add code examples to creation and datetime module's dosctrings

feature_engine/creation/cyclical_features.py

@@ -100,6 +100,25 @@ class CyclicalFeatures(
     https://towardsdatascience.com/cyclical-features-encoding-its-about-time-ce23581845ca
 
     http://blog.davidkaleko.com/feature-engineering-cyclical-features.html
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.creation import CyclicalFeatures
+    >>> X = pd.DataFrame(dict(x= [1,4,3,3,4,2,1,2]))
+    >>> cf = CyclicalFeatures()
+    >>> cf.fit(X)
+    >>> cf.transform(X)
+       x         x_sin         x_cos
+    0  1  1.000000e+00  6.123234e-17
+    1  4 -2.449294e-16  1.000000e+00
+    2  3 -1.000000e+00 -1.836970e-16
+    3  3 -1.000000e+00 -1.836970e-16
+    4  4 -2.449294e-16  1.000000e+00
+    5  2  1.224647e-16 -1.000000e+00
+    6  1  1.000000e+00  6.123234e-17
+    7  2  1.224647e-16 -1.000000e+00
     """
 
     def __init__(

feature_engine/creation/math_features.py

@@ -100,6 +100,36 @@ class MathFeatures(BaseCreation):
 
     In insurance, we can sum the damage to various parts of a car to obtain the
     total damage.
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.creation import MathFeatures
+    >>> X = pd.DataFrame(dict(x1 = [1,2,3], x2 = [4,5,6]))
+    >>> mf = MathFeatures(variables = ["x1","x2"], func = "sum")
+    >>> mf.fit(X)
+    >>> mf.transform(X)
+       x1  x2  sum_x1_x2
+    0   1   4          5
+    1   2   5          7
+    2   3   6          9
+
+    >>> mf = MathFeatures(variables = ["x1","x2"], func = "prod")
+    >>> mf.fit(X)
+    >>> mf.transform(X)
+       x1  x2  prod_x1_x2
+    0   1   4           4
+    1   2   5          10
+    2   3   6          18
+
+    >>> mf = MathFeatures(variables = ["x1","x2"], func = "mean")
+    >>> mf.fit(X)
+    >>> mf.transform(X))
+       x1  x2  mean_x1_x2
+    0   1   4         2.5
+    1   2   5         3.5
+    2   3   6         4.5
     """
 
     def __init__(

feature_engine/creation/relative_features.py

@@ -105,6 +105,22 @@ class RelativeFeatures(BaseCreation):
 
     - Ratio between income and debt to create the debt_to_income_ratio.
     - Subtraction of rent from income to obtain the disposable_income.
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.creation import RelativeFeatures
+    >>> X = pd.DataFrame(dict(x1 = [1,2,3], x2 = [4,5,6], x3 = [3,4,5]))
+    >>> rf = RelativeFeatures(variables = ["x1","x2"],
+    >>>                     reference = ["x3"],
+    >>>                     func = ["div"])
+    >>> rf.fit(X)
+    >>> rf.transform(X)
+       x1  x2  x3  x1_div_x3  x2_div_x3
+    0   1   4   3   0.333333   1.333333
+    1   2   5   4   0.500000   1.250000
+    2   3   6   5   0.600000   1.200000
     """
 
     def __init__(

feature_engine/datetime/datetime.py

@@ -152,6 +152,20 @@ class DatetimeFeatures(BaseEstimator, TransformerMixin, GetFeatureNamesOutMixin)
     --------
     pandas.to_datetime
     pandas.dt
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from feature_engine.datetime import DatetimeFeatures
+    >>> X = pd.DataFrame(dict(date = ["2022-09-18", "2022-10-27", "2022-12-24"]))
+    >>> dtf = DatetimeFeatures(features_to_extract = ["year", "month", "day_of_month"])
+    >>> dtf.fit(X)
+    >>> dtf.transform(X)
+        date_year  date_month  date_day_of_month
+    0       2022           9                 18
+    1       2022          10                 27
+    2       2022          12                 24
     """
 
     def __init__(