PYPI commit

LICENSE

@@ -1,21 +1,21 @@
-MIT License
-
-Copyright (c) 2020 nikhil kala
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+MIT License
+
+Copyright (c) 2020 nikhil kala
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE.txt

@@ -0,0 +1,17 @@
+MIT License
+Copyright (c) 2020 Nikhil Kala
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1 +1 @@
-# Categorical-Encode
+# categorical-encode

categorical_encode/__init__.py

@@ -0,0 +1 @@
+

categorical_encode/categorical.py

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+
+def categorical(
+    dataframe=None,
+    normalize=False,
+    drop_columns=[],
+    drop_na=False,
+    target_columns=[],
+              ):
+    """
+    :rtype: Pandas DataFrame
+    :param dataframe: The Input DataFrame(X) which you want to categorically encode.
+    :param normalize: This parameter determines if it will be between 0-1(1 included) or 1 to no. of classes (1 - no. of classes). default:False
+    :param drop_columns:  This specifies the dataframe columns that need to be dropped as they are useless. default: No Columns
+    :param drop_na: This drops empty values (NaN) if is set to True. default: False
+    :param target_columns: This creates the target DataFrame(Y) without applying any Encoding. default: No Columns
+    :return: This Returns Two DataFrame(X,Y) if target_columns are provided Else only the Input dataframe(X) which is encoded.
+    """
+
+    import pandas as pd
+    if not isinstance(dataframe, pd.DataFrame):
+        raise ValueError('Type Error : Expects pd.DataFrame for the parameter -> "dataframe"'
+                         )
+    if len(dataframe.columns) == 0:
+        raise ValueError('Data Error : The parameter -> "dataframe" is empty'
+                         )
+
+    # This Drops the columns if columns are provided for the dataframe using parameter: drop_columns
+
+    dataframe.drop(columns=drop_columns, inplace=True)
+
+    # This drops empty values (NaN) if drop_na flag is set to True.
+
+    dataframe.dropna(inplace=drop_na)
+
+    # Initializes the target_dataframe
+
+    target_dataframe = pd.DataFrame(dataframe[target_columns])
+
+    # If target_columns is provided target_columns are not encoded and removed from the input dataframe.
+
+    if len(target_columns) > 0:
+        dataframe.drop(columns=target_columns, inplace=True)
+
+    for i in dataframe.columns:
+        lst = dataframe[i].unique()
+        dic = dict(zip(lst, range(1, len(lst) + 1)))
+        dataframe[i] = dataframe[i].map(dic)
+
+        # The normalize flag determines if it will be between 0-1 or 1 to no. of classes (1 - no. of classes).
+
+        if normalize:
+            dataframe[i] /= len(lst)
+
+    # If target_columns is provided then it is returned as a Dataframe(Y) along with Input Dataframe(X).
+
+    if len(target_columns) > 0:
+        return (dataframe, target_dataframe)
+    else:
+        return dataframe

setup.cfg

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+# Inside of setup.cfg
+[metadata]
+description-file = README.md

setup.py

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+from distutils.core import setup
+setup(
+  name = 'categorical_encode',         
+  packages = ['categorical_encode'],
+  version = '0.1',     
+  license='MIT',
+  description = 'This Library converts categorical data of any kind {integer, float, strings} into discrete values{1,2,3... # Classes}.',
+  author = 'Nikhil Kala',                   
+  author_email = 'nikhilkala8@gmail.com',      
+  url = 'https://github.com/nikhilkala/Categorical-Encode',  
+  download_url = 'https://github.com/nikhilkala/Categorical-Encode/archive/v0.1.tar.gz',
+  keywords = ['Machine Learning', 'Categorical Data', 'Encoding', 'Deep Learning','Pandas', 'DataFrame', 'Normalization'],  
+  install_requires=[            
+          'pandas',
+      ],
+  classifiers=[
+    'Development Status :: 4 - Beta',      
+    'Intended Audience :: Developers',      
+    'Topic :: Software Development :: Build Tools',
+    'License :: OSI Approved :: MIT License',   
+    'Programming Language :: Python :: 3',      
+    'Programming Language :: Python :: 3.4',
+    'Programming Language :: Python :: 3.5',
+    'Programming Language :: Python :: 3.6',
+    'Programming Language :: Python :: 3.7',
+    'Programming Language :: Python :: 3.8'
+  ],
+)