# Python Script

Extends functionalities through Python scripting.

Inputs

- Data (Orange.data.Table): input dataset bound to `in_data` variable
- Learner (Orange.classification.Learner): input learner bound to `in_learner` variable
- Classifier (Orange.classification.Learner): input classifier bound to `in_classifier` variable
- Object: input Python object bound to `in_object` variable

Outputs

- Data (Orange.data.Table): dataset retrieved from `out_data` variable
- Learner (Orange.classification.Learner): learner retrieved from `out_learner` variable
- Classifier (Orange.classification.Learner): classifier retrieved from `out_classifier` variable
- Object: Python object retrieved from `out_object` variable

Python Script widget can be used to run a python script in the input, when a suitable functionality is not implemented in an existing widget. The script has `in_data`, `in_distance`, `in_learner`, `in_classifier` and `in_object` variables (from input signals) in its local namespace. If a signal is not connected or it did not yet receive any data, those variables contain `None`.

After the script is executed variables from the script’s local namespace are extracted and used as outputs of the widget. The widget can be further connected to other widgets for visualizing the output.

For instance the following script would simply pass on all signals it receives:

    out_data = in_data
    out_distance = in_distance
    out_learner = in_learner
    out_classifier = in_classifier
    out_object = in_object

Note: You should not modify the input objects in place.

1. Info box contains names of basic operators for Orange Python script.
2. The Library control can be used to manage multiple scripts. Pressing “+” will add a new entry and open it in the Python script editor. When the script is modified, its entry in the Library will change to indicate it has unsaved changes. Pressing Update will save the script (keyboard shortcut “Ctrl+S”). A script can be removed by selecting it and pressing the “-“ button.
3. Pressing Execute in the Run box executes the script (keyboard shortcut “Ctrl+R”). Any script output (from `print`) is captured and displayed in the Console below the script.
4. The Python script editor on the left can be used to edit a script (it supports some rudimentary syntax highlighting).
5. Console displays the output of the script.

## Examples

Python Script widget is intended to extend functionalities for advanced users. Classes from Orange library are described in the documentation. To find further information about orange Table class see Table, Domain, and Variable documentation.

One can, for example, do batch filtering by attributes. We used zoo.tab for the example and we filtered out all the attributes that have more than 5 discrete values. This in our case removed only ‘leg’ attribute, but imagine an example where one would have many such attributes.

    from Orange.data import Domain, Table
    domain = Domain([attr for attr in in_data.domain.attributes
                     if attr.is_continuous or len(attr.values) <= 5],
                    in_data.domain.class_vars)
    out_data = Table(domain, in_data)

The second example shows how to round all the values in a few lines of code. This time we used wine.tab and rounded all the values to whole numbers.

    import numpy as np
    out_data = in_data.copy()
    #copy, otherwise input data will be overwritten
    np.round(out_data.X, 0, out_data.X)

The third example introduces some Gaussian noise to the data. Again we make a copy of the input data, then walk through all the values with a double for loop and add random noise.

    import random
    from Orange.data import Domain, Table
    new_data = in_data.copy()
    for inst in new_data:
      for f in inst.domain.attributes:
        inst[f] += random.gauss(0, 0.02)
    out_data = new_data

The final example uses Orange3-Text add-on. Python Script is very useful for custom preprocessing in text mining, extracting new features from strings, or utilizing advanced nltk or gensim functions. Below, we simply tokenized our input data from deerwester.tab by splitting them by whitespace.

    print('Running Preprocessing ...')
    tokens = [doc.split(' ') for doc in in_data.documents]
    print('Tokens:', tokens)
    out_object = in_data
    out_object.store_tokens(tokens)

You can add a lot of other preprocessing steps to further adjust the output. The output of Python Script can be used with any widget that accepts the type of output your script produces. In this case, connection is green, which signalizes the right type of input for Word Cloud widget.