT,\r",
       "      separated_ind BYTEINT,\r",
       "      ca_resident_ind BYTEINT,\r",
       "      ny_resident_ind BYTEINT,\r",
       "      tx_resident_ind BYTEINT,\r",
       "      il_resident_ind BYTEINT,\r",
       "      az_resident_ind BYTEINT,\r",
       "      oh_resident_ind BYTEINT,\r",
       "      ck_acct_ind BYTEINT,\r",
       "      sv_acct_ind BYTEINT,\r",
       "      cc_acct_ind BYTEINT,\r",
       "      ck_avg_bal FLOAT,\r",
       "      sv_avg_bal FLOAT,\r",
       "      cc_avg_bal FLOAT,\r",
       "      ck_avg_tran_amt FLOAT,\r",
       "      sv_avg_tran_amt FLOAT,\r",
       "      cc_avg_tran_amt FLOAT,\r",
       "      q1_trans_cnt INTEGER,\r",
       "      q2_trans_cnt INTEGER,\r",
       "      q3_trans_cnt INTEGER,\r",
       "      q4_trans_cnt INTEGER)\r",
       "UNIQUE PRIMARY INDEX ( cust_id );</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.06_13.13.08.833_EDT\",\"historyID\":327,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"Request Text\",\"columnTypeName\":\"VARCHAR\",\"length\":989,\"scale\":989}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show table TRNG_XSP.TWM_ADS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Have a DDL for our data infront of us "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```sql\n",
    "CREATE SET TABLE TRNG_XSP.TWM_ADS ,FALLBACK ,\n",
    "     NO BEFORE JOURNAL,\n",
    "     NO AFTER JOURNAL,\n",
    "     CHECKSUM = DEFAULT,\n",
    "     DEFAULT MERGEBLOCKRATIO,\n",
    "     MAP = TD_MAP1\n",
    "     (\n",
    "      cust_id INTEGER,\n",
    "      tot_income DECIMAL(15,1),\n",
    "      tot_age INTEGER,\n",
    "      tot_cust_years INTEGER,\n",
    "      tot_children INTEGER,\n",
    "      single_ind BYTEINT,\n",
    "      female_ind BYTEINT,\n",
    "      married_ind BYTEINT,\n",
    "      separated_ind BYTEINT,\n",
    "      ca_resident_ind BYTEINT,\n",
    "      ny_resident_ind BYTEINT,\n",
    "      tx_resident_ind BYTEINT,\n",
    "      il_resident_ind BYTEINT,\n",
    "      az_resident_ind BYTEINT,\n",
    "      oh_resident_ind BYTEINT,\n",
    "      ck_acct_ind BYTEINT,\n",
    "      sv_acct_ind BYTEINT,\n",
    "      cc_acct_ind BYTEINT,\n",
    "      ck_avg_bal FLOAT,\n",
    "      sv_avg_bal FLOAT,\n",
    "      cc_avg_bal FLOAT,\n",
    "      ck_avg_tran_amt FLOAT,\n",
    "      sv_avg_tran_amt FLOAT,\n",
    "      cc_avg_tran_amt FLOAT,\n",
    "      q1_trans_cnt INTEGER,\n",
    "      q2_trans_cnt INTEGER,\n",
    "      q3_trans_cnt INTEGER,\n",
    "      q4_trans_cnt INTEGER)\n",
    "UNIQUE PRIMARY INDEX ( cust_id );\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select a sample from input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"cust_id\",\"tot_income\",\"tot_age\",\"tot_cust_years\",\"tot_children\",\"single_ind\",\"female_ind\",\"married_ind\",\"separated_ind\",\"ca_resident_ind\",\"ny_resident_ind\",\"tx_resident_ind\",\"il_resident_ind\",\"az_resident_ind\",\"oh_resident_ind\",\"ck_acct_ind\",\"sv_acct_ind\",\"cc_acct_ind\",\"ck_avg_bal\",\"sv_avg_bal\",\"cc_avg_bal\",\"ck_avg_tran_amt\",\"sv_avg_tran_amt\",\"cc_avg_tran_amt\",\"q1_trans_cnt\",\"q2_trans_cnt\",\"q3_trans_cnt\",\"q4_trans_cnt\"\n\"20445135\",\"143377.5\",\"58\",\"5\",\"3\",\"0\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"0\",\"0\",\"81464.664\",\"0\",\"0\",\"-390.4937777777778\",\"0\",\"0\",\"3\",\"8\",\"22\",\"12\"\n\"16356612\",\".0\",\"17\",\"9\",\"3\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\"\n\"28628313\",\"8251.1\",\"59\",\"9\",\"2\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"0\",\"0\",\"1\",\"1\",\"1\",\"3482.1675\",\"398.53636363636366\",\"1104.685625\",\"-16.715477272727274\",\"3.7759375\",\"1.2335227272727274\",\"48\",\"55\",\"47\",\"26\"\n\"21802928\",\"115148.8\",\"34\",\"7\",\"2\",\"0\",\"1\",\"1\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"0\",\"0\",\"14358.757\",\"0\",\"0\",\"-5.982914893617021\",\"21\",\"39\",\"36\",\"45\"\n\"29995240\",\"49211.8\",\"37\",\"7\",\"0\",\"1\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"0\",\"1\",\"4080.614518248175\",\"0\",\"2590.5967445255474\",\"-12.099350364963504\",\"0\",\"-3.846868613138686\",\"38\",\"33\",\"30\",\"36\"\n\"25902909\",\"10399.5\",\"55\",\"2\",\"0\",\"0\",\"0\",\"1\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"1\",\"1\",\"37719.9405\",\"742.346804347826\",\"727.8260869565217\",\"-15.386427536231883\",\"12.91404347826087\",\"-12.782608695652174\",\"10\",\"10\",\"71\",\"47\"\n\"23174094\",\"71835.7\",\"55\",\"1\",\"0\",\"0\",\"1\",\"1\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"1\",\"0\",\"1830.643\",\"10552.666666666666\",\"0\",\"-3.922846153846154\",\"-6.845423076923077\",\"3\",\"22\",\"17\",\"36\"\n\"25899660\",\"3930.0\",\"49\",\"6\",\"0\",\"1\",\"0\",\"0\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"1\",\"1\",\"551.6519491525423\",\"717.8787288135593\",\"1187.0845762711865\",\"38.32803389830509\",\"24.028983050847458\",\"17.751169491525424\",\"38\",\"30\",\"21\",\"29\"\n\"25897209\",\"17147.1\",\"60\",\"7\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"0\",\"0\",\"0\",\"0\",\"1\",\"1\",\"1\",\"31788.8811185567\",\"2195.376943298969\",\"4549.077731958763\",\"-17.147654639175258\",\"15.506664948453608\",\"44.68818556701031\",\"71\",\"71\",\"32\",\"20\"\n\"13625870\",\"8799.0\",\"28\",\"7\",\"1\",\"0\",\"1\",\"1\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"0\",\"1\",\"1\",\"1\",\"2863.5853222748815\",\"1310.1871137440758\",\"2073.6462890995263\",\"-105.70832227488151\",\"44.35816587677725\",\"1.446739336492891\",\"125\",\"22\",\"35\",\"29\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory51140\"><style>\n",
       "      table.tdhistory51140 { display: block !important; min-height: 294px !important; overflow: auto !important; height: 294px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory51140 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory51140:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory51140 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory51140 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory51140 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory51140\"></th><th class=\"tdhistory51140\">cust_id</th><th class=\"tdhistory51140\">tot_income</th><th class=\"tdhistory51140\">tot_age</th><th class=\"tdhistory51140\">tot_cust_years</th><th class=\"tdhistory51140\">tot_children</th><th class=\"tdhistory51140\">single_ind</th><th class=\"tdhistory51140\">female_ind</th><th class=\"tdhistory51140\">married_ind</th><th class=\"tdhistory51140\">separated_ind</th><th class=\"tdhistory51140\">ca_resident_ind</th><th class=\"tdhistory51140\">ny_resident_ind</th><th class=\"tdhistory51140\">tx_resident_ind</th><th class=\"tdhistory51140\">il_resident_ind</th><th class=\"tdhistory51140\">az_resident_ind</th><th class=\"tdhistory51140\">oh_resident_ind</th><th class=\"tdhistory51140\">ck_acct_ind</th><th class=\"tdhistory51140\">sv_acct_ind</th><th class=\"tdhistory51140\">cc_acct_ind</th><th class=\"tdhistory51140\">ck_avg_bal</th><th class=\"tdhistory51140\">sv_avg_bal</th><th class=\"tdhistory51140\">cc_avg_bal</th><th class=\"tdhistory51140\">ck_avg_tran_amt</th><th class=\"tdhistory51140\">sv_avg_tran_amt</th><th class=\"tdhistory51140\">cc_avg_tran_amt</th><th class=\"tdhistory51140\">q1_trans_cnt</th><th class=\"tdhistory51140\">q2_trans_cnt</th><th class=\"tdhistory51140\">q3_trans_cnt</th><th class=\"tdhistory51140\">q4_trans_cnt</th></tr></thead><tbody class=\"tdhistory51140 resultset-body\"><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory51140\">20445135</td><td class=\"tdhistory51140\">143377.5</td><td class=\"tdhistory51140\">58</td><td class=\"tdhistory51140\">5</td><td class=\"tdhistory51140\">3</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">81464.664</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">-390.4937777777778</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">3</td><td class=\"tdhistory51140\">8</td><td class=\"tdhistory51140\">22</td><td class=\"tdhistory51140\">12</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory51140\">16356612</td><td class=\"tdhistory51140\">.0</td><td class=\"tdhistory51140\">17</td><td class=\"tdhistory51140\">9</td><td class=\"tdhistory51140\">3</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory51140\">28628313</td><td class=\"tdhistory51140\">8251.1</td><td class=\"tdhistory51140\">59</td><td class=\"tdhistory51140\">9</td><td class=\"tdhistory51140\">2</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">3482.1675</td><td class=\"tdhistory51140\">398.53636363636366</td><td class=\"tdhistory51140\">1104.685625</td><td class=\"tdhistory51140\">-16.715477272727274</td><td class=\"tdhistory51140\">3.7759375</td><td class=\"tdhistory51140\">1.2335227272727274</td><td class=\"tdhistory51140\">48</td><td class=\"tdhistory51140\">55</td><td class=\"tdhistory51140\">47</td><td class=\"tdhistory51140\">26</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory51140\">21802928</td><td class=\"tdhistory51140\">115148.8</td><td class=\"tdhistory51140\">34</td><td class=\"tdhistory51140\">7</td><td class=\"tdhistory51140\">2</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">14358.757</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">-5.982914893617021</td><td class=\"tdhistory51140\">21</td><td class=\"tdhistory51140\">39</td><td class=\"tdhistory51140\">36</td><td class=\"tdhistory51140\">45</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory51140\">29995240</td><td class=\"tdhistory51140\">49211.8</td><td class=\"tdhistory51140\">37</td><td class=\"tdhistory51140\">7</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">4080.614518248175</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">2590.5967445255474</td><td class=\"tdhistory51140\">-12.099350364963504</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">-3.846868613138686</td><td class=\"tdhistory51140\">38</td><td class=\"tdhistory51140\">33</td><td class=\"tdhistory51140\">30</td><td class=\"tdhistory51140\">36</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory51140\">25902909</td><td class=\"tdhistory51140\">10399.5</td><td class=\"tdhistory51140\">55</td><td class=\"tdhistory51140\">2</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">37719.9405</td><td class=\"tdhistory51140\">742.346804347826</td><td class=\"tdhistory51140\">727.8260869565217</td><td class=\"tdhistory51140\">-15.386427536231883</td><td class=\"tdhistory51140\">12.91404347826087</td><td class=\"tdhistory51140\">-12.782608695652174</td><td class=\"tdhistory51140\">10</td><td class=\"tdhistory51140\">10</td><td class=\"tdhistory51140\">71</td><td class=\"tdhistory51140\">47</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory51140\">23174094</td><td class=\"tdhistory51140\">71835.7</td><td class=\"tdhistory51140\">55</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1830.643</td><td class=\"tdhistory51140\">10552.666666666666</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">-3.922846153846154</td><td class=\"tdhistory51140\">-6.845423076923077</td><td class=\"tdhistory51140\">3</td><td class=\"tdhistory51140\">22</td><td class=\"tdhistory51140\">17</td><td class=\"tdhistory51140\">36</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory51140\">25899660</td><td class=\"tdhistory51140\">3930.0</td><td class=\"tdhistory51140\">49</td><td class=\"tdhistory51140\">6</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">551.6519491525423</td><td class=\"tdhistory51140\">717.8787288135593</td><td class=\"tdhistory51140\">1187.0845762711865</td><td class=\"tdhistory51140\">38.32803389830509</td><td class=\"tdhistory51140\">24.028983050847458</td><td class=\"tdhistory51140\">17.751169491525424</td><td class=\"tdhistory51140\">38</td><td class=\"tdhistory51140\">30</td><td class=\"tdhistory51140\">21</td><td class=\"tdhistory51140\">29</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory51140\">25897209</td><td class=\"tdhistory51140\">17147.1</td><td class=\"tdhistory51140\">60</td><td class=\"tdhistory51140\">7</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">31788.8811185567</td><td class=\"tdhistory51140\">2195.376943298969</td><td class=\"tdhistory51140\">4549.077731958763</td><td class=\"tdhistory51140\">-17.147654639175258</td><td class=\"tdhistory51140\">15.506664948453608</td><td class=\"tdhistory51140\">44.68818556701031</td><td class=\"tdhistory51140\">71</td><td class=\"tdhistory51140\">71</td><td class=\"tdhistory51140\">32</td><td class=\"tdhistory51140\">20</td></tr><tr><th class=\"tdhistory51140\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory51140\">13625870</td><td class=\"tdhistory51140\">8799.0</td><td class=\"tdhistory51140\">28</td><td class=\"tdhistory51140\">7</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">0</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">1</td><td class=\"tdhistory51140\">2863.5853222748815</td><td class=\"tdhistory51140\">1310.1871137440758</td><td class=\"tdhistory51140\">2073.6462890995263</td><td class=\"tdhistory51140\">-105.70832227488151</td><td class=\"tdhistory51140\">44.35816587677725</td><td class=\"tdhistory51140\">1.446739336492891</td><td class=\"tdhistory51140\">125</td><td class=\"tdhistory51140\">22</td><td class=\"tdhistory51140\">35</td><td class=\"tdhistory51140\">29</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.06_08.35.42.359_EDT\",\"historyID\":322,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":10,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"cust_id\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"tot_income\",\"columnTypeName\":\"DECIMAL\",\"length\":1,\"precision\":15,\"scale\":1},{\"columnNumber\":2,\"columnName\":\"tot_age\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":3,\"columnName\":\"tot_cust_years\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":4,\"columnName\":\"tot_children\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":5,\"columnName\":\"single_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":6,\"columnName\":\"female_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":7,\"columnName\":\"married_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":8,\"columnName\":\"separated_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":9,\"columnName\":\"ca_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":10,\"columnName\":\"ny_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":11,\"columnName\":\"tx_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":12,\"columnName\":\"il_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":13,\"columnName\":\"az_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":14,\"columnName\":\"oh_resident_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":15,\"columnName\":\"ck_acct_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":16,\"columnName\":\"sv_acct_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":17,\"columnName\":\"cc_acct_ind\",\"columnTypeName\":\"BYTEINT\"},{\"columnNumber\":18,\"columnName\":\"ck_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":19,\"columnName\":\"sv_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":20,\"columnName\":\"cc_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":21,\"columnName\":\"ck_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":22,\"columnName\":\"sv_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":23,\"columnName\":\"cc_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":24,\"columnName\":\"q1_trans_cnt\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":25,\"columnName\":\"q2_trans_cnt\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":26,\"columnName\":\"q3_trans_cnt\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":27,\"columnName\":\"q4_trans_cnt\",\"columnTypeName\":\"INTEGER\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SELECT * FROM TRNG_XSP.TWM_ADS sample 10;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"Count(*)\"\n\"10458\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory136400\"><style>\n",
       "      table.tdhistory136400 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory136400 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory136400:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory136400 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory136400 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory136400 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory136400\"></th><th class=\"tdhistory136400\">Count(*)</th></tr></thead><tbody class=\"tdhistory136400 resultset-body\"><tr><th class=\"tdhistory136400\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory136400\">10458</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.08_14.16.25.976_EDT\",\"historyID\":771,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"Count(*)\",\"columnTypeName\":\"INTEGER\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- show the number of records in data\n",
    "select count(*) from TRNG_XSP.TWM_ADS;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize the data \n",
    "this way clustering works better  \n",
    "much better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CALL TRNG_XSP.td_analyze('vartran',\n",
