Multiple Input Tables

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Replace Placeholder

The ‘Replace Placeholder’ task is searching for a specified placeholder e.g. ‘X’ in a column A and replaces this placeholder with the item provided in a second column B. The ‘Replace Placeholder task is used to replace placeholder-variables in a list that are replaced by the final values provided in a second list. Go to Flow Gallery and Copy & Edit the Replace Placeholder Flow for example usecases.
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Inject by Table

The ‘Inject by Table’ task finds and replaces substrings in code or text that is saved in a ZIPPYDOC Word-by-Word table. Word-by-Word tables are also designated as ‘Text’ tables. The task works similar to the ‘Inject’ task but allows to use a replacement table instead of a single replacement item to replace the found items. To import and store code or text, the ZIPPYDOC plattform converts this text into a Word-by-Word table that contains one column with all Words and a second column with the following Non-Word. This format is very powerful to wrangle with text. With the Inject  by Table it...
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Row by Row

The ‘Row by Row’ task is applied on table that are stored in a top-to-down list shape: Month Revenues Jan 234 Feb 332 Mar 343 Apr 444 For those data lists, Row by Row can add a column containing: Trailing N Rows Average Index from Row-N Absolute Row-on-Row Growth Relative Row-on-Row Growth Compound Row-on-Row-N Growth This task is in particular handy together with the ‘Header to Label’ task, as many input tables are sorted left-to-right for date columns. With the header to label task, those tables get converted in top-to-down row by row lists prior to running the Row by Row task. Go to Flow Gallery and Copy & Edit the Row by...
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Date & Time

The ‘Date & Time’ converts date and time formats. In the table overview, the formatted date is shown, if you click on the header of the table-frame, the view toggles to the stored data view, showing the underlying value. The Date & Time task supports the following conversions: Complete Date: A string like 23./24.08.-12.09.2020 converts to 23.08.2020/24.08.2020-12.09.2020 Expand Date: A string like 23.08.2020/25.08.2020-12.09.2020 converts to a data list 23.08.2020, 25.08.2020, 26.08.2020 etc. Convert Date: A date like 05.01.2020 converts to Begin of Month/End of Month/Quarter/Year values, e.g. to 31.01.2020 as end of month value. Note that data input in this mode needs...
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Custom Row

The ‘Custom Row’ task is adding or inserting a custom defined row below or above other rows. With the ‘Select Position’ Field, you define where the to be inserted row shall be positioned. With the ‘Select Type’ Field, you define if the inserted row shall be inserted after each row, or if it shall only be inserted at a specified row position, e.g. each time after a cell has been found with a specified value. The ‘Custom Row’ task is very powerful to e.g. add partial sums or averages to a data list, or to insert rows with comments. Go to Flow Gallery and...
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Inject

The ‘Inject’ task finds and replaces strings in code. It requires input tables that are saved in a ZIPPYDOC Word-by-Word table. Word-by-Word tables are also designated as ‘Text’ tables. To import and store code, the ZIPPYDOC plattform converts this text into a Word-by-Word table that contains one column with all Words and a second column with the following Non-Word. This format is very powerful to wrangle with text or code. With Extract Task it is easy to wrangle with code, as it allows to extract code snippets based on certain framing conditions, e.g. if the code-snippet was contained in a pattern...
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Extract

The ‘Extract’ task is extracting substrings in code that is saved in a ZIPPYDOC Word-by-Word table. Word-by-Word tables are also designated as ‘Text’ tables. To import and store code, the ZIPPYDOC plattform converts this text into a Word-by-Word table that contains one column with all Words and a second column with the following Non-Word. This format is very powerful to wrangle with text. With Extract Task it is easy to wrangle with code, as it allows to extract code snippets based on certain framing conditions, e.g. if the code-snippet was contained in a pattern like f(x){} or in a pattern like...
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Range Lookup

The ‘Ranges Lookup’ task is searching and extracting ranges in records of a table column. The ‘Ranges Lookup’ task is very powerful to e.g. understand range-patterns of long lists of data. If e.g. a sales list contains various customers that shall be allocated to ranges of e.g. 0 <= x < 100, 100 <= x < 1000 or to equal ranges, this can easily be done with this task. Go to Flow Gallery and Copy & Edit the Range Lookup Flow for example usecases.
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Find Group

The ‘Find Group’ task is searching and extracting groups of records of a table column. The ‘Find Group’ task is very powerful to e.g. understand the common groups in sales buckets. If a list of data contains two columns, one is the column of the purchase number, the second the item of purchases contained in that purchase, Find Group finds all common subgroups of purchase items for those purchases. This task is very powerful to understand if similar groups appear as a sales pattern or in similar use cases. Go to Flow Gallery and Copy & Edit the Find Group Flow for example...
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Word Lookup

The ‘Word Lookup’ task is searching and extracting strings in all records of a table column and labels those strings. The ‘Word Lookup’ task is very powerful to extract string patterns in a data list. If e.g. a contact list contains various e-mail formats like name@gmail.com, prename.surname@out-look.com etc. , phone numbers and more, word lookup extracts the found patterns, e.g. the prename, surname, name and domain in the mentioned example. Go to Flow Gallery and Copy & Edit the Word Lookup Flow for example usecases.
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