check_numerical_variables#

check_numerical_variables() checks that the variables in the list are of type numerical.

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

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="T"),
})

print(df.head())

We see the resulting dataframe below:

    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

Let’s now check that 2 of the variables are of type numerical:

from feature_engine.variable_handling import check_numerical_variables

var_num = check_numerical_variables(df, ['Age', 'Marks'])

var_num

If the variables are numerical, the function returns their names in a list:

['Age', 'Marks']

If we pass a variable that is not of type numerical, check_numerical_variables() will return an error:

check_numerical_variables(df, ['Age', 'Name'])

Below we see the error message:

TypeError: Some of the variables are not numerical. Please cast them as numerical
before using this transformer.
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