find_categorical_and_numerical_variables#

With find_categorical_and_numerical_variables() you can automatically capture in 2 separate lists the names of all the categorical and numerical variables in the dataset, respectively.

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

import pandas as pd
from sklearn.datasets import make_classification

X, y = make_classification(
    n_samples=1000,
    n_features=4,
    n_redundant=1,
    n_clusters_per_class=1,
    weights=[0.50],
    class_sep=2,
    random_state=1,
)

# transform arrays into pandas df and series
colnames = [f"num_var_{i+1}" for i in range(4)]
X = pd.DataFrame(X, columns=colnames)

X["cat_var1"] = ["Hello"] * 1000
X["cat_var2"] = ["Bye"] * 1000

X["date1"] = pd.date_range("2020-02-24", periods=1000, freq="T")
X["date2"] = pd.date_range("2021-09-29", periods=1000, freq="H")
X["date3"] = ["2020-02-24"] * 1000

print(X.head())

Below we see the resulting dataframe:

   num_var_1  num_var_2  num_var_3  num_var_4 cat_var1 cat_var2  \
0  -1.558594   1.634123   1.556932   2.869318    Hello      Bye
1   1.499925   1.651008   1.159977   2.510196    Hello      Bye
2   0.277127  -0.263527   0.532159   0.274491    Hello      Bye
3  -1.139190  -1.131193   2.296540   1.189781    Hello      Bye
4  -0.530061  -2.280109   2.469580   0.365617    Hello      Bye

                date1               date2       date3
0 2020-02-24 00:00:00 2021-09-29 00:00:00  2020-02-24
1 2020-02-24 00:01:00 2021-09-29 01:00:00  2020-02-24
2 2020-02-24 00:02:00 2021-09-29 02:00:00  2020-02-24
3 2020-02-24 00:03:00 2021-09-29 03:00:00  2020-02-24
4 2020-02-24 00:04:00 2021-09-29 04:00:00  2020-02-24

We can now use find_categorical_and_numerical_variables() to capture categorical and numerical variables in separate lists. So let’s do that and then display the lists:

from feature_engine.variable_handling import find_categorical_and_numerical_variables

var_cat, var_num = find_categorical_and_numerical_variables(X)

var_cat, var_num

Below we see the names of the categorical variables, followed by the names of the numerical variables:

(['cat_var1', 'cat_var2'],
 ['num_var_1', 'num_var_2', 'num_var_3', 'num_var_4'])

We can also use find_categorical_and_numerical_variables() with a list of variables, to indentify their types:

var_cat, var_num = find_categorical_and_numerical_variables(X, ["num_var_1", "cat_var1"])

var_cat, var_num

We see the resulting lists below:

(['cat_var1'], ['num_var_1'])

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

find_categorical_and_numerical_variables(X, ["num_var_1", "cat_var1", "date1"])

Below the error message:

TypeError: Some of the variables are neither numerical nor categorical.
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