adds drop NA transformer closes #183 (#215)

* Intial Commit for NA transformer

* Intial Commit for NA transformer

* copy_and_safe removed_data

* Minor changes -> method name and example

* Comments and Examples

* Comments and Examples

* Comments and Examples

* Comments - documentations

* Minor errors

* Documentation changes

* test cases

* Style changes

* Style changes-2

* fix bug, reword docstrings, update notebook

* change method name in docstrings

Co-authored-by: Soledad Galli <[email protected]>

Author: pradumna123

docs/imputation/DropMissingData.rst

@@ -0,0 +1,75 @@
+DropMissingData
+===============
+
+API Reference
+-------------
+
+.. autoclass:: feature_engine.imputation.DropMissingData
+    :members:
+
+Example
+-------
+
+DropMissingData() deletes rows with NA values. It works with numerical and categorical
+variables. The user can pass a list of variables for which to delete rows with NA.
+Alternatively, DropMissingData() will default to all variables. The trasformer has the
+option to learn the variables with NA in the train set, and then remove observations
+with NA in only those variables.
+
+.. code:: python
+
+	import numpy as np
+	import pandas as pd
+	from sklearn.model_selection import train_test_split
+
+	from feature_engine.imputation import DropMissingData
+
+	# Load dataset
+	data = pd.read_csv('houseprice.csv')
+
+	# Separate into train and test sets
+	X_train, X_test, y_train, y_test = train_test_split(
+    	data.drop(['Id', 'SalePrice'], axis=1),
+        data['SalePrice'],
+        test_size=0.3,
+        random_state=0)
+
+	# set up the imputer
+	missingdata_imputer = DropMissingData(variables=['LotFrontage', 'MasVnrArea'])
+
+	# fit the imputer
+	missingdata_imputer.fit(X_train)
+
+	# transform the data
+	train_t= missingdata_imputer.transform(X_train)
+	test_t= missingdata_imputer.transform(X_test)
+
+    # Number of NA before the transformation:
+    X_train['LotFrontage'].isna().sum()
+
+.. code:: python
+
+    189
+
+.. code:: python
+
+    # Number of NA after the transformation:
+	train_t['LotFrontage'].isna().sum()
+
+.. code:: python
+
+    0
+
+.. code:: python
+
+    # Number of rows before and after transformation
+    print(X_train.shape)
+	print(train_t.shape)
+
+.. code:: python
+
+    (1022, 79)
+    (829, 79)
+
+
+

docs/imputation/index.rst

@@ -14,4 +14,5 @@ from data or arbitrary values pre-defined by the user.
    EndTailImputer
    CategoricalImputer
    RandomSampleImputer
-   AddMissingIndicator
+   AddMissingIndicator
+   DropMissingData

docs/index.rst

@@ -102,6 +102,7 @@ Missing Data Imputation: Imputers
 - :doc:`imputation/CategoricalImputer`: replaces missing data in categorical variables with the string 'Missing' or by the most frequent category
 - :doc:`imputation/RandomSampleImputer`: replaces missing data with random samples of the variable
 - :doc:`imputation/AddMissingIndicator`: adds a binary missing indicator to flag observations with missing data
+- :doc:`imputation/DropMissingData`: removes rows containing NA values from dataframe
 
 Categorical Variable Encoders: Encoders
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~