    "    'database=TRNG_XSP;\n",
    "    tablename=TWM_ADS;\n",
    "    outputstyle=view;\n",
    "    outputdatabase=demo_db;\n",
    "    outputtablename=ClusterInput;\n",
    "    keycolumns=cust_id;\n",
    "    zscore=columns(tot_income,tot_age,tot_cust_years,\n",
    "    tot_children,single_ind,female_ind,married_ind,\n",
    "    separated_ind,ca_resident_ind,ny_resident_ind,\n",
    "    tx_resident_ind,il_resident_ind,az_resident_ind,\n",
    "    oh_resident_ind,ck_acct_ind,sv_acct_ind,cc_acct_ind,\n",
    "    ck_avg_bal,sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,\n",
    "    sv_avg_tran_amt,cc_avg_tran_amt,\n",
    "    q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt);\n",
    "    retain=columns(cust_id);');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify that normalized data looks sound "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": 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      ]
     },
     "execution_count": 9,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.06_13.17.17.950_EDT\",\"historyID\":329,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":10,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"cust_id\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"tot_income\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":2,\"columnName\":\"tot_age\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":3,\"columnName\":\"tot_cust_years\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":4,\"columnName\":\"tot_children\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":5,\"columnName\":\"single_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":6,\"columnName\":\"female_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":7,\"columnName\":\"married_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":8,\"columnName\":\"separated_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":9,\"columnName\":\"ca_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":10,\"columnName\":\"ny_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":11,\"columnName\":\"tx_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":12,\"columnName\":\"il_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":13,\"columnName\":\"az_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":14,\"columnName\":\"oh_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":15,\"columnName\":\"ck_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":16,\"columnName\":\"sv_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":17,\"columnName\":\"cc_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":18,\"columnName\":\"ck_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":19,\"columnName\":\"sv_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":20,\"columnName\":\"cc_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":21,\"columnName\":\"ck_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":22,\"columnName\":\"sv_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":23,\"columnName\":\"cc_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":24,\"columnName\":\"q1_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":25,\"columnName\":\"q2_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":26,\"columnName\":\"q3_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":27,\"columnName\":\"q4_trans_cnt\",\"columnTypeName\":\"FLOAT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SELECT * FROM demo_db.ClusterInput SAMPLE 10;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference calls to VAL functions  \n",
    "this is just for memo \n",
    "later on code will incapsulate that into a procedure  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    },
    {
     "ename": "WARNING",
     "evalue": "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets.",
     "output_type": "error",
     "traceback": [
      "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets."
     ]
    },
    {
     "data": {
      "application/vnd.teradata.resultset": "\"ctime\",\"v\"\n\"2021-09-06 13:22:36.450000\",\"Starting Procedure\"\n\"2021-09-06 13:22:37.030000\",\"Data randomly assigned to clusters initially\"\n\"2021-09-06 13:22:37.060000\",\"Starting K-means loop 1\"\n\"2021-09-06 13:22:39.860000\",\"Completed K-means loop 1\"\n\"2021-09-06 13:22:40.010000\",\"Comparison complete, cluster aggregate difference is  7.59370900736606E 000\"\n\"2021-09-06 13:22:40.260000\",\"Starting K-means loop 2\"\n\"2021-09-06 13:22:40.950000\",\"Completed K-means loop 2\"\n\"2021-09-06 13:22:41.090000\",\"Comparison complete, cluster aggregate difference is  2.74626936878873E 000\"\n\"2021-09-06 13:22:41.340000\",\"Starting K-means loop 3\"\n\"2021-09-06 13:22:42.040000\",\"Completed K-means loop 3\"\n\"2021-09-06 13:22:42.170000\",\"Comparison complete, cluster aggregate difference is  4.33165407752225E-001\"\n\"2021-09-06 13:22:42.410000\",\"Starting K-means loop 4\"\n\"2021-09-06 13:22:43.110000\",\"Completed K-means loop 4\"\n\"2021-09-06 13:22:43.270000\",\"Comparison complete, cluster aggregate difference is  4.47827815060156E-001\"\n\"2021-09-06 13:22:43.500000\",\"Starting K-means loop 5\"\n\"2021-09-06 13:22:44.210000\",\"Completed K-means loop 5\"\n\"2021-09-06 13:22:44.350000\",\"Comparison complete, cluster aggregate difference is  3.45526283017531E-001\"\n\"2021-09-06 13:22:44.600000\",\"Starting K-means loop 6\"\n\"2021-09-06 13:22:45.300000\",\"Completed K-means loop 6\"\n\"2021-09-06 13:22:45.430000\",\"Comparison complete, cluster aggregate difference is  2.17044259594809E-001\"\n\"2021-09-06 13:22:45.660000\",\"Starting K-means loop 7\"\n\"2021-09-06 13:22:46.340000\",\"Completed K-means loop 7\"\n\"2021-09-06 13:22:46.480000\",\"Comparison complete, cluster aggregate difference is  2.02154961306909E-001\"\n\"2021-09-06 13:22:46.710000\",\"Starting K-means loop 8\"\n\"2021-09-06 13:22:47.390000\",\"Completed K-means loop 8\"\n\"2021-09-06 13:22:47.520000\",\"Comparison complete, cluster aggregate difference is  2.27687040545491E-001\"\n\"2021-09-06 13:22:47.740000\",\"Starting K-means loop 9\"\n\"2021-09-06 13:22:48.420000\",\"Completed K-means loop 9\"\n\"2021-09-06 13:22:48.550000\",\"Comparison complete, cluster aggregate difference is  2.92090290248794E-001\"\n\"2021-09-06 13:22:48.800000\",\"Starting K-means loop 10\"\n\"2021-09-06 13:22:49.520000\",\"Completed K-means loop 10\"\n\"2021-09-06 13:22:49.670000\",\"Comparison complete, cluster aggregate difference is  3.07161191121025E-001\"\n\"2021-09-06 13:22:49.700000\",\"Kmeans reached maximum iterations (10) so terminating procedure\"\n\"2021-09-06 13:22:50.690000\",\"Procedure Complete\"",
      "text/html": [
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       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory643935\"></th><th class=\"tdhistory643935\">ctime</th><th class=\"tdhistory643935\">v</th></tr></thead><tbody class=\"tdhistory643935 resultset-body\"><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory643935\">2021-09-06 13:22:36.450000</td><td class=\"tdhistory643935\">Starting Procedure</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory643935\">2021-09-06 13:22:37.030000</td><td class=\"tdhistory643935\">Data randomly assigned to clusters initially</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory643935\">2021-09-06 13:22:37.060000</td><td class=\"tdhistory643935\">Starting K-means loop 1</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory643935\">2021-09-06 13:22:39.860000</td><td class=\"tdhistory643935\">Completed K-means loop 1</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory643935\">2021-09-06 13:22:40.010000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  7.59370900736606E 000</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory643935\">2021-09-06 13:22:40.260000</td><td class=\"tdhistory643935\">Starting K-means loop 2</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory643935\">2021-09-06 13:22:40.950000</td><td class=\"tdhistory643935\">Completed K-means loop 2</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory643935\">2021-09-06 13:22:41.090000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  2.74626936878873E 000</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory643935\">2021-09-06 13:22:41.340000</td><td class=\"tdhistory643935\">Starting K-means loop 3</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory643935\">2021-09-06 13:22:42.040000</td><td class=\"tdhistory643935\">Completed K-means loop 3</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">11</th><td class=\"tdhistory643935\">2021-09-06 13:22:42.170000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  4.33165407752225E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">12</th><td class=\"tdhistory643935\">2021-09-06 13:22:42.410000</td><td class=\"tdhistory643935\">Starting K-means loop 4</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">13</th><td class=\"tdhistory643935\">2021-09-06 13:22:43.110000</td><td class=\"tdhistory643935\">Completed K-means loop 4</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">14</th><td class=\"tdhistory643935\">2021-09-06 13:22:43.270000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  4.47827815060156E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">15</th><td class=\"tdhistory643935\">2021-09-06 13:22:43.500000</td><td class=\"tdhistory643935\">Starting K-means loop 5</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">16</th><td class=\"tdhistory643935\">2021-09-06 13:22:44.210000</td><td class=\"tdhistory643935\">Completed K-means loop 5</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">17</th><td class=\"tdhistory643935\">2021-09-06 13:22:44.350000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  3.45526283017531E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">18</th><td class=\"tdhistory643935\">2021-09-06 13:22:44.600000</td><td class=\"tdhistory643935\">Starting K-means loop 6</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">19</th><td class=\"tdhistory643935\">2021-09-06 13:22:45.300000</td><td class=\"tdhistory643935\">Completed K-means loop 6</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">20</th><td class=\"tdhistory643935\">2021-09-06 13:22:45.430000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  2.17044259594809E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">21</th><td class=\"tdhistory643935\">2021-09-06 13:22:45.660000</td><td class=\"tdhistory643935\">Starting K-means loop 7</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">22</th><td class=\"tdhistory643935\">2021-09-06 13:22:46.340000</td><td class=\"tdhistory643935\">Completed K-means loop 7</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">23</th><td class=\"tdhistory643935\">2021-09-06 13:22:46.480000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  2.02154961306909E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">24</th><td class=\"tdhistory643935\">2021-09-06 13:22:46.710000</td><td class=\"tdhistory643935\">Starting K-means loop 8</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">25</th><td class=\"tdhistory643935\">2021-09-06 13:22:47.390000</td><td class=\"tdhistory643935\">Completed K-means loop 8</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">26</th><td class=\"tdhistory643935\">2021-09-06 13:22:47.520000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  2.27687040545491E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">27</th><td class=\"tdhistory643935\">2021-09-06 13:22:47.740000</td><td class=\"tdhistory643935\">Starting K-means loop 9</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">28</th><td class=\"tdhistory643935\">2021-09-06 13:22:48.420000</td><td class=\"tdhistory643935\">Completed K-means loop 9</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">29</th><td class=\"tdhistory643935\">2021-09-06 13:22:48.550000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  2.92090290248794E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">30</th><td class=\"tdhistory643935\">2021-09-06 13:22:48.800000</td><td class=\"tdhistory643935\">Starting K-means loop 10</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">31</th><td class=\"tdhistory643935\">2021-09-06 13:22:49.520000</td><td class=\"tdhistory643935\">Completed K-means loop 10</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">32</th><td class=\"tdhistory643935\">2021-09-06 13:22:49.670000</td><td class=\"tdhistory643935\">Comparison complete, cluster aggregate difference is  3.07161191121025E-001</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">33</th><td class=\"tdhistory643935\">2021-09-06 13:22:49.700000</td><td class=\"tdhistory643935\">Kmeans reached maximum iterations (10) so terminating procedure</td></tr><tr><th class=\"tdhistory643935\" style= \"background: rgba(243, 243, 243, 0.75)\">34</th><td class=\"tdhistory643935\">2021-09-06 13:22:50.690000</td><td class=\"tdhistory643935\">Procedure Complete</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 11,
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     "output_type": "execute_result"
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      ]
     },
     "execution_count": 11,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.06_13.22.50.792_EDT\",\"historyID\":332,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":6,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"clusterid\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"cnt\",\"columnTypeName\":\"BIGINT\"},{\"columnNumber\":2,\"columnName\":\"tot_income\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":3,\"columnName\":\"tot_age\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":4,\"columnName\":\"tot_cust_years\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":5,\"columnName\":\"tot_children\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":6,\"columnName\":\"single_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":7,\"columnName\":\"female_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":8,\"columnName\":\"married_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":9,\"columnName\":\"separated_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":10,\"columnName\":\"ca_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":11,\"columnName\":\"ny_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":12,\"columnName\":\"tx_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":13,\"columnName\":\"il_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":14,\"columnName\":\"az_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":15,\"columnName\":\"oh_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":16,\"columnName\":\"ck_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":17,\"columnName\":\"sv_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":18,\"columnName\":\"cc_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":19,\"columnName\":\"ck_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":20,\"columnName\":\"sv_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":21,\"columnName\":\"cc_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":22,\"columnName\":\"ck_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":23,\"columnName\":\"sv_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":24,\"columnName\":\"cc_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":25,\"columnName\":\"q1_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":26,\"columnName\":\"q2_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":27,\"columnName\":\"q3_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":28,\"columnName\":\"q4_trans_cnt\",\"columnTypeName\":\"FLOAT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- single call to make a cluster model\n",
    "call TRNG_XSP.td_analyze(\n",
    "    'Kmeans',\n",
    "    'database=demo_db;\n",
    "    tablename=ClusterInput;\n",
    "    columns=tot_income,tot_age,tot_cust_years,\n",
    "    tot_children,single_ind,female_ind,married_ind,\n",
    "    separated_ind,ca_resident_ind,ny_resident_ind,\n",
    "    tx_resident_ind,il_resident_ind,az_resident_ind,\n",
    "    oh_resident_ind,ck_acct_ind,sv_acct_ind,cc_acct_ind,\n",
    "    ck_avg_bal,sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,\n",
    "    sv_avg_tran_amt,cc_avg_tran_amt,\n",
    "    q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt;\n",
    "    outputdatabase=demo_db;\n",
    "    outputtablename=ClusterModel;\n",
    "    operatordatabase=TRNG_XSP;\n",
    "    kvalue=3;\n",
    "    iterations=10;\n",
    "    threshold=0.1;');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster model has `2 * Number of clusters` rows  \n",
    "`negative items` hold variance of all points in cluster (std deveation)  \n",
    "`positive items` hold mean of all points in cluster (loc) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform actual clustering  \n",
    "just for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    },
    {
     "ename": "WARNING",
     "evalue": "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets.",
     "output_type": "error",
     "traceback": [
      "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets."
