.. _find_num_vars:

.. currentmodule:: feature_engine.variable_handling

find_numerical_variables
========================

:class:`find_numerical_variables()` returns a list with the names of the numerical
variables in the dataset.

Let's create a toy dataset with numerical, categorical and datetime variables:

.. code:: python

    import pandas as pd
    df = pd.DataFrame({
        "Name": ["tom", "nick", "krish", "jack"],
        "City": ["London", "Manchester", "Liverpool", "Bristol"],
        "Age": [20, 21, 19, 18],
        "Marks": [0.9, 0.8, 0.7, 0.6],
        "dob": pd.date_range("2020-02-24", periods=4, freq="min"),
    })

    print(df.head())

We see the resulting dataframe below:

.. code:: python

        Name        City  Age  Marks                 dob
    0    tom      London   20    0.9 2020-02-24 00:00:00
    1   nick  Manchester   21    0.8 2020-02-24 00:01:00
    2  krish   Liverpool   19    0.7 2020-02-24 00:02:00
    3   jack     Bristol   18    0.6 2020-02-24 00:03:00

With :class:`find_numerical_variables()` we capture the names of all the numerical
variables in a list. So let's do that and then display the list:

.. code:: python

    from feature_engine.variable_handling import find_numerical_variables

    var_num = find_numerical_variables(df)

    var_num

We see the names of the numerical variables in the list below:

.. code:: python

    ['Age', 'Marks']

If there are no numerical variables in the dataset, :class:`find_numerical_variables()`
will raise an error. For example, the command
`find_numerical_variables(df[["Name", "City", "dob"]])` results in a `TypeError` because
there are no numerical variables in that subset of the data.

We can return an empty list when no variables are found. To return an empty list, we
need to set `return_empty` to `True`:

.. code:: python

    find_numerical_variables(df[["Name", "City", "dob"]], return_empty=True)

The previous commands returns an empty list: `[]`.