Add code examples in transformation module's docstrings (#646)

* Adding code examples for Transformation module

* Fixing details in the examples

Author: datacubeR

feature_engine/transformation/arcsin.py

@@ -80,6 +80,24 @@ class ArcsinTransformer(BaseNumericalTransformer):
     transform:
         Apply the arcsin transformation.
 
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import ArcsinTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.beta(1, 1, size = 100)))
+    >>> ast = ArcsinTransformer()
+    >>> ast.fit(X)
+    >>> X = ast.transform(X)
+    >>> X.head()
+              x
+    0  0.785437
+    1  0.253389
+    2  0.144664
+    3  0.783236
+    4  0.650777
     """
 
     def __init__(

feature_engine/transformation/boxcox.py

@@ -93,6 +93,25 @@ class BoxCoxTransformer(BaseNumericalTransformer):
     .. [1] Box and Cox. "An Analysis of Transformations". Read at a RESEARCH MEETING,
         1964.
         https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1964.tb00553.x
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import BoxCoxTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
+    >>> bct = BoxCoxTransformer()
+    >>> bct.fit(X)
+    >>> X = bct.transform(X)
+    >>> X.head()
+              x
+    0  0.505485
+    1 -0.137595
+    2  0.662654
+    3  1.607518
+    4 -0.232237
     """
 
     def __init__(

feature_engine/transformation/log.py

@@ -77,6 +77,24 @@ class LogTransformer(BaseNumericalTransformer):
     transform:
         Transform the variables using the logarithm.
 
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import LogTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
+    >>> lt = LogTransformer()
+    >>> lt.fit(X)
+    >>> X = lt.transform(X)
+    >>> X.head()
+            x
+    0  0.496714
+    1 -0.138264
+    2  0.647689
+    3  1.523030
+    4 -0.234153
     """
 
     def __init__(
@@ -263,6 +281,24 @@ class LogCpTransformer(BaseNumericalTransformer, FitFromDictMixin):
     transform:
         Transform the variables with the logarithm of x plus C.
 
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import LogCpTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
+    >>> lct = LogCpTransformer()
+    >>> lct.fit(X)
+    >>> X = lct.transform(X)
+    >>> X.head()
+              x
+    0  0.944097
+    1  0.586701
+    2  1.043204
+    3  1.707159
+    4  0.541405
     """
 
     def __init__(

feature_engine/transformation/power.py

@@ -74,6 +74,24 @@ class PowerTransformer(BaseNumericalTransformer):
     transform:
         Apply the power transformation to the variables.
 
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import PowerTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
+    >>> pt = PowerTransformer()
+    >>> pt.fit(X)
+    >>> X = pt.transform(X)
+    >>> X.head()
+              x
+    0  1.281918
+    1  0.933203
+    2  1.382432
+    3  2.141518
+    4  0.889517
     """
 
     def __init__(

feature_engine/transformation/reciprocal.py

@@ -73,6 +73,24 @@ class ReciprocalTransformer(BaseNumericalTransformer):
     transform:
         Apply the reciprocal 1 / x transformation.
 
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import ReciprocalTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = 10 - np.random.exponential(size = 100)))
+    >>> rt = ReciprocalTransformer()
+    >>> rt.fit(X)
+    >>> X = rt.transform(X)
+    >>> X.head()
+            x
+    0  0.104924
+    1  0.143064
+    2  0.115164
+    3  0.110047
+    4  0.101726
     """
 
     def __init__(

feature_engine/transformation/yeojohnson.py

@@ -74,11 +74,30 @@ class YeoJohnsonTransformer(BaseNumericalTransformer):
     References
     ----------
     .. [1] Yeo, In-Kwon and Johnson, Richard (2000).
-       A new family of power transformations to improve normality or symmetry.
-       Biometrika, 87, 954-959.
+        A new family of power transformations to improve normality or symmetry.
+        Biometrika, 87, 954-959.
 
     .. [2] Weisberg S. "Yeo-Johnson Power Transformations".
         https://www.stat.umn.edu/arc/yjpower.pdf
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> import pandas as pd
+    >>> from feature_engine.transformation import YeoJohnsonTransformer
+    >>> np.random.seed(42)
+    >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100) - 10))
+    >>> yjt = YeoJohnsonTransformer()
+    >>> yjt.fit(X)
+    >>> X = yjt.transform(X)
+    >>> X.head()
+                   x
+    0 -267042.906453
+    1 -444357.138990
+    2 -221626.115742
+    3  -23647.632651
+    4 -467264.993249
     """
 
     def __init__(