     ]
    },
    {
     "data": {
      "application/vnd.teradata.resultset": "\"clusterid\",\"cust_id\"\n\"2\",\"29982260\"\n\"5\",\"16358856\"\n\"2\",\"21812720\"\n\"2\",\"28620501\"\n\"3\",\"14991944\"\n\"5\",\"16360596\"\n\"3\",\"24530760\"\n\"1\",\"29994888\"\n\"3\",\"25890160\"\n\"3\",\"27268200\"\n\"2\",\"17714359\"\n\"4\",\"24532254\"\n\"5\",\"31343480\"\n\"3\",\"20445540\"\n\"5\",\"14991636\"\n\"2\",\"17717037\"\n\"1\",\"28632513\"\n\"1\",\"20446905\"\n\"5\",\"21806944\"\n\"2\",\"27256480\"\n\"2\",\"13633650\"\n\"5\",\"23168365\"\n\"2\",\"19087880\"\n\"1\",\"24533460\"\n\"5\",\"31341203\"",
      "text/html": [
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       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory687903\"></th><th class=\"tdhistory687903\">clusterid</th><th class=\"tdhistory687903\">cust_id</th></tr></thead><tbody class=\"tdhistory687903 resultset-body\"><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">29982260</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">16358856</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">21812720</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">28620501</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory687903\">3</td><td class=\"tdhistory687903\">14991944</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">16360596</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory687903\">3</td><td class=\"tdhistory687903\">24530760</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory687903\">1</td><td class=\"tdhistory687903\">29994888</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory687903\">3</td><td class=\"tdhistory687903\">25890160</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory687903\">3</td><td class=\"tdhistory687903\">27268200</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">11</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">17714359</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">12</th><td class=\"tdhistory687903\">4</td><td class=\"tdhistory687903\">24532254</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">13</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">31343480</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">14</th><td class=\"tdhistory687903\">3</td><td class=\"tdhistory687903\">20445540</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">15</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">14991636</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">16</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">17717037</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">17</th><td class=\"tdhistory687903\">1</td><td class=\"tdhistory687903\">28632513</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">18</th><td class=\"tdhistory687903\">1</td><td class=\"tdhistory687903\">20446905</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">19</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">21806944</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">20</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">27256480</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">21</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">13633650</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">22</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">23168365</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">23</th><td class=\"tdhistory687903\">2</td><td class=\"tdhistory687903\">19087880</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">24</th><td class=\"tdhistory687903\">1</td><td class=\"tdhistory687903\">24533460</td></tr><tr><th class=\"tdhistory687903\" style= \"background: rgba(243, 243, 243, 0.75)\">25</th><td class=\"tdhistory687903\">5</td><td class=\"tdhistory687903\">31341203</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.07_09.29.31.783_EDT\",\"historyID\":381,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":25,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"clusterid\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"cust_id\",\"columnTypeName\":\"INTEGER\"}]}"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.teradata.resultset": "\"ctime\",\"v\"\n\"2021-09-07 09:29:30.970000\",\"Starting Procedure\"\n\"2021-09-07 09:29:30.990000\",\"About to assign clusters in \"\"demo_db\"\".\"\"ClusterModel\"\" to input data.\"\n\"2021-09-07 09:29:31.690000\",\"Assignment complete creating Score Table \"\"demo_db\"\".\"\"ClusterScore\"\".\"\n\"2021-09-07 09:29:31.700000\",\"Procedure Complete\"",
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       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory720639\"></th><th class=\"tdhistory720639\">ctime</th><th class=\"tdhistory720639\">v</th></tr></thead><tbody class=\"tdhistory720639 resultset-body\"><tr><th class=\"tdhistory720639\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory720639\">2021-09-07 09:29:30.970000</td><td class=\"tdhistory720639\">Starting Procedure</td></tr><tr><th class=\"tdhistory720639\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory720639\">2021-09-07 09:29:30.990000</td><td class=\"tdhistory720639\">About to assign clusters in \"demo_db\".\"ClusterModel\" to input data.</td></tr><tr><th class=\"tdhistory720639\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory720639\">2021-09-07 09:29:31.690000</td><td class=\"tdhistory720639\">Assignment complete creating Score Table \"demo_db\".\"ClusterScore\".</td></tr><tr><th class=\"tdhistory720639\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory720639\">2021-09-07 09:29:31.700000</td><td class=\"tdhistory720639\">Procedure Complete</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.07_09.29.31.797_EDT\",\"historyID\":382,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":4,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"ctime\",\"columnTypeName\":\"TIMESTAMP\",\"length\":6,\"scale\":6},{\"columnNumber\":1,\"columnName\":\"v\",\"columnTypeName\":\"VARCHAR\",\"length\":2048,\"scale\":2048}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call TRNG_XSP.td_analyze(\n",
    "    'KmeansScore',\n",
    "    'database=demo_db;\n",
    "    tablename=ClusterInput;\n",
    "    outputdatabase=demo_db;\n",
    "    outputtablename=ClusterScore;\n",
    "    index=cust_id;\n",
    "    modeldatabase=demo_db;\n",
    "    modeltablename=ClusterModel;\n",
    "    samplescoresize=25;\n",
    "    operatordatabase=TRNG_XSP;');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Inertia for K-clusters  \n",
    "below is a `python` code to generate sql with a query that calculates inertia for cluster distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "TABLE_NAME = \"demo_db.ClusterInput\" #  scaled table with data\n",
    "CLUSTER_TABLE = \"demo_db.ClusterScore\" # all data scored to each cluster\n",
    "CLUSTER_MODEL = \"demo_db.ClusterModel\" # cluster model metadata table\n",
    "KEY = \"cust_id\" # key field in a table\n",
    "CLUSTER_ID = \"clusterid\"\n",
    "\n",
    "# fields description from DDL\n",
    "s = '''\n",
    "      tot_income DECIMAL(15,1),\n",
    "      tot_age INTEGER,\n",
    "      tot_cust_years INTEGER,\n",
    "      tot_children INTEGER,\n",
    "      single_ind BYTEINT,\n",
    "      female_ind BYTEINT,\n",
    "      married_ind BYTEINT,\n",
    "      separated_ind BYTEINT,\n",
    "      ca_resident_ind BYTEINT,\n",
    "      ny_resident_ind BYTEINT,\n",
    "      tx_resident_ind BYTEINT,\n",
    "      il_resident_ind BYTEINT,\n",
    "      az_resident_ind BYTEINT,\n",
    "      oh_resident_ind BYTEINT,\n",
    "      ck_acct_ind BYTEINT,\n",
    "      sv_acct_ind BYTEINT,\n",
    "      cc_acct_ind BYTEINT,\n",
    "      ck_avg_bal FLOAT,\n",
    "      sv_avg_bal FLOAT,\n",
    "      cc_avg_bal FLOAT,\n",
    "      ck_avg_tran_amt FLOAT,\n",
    "      sv_avg_tran_amt FLOAT,\n",
    "      cc_avg_tran_amt FLOAT,\n",
    "      q1_trans_cnt INTEGER,\n",
    "      q2_trans_cnt INTEGER,\n",
    "      q3_trans_cnt INTEGER,\n",
    "      q4_trans_cnt INTEGER\n",
    "'''\n",
    "\n",
    "# extract all fields from fields definition above\n",
    "fields = [f for i,f in enumerate(s.split()) if i%2 ==0]\n",
    "\n",
    "sql_string = '''\n",
    "with inertia_items as (\n",
    "select \n",
    "  b.clusterid, \n",
    "'''\n",
    "for f in fields:\n",
    "    sql_string += f\"  (a.{f} - c.{f})**2 + \\n\"\n",
    "last_plus_pos = sql_string.find(\" +\", -4)\n",
    "sql_string = sql_string[:last_plus_pos]\n",
    "sql_string += \" as inertia\" +\\\n",
    "f'''\n",
    "from \n",
    "  {TABLE_NAME} a join \n",
    "  {CLUSTER_TABLE} b \n",
    "  on a.{KEY} = b.{KEY} join \n",
    "  {CLUSTER_MODEL} c \n",
    "  on c.{CLUSTER_ID} = b.{CLUSTER_ID})\n",
    "select CAST(sum(inertia) as int) inertia from inertia_items\n",
    "'''\n",
    "print(sql_string)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"inertia\"\n\"232044\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory340146\"><style>\n",
       "      table.tdhistory340146 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory340146 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory340146:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory340146 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory340146 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory340146 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory340146\"></th><th class=\"tdhistory340146\">inertia</th></tr></thead><tbody class=\"tdhistory340146 resultset-body\"><tr><th class=\"tdhistory340146\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory340146\">232044</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 152,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.07_13.27.25.877_EDT\",\"historyID\":476,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"inertia\",\"columnTypeName\":\"INTEGER\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with inertia_items as (\n",
    "select \n",
    "  b.clusterid, \n",
    "  (a.tot_income - c.tot_income)**2 + \n",
    "  (a.tot_age - c.tot_age)**2 + \n",
    "  (a.tot_cust_years - c.tot_cust_years)**2 + \n",
    "  (a.tot_children - c.tot_children)**2 + \n",
    "  (a.single_ind - c.single_ind)**2 + \n",
    "  (a.female_ind - c.female_ind)**2 + \n",
    "  (a.married_ind - c.married_ind)**2 + \n",
    "  (a.separated_ind - c.separated_ind)**2 + \n",
    "  (a.ca_resident_ind - c.ca_resident_ind)**2 + \n",
    "  (a.ny_resident_ind - c.ny_resident_ind)**2 + \n",
    "  (a.tx_resident_ind - c.tx_resident_ind)**2 + \n",
    "  (a.il_resident_ind - c.il_resident_ind)**2 + \n",
    "  (a.az_resident_ind - c.az_resident_ind)**2 + \n",
    "  (a.oh_resident_ind - c.oh_resident_ind)**2 + \n",
    "  (a.ck_acct_ind - c.ck_acct_ind)**2 + \n",
    "  (a.sv_acct_ind - c.sv_acct_ind)**2 + \n",
    "  (a.cc_acct_ind - c.cc_acct_ind)**2 + \n",
    "  (a.ck_avg_bal - c.ck_avg_bal)**2 + \n",
    "  (a.sv_avg_bal - c.sv_avg_bal)**2 + \n",
    "  (a.cc_avg_bal - c.cc_avg_bal)**2 + \n",
    "  (a.ck_avg_tran_amt - c.ck_avg_tran_amt)**2 + \n",
    "  (a.sv_avg_tran_amt - c.sv_avg_tran_amt)**2 + \n",
    "  (a.cc_avg_tran_amt - c.cc_avg_tran_amt)**2 + \n",
    "  (a.q1_trans_cnt - c.q1_trans_cnt)**2 + \n",
    "  (a.q2_trans_cnt - c.q2_trans_cnt)**2 + \n",
    "  (a.q3_trans_cnt - c.q3_trans_cnt)**2 + \n",
    "  (a.q4_trans_cnt - c.q4_trans_cnt)**2 as inertia\n",
    "from \n",
    "  demo_db.ClusterInput a join \n",
    "  demo_db.ClusterScore b \n",
    "  on a.cust_id = b.cust_id join \n",
    "  demo_db.ClusterModel c \n",
    "  on c.clusterid = b.clusterid)\n",
    "select CAST(sum(inertia) as int) inertia from inertia_items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a clustering model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 125,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace procedure demo_db.drop_my_table(in table_name varchar(100))\n",
    "BEGIN\n",
    "  DECLARE exit handler for sqlexception\n",
    "    begin\n",
    "    -- it is an empty handler\n",
    "    end;\n",
    "    \n",
    "  CALL DBC.SysExecSQL('Drop table '||table_name,1);    \n",
    "END;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 142,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GRANT EXECUTE PROCEDURE ON demo_db.drop_my_table TO demo_db;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 372,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "REPLACE PROCEDURE demo_db.cluster_data(\n",
    "    -- Procedure runs clustering over the data and calculates inertia for the clusterization\n",
    "    OUT inertia BIGINT, -- the vale of inertia\n",
    "    IN CLUSTER_NUM INTEGER,  -- number of clusters\n",
    "    IN IN_DB varchar(100), -- database of incoming table\n",
    "    IN MODEL_DB varchar(100), -- database of table with cluster model\n",
    "    IN IN_TABLE_NAME varchar(100), -- input table with data to cluster\n",
    "    IN COLS varchar(1000), -- columns of table to treat as dimentions\n",
    "    IN MODEL_TABLE_NAME varchar(1000), -- table name of cluster model to store\n",
    "    IN OPERATOR_DB varchar(100), -- database where VAL operator resides\n",
    "    IN ITERATIONS INTEGER,  -- number of iterations in clustering \n",
    "    -- end of model parameters\n",
    "    IN OUT_DB varchar(100), -- output database with clustered points\n",
    "    IN OUT_TABLE_NAME varchar(100), -- output table name with clustered points\n",
    "    IN KEY_COL_NAME varchar(100) -- key column that identifies objects - that will be in the output table\n",
    ")\n",
    "BEGIN\n",
    "DECLARE cluster_param varchar(1000); -- param for clustering model\n",
    "DECLARE run_cluster_param varchar(1000); --param for actual clustering\n",
    "DECLARE i BIGINT;\n",
    "\n",
    "--     'database=demo_db;\n",
    "--     tablename=ClusterInput;\n",
    "--     columns=tot_income,tot_age,tot_cust_years,\n",
    "--     tot_children,single_ind,female_ind,married_ind,\n",
    "--     separated_ind,ca_resident_ind,ny_resident_ind,\n",
    "--     tx_resident_ind,il_resident_ind,az_resident_ind,\n",
    "--     oh_resident_ind,ck_acct_ind,sv_acct_ind,cc_acct_ind,\n",
    "--     ck_avg_bal,sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,\n",
    "--     sv_avg_tran_amt,cc_avg_tran_amt,\n",
    "--     q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt;\n",
    "--     outputdatabase=demo_db;\n",
    "--     outputtablename=ClusterModel;\n",
    "--     operatordatabase=TRNG_XSP;\n",
    "--     kvalue=3;\n",
    "--     iterations=10;\n",
    "--     threshold=0.1;'\n",
    "\n",
    "SET cluster_param='database='||IN_DB||';tablename='||IN_TABLE_NAME||';columns='||COLS;\n",
    "SET cluster_param=cluster_param||';outputdatabase='||MODEL_DB||';outputtablename='||MODEL_TABLE_NAME||';operatordatabase='||OPERATOR_DB;\n",
    "SET cluster_param=cluster_param||';kvalue='||CAST(CLUSTER_NUM as VARCHAR(3))||';iterations='||CAST(ITERATIONS as VARCHAR(3))||';threshold=0.001;';\n",
