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Sort Data Transform

The Sort Data transform allows you to reorder the rows in your dataset based on values in one or more columns. This is useful for organizing your data, preparing it for analysis, or improving its readability.

Basic Usage​

To sort your dataset:

  1. Select the Sort Data transform from the transform menu.
  2. Choose one or more columns to sort by in the "Column" dropdown.
  3. For each selected column, specify the sort order (ascending or descending).
  4. Apply the transformation.

Configuration Options​

Basic Options​

  • Column: Select one or more columns to sort by. The order of selection determines the priority of sorting.
  • Sort Order: For each selected column, choose either:
    • Ascending (A to Z, 0 to 9)
    • Descending (Z to A, 9 to 0)
note

You can sort by multiple columns. The transform will sort by the first column, then by the second column for any ties, and so on.

tip

NaN (Not a Number) values will always be placed at the end of the sorted data, regardless of the sort order.

Examples​

Here's an example of how to use the Sort Data transform:

Example: Sorting a Sales Dataset

Input Dataset:

DateProductSalesRegion
2023-05-15B1500North
2023-05-14A1000South
2023-05-16C2000East
2023-05-15A1200West
2023-05-14B800North

Configuration:

  • Column 1: Date (Ascending)
  • Column 2: Product (Descending)

Result:

DateProductSalesRegion
2023-05-14B800North
2023-05-14A1000South
2023-05-15B1500North
2023-05-15A1200West
2023-05-16C2000East

The data is first sorted by Date in ascending order, then for each date, it's sorted by Product in descending order.

Best Practices​

  1. Prioritize Columns: When sorting by multiple columns, consider the logical order that makes the most sense for your data analysis.

  2. Consistent Sorting: For datasets that you frequently work with, try to maintain a consistent sorting approach to make data exploration more intuitive.

  3. Check for Data Types: Ensure that the columns you're sorting by have consistent data types. Mixing data types in a column can lead to unexpected sorting results.

  4. Handle Missing Values: Remember that NaN values will always be sorted to the end. Consider how this might affect your analysis.

  5. Preserve Original Order: If the original order of your data is meaningful, consider creating a new column with row numbers before sorting.

Troubleshooting​

  • If sorting doesn't produce the expected results, check the data types of your sorting columns. Mixed data types can lead to unexpected sorting behavior.
  • For date-based sorting, ensure all date values are in a consistent format and are recognized as date objects by the system.
  • If sorting by a numeric column doesn't work as expected, check for any non-numeric values or hidden characters in the column.