Merge pull request #47 from UBC-MDS/cor_map

Added scaling example for readme

Author: chuangw6

README.md

@@ -15,10 +15,10 @@ $ pip install -i https://test.pypi.org/simple/ eda_utils_py
 ## Functions
 
 The four functions contained in this package are as follows:
-- `cor_map`: A function to plot a correlation matrix of numeric columns in the dataframe
+- `imputer`: A function to impute missing values
 - `outlier_identifier`: A function to identify and deal with outliers
+- `cor_map`: A function to plot a correlation matrix of numeric columns in the dataframe
 - `scale` A function to scale numerical values in the dataset
-- `imputer`: A function to impute missing values
 
 
 ## Our Place in the Python Ecosystem
@@ -33,9 +33,9 @@ While Python packages with similar functionalities exist, this package aims to s
 - Please see a list of dependencies [here](pyproject.toml).
 
 ## Usage
-The eda_utils_py package help you to build exploratory data analysis.
+The eda_utils_py package will help you in your exploratory data analysis portion of your work.
 
-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. 
+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. Depending on the function, the generated output can be obtained in object or graphical form. 
 
 ```python
 import pandas as pd
@@ -59,39 +59,56 @@ data_with_outlier = pd.DataFrame({
          'SepalWidthCm':[1.4, 1.4, 1.3, 1.2, 1.2, 1.3, 1.6, 1.3],
          'PetalWidthCm':[0.2, 0.1, 30, 0.2, 0.3, 0.1, 0.4, 0.5]
          })
+         
+data_with_scale = pd.DataFrame({'SepalLengthCm':[1, 0, 0, 3, 4], 
+         'SepalWidthCm':[4, 1, 1, 0, 1], 
+         'PetalWidthCm':[2, 0, 0, 2, 1],
+         'Species':['Iris-setosa','Iris-virginica', 'Iris-germanica', 'Iris-virginica','Iris-germanica']})      
 ```
 
-The eda_utils_py will help you to:
-- Diagnose data quality: Resolve skewed data by identifing missing data and outlier and provide corresponding remedy.
+The eda_utils_py package contains functions that will help you to:
+- **Impute**: Resolve skewed data by identifying missing data and outlier and provide corresponding remedy.
 
 ```python
 imputer(data_with_NA)
 ```
-Output:
+Output of `imputer()`:
 
 ![imputer_output](images/imputer_output.png)
 
+- **Identify Outliers**: Identify and deal with outliers in the dataset.
+
 ```python
 outlier_identifier(data_with_outlier, method = "median")
 ```
-Output:
+Output of `outlier_identifier()`:
 
 ![outlier_output](images/outlier_output.png)
 
-- This package can help you easily plot a correlation matrix along with its values to help explore data.
+- **Correlation Heatmap Plotting**: Easily plot a correlation matrix along with its values to help explore data.
 
 ```python
 numerical_columns = ['SepalLengthCm','SepalWidthCm','PetalWidthCm']
 
 cor_map(data, numerical_columns, col_scheme = 'purpleorange')
 
 ```
-Output:
+Output of `cor_map()`:
 
 ![cor_map_output](images/cor_map.output.png)
 
-- Machine learning pereperation: Perform column transformations, derive scaler automatically to fulfill further machine learning need
-    
+- **Scaling**: Scale the data in preperation for future use in machine learning projects.
+
+```python
+numerical_columns = ['SepalLengthCm','SepalWidthCm','PetalWidthCm']
+
+scale(data, numerical_columns, scaler="minmax")
+
+```
+Output of `scale()`:
+
+![scale_output](images/scale_output.png)
+
 ## Documentation
 
 The official documentation is hosted on Read the Docs: https://eda_utils_py.readthedocs.io/en/latest/