    "\n",
    "-- create a cluster model\n",
    "call TRNG_XSP.td_analyze(\n",
    "    'Kmeans', cluster_param);\n",
    "    \n",
    "-- call TRNG_XSP.td_analyze(\n",
    "--     'KmeansScore',\n",
    "--     'database=demo_db;\n",
    "--     tablename=ClusterInput;\n",
    "--     outputdatabase=demo_db;\n",
    "--     outputtablename=ClusterScore;\n",
    "--     index=cust_id;\n",
    "--     modeldatabase=demo_db;\n",
    "--     modeltablename=ClusterModel;\n",
    "--     operatordatabase=TRNG_XSP;');\n",
    "\n",
    "-- run actual clustering \n",
    "SET run_cluster_param = 'database='||IN_DB||';tablename='||IN_TABLE_NAME||';outputdatabase='||OUT_DB;\n",
    "SET run_cluster_param = run_cluster_param||';outputtablename='||OUT_TABLE_NAME||';index='||KEY_COL_NAME;\n",
    "SET run_cluster_param = run_cluster_param||';modeldatabase='||MODEL_DB||';modeltablename='||MODEL_TABLE_NAME||';operatordatabase='||OPERATOR_DB||';';\n",
    "\n",
    "call TRNG_XSP.td_analyze(\n",
    "     'KmeansScore', run_cluster_param);\n",
    "     \n",
    "-- calculate inertia value\n",
    "select CAST(sum(inertia) as int) into i from  (\n",
    "select \n",
    "  b.clusterid, \n",
    "  (a.tot_income - c.tot_income)**2 + \n",
    "  (a.tot_age - c.tot_age)**2 + \n",
    "  (a.tot_cust_years - c.tot_cust_years)**2 + \n",
    "  (a.tot_children - c.tot_children)**2 + \n",
    "  (a.single_ind - c.single_ind)**2 + \n",
    "  (a.female_ind - c.female_ind)**2 + \n",
    "  (a.married_ind - c.married_ind)**2 + \n",
    "  (a.separated_ind - c.separated_ind)**2 + \n",
    "  (a.ca_resident_ind - c.ca_resident_ind)**2 + \n",
    "  (a.ny_resident_ind - c.ny_resident_ind)**2 + \n",
    "  (a.tx_resident_ind - c.tx_resident_ind)**2 + \n",
    "  (a.il_resident_ind - c.il_resident_ind)**2 + \n",
    "  (a.az_resident_ind - c.az_resident_ind)**2 + \n",
    "  (a.oh_resident_ind - c.oh_resident_ind)**2 + \n",
    "  (a.ck_acct_ind - c.ck_acct_ind)**2 + \n",
    "  (a.sv_acct_ind - c.sv_acct_ind)**2 + \n",
    "  (a.cc_acct_ind - c.cc_acct_ind)**2 + \n",
    "  (a.ck_avg_bal - c.ck_avg_bal)**2 + \n",
    "  (a.sv_avg_bal - c.sv_avg_bal)**2 + \n",
    "  (a.cc_avg_bal - c.cc_avg_bal)**2 + \n",
    "  (a.ck_avg_tran_amt - c.ck_avg_tran_amt)**2 + \n",
    "  (a.sv_avg_tran_amt - c.sv_avg_tran_amt)**2 + \n",
    "  (a.cc_avg_tran_amt - c.cc_avg_tran_amt)**2 + \n",
    "  (a.q1_trans_cnt - c.q1_trans_cnt)**2 + \n",
    "  (a.q2_trans_cnt - c.q2_trans_cnt)**2 + \n",
    "  (a.q3_trans_cnt - c.q3_trans_cnt)**2 + \n",
    "  (a.q4_trans_cnt - c.q4_trans_cnt)**2 as inertia\n",
    "from \n",
    "  demo_db.ClusterInput a join \n",
    "  demo_db.ClusterScore b \n",
    "  on a.cust_id = b.cust_id join \n",
    "  demo_db.ClusterModel c \n",
    "  on c.clusterid = b.clusterid) ttt;\n",
    "\n",
    "SET inertia = i;\n",
    "\n",
    "END;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 373,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GRANT EXECUTE PROCEDURE ON demo_db.cluster_data TO demo_db;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "WARNING",
     "evalue": "[Teradata Database] [Warning 5527] Stored Procedure Created with Warnings.\n\nSPL5000:W(L45), E(3807):Object 'cluster_quality' does not exist.\nSPL5013:W(L45), Identifier (k) reference is ambiguous and will be treated as local variable during execution.\nSPL5013:W(L45), Identifier (current_inertia) reference is ambiguous and will be treated as local variable during execution.",
     "output_type": "error",
     "traceback": [
      "[Teradata Database] [Warning 5527] Stored Procedure Created with Warnings.\n\nSPL5000:W(L45), E(3807):Object 'cluster_quality' does not exist.\nSPL5013:W(L45), Identifier (k) reference is ambiguous and will be treated as local variable during execution.\nSPL5013:W(L45), Identifier (current_inertia) reference is ambiguous and will be treated as local variable during execution."
     ]
    }
   ],
   "source": [
    "REPLACE PROCEDURE demo_db.cluster_data_multiple(\n",
    "    --///////////////////////////////////////////////////////////////////////////////////////////////////////\n",
    "    -- Procedure calculates inertia for clustering data from strat_clusters to max_clusters\n",
    "    -- results are stored in volatile table cluster_quality (cluster_num, inertia)\n",
    "    -- Next step this table is used to define optimal clusters number\n",
    "    --///////////////////////////////////////////////////////////////////////////////////////////////////////\n",
    "    \n",
    "    IN start_clusters Integer,\n",
    "    IN max_clusters INTEGER,\n",
    "    -- Procedure runs clustering over the data and calculates inertia for the clusterization\n",
    "    IN IN_DB varchar(100), -- database of incoming table\n",
    "    IN MODEL_DB varchar(100), -- database of table with cluster model\n",
    "    IN IN_TABLE_NAME varchar(100), -- input table with data to cluster\n",
    "    IN COLS varchar(1000), -- columns of table to treat as dimentions\n",
    "    IN MODEL_TABLE_NAME varchar(1000), -- table name of cluster model to store\n",
    "    IN OPERATOR_DB varchar(100), -- database where VAL operator resides\n",
    "    IN ITERATIONS INTEGER,  -- number of iterations in clustering \n",
    "    -- end of model parameters\n",
    "    IN OUT_DB varchar(100), -- output database with clustered points\n",
    "    IN OUT_TABLE_NAME varchar(100), -- output table name with clustered points\n",
    "    IN KEY_COL_NAME varchar(100) -- key column that identifies objects - that will be in the output table\n",
    ")\n",
    "SQL Security invoker\n",
    "BEGIN\n",
    "  DECLARE k INT DEFAULT 2; -- current number of clusters\n",
    "  DECLARE current_inertia BIGINT; -- inertia for current K number of clusters\n",
    "\n",
    "  CALL demo_db.drop_my_table('cluster_quality'); -- drop table if it exists before creating\n",
    "  \n",
    "  -- table to store inertia for all clusters\n",
    "  CREATE MULTISET VOLATILE TABLE cluster_quality (cluster_num SMALLINT, inertia BIGINT) \n",
    "  PRIMARY INDEX ( cluster_num ) ON COMMIT PRESERVE ROWS;\n",
    "  \n",
    "  SET k = start_clusters;\n",
    "  -- main loop to create cluster models and calculate inertia\n",
    "  WHILE k < max_clusters+1\n",
    "  DO\n",
    "    call demo_db.cluster_data(current_inertia, k, IN_DB, MODEL_DB, IN_TABLE_NAME, \n",
    "        COLS, MODEL_TABLE_NAME, OPERATOR_DB, ITERATIONS, OUT_DB, OUT_TABLE_NAME, KEY_COL_NAME);\n",
    "    insert into cluster_quality (cluster_num, inertia) values (k, current_inertia);\n",
    "    SET k = k+1;\n",
    "  END WHILE;\n",
    "\n",
    "END;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 349,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop table cluster_quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- calculate inertia for a range of clusters\n",
    "call demo_db.cluster_data_multiple (2,30,'demo_db','demo_db','ClusterInput', \n",
    "    'tot_income,tot_age,tot_cust_years,tot_children,single_ind,female_ind,married_ind,separated_ind,ca_resident_ind,ny_resident_ind,tx_resident_ind,il_resident_ind,az_resident_ind,oh_resident_ind,ck_acct_ind,sv_acct_ind,cc_acct_ind,ck_avg_bal,sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,sv_avg_tran_amt,cc_avg_tran_amt,q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt',\n",
    "'ClusterModel','TRNG_XSP',50, 'demo_db', 'ClusterScore', 'cust_id');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"cluster_num\",\"inertia\"\n\"2\",\"271190\"\n\"3\",\"246791\"\n\"4\",\"239943\"\n\"5\",\"226265\"\n\"6\",\"222309\"\n\"7\",\"213951\"\n\"8\",\"210696\"\n\"9\",\"204312\"\n\"10\",\"200370\"\n\"11\",\"189021\"\n\"12\",\"182910\"\n\"13\",\"177754\"\n\"14\",\"175222\"\n\"15\",\"163347\"\n\"16\",\"172350\"\n\"17\",\"161131\"\n\"18\",\"164589\"\n\"19\",\"162013\"\n\"20\",\"155786\"\n\"21\",\"150882\"\n\"22\",\"150441\"\n\"23\",\"153922\"\n\"24\",\"147075\"\n\"25\",\"144324\"\n\"26\",\"141358\"\n\"27\",\"146489\"\n\"28\",\"144851\"\n\"29\",\"136931\"\n\"30\",\"138783\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory581657\"><style>\n",
       "      table.tdhistory581657 { display: block !important; min-height: 588px !important; overflow: auto !important; height: 588px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory581657 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory581657:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory581657 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory581657 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory581657 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory581657\"></th><th class=\"tdhistory581657\">cluster_num</th><th class=\"tdhistory581657\">inertia</th></tr></thead><tbody class=\"tdhistory581657 resultset-body\"><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory581657\">2</td><td class=\"tdhistory581657\">271190</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory581657\">3</td><td class=\"tdhistory581657\">246791</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory581657\">4</td><td class=\"tdhistory581657\">239943</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory581657\">5</td><td class=\"tdhistory581657\">226265</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory581657\">6</td><td class=\"tdhistory581657\">222309</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory581657\">7</td><td class=\"tdhistory581657\">213951</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory581657\">8</td><td class=\"tdhistory581657\">210696</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory581657\">9</td><td class=\"tdhistory581657\">204312</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory581657\">10</td><td class=\"tdhistory581657\">200370</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory581657\">11</td><td class=\"tdhistory581657\">189021</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">11</th><td class=\"tdhistory581657\">12</td><td class=\"tdhistory581657\">182910</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">12</th><td class=\"tdhistory581657\">13</td><td class=\"tdhistory581657\">177754</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">13</th><td class=\"tdhistory581657\">14</td><td class=\"tdhistory581657\">175222</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">14</th><td class=\"tdhistory581657\">15</td><td class=\"tdhistory581657\">163347</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">15</th><td class=\"tdhistory581657\">16</td><td class=\"tdhistory581657\">172350</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">16</th><td class=\"tdhistory581657\">17</td><td class=\"tdhistory581657\">161131</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">17</th><td class=\"tdhistory581657\">18</td><td class=\"tdhistory581657\">164589</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">18</th><td class=\"tdhistory581657\">19</td><td class=\"tdhistory581657\">162013</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">19</th><td class=\"tdhistory581657\">20</td><td class=\"tdhistory581657\">155786</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">20</th><td class=\"tdhistory581657\">21</td><td class=\"tdhistory581657\">150882</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">21</th><td class=\"tdhistory581657\">22</td><td class=\"tdhistory581657\">150441</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">22</th><td class=\"tdhistory581657\">23</td><td class=\"tdhistory581657\">153922</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">23</th><td class=\"tdhistory581657\">24</td><td class=\"tdhistory581657\">147075</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">24</th><td class=\"tdhistory581657\">25</td><td class=\"tdhistory581657\">144324</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">25</th><td class=\"tdhistory581657\">26</td><td class=\"tdhistory581657\">141358</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">26</th><td class=\"tdhistory581657\">27</td><td class=\"tdhistory581657\">146489</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">27</th><td class=\"tdhistory581657\">28</td><td class=\"tdhistory581657\">144851</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">28</th><td class=\"tdhistory581657\">29</td><td class=\"tdhistory581657\">136931</td></tr><tr><th class=\"tdhistory581657\" style= \"background: rgba(243, 243, 243, 0.75)\">29</th><td class=\"tdhistory581657\">30</td><td class=\"tdhistory581657\">138783</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.08_14.41.34.609_EDT\",\"historyID\":789,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":29,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"cluster_num\",\"columnTypeName\":\"SMALLINT\"},{\"columnNumber\":1,\"columnName\":\"inertia\",\"columnTypeName\":\"BIGINT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select * from cluster_quality order by cluster_num;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result Set ID: /home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.08_11.23.20.591_EDT\n",
      "History ID:    720\n",
      "Rows:          21 of 21\n",
      "Parts:         2\n",
      "Column Definitions:\n",
      "    cluster_num: SMALLINT\n",
      "    inertia: BIGINT\n"
     ]
    }
   ],
   "source": [
    "%meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### %chart\n",
       "Display a Vega-Lite visualization of a query result set or an existing Vega or VegaLite chart.\n",
       "\n",
       "%chart x=&lt;XColumnName&gt;, y=&lt;YColumnName&gt;,</br>\n",
       "[title=&lt;ChartTitle&gt;,] [id=&lt;ResultSetID&gt;|&lt;HistoryID&gt;,] [part=&lt;PartNum&gt;]</br>\n",
       "[labelx=&lt;XLabel&gt;,] [labely=&lt;YLabel&gt;,]</br>\n",
       "[gridx=&lt;ttrue|false&gt;,] [gridy=&lt;true|false&gt;,]</br>\n",
       "[typex=&lt;q|n|o|t&gt;,] [typey=&lt;q|n|o|t&gt;,]</br>\n",
       "[mark=&lt;area|bar|circle|line|point|rect|rule|square|text|tick&gt;,]</br>\n",
       "[color=&lt;CSSColorSpec&gt;,] [height=&lt;ChartHeight&gt;,] [width=&lt;ChartWidth&gt;]</br>\n",
       "[spec=&lt;ChartSpec&gt;, [data=&lt;ChartData&gt;]]\n",
       "\n",
       "* **typex** and **typey** values: q=quantitative n=nominal o=ordinal t=temporal\n",
       "  * (defaults based on column type: numeric types -> quantitative, time times -> temporal, other types -> nominal)\n",
       "* **&lt;ResultSetID&gt;** is the timestamp/directory name of the result set.\n",
       "* **&lt;HistoryID&gt;** is the id of the history item (see **%history** command).\n",
       "* If the **id** is not specified, the most recently accessed result set is displayed.\n",
       "* The **spec** parameter is used to display an existing chart spec.\n",
       "* If **spec** is used, only **data**, **id**, and **part** parameters are supported."
