Merge pull request #11 from UBC-MDS/cor_map

Updated README and added cor_map docstring

Author: wangjc640

README.md

@@ -2,25 +2,39 @@
 
 ![](https://github.com/chuangw46/eda_utils_py/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/chuangw46/eda_utils_py/branch/main/graph/badge.svg)](https://codecov.io/gh/chuangw46/eda_utils_py) ![Release](https://github.com/chuangw46/eda_utils_py/workflows/Release/badge.svg) [![Documentation Status](https://readthedocs.org/projects/eda_utils_py/badge/?version=latest)](https://eda_utils_py.readthedocs.io/en/latest/?badge=latest)
 
-Python package that contains util functions for eda process
+## Overview 
+
+As data rarely comes ready to be used and analyzed for machine learning right away, this package aims to help speed up the process of cleaning and doing initial exploratory data anslysis (EDA). The package focuses on the tasks of dealing with outlier and missing values, scaling and correlation visualization.
 
 ## Installation
 
 ```bash
 $ pip install -i https://test.pypi.org/simple/ eda_utils_py
 ```
 
-## Features
+## Functions
+
+The four functions contained in this package are as follows:
+- Function 1: A function to identify and impute missing values
+- Function 2: A function to identify and deal with outliers
+- Function 3: A function to scale numerical values in the dataset
+- `cor_map`: A function to plot a correlation matrix of numeric columns in the dataframe
+
+
+## Our Place in the Python Ecosystem
+
+While Python packages with similar functionalities exist, this package aims to simplify the amount of code necessary for these functions and outputs. Packages with similar functionality are as follows:
 
-- TODO
+- [Sklearn.preprocessing]( https://scikit-learn.org/stable/modules/preprocessing.html)
+- [Altair Heatmap](https://altair-viz.github.io/gallery/layered_heatmap_text.html)
 
 ## Dependencies
 
-- TODO
+- TBD
 
 ## Usage
 
-- TODO
+- TBD
 
 ## Documentation
 

eda_utils_py/eda_utils_py.py

@@ -0,0 +1,28 @@
+
+def cor_map(dataframe, num_col):
+    """
+    A function to implement a correlation heatmap including coefficients based on given numeric columns of a data frame.
+
+    Args:
+        dataframe (pandas.DataFrame): The data frame to be used for EDA.
+        num_col (list):  A list of string of column names with numeric data from the data frame.
+
+    Returns:
+        (altair): A correlation heatmap plot with correlation coefficient labels based on the numeric columns specified by user.
+
+    Examples: 
+        import pandas as pd
+        from eda_utils_py import cor_map
+
+        data = pd.DataFrame({
+            'SepalLengthCm':[5.1, 4.9, 4.7],
+            'SepalWidthCm':[1.4, 1.4, 1.3],
+            'PetalWidthCm:[0.2, 0.2, 0.2],
+            'Species':['Iris-setosa','Iris-virginica']
+        })
+
+        numerical_columns = ['SepalLengthCm','SepalWidthCm','PetalWidthCm']
+        
+        cor_map(data, numerical_columns)
+        
+    """