Export to Excel
Export an Analyze data table to PlaidCloud Document as a Microsoft Excel file. PlaidCloud Analyze supports modern versions of Microsoft Excel (2007-2016) as well as legacy versions (2000/2003).
Export File Selector
The file selector in this transform allows you to choose a destination store the exported result in a PlaidCloud Document.
You choose a directory and specify a file name for the target file.
The Dynamic option allows specification of a table using text, including variables. This is useful when employing variable driven workflows where table and view references are relative to the variables specified.
An example that uses the
current_month variable to dynamically point to source table:
When a specific table is desired as the source for the export, leave the Dynamic box unchecked and select the source table.
Table Explorer is always avaible with any table selection. Click on the Table Explorer button to the right of the table selection and a Table Explorer window will open.
Selecting a Document Account
Choose a PlaidCloud Document account for which you have access. This will provide you with the ability to select a directory next selection.
Target Directory Path
Select the Browse icon to the right of the Target Directory Path and navigate to the location you want the file saved to.
Target File Name
Specify the name the exported file should be saved as.
Target Sheet Name
Specify the target sheet name, the default is Sheet1
Selecting File Compression
All exported files are uncompressed, but the following compression options are available:
- No Compression
Write Header To First Row
If this checkbox is selected the table headers will be exported to the first row. If it is not there will be no headers in the exported file.
Table Data Selection
Data Mapper Configuration
The Data Mapper is used to map columns from the source data to the target data table.
Inspection and Populating the Mapper
Using the Inspect Source menu button provides additional ways to map columns from source to target:
- Populate Both Mapping Tables: Propagates all values from the source data table into the target data table. This is done by default.
- Populate Source Mapping Table Only: Maps all values in the source data table only. This is helpful when modifying an existing workflow when source column structure has changed.
- Populate Target Mapping Table Only: Propagates all values into the target data table only.
If the source and target column options aren’t enough, other columns can be added into the target data table in several different ways:
- Propagate All will insert all source columns into the target data table, whether they already existed or not.
- Propagate Selected will insert selected source column(s) only.
- Right click on target side and select Insert Row to insert a row immediately above the currently selected row.
- Right click on target side and select Append Row to insert a row at the bottom (far right) of the target data table.
To delete columns from the target data table, select the desired column(s), then right click and select Delete.
Chaging Column Order
To rearrange columns in the target data table, select the desired column(s). You can use either:
- Bulk Move Arrows: Select the desired move option from the arrows in the upper right
- Context Menu: Right clikc and select Move to Top, Move Up, Move Down, or Move to Bottom.
Reduce Result to Distinct Records Only
To return only distinct options, select the Distinct menu option. This will toggle a set of checkboxes for each column in the source. Simply check any box next to the corresponding column to return only distinct results.
Depending on the situation, you may want to consider use of Summarization instead.
The distinct process retains the first unique record found and discards the rest. You may want to apply a sort on the data if it is important for consistency between runs.
Aggregation and Grouping
To aggregate results, select the Summarize menu option. This will toggle a set of select boxes for each column in the target data table. Choose an appropriate summarization method for each column.
- Group By
- Count (including nulls)
- Standard Deviation
- Sample Standard Deviation
- Population Standard Deviation
- Sample Variance
- Population Variance
- Advanced Non-Group_By
For advanced data mapper usage such as expressions, cleaning, and constants, please see the Advanced Data Mapper Usage
For more aggregation details, see the Analyze overview page here.
To allow for maximum flexibility, data filters are available on the source data and the target data. For larger data sets, it can be especially beneficial to filter out rows on the source so the remaining operations are performed on a smaller data set.
Select Subset Of Data
This filter type provides a way to filter the inbound source data based on the specified conditions.
Apply Secondary Filter To Result Data
This filter type provides a way to apply a filter to the post-transformed result data based on the specified conditions. The ability to apply a filter on the post-transformed result allows for exclusions based on results of complex calcuations, summarizaitons, or window functions.
Final Data Table Slicing (Limit)
The row slicing capability provides the ability to limit the rows in the result set based on a range and starting point.
The filter syntax utilizes Python SQLAlchemy which is the same syntax as other expressions.
View examples and expression functions in the Expressions area.
No examples yet...