      ]
     },
     "execution_count": 443,
     "metadata": {
      "application/vnd.teradata.resultset": "helpdata"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%help chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
       "data": {
        "format": {
         "type": "csv"
        },
        "values": "\"cluster_num\",\"inertia\"\n\"2\",\"271190\"\n\"3\",\"246791\"\n\"4\",\"239943\"\n\"5\",\"226265\"\n\"6\",\"222309\"\n\"7\",\"213951\"\n\"8\",\"210696\"\n\"9\",\"204312\"\n\"10\",\"200370\"\n\"11\",\"189021\"\n\"12\",\"182910\"\n\"13\",\"177754\"\n\"14\",\"175222\"\n\"15\",\"163347\"\n\"16\",\"172350\"\n\"17\",\"161131\"\n\"18\",\"164589\"\n\"19\",\"162013\"\n\"20\",\"155786\"\n\"21\",\"150882\"\n\"22\",\"150441\"\n\"23\",\"153922\"\n\"24\",\"147075\"\n\"25\",\"144324\"\n\"26\",\"141358\"\n\"27\",\"146489\"\n\"28\",\"144851\"\n\"29\",\"136931\"\n\"30\",\"138783\"\n"
       },
       "encoding": {
        "x": {
         "axis": {
          "grid": true,
          "title": "Number of clusters"
         },
         "field": "cluster_num",
         "type": "quantitative"
        },
        "y": {
         "axis": {
          "grid": true,
          "title": "Inertia"
         },
         "field": "inertia",
         "type": "quantitative"
        }
       },
       "height": "300",
       "mark": {
        "color": "#4C78A8",
        "type": "line"
       },
       "title": "Inertia by cluster#",
       "width": "600"
      },
      "image/png": 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"
     },
     "execution_count": 7,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%chart x=cluster_num, y=inertia, title=\"Inertia by cluster#\", height=300, width=600,labelx=Number of clusters, labely=Inertia, mark=line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is a thrilling but yet quite simple query that tries to implement an elbow method for definition of number of clusters  \n",
    "Main idea is that rate of decrease for inertia decreases to some point and then becomes stable.  \n",
    "This you can see on smoothed inertia diff (see picture **Normalized smoothed inertia diff** below)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 7 rows affected"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace view demo_db.v_elbow as \n",
    "-- single inertia diff\n",
    "with inertia_filtered as (\n",
    "select \n",
    "  cluster_num, \n",
    "  inertia, \n",
    "  inertia_diff\n",
    "from (\n",
    "select cluster_num, inertia, -mdiff(inertia, 1, cluster_num) as inertia_diff  \n",
    "from cluster_quality \n",
    "qualify coalesce(inertia_diff, 1) > 0) a ), \n",
    "\n",
    "-- remove points with increasing inertia\n",
    "inertia_filtered_diff as (\n",
    "select  \n",
    "  cluster_num, \n",
    "  inertia, \n",
    "  -mdiff(inertia, 1, cluster_num) as inertia_diff\n",
    "from inertia_filtered), \n",
    "\n",
    "-- min-max scale inertia diff\n",
    "inertia_normalized as (\n",
    "select \n",
    "  cluster_num, \n",
    "  cast((inertia_diff - min_in_diff)/(max_in_diff - min_in_diff) as decimal(5,3)) as normed_diff,\n",
    "  inertia, \n",
    "  inertia_diff, \n",
    "  cast(min(inertia_diff) over () as float) as min_in_diff,\n",
    "  max(inertia_diff) over () as max_in_diff\n",
    "from inertia_filtered_diff\n",
    "      where inertia_diff is not null), \n",
    "\n",
    "-- smoothing inertia diff\n",
    "smoothed_normed_diff as (\n",
    "select \n",
    "  cluster_num, \n",
    "  normed_diff, \n",
    "  cast(avg(normed_diff) over (order by cluster_num rows between 3 PRECEDING and 0 preceding) as decimal(3,2)) as smoothed_diff\n",
    "from inertia_normalized), \n",
    "\n",
    "-- adding double diff for smoothed_diff      \n",
    "smoothed_normed_double_diff as (\n",
    "select \n",
    "      cluster_num, normed_diff, smoothed_diff, -mdiff(smoothed_diff, 1, cluster_num) as double_diff\n",
    "from smoothed_normed_diff),\n",
    "      \n",
    "-- adding smoothing for double_diff\n",
    "double_smoothed_double_diff as (\n",
    "select \n",
    "      cluster_num, normed_diff, double_diff, smoothed_diff,\n",
    "      cast(avg(double_diff) over (order by cluster_num rows between 3 PRECEDING and 0 preceding) as decimal(5,2)) as smoothed_double_diff\n",
    "from smoothed_normed_double_diff),\n",
    "\n",
    "-- and a final part adding a stop_flag - this is a minimum of smoothed_double_diff from current point and two points ahead\n",
    "-- if theis value is lower then a thereshold (a value close to zero) - it is an elbow point\n",
    "elbow_view as (\n",
    "select\n",
    "  cluster_num, \n",
    "  normed_diff, \n",
    "  smoothed_diff, \n",
    "  double_diff,\n",
    "  smoothed_double_diff,\n",
    "  min(smoothed_double_diff) over (order by cluster_num rows between 0 preceding and 2 following) as stop_flag\n",
    "from double_smoothed_double_diff)\n",
    "\n",
    "select * from elbow_view;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 80,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_05.34.02.257_EDT\",\"historyID\":850,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":23,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"cluster_num\",\"columnTypeName\":\"SMALLINT\"},{\"columnNumber\":1,\"columnName\":\"normed_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":3,\"precision\":5,\"scale\":3},{\"columnNumber\":2,\"columnName\":\"smoothed_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":3,\"scale\":2},{\"columnNumber\":3,\"columnName\":\"double_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":3,\"scale\":2},{\"columnNumber\":4,\"columnName\":\"smoothed_double_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":5,\"scale\":2},{\"columnNumber\":5,\"columnName\":\"stop_flag\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":5,\"scale\":2}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select * from demo_db.v_elbow order by cluster_num;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalized difference in neighbouring inertia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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         "axis": {
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"
     },
     "execution_count": 10,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%chart x = cluster_num, y = norm_inertia_diff, width = 600, height = 300, labley = inertia diff, \n",
    "lablex=cluster number, title=Normalized inertia diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
       "data": {
        "format": {
         "type": "csv"
        },
        "values": "\"cluster_num\",\"normed_diff\",\"double_diff\",\"smoothed_diff\",\"running_double_diff\"\n\"3\",\"1.000\",\"\",\"1.00\",\"\"\n\"4\",\".371\",\".31\",\".69\",\".31\"\n\"5\",\".616\",\".03\",\".66\",\".17\"\n\"6\",\".267\",\".10\",\".56\",\".15\"\n\"7\",\".425\",\".14\",\".42\",\".14\"\n\"8\",\".242\",\".03\",\".39\",\".07\"\n\"9\",\".354\",\".07\",\".32\",\".08\"\n\"10\",\".267\",\".00\",\".32\",\".06\"\n\"11\",\".532\",\"-.03\",\".35\",\".02\"\n\"12\",\".344\",\"-.02\",\".37\",\".00\"\n\"13\",\".310\",\".01\",\".36\",\"-.01\"\n\"14\",\".216\",\".01\",\".35\",\"-.01\"\n\"15\",\".551\",\"-.01\",\".36\",\".00\"\n\"17\",\".205\",\".04\",\".32\",\".01\"\n\"19\",\".094\",\".05\",\".27\",\".02\"\n\"20\",\".348\",\"-.03\",\".30\",\".01\"\n\"21\",\".301\",\".06\",\".24\",\".03\"\n\"22\",\".141\",\".02\",\".22\",\".02\"\n\"24\",\".246\",\"-.04\",\".26\",\".00\"\n\"25\",\".224\",\".03\",\".23\",\".02\"\n\"26\",\".232\",\".02\",\".21\",\".01\"\n\"28\",\".000\",\".03\",\".18\",\".01\"\n\"29\",\".409\",\"-.04\",\".22\",\".01\"\n"
       },
       "encoding": {
        "x": {
         "axis": {
          "grid": true,
          "title": "cluster_num"
         },
         "field": "cluster_num",
         "type": "quantitative"
        },
        "y": {
         "axis": {
          "grid": true,
          "title": "smoothed_diff"
         },
         "field": "smoothed_diff",
         "type": "quantitative"
        }
       },
       "height": "300",
       "mark": {
        "color": "#4C78A8",
        "type": "bar"
       },
       "title": "Normalized smoothed inertia diff",
       "width": "600"
      },
      "image/png": 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"
     },
     "execution_count": 39,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%chart x = cluster_num, y = smoothed_diff, width = 600, height = 300, labley = inertia diff, \n",
    "lablex=cluster number, title=Normalized smoothed inertia diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
       "data": {
        "format": {
         "type": "csv"
        },
        "values": "\"cluster_num\",\"normed_diff\",\"double_diff\",\"smoothed_diff\",\"running_double_diff\"\n\"3\",\"1.000\",\"\",\"1.00\",\"\"\n\"4\",\".371\",\".31\",\".69\",\".31\"\n\"5\",\".616\",\".03\",\".66\",\".17\"\n\"6\",\".267\",\".10\",\".56\",\".15\"\n\"7\",\".425\",\".14\",\".42\",\".14\"\n\"8\",\".242\",\".03\",\".39\",\".07\"\n\"9\",\".354\",\".07\",\".32\",\".08\"\n\"10\",\".267\",\".00\",\".32\",\".06\"\n\"11\",\".532\",\"-.03\",\".35\",\".02\"\n\"12\",\".344\",\"-.02\",\".37\",\".00\"\n\"13\",\".310\",\".01\",\".36\",\"-.01\"\n\"14\",\".216\",\".01\",\".35\",\"-.01\"\n\"15\",\".551\",\"-.01\",\".36\",\".00\"\n\"17\",\".205\",\".04\",\".32\",\".01\"\n\"19\",\".094\",\".05\",\".27\",\".02\"\n\"20\",\".348\",\"-.03\",\".30\",\".01\"\n\"21\",\".301\",\".06\",\".24\",\".03\"\n\"22\",\".141\",\".02\",\".22\",\".02\"\n\"24\",\".246\",\"-.04\",\".26\",\".00\"\n\"25\",\".224\",\".03\",\".23\",\".02\"\n\"26\",\".232\",\".02\",\".21\",\".01\"\n\"28\",\".000\",\".03\",\".18\",\".01\"\n\"29\",\".409\",\"-.04\",\".22\",\".01\"\n"
       },
       "encoding": {
        "x": {
         "axis": {
          "grid": true,
          "title": "cluster_num"
         },
         "field": "cluster_num",
         "type": "quantitative"
        },
        "y": {
         "axis": {
          "grid": true,
          "title": "double_diff"
         },
         "field": "double_diff",
         "type": "quantitative"
        }
       },
       "height": "300",
       "mark": {
        "color": "#4C78A8",
        "type": "bar"
       },
       "title": "Normalized smoothed inertia double diff",
       "width": "600"
      },
      "image/png": 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"
     },
     "execution_count": 40,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%chart x = cluster_num, y = double_diff, width = 600, height = 300, labley = inertia diff, \n",
    "lablex=cluster number, title=Normalized smoothed inertia double diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v3+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v3.json",
       "data": {
        "format": {
         "type": "csv"
        },
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       },
       "encoding": {
        "x": {
         "axis": {
          "grid": true,
          "title": "cluster_num"
         },
         "field": "cluster_num",
         "type": "quantitative"
        },
        "y": {
         "axis": {
          "grid": true,
          "title": "smoothed_double_diff"
         },
         "field": "smoothed_double_diff",
         "type": "quantitative"
        }
       },
       "height": "300",
       "mark": {
        "color": "#4C78A8",
        "type": "bar"
       },
       "title": "Double diff averaged in window of 3",
       "width": "600"
      },
      "image/png": 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"
     },
     "execution_count": 48,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%chart x = cluster_num, y = smoothed_double_diff, width = 600, height = 300, labley = inertia diff, \n",
    "lablex=cluster number, title=Double diff averaged in window of 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting 1st point from elbow view - this gives us the optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"cluster_num\",\"normed_diff\",\"smoothed_diff\",\"double_diff\",\"smoothed_double_diff\",\"stop_flag\"\n\"9\",\".354\",\".32\",\".07\",\".08\",\".02\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory350861\"><style>\n",
       "      table.tdhistory350861 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory350861 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory350861:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory350861 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory350861 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory350861 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory350861\"></th><th class=\"tdhistory350861\">cluster_num</th><th class=\"tdhistory350861\">normed_diff</th><th class=\"tdhistory350861\">smoothed_diff</th><th class=\"tdhistory350861\">double_diff</th><th class=\"tdhistory350861\">smoothed_double_diff</th><th class=\"tdhistory350861\">stop_flag</th></tr></thead><tbody class=\"tdhistory350861 resultset-body\"><tr><th class=\"tdhistory350861\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory350861\">9</td><td class=\"tdhistory350861\">.354</td><td class=\"tdhistory350861\">.32</td><td class=\"tdhistory350861\">.07</td><td class=\"tdhistory350861\">.08</td><td class=\"tdhistory350861\">.02</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_05.33.00.173_EDT\",\"historyID\":848,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"cluster_num\",\"columnTypeName\":\"SMALLINT\"},{\"columnNumber\":1,\"columnName\":\"normed_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":3,\"precision\":5,\"scale\":3},{\"columnNumber\":2,\"columnName\":\"smoothed_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":3,\"scale\":2},{\"columnNumber\":3,\"columnName\":\"double_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":3,\"scale\":2},{\"columnNumber\":4,\"columnName\":\"smoothed_double_diff\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":5,\"scale\":2},{\"columnNumber\":5,\"columnName\":\"stop_flag\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":5,\"scale\":2}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select top 1 * from demo_db.v_elbow where stop_flag <=.02 order by cluster_num;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### END -- defining the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"inertia\"\n\"     270867\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory37189\"><style>\n",
       "      table.tdhistory37189 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory37189 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory37189:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory37189 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory37189 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory37189 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory37189\"></th><th class=\"tdhistory37189\">inertia</th></tr></thead><tbody class=\"tdhistory37189 resultset-body\"><tr><th class=\"tdhistory37189\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory37189\">     270867</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 282,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.08_07.00.51.671_EDT\",\"historyID\":606,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"inertia\",\"columnTypeName\":\"VARCHAR\",\"length\":1000,\"scale\":1000}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- this is a single clusterisation call\n",
