# Transform

The Transform node is one of the most important nodes, allowing modification and/or remapping of source data before uploading it to some destination.

## Anatomy

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1. The name of a column from the source data.
2. The name of a column in the destination data.
3. The type of transformation to perform.
4. The arguments for a given transformation type.
   1. These aren't always required (e.g., one-to-one transformations).
5. Button to import mappings from a JSON file.
6. Button to export mappings as a JSON file.
7. Button that zooms and focuses the view on the node.
8. Button to add a new mapping.
9. Button to delete an existing mapping.

## Transformation Types

### One-to-One

The one-to-one transformation is very simple: the value of the source column is left untouched and merely mapped to the target column. No arguments are required.

### Boolean

The boolean transformation normalizes the value of the source column into either `true` or `false` based on the list of defined "Truthy Values", which are formatted as a JSON list.

For example, if the Truthy Values are defined as `["yes", "true"]` and the source column has the value `no`, the target column will have a value of `false`. Alternatively, if the source column has a value of `yes` or `true`, the target column will have a value of `true`.

{% hint style="info" %}
While the Truthy Values are case sensitive, the source column value is always compared in lowercase.
{% endhint %}

### Date

The date transformation converts a date from one format to another. It takes two arguments: the "From" date format and the "To" date format. There's a predefined list of common date formats available or, if those don't fit your requirements, you can define any date format using [Python's `strftime()` and `strptime()` format codes](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes).

For example, if I have the date `2026-01-01` and I want to convert it to the format `MM-DD-YY`, I'd select `YYYY-MM-DD` from the list of predefined date formats for the "From" argument, and I'd input `%m-%d-%y` for the "To" argument.

### Python

{% hint style="info" %}
The source column is unused when using the Python transformation.
{% endhint %}

The Python transformation executes custom Python code on an entire row, with the final value being written to the target column. There are two special variables available in scope: `record_value` and `retvalue`. To access the row, you use the `record_value` variable, which is a dictionary whose keys are the column names (e.g., `record_value["column_1"]`). To pass a value to the target column, you set `retvalue` to that value.

As an example:

```python
count = int(record_value["count"])
product_name = record_value["product_name"]

if count > 1:
   retvalue = product_name + "s"
else:
   retvalue = product_name
```

Also, there's a restricted list of modules that can be imported. The following are currently available:

* `collections`
* `datetime`
* `functools`
* `hashlib`
* `itertools`
* `json`
* `math`
* `operator`
* `random`
* `re`
* `string`
* `time`

### Value Mapping

The Value Mapping transformation, which takes a Value Mapping Set as it's only argument, attempts to match the source column value against a list Value Mapping "From" values and assigns the target column the associated "To" value, if there's a match. If there's no match, and no default value is set on the Value Mapping Set, the transformation will fail.

For more information on Value Mappings and Value Mapping Sets, check out [the associated documentation](https://glyue.docs.sandboxbanking.com/etl/value-mappings).