Merge pull request #13 from wangjc640/main

update docstring and readme

Author: micahkwok

README.md

@@ -33,8 +33,15 @@ While Python packages with similar functionalities exist, this package aims to s
 - TBD
 
 ## Usage
+The eda_utils_py package help you to build exploratory data analysis.
 
-- TBD
+eda_utils_py includes multiple custom functions to perform initial exploratory analysis on any input data describing the structure and the relationships present in the data. The generated output can be obtained in both object and graphical form. 
+
+The eda_utils_py is capable of :
+- Diagnose data quality : Resolve skewed data by identifing missing data and outlier and provide corresponding remedy.
+- Discover data: Plot correlation mattrix to help explore data to understand the data and find scenarios for performing the analysis.
+- Machine learning pereperation : Perform column transformations, derive scaler automatically to fulfill further machine learning need
+    
 
 ## Documentation
 

eda_utils_py/eda_utils_py.py

@@ -26,3 +26,32 @@ def cor_map(dataframe, num_col):
         cor_map(data, numerical_columns)
         
     """
+
+
+def outlier_identifier(dataframe, columns = None, method = "somefunction"):
+    """
+    A function that identify and deal with outliers based on the method the user choose
+
+    Key arguments: 
+        dataframe [pandas.DataFrame]: 
+            The target dataframe where the function is performed.
+        columns [list] : None
+            The target columns where the function needed to be performed. Defualt is None, the function will check all columns
+        method [string] : "somefunction"
+            The method of dealing with outliers. 
+        
+    Returns:
+        dataframe :
+            The dataframe which the outlier has already process by the chosen method
+    
+    Examples:
+        data = pd.DataFrame({
+            'SepalLengthCm':[5.1, 4.9, 4.7],
+            'SepalWidthCm':[1.4, 1.4, 9999999.99],
+            'PetalWidthCm:[0.2, 0.2, 0.2],
+            'Species':['Iris-setosa','Iris-virginica']
+        })
+
+        outlier_identifier(data)
+
+    """