    "-- it returns inertia\n",
    "call demo_db.cluster_data (w,2,'demo_db','demo_db','ClusterInput', \n",
    "    'tot_income,tot_age,tot_cust_years,tot_children,single_ind,female_ind,married_ind,separated_ind,ca_resident_ind,ny_resident_ind,tx_resident_ind,il_resident_ind,az_resident_ind,oh_resident_ind,ck_acct_ind,sv_acct_ind,cc_acct_ind,ck_avg_bal,sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,sv_avg_tran_amt,cc_avg_tran_amt,q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt',\n",
    "'ClusterModel','TRNG_XSP',300, 'demo_db', 'ClusterScore', 'cust_id');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we can clearly see that the number of clusters is 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now cluster the data with the optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"inertia\"\n\"187209\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory117999\"><style>\n",
       "      table.tdhistory117999 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory117999 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory117999:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory117999 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory117999 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory117999 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory117999\"></th><th class=\"tdhistory117999\">inertia</th></tr></thead><tbody class=\"tdhistory117999 resultset-body\"><tr><th class=\"tdhistory117999\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory117999\">187209</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 265,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_13.01.45.259_EDT\",\"historyID\":1035,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"inertia\",\"columnTypeName\":\"BIGINT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call demo_db.cluster_data (w,12,'demo_db','demo_db','ClusterInput', \n",
    "    'tot_income,tot_age,tot_cust_years,tot_children,\n",
    "    single_ind,female_ind,married_ind,separated_ind,\n",
    "    ca_resident_ind,ny_resident_ind,tx_resident_ind,\n",
    "    il_resident_ind,az_resident_ind,oh_resident_ind,\n",
    "    ck_acct_ind,sv_acct_ind,cc_acct_ind,ck_avg_bal,\n",
    "    sv_avg_bal,cc_avg_bal,ck_avg_tran_amt,sv_avg_tran_amt,\n",
    "    cc_avg_tran_amt,q1_trans_cnt,q2_trans_cnt,q3_trans_cnt,q4_trans_cnt',\n",
    "'ClusterModel','TRNG_XSP',300, 'demo_db', 'ClusterScore', 'cust_id');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"clusterid\",\"cnt\",\"tot_income\",\"tot_age\",\"tot_cust_years\",\"tot_children\",\"single_ind\",\"female_ind\",\"married_ind\",\"separated_ind\",\"ca_resident_ind\",\"ny_resident_ind\",\"tx_resident_ind\",\"il_resident_ind\",\"az_resident_ind\",\"oh_resident_ind\",\"ck_acct_ind\",\"sv_acct_ind\",\"cc_acct_ind\",\"ck_avg_bal\",\"sv_avg_bal\",\"cc_avg_bal\",\"ck_avg_tran_amt\",\"sv_avg_tran_amt\",\"cc_avg_tran_amt\",\"q1_trans_cnt\",\"q2_trans_cnt\",\"q3_trans_cnt\",\"q4_trans_cnt\"\n\"1\",\"322\",\"-0.22019215234420347\",\"0.04959841956181648\",\"-0.100523855500181\",\"-0.25093676973664314\",\"0.04522082040255506\",\"0.011372548781866554\",\"0.011425172768187464\",\"-0.2620487128321679\",\"-0.5572488813712185\",\"-0.4088856808449036\",\"-0.3535533905932748\",\"-0.28467871041242904\",\"-0.1821950074697113\",\"5.610549070239478\",\"0.09351946247747657\",\"-0.25971900473907783\",\"0.14294322509357996\",\"-0.046338376723782944\",\"-0.228008076428952\",\"-0.01020739406032096\",\"0.00876195148047832\",\"-0.07156098929694285\",\"-0.03982440752382195\",\"-0.06688296822077912\",\"-0.026816219094236596\",\"-0.2933079428428316\",\"0.10270392857011554\"\n\"2\",\"1896\",\"-0.24352700467473432\",\"-0.40339307092719157\",\"-0.15572339055621165\",\"-0.4914204492485305\",\"1.2746495681648955\",\"0.0988605143439992\",\"-0.9465405888256554\",\"-0.2620487128321615\",\"0.18574464563835189\",\"-0.4088856808448942\",\"-0.3535533905932763\",\"0.16195766936466244\",\"0.06007102006566598\",\"-0.17823567488329725\",\"0.6286016530783701\",\"0.11425871342808804\",\"0.2781913261953246\",\"0.056217317657090655\",\"-0.13634005067437865\",\"-0.11641334261058171\",\"0.03221864003044082\",\"-0.07812799431119292\",\"-0.014561436821255814\",\"0.38474298256422895\",\"0.3422905437505422\",\"0.09141362172749765\",\"0.12617699561062645\"\n\"3\",\"1108\",\"-0.6494230011494773\",\"-1.2665452660531034\",\"0.17743312879430848\",\"-0.4450194741213295\",\"1.2053650491315915\",\"0.06180238924629285\",\"-0.8778449803884208\",\"-0.26204871283216663\",\"0.3469550261514311\",\"-0.1332199736326809\",\"-0.07785832789238185\",\"-0.0790427543673495\",\"-0.11054205214035413\",\"-0.1782356748832976\",\"-1.5115597141208132\",\"0.19935307676063196\",\"-0.970509985261295\",\"-0.48189321857611\",\"0.7912294931519204\",\"-0.32929567179750613\",\"0.2075263621567123\",\"0.5596541222495867\",\"-0.031100679238181585\",\"-0.8625386511990424\",\"-0.7113953902321879\",\"-0.5884310124971528\",\"-0.5262878896603241\"\n\"4\",\"300\",\"1.5503686430601615\",\"0.3095504784245059\",\"0.19119160867954132\",\"0.3762879916575621\",\"-0.39256736841302775\",\"-0.428864676702738\",\"0.3220383136486141\",\"0.0234206037093751\",\"0.022856649032514477\",\"-0.09488440612503701\",\"0.11313708498984786\",\"0.5254355626469404\",\"-0.049875883294833445\",\"-0.17823567488329928\",\"0.6607105732001444\",\"-0.2962474910585704\",\"-0.158158015340283\",\"3.323764969062942\",\"0.25834972884692803\",\"0.46672082702114553\",\"-3.549781847775029\",\"0.3432171042001884\",\"0.059914569611912424\",\"0.27422926470980424\",\"0.16964880517812\",\"0.27121939922062843\",\"-0.16337576249514035\"\n\"5\",\"839\",\"0.12392408817193203\",\"0.08270198703537003\",\"-0.07160133781797604\",\"-0.09998294918679503\",\"-0.00491899040908052\",\"-0.13320287983314322\",\"0.123009655779714\",\"-0.2620487128321683\",\"-0.5572488813712146\",\"-0.40888568084490007\",\"2.828427124746207\",\"-0.28467871041243226\",\"-0.18219500746971085\",\"-0.17823567488329792\",\"0.47671711197445377\",\"0.23107756329713414\",\"0.1388723350373694\",\"-0.0027506234582170995\",\"-0.14605088918448722\",\"-0.029329844045658526\",\"0.08440341266282855\",\"-0.06730081662314348\",\"-0.011639964551522834\",\"0.3390118458756964\",\"0.25777642977977766\",\"0.02871721691283442\",\"0.12661565796356677\"\n\"6\",\"2659\",\"0.02019662579514877\",\"0.3901589295862247\",\"0.03976949786688855\",\"0.31478509644387376\",\"-0.7654980477744635\",\"0.06825531082508793\",\"0.5984735611167856\",\"-0.26204871283215947\",\"0.22372931101948604\",\"-0.4088856808448961\",\"-0.35355339059327634\",\"0.07235501043805864\",\"0.1398410063443919\",\"-0.1782356748832971\",\"0.6345445548040243\",\"0.00486867728929298\",\"0.20301034765127055\",\"0.033156563664931755\",\"-0.14844761755458213\",\"-0.08664517688168095\",\"0.1130434109259557\",\"-0.09648775222507647\",\"-0.03140902026032414\",\"0.310889814916551\",\"0.23146011681460288\",\"0.3435561260009716\",\"0.21033654991461803\"\n\"7\",\"1079\",\"0.06551800769706566\",\"0.12267233167390389\",\"-0.09012703869524606\",\"-0.06552060045436463\",\"0.046723543049778764\",\"0.06762202281239882\",\"0.03176449132254316\",\"-0.26204871283216696\",\"-0.5572488813712133\",\"2.4456713620629675\",\"-0.35355339059327606\",\"-0.2846787104124331\",\"-0.1821950074697119\",\"-0.17823567488329764\",\"0.31009289863849465\",\"0.24086005931518867\",\"0.39659247941173636\",\"0.009371165229668096\",\"-0.06843571683020025\",\"0.07357566588634638\",\"0.028068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      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory348902\"><style>\n",
       "      table.tdhistory348902 { display: block !important; min-height: 273px !important; overflow: auto !important; height: 273px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory348902 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
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       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory348902\"></th><th class=\"tdhistory348902\">clusterid</th><th class=\"tdhistory348902\">cnt</th><th class=\"tdhistory348902\">tot_income</th><th class=\"tdhistory348902\">tot_age</th><th class=\"tdhistory348902\">tot_cust_years</th><th class=\"tdhistory348902\">tot_children</th><th class=\"tdhistory348902\">single_ind</th><th class=\"tdhistory348902\">female_ind</th><th class=\"tdhistory348902\">married_ind</th><th class=\"tdhistory348902\">separated_ind</th><th class=\"tdhistory348902\">ca_resident_ind</th><th class=\"tdhistory348902\">ny_resident_ind</th><th class=\"tdhistory348902\">tx_resident_ind</th><th class=\"tdhistory348902\">il_resident_ind</th><th class=\"tdhistory348902\">az_resident_ind</th><th class=\"tdhistory348902\">oh_resident_ind</th><th class=\"tdhistory348902\">ck_acct_ind</th><th class=\"tdhistory348902\">sv_acct_ind</th><th 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      ]
     },
     "execution_count": 157,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_11.38.55.682_EDT\",\"historyID\":927,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":9,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"clusterid\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"cnt\",\"columnTypeName\":\"BIGINT\"},{\"columnNumber\":2,\"columnName\":\"tot_income\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":3,\"columnName\":\"tot_age\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":4,\"columnName\":\"tot_cust_years\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":5,\"columnName\":\"tot_children\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":6,\"columnName\":\"single_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":7,\"columnName\":\"female_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":8,\"columnName\":\"married_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":9,\"columnName\":\"separated_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":10,\"columnName\":\"ca_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":11,\"columnName\":\"ny_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":12,\"columnName\":\"tx_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":13,\"columnName\":\"il_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":14,\"columnName\":\"az_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":15,\"columnName\":\"oh_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":16,\"columnName\":\"ck_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":17,\"columnName\":\"sv_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":18,\"columnName\":\"cc_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":19,\"columnName\":\"ck_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":20,\"columnName\":\"sv_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":21,\"columnName\":\"cc_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":22,\"columnName\":\"ck_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":23,\"columnName\":\"sv_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":24,\"columnName\":\"cc_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":25,\"columnName\":\"q1_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":26,\"columnName\":\"q2_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":27,\"columnName\":\"q3_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":28,\"columnName\":\"q4_trans_cnt\",\"columnTypeName\":\"FLOAT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select * from demo_db.ClusterModel where clusterid >0 order by clusterid;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"clusterid\",\"cust_id\"\n\"7\",\"25897247\"\n\"9\",\"21805952\"\n\"2\",\"24535494\"\n\"2\",\"16352904\"\n\"6\",\"13626380\"\n\"2\",\"17715126\"\n\"3\",\"14996718\"\n\"3\",\"17717206\"\n\"2\",\"25902814\"\n\"6\",\"27259000\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory90821\"><style>\n",
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       "      th.tdhistory90821 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory90821:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory90821 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory90821 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory90821 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory90821\"></th><th class=\"tdhistory90821\">clusterid</th><th class=\"tdhistory90821\">cust_id</th></tr></thead><tbody class=\"tdhistory90821 resultset-body\"><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory90821\">7</td><td class=\"tdhistory90821\">25897247</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory90821\">9</td><td class=\"tdhistory90821\">21805952</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory90821\">2</td><td class=\"tdhistory90821\">24535494</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory90821\">2</td><td class=\"tdhistory90821\">16352904</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory90821\">6</td><td class=\"tdhistory90821\">13626380</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory90821\">2</td><td class=\"tdhistory90821\">17715126</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory90821\">3</td><td class=\"tdhistory90821\">14996718</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory90821\">3</td><td class=\"tdhistory90821\">17717206</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory90821\">2</td><td class=\"tdhistory90821\">25902814</td></tr><tr><th class=\"tdhistory90821\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory90821\">6</td><td class=\"tdhistory90821\">27259000</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 159,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_11.39.15.810_EDT\",\"historyID\":929,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":10,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"clusterid\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"cust_id\",\"columnTypeName\":\"INTEGER\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select top 10 * from demo_db.clusterscore;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a procedure for TG/CG split  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "ename": "WARNING",
     "evalue": "[Teradata Database] [Warning 5527] Stored Procedure Created with Warnings.\n\nSPL5000:W(L19), E(3803):Table 'tg_cg_split' already exists.",
     "output_type": "error",
     "traceback": [
      "[Teradata Database] [Warning 5527] Stored Procedure Created with Warnings.\n\nSPL5000:W(L19), E(3803):Table 'tg_cg_split' already exists."
     ]
    }
   ],
   "source": [
    "REPLACE PROCEDURE demo_db.tgcg_split(\n",
    "    -- Procedure runs actual TG CG split\n",
    "    IN cg_ratio decimal(3,2), -- fraction of data that goes into Control Group\n",
    "    IN db_name varchar(100), -- database to hold ClusterScore table (not used at the moment!!!)\n",
    "    IN cluster_score_table_name varchar(100), -- ClusterScore table name (not used at the moment!!!)\n",
    "    -- need to make dynamic cusror - if needed\n",
    "    OUT test varchar(1000)\n",
    ") \n",
    "BEGIN\n",
    "  DECLARE sql_text varchar(1000);\n",
    "    \n",
    "  -- drop table for TG-CG split if it exists\n",
    "  call demo_db.drop_my_table('demo_db.tg_cg_split');\n",
    "  -- create table to hold TG-CG split\n",
    "  CREATE TABLE demo_db.tg_cg_split (\n",
    "      gr varchar(2),\n",
    "      clusterid INTEGER,\n",
    "      cust_id INTEGER) \n",
    "  primary index (cust_id);\n",
    "  \n",
    "  \n",
    "  -- main loop over the clusters\n",
    "  FOR clusters AS cur1 CURSOR FOR \n",
    "    SELECT  distinct clusterid \n",
    "    FROM  demo_db.clustermodel \n",
    "    where clusterid > 0 order by 1\n",
    "  DO\n",
    "    --the below code does not work in TSQL - so we make it dynamic\n",
    "    --insert into demo_db.tg_cg_split select 'CG', clusterid, cust_id from demo_db.ClusterScore where clusterid = 1 sample RANDOMIZED ALLOCATION 0.05\n",
    "    -- we use smaple clause to select cg_ratio of population as Control Group for the cluster \n",
    "    set sql_text = 'insert into demo_db.tg_cg_split \n",
    "    select ''CG'', clusterid, cust_id  from demo_db.ClusterScore \n",
    "    where clusterid = '||CAST(clusters.clusterid as char(3))||' sample RANDOMIZED ALLOCATION ' || CAST (cg_ratio as char(5))||';'; \n",
    "    CALL DBC.SysExecSQL(sql_text);\n",
    "        \n",
    "    -- remaining rows belong to TG and we insert them appropriately\n",
    "    insert into demo_db.tg_cg_split \n",
    "    select 'TG', clusterid, cust_id from demo_db.ClusterScore \n",
    "    where \n",
    "      clusterid = clusters.clusterid and \n",
    "      cust_id not in (select cust_id from demo_db.tg_cg_split);  \n",
    "  END FOR;\n",
    "  set test = sql_text;\n",
    "  \n",
    "END;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"test\"\n\"insert into demo_db.tg_cg_split \n    select 'CG', clusterid, cust_id  from demo_db.ClusterScore \n    where clusterid = 12  sample RANDOMIZED ALLOCATION .20  ;\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory900303\"><style>\n",
       "      table.tdhistory900303 { display: block !important; min-height: 105px !important; overflow: auto !important; height: 105px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory900303 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory900303:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory900303 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory900303 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory900303 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory900303\"></th><th class=\"tdhistory900303\">test</th></tr></thead><tbody class=\"tdhistory900303 resultset-body\"><tr><th class=\"tdhistory900303\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory900303\">insert into demo_db.tg_cg_split \n",
       "    select 'CG', clusterid, cust_id  from demo_db.ClusterScore \n",
       "    where clusterid = 12  sample RANDOMIZED ALLOCATION .20  ;</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 276,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_14.10.03.158_EDT\",\"historyID\":1046,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"test\",\"columnTypeName\":\"VARCHAR\",\"length\":1000,\"scale\":1000}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- perform a split\n",
    "call demo_db.tgcg_split(0.2,'2','3',ttt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": "\"clusterid\",\"tg_cnt\",\"cg_cnt\",\"cg_ratio\"\n\"1\",\"716\",\"179\",\".20\"\n\"2\",\"774\",\"194\",\".20\"\n\"3\",\"112\",\"28\",\".20\"\n\"4\",\"650\",\"162\",\".20\"\n\"5\",\"528\",\"132\",\".20\"\n\"6\",\"1263\",\"316\",\".20\"\n\"7\",\"325\",\"81\",\".20\"\n\"8\",\"28\",\"7\",\".20\"\n\"9\",\"869\",\"217\",\".20\"\n\"10\",\"258\",\"64\",\".20\"\n\"11\",\"1443\",\"361\",\".20\"\n\"12\",\"1390\",\"347\",\".20\"",
      "text/html": [
       "<div class = \"td-resultset-table-div\" style = \"max-height: 100%; overflow-y: auto\">\n",
       "      <table class=\"tdhistory359754\"><style>\n",
       "      table.tdhistory359754 { display: block !important; min-height: 336px !important; overflow: auto !important; height: 336px !important; width: 100% !important; border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory359754 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory359754:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory359754 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory359754 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory359754 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory359754\"></th><th class=\"tdhistory359754\">clusterid</th><th class=\"tdhistory359754\">tg_cnt</th><th class=\"tdhistory359754\">cg_cnt</th><th class=\"tdhistory359754\">cg_ratio</th></tr></thead><tbody class=\"tdhistory359754 resultset-body\"><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory359754\">1</td><td class=\"tdhistory359754\">716</td><td class=\"tdhistory359754\">179</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">2</th><td class=\"tdhistory359754\">2</td><td class=\"tdhistory359754\">774</td><td class=\"tdhistory359754\">194</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">3</th><td class=\"tdhistory359754\">3</td><td class=\"tdhistory359754\">112</td><td class=\"tdhistory359754\">28</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">4</th><td class=\"tdhistory359754\">4</td><td class=\"tdhistory359754\">650</td><td class=\"tdhistory359754\">162</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">5</th><td class=\"tdhistory359754\">5</td><td class=\"tdhistory359754\">528</td><td class=\"tdhistory359754\">132</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory359754\">6</td><td class=\"tdhistory359754\">1263</td><td class=\"tdhistory359754\">316</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory359754\">7</td><td class=\"tdhistory359754\">325</td><td class=\"tdhistory359754\">81</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory359754\">8</td><td class=\"tdhistory359754\">28</td><td class=\"tdhistory359754\">7</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory359754\">9</td><td class=\"tdhistory359754\">869</td><td class=\"tdhistory359754\">217</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory359754\">10</td><td class=\"tdhistory359754\">258</td><td class=\"tdhistory359754\">64</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">11</th><td class=\"tdhistory359754\">11</td><td class=\"tdhistory359754\">1443</td><td class=\"tdhistory359754\">361</td><td class=\"tdhistory359754\">.20</td></tr><tr><th class=\"tdhistory359754\" style= \"background: rgba(243, 243, 243, 0.75)\">12</th><td class=\"tdhistory359754\">12</td><td class=\"tdhistory359754\">1390</td><td class=\"tdhistory359754\">347</td><td class=\"tdhistory359754\">.20</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 277,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_14.10.07.941_EDT\",\"historyID\":1047,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":12,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"clusterid\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":1,\"columnName\":\"tg_cnt\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":2,\"columnName\":\"cg_cnt\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":3,\"columnName\":\"cg_ratio\",\"columnTypeName\":\"DECIMAL\",\"length\":2,\"precision\":3,\"scale\":2}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-- display the stats\n",
    "with tg_cg_stat as (\n",
    "select gr, \n",
    "  clusterid, \n",
    "  count(distinct cust_id) cnt,\n",
    "  max(cnt) over (partition by clusterid) as tg_cnt,\n",
    "  min(cnt) over (partition by clusterid) as cg_cnt,\n",
    "  cast(cast(cg_cnt as float)/(cg_cnt+tg_cnt)  as decimal(3,2)) as cg_ratio\n",
    "from demo_db.tg_cg_split group by gr, clusterid)\n",
    "select clusterid, tg_cnt, cg_cnt, cg_ratio from tg_cg_stat where gr = 'CG' order by clusterid;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check that we match in split and in incoming data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "select a.* from demo_db.tg_cg_split a \n",
    "right join demo_db.clusterscore b on a.cust_id = b.cust_id\n",
    "where a.clusterid is null;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify that KS-test works fine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Unable to run SQL: Unable to run SQL query: Database reported error:3804:View 'tg_cg_split_result' already exists.",
     "output_type": "error",
     "traceback": [
      "Unable to run SQL: Unable to run SQL query: Database reported error:3804:View 'tg_cg_split_result' already exists."
     ]
    }
   ],
   "source": [
    "create view demo_db.tg_cg_split_result  as \n",
    "select b.gr, a.*\n",
    "from demo_db.ClusterInput a\n",
    "join demo_db.tg_cg_split b on \n",
    "a.cust_id = b.cust_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.teradata.resultset": 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       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory363915\"></th><th class=\"tdhistory363915\">gr</th><th class=\"tdhistory363915\">cust_id</th><th class=\"tdhistory363915\">tot_income</th><th class=\"tdhistory363915\">tot_age</th><th class=\"tdhistory363915\">tot_cust_years</th><th class=\"tdhistory363915\">tot_children</th><th class=\"tdhistory363915\">single_ind</th><th class=\"tdhistory363915\">female_ind</th><th class=\"tdhistory363915\">married_ind</th><th class=\"tdhistory363915\">separated_ind</th><th class=\"tdhistory363915\">ca_resident_ind</th><th class=\"tdhistory363915\">ny_resident_ind</th><th class=\"tdhistory363915\">tx_resident_ind</th><th class=\"tdhistory363915\">il_resident_ind</th><th class=\"tdhistory363915\">az_resident_ind</th><th class=\"tdhistory363915\">oh_resident_ind</th><th class=\"tdhistory363915\">ck_acct_ind</th><th class=\"tdhistory363915\">sv_acct_ind</th><th 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class=\"tdhistory363915\">-0.3535533905932738</td><td class=\"tdhistory363915\">3.5127319445533725</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">0.660710573200143</td><td class=\"tdhistory363915\">0.8799697684291027</td><td class=\"tdhistory363915\">0.7721099961494127</td><td class=\"tdhistory363915\">-0.0785866196229616</td><td class=\"tdhistory363915\">0.12814306729110572</td><td class=\"tdhistory363915\">-0.2675931979088154</td><td class=\"tdhistory363915\">0.6460689061267715</td><td class=\"tdhistory363915\">0.23131771157666225</td><td class=\"tdhistory363915\">-0.006505272631487507</td><td class=\"tdhistory363915\">-0.8038558163219623</td><td class=\"tdhistory363915\">-0.6102825214199324</td><td class=\"tdhistory363915\">0.1108315075394882</td><td class=\"tdhistory363915\">5.196196805866019</td></tr><tr><th class=\"tdhistory363915\" style= \"background: rgba(243, 243, 243, 0.75)\">6</th><td class=\"tdhistory363915\">TG</td><td class=\"tdhistory363915\">31340099</td><td class=\"tdhistory363915\">-0.36458068368363494</td><td class=\"tdhistory363915\">1.6954563638679414</td><td class=\"tdhistory363915\">0.41537389857422047</td><td class=\"tdhistory363915\">-1.221265974529421</td><td class=\"tdhistory363915\">-0.7654980477744517</td><td class=\"tdhistory363915\">0.8871760477565425</td><td class=\"tdhistory363915\">-0.9465405888256543</td><td class=\"tdhistory363915\">3.8160843806184372</td><td class=\"tdhistory363915\">-0.5572488813712179</td><td class=\"tdhistory363915\">-0.4088856808449032</td><td class=\"tdhistory363915\">2.8284271247461903</td><td class=\"tdhistory363915\">-0.2846787104124296</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">-1.5135220179034112</td><td class=\"tdhistory363915\">0.8799697684291027</td><td class=\"tdhistory363915\">0.7721099961494127</td><td class=\"tdhistory363915\">-0.4836724036294333</td><td class=\"tdhistory363915\">-0.24312163426108296</td><td class=\"tdhistory363915\">-0.26181683561954805</td><td class=\"tdhistory363915\">0.20815063577634224</td><td class=\"tdhistory363915\">-0.17630302080018503</td><td class=\"tdhistory363915\">-0.05246799184933447</td><td class=\"tdhistory363915\">-0.6493602062382507</td><td class=\"tdhistory363915\">-0.4371247800845646</td><td class=\"tdhistory363915\">-0.4359453959189445</td><td class=\"tdhistory363915\">0.3011048550936812</td></tr><tr><th class=\"tdhistory363915\" style= \"background: rgba(243, 243, 243, 0.75)\">7</th><td class=\"tdhistory363915\">TG</td><td class=\"tdhistory363915\">20450235</td><td class=\"tdhistory363915\">-0.4870065793132995</td><td class=\"tdhistory363915\">0.19016532724398505</td><td class=\"tdhistory363915\">-0.2571729711098171</td><td class=\"tdhistory363915\">1.51349117158239</td><td class=\"tdhistory363915\">-0.7654980477744517</td><td class=\"tdhistory363915\">-1.1271719998852119</td><td class=\"tdhistory363915\">1.0564787308705603</td><td class=\"tdhistory363915\">-0.26204871283216735</td><td class=\"tdhistory363915\">-0.5572488813712179</td><td class=\"tdhistory363915\">-0.4088856808449032</td><td class=\"tdhistory363915\">-0.3535533905932738</td><td class=\"tdhistory363915\">-0.2846787104124296</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">-1.5135220179034112</td><td class=\"tdhistory363915\">-1.1364026764069088</td><td class=\"tdhistory363915\">-1.2951522516054665</td><td class=\"tdhistory363915\">-0.4836724036294333</td><td class=\"tdhistory363915\">-0.32587665081338485</td><td class=\"tdhistory363915\">-0.4708520225485024</td><td class=\"tdhistory363915\">0.20815063577634224</td><td class=\"tdhistory363915\">-0.20875753732069494</td><td class=\"tdhistory363915\">-0.03196210297244371</td><td class=\"tdhistory363915\">-0.958351426405674</td><td class=\"tdhistory363915\">-0.913308568756826</td><td class=\"tdhistory363915\">-0.846028073512769</td><td class=\"tdhistory363915\">-0.8604423874624668</td></tr><tr><th class=\"tdhistory363915\" style= \"background: rgba(243, 243, 243, 0.75)\">8</th><td class=\"tdhistory363915\">CG</td><td class=\"tdhistory363915\">23175148</td><td class=\"tdhistory363915\">-0.4104760253081829</td><td class=\"tdhistory363915\">-0.27699395998413934</td><td class=\"tdhistory363915\">-0.2571729711098171</td><td class=\"tdhistory363915\">0.14611259852648448</td><td class=\"tdhistory363915\">-0.7654980477744517</td><td class=\"tdhistory363915\">0.8871760477565425</td><td class=\"tdhistory363915\">1.0564787308705603</td><td class=\"tdhistory363915\">-0.26204871283216735</td><td class=\"tdhistory363915\">-0.5572488813712179</td><td class=\"tdhistory363915\">-0.4088856808449032</td><td class=\"tdhistory363915\">-0.3535533905932738</td><td class=\"tdhistory363915\">-0.2846787104124296</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">-1.5135220179034112</td><td class=\"tdhistory363915\">-1.1364026764069088</td><td class=\"tdhistory363915\">-1.2951522516054665</td><td class=\"tdhistory363915\">-0.4836724036294333</td><td class=\"tdhistory363915\">-0.32587665081338485</td><td class=\"tdhistory363915\">-0.4708520225485024</td><td class=\"tdhistory363915\">0.20815063577634224</td><td class=\"tdhistory363915\">-0.20875753732069494</td><td class=\"tdhistory363915\">-0.03196210297244371</td><td class=\"tdhistory363915\">-0.958351426405674</td><td class=\"tdhistory363915\">-0.913308568756826</td><td class=\"tdhistory363915\">-0.846028073512769</td><td class=\"tdhistory363915\">-0.8604423874624668</td></tr><tr><th class=\"tdhistory363915\" style= \"background: rgba(243, 243, 243, 0.75)\">9</th><td class=\"tdhistory363915\">TG</td><td class=\"tdhistory363915\">28620753</td><td class=\"tdhistory363915\">-0.7855078747714157</td><td class=\"tdhistory363915\">-1.4708454717893462</td><td class=\"tdhistory363915\">0.7516473334162392</td><td class=\"tdhistory363915\">-0.5375766880014683</td><td class=\"tdhistory363915\">1.30633905978901</td><td class=\"tdhistory363915\">-1.1271719998852119</td><td class=\"tdhistory363915\">-0.9465405888256543</td><td class=\"tdhistory363915\">-0.26204871283216735</td><td class=\"tdhistory363915\">-0.5572488813712179</td><td class=\"tdhistory363915\">-0.4088856808449032</td><td class=\"tdhistory363915\">-0.3535533905932738</td><td class=\"tdhistory363915\">-0.2846787104124296</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">-1.5135220179034112</td><td class=\"tdhistory363915\">0.8799697684291027</td><td class=\"tdhistory363915\">-1.2951522516054665</td><td class=\"tdhistory363915\">-0.4836724036294333</td><td class=\"tdhistory363915\">0.011832782582619078</td><td class=\"tdhistory363915\">-0.4708520225485024</td><td class=\"tdhistory363915\">0.20815063577634224</td><td class=\"tdhistory363915\">0.5799876082416803</td><td class=\"tdhistory363915\">-0.03196210297244371</td><td class=\"tdhistory363915\">-0.8921390220840832</td><td class=\"tdhistory363915\">-0.7834402627553002</td><td class=\"tdhistory363915\">-0.5726396217835527</td><td class=\"tdhistory363915\">-0.4456040865495568</td></tr><tr><th class=\"tdhistory363915\" style= \"background: rgba(243, 243, 243, 0.75)\">10</th><td class=\"tdhistory363915\">TG</td><td class=\"tdhistory363915\">17717206</td><td class=\"tdhistory363915\">-0.7855078747714157</td><td class=\"tdhistory363915\">-1.730378409138304</td><td class=\"tdhistory363915\">0.7516473334162392</td><td class=\"tdhistory363915\">0.14611259852648448</td><td class=\"tdhistory363915\">1.30633905978901</td><td class=\"tdhistory363915\">-1.1271719998852119</td><td class=\"tdhistory363915\">-0.9465405888256543</td><td class=\"tdhistory363915\">-0.26204871283216735</td><td class=\"tdhistory363915\">1.7945302959412102</td><td class=\"tdhistory363915\">-0.4088856808449032</td><td class=\"tdhistory363915\">-0.3535533905932738</td><td class=\"tdhistory363915\">-0.2846787104124296</td><td class=\"tdhistory363915\">-0.18219500746971076</td><td class=\"tdhistory363915\">-0.17823567488329875</td><td class=\"tdhistory363915\">-1.5135220179034112</td><td class=\"tdhistory363915\">-1.1364026764069088</td><td class=\"tdhistory363915\">-1.2951522516054665</td><td class=\"tdhistory363915\">-0.4836724036294333</td><td class=\"tdhistory363915\">-0.32587665081338485</td><td class=\"tdhistory363915\">-0.4708520225485024</td><td class=\"tdhistory363915\">0.20815063577634224</td><td class=\"tdhistory363915\">-0.20875753732069494</td><td class=\"tdhistory363915\">-0.03196210297244371</td><td class=\"tdhistory363915\">-0.958351426405674</td><td class=\"tdhistory363915\">-0.913308568756826</td><td class=\"tdhistory363915\">-0.846028073512769</td><td class=\"tdhistory363915\">-0.8604423874624668</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 279,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_14.10.15.473_EDT\",\"historyID\":1049,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":10,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"gr\",\"columnTypeName\":\"VARCHAR\",\"length\":2,\"scale\":2},{\"columnNumber\":1,\"columnName\":\"cust_id\",\"columnTypeName\":\"INTEGER\"},{\"columnNumber\":2,\"columnName\":\"tot_income\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":3,\"columnName\":\"tot_age\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":4,\"columnName\":\"tot_cust_years\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":5,\"columnName\":\"tot_children\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":6,\"columnName\":\"single_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":7,\"columnName\":\"female_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":8,\"columnName\":\"married_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":9,\"columnName\":\"separated_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":10,\"columnName\":\"ca_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":11,\"columnName\":\"ny_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":12,\"columnName\":\"tx_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":13,\"columnName\":\"il_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":14,\"columnName\":\"az_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":15,\"columnName\":\"oh_resident_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":16,\"columnName\":\"ck_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":17,\"columnName\":\"sv_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":18,\"columnName\":\"cc_acct_ind\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":19,\"columnName\":\"ck_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":20,\"columnName\":\"sv_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":21,\"columnName\":\"cc_avg_bal\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":22,\"columnName\":\"ck_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":23,\"columnName\":\"sv_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":24,\"columnName\":\"cc_avg_tran_amt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":25,\"columnName\":\"q1_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":26,\"columnName\":\"q2_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":27,\"columnName\":\"q3_trans_cnt\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":28,\"columnName\":\"q4_trans_cnt\",\"columnTypeName\":\"FLOAT\"}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select top 10 * from demo_db.tg_cg_split_result;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Success: 0 rows affected"
      ]
     },
     "execution_count": 280,
     "metadata": {
      "application/vnd.teradata.resultset": ""
     },
     "output_type": "execute_result"
    },
    {
     "ename": "WARNING",
     "evalue": "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets.",
     "output_type": "error",
     "traceback": [
      "[Teradata Database] [Warning 3212] The stored procedure returned one or more result sets."
     ]
    },
    {
     "data": {
      "application/vnd.teradata.resultset": "\"M\",\"N\",\"D\",\"SmirnovPValue\",\"SmirnovPText\",\"SmirnovCallP_5E-2\"\n\"2088\",\"8356\",\"0.43773946360153554\",\"0.005\",\"<0.005\",\"p\"",
      "text/html": [
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       "      th.tdhistory411637 { border: 1px solid rgb(198,198,198) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: center !important; white-space: normal !important; color: var(--jp-ui-font-color1) !important; }\n",
       "      th.tdhistory411637:last-child { text-align: left !important; }\n",
       "      tbody.tdhistory411637 tr:nth-child(even) { background: rgba(243, 243, 243, 0.75) !important; }\n",
       "      tbody.tdhistory411637 tr:nth-child(odd) { background: var(--jp-ui-inverse-font-color1) !important; }\n",
       "      td.tdhistory411637 { border: 1px solid rgb(207, 207, 207) !important; border-collapse: collapse !important; ; padding: 2px 5px !important; ; font-size: 13px !important; ; text-align: left !important;          white-space: nowrap !important; overflow:hidden !important; text-overflow:ellipsis !important;; }\n",
       "    </style><thead><tr style= \"background: rgba(243, 243, 243, 0.75)\"><th class=\"tdhistory411637\"></th><th class=\"tdhistory411637\">M</th><th class=\"tdhistory411637\">N</th><th class=\"tdhistory411637\">D</th><th class=\"tdhistory411637\">SmirnovPValue</th><th class=\"tdhistory411637\">SmirnovPText</th><th class=\"tdhistory411637\">SmirnovCallP_5E-2</th></tr></thead><tbody class=\"tdhistory411637 resultset-body\"><tr><th class=\"tdhistory411637\" style= \"background: rgba(243, 243, 243, 0.75)\">1</th><td class=\"tdhistory411637\">2088</td><td class=\"tdhistory411637\">8356</td><td class=\"tdhistory411637\">0.43773946360153554</td><td class=\"tdhistory411637\">0.005</td><td class=\"tdhistory411637\"><0.005</td><td class=\"tdhistory411637\">p</td></tr></tbody></table></div>"
      ]
     },
     "execution_count": 280,
     "metadata": {
      "application/vnd.teradata.resultset": "{\"resultSetID\":\"/home/tdatuser/jupter_notebooks/Teradata/Resultsets/2021.09.09_14.10.20.662_EDT\",\"historyID\":1050,\"chunkID\":\"00001\",\"sessionid\":\"\",\"portnum\":\"\",\"dockermode\":\"standalone\",\"totalRowCount\":1,\"chunkCount\":0,\"rowLimit\":0,\"columnMetadata\":[{\"columnName\":\"M\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":1,\"columnName\":\"N\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":2,\"columnName\":\"D\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":3,\"columnName\":\"SmirnovPValue\",\"columnTypeName\":\"FLOAT\"},{\"columnNumber\":4,\"columnName\":\"SmirnovPText\",\"columnTypeName\":\"VARCHAR\",\"length\":6,\"scale\":6},{\"columnNumber\":5,\"columnName\":\"SmirnovCallP_5E-2\",\"columnTypeName\":\"VARCHAR\",\"length\":1,\"scale\":1}]}"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call TRNG_XSP.td_analyze(\n",
    "'kstest',\n",
    "'database=demo_db;\n",
    "tablename=tg_cg_split_result;\n",
    "columnofinterest=sv_avg_bal;\n",
    "teststyle=s;\n",
    "columns=gr;\n",
    "statsdatabase=TRNG_XSP;');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Teradata SQL",
   "language": "Teradata SQL",
   "name": "teradatasql"
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   "file_extension": ".tdrs",
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   "name": "Teradata SQL",
   "nbconvert_exporter": "",
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   "version": "16.20"
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