fix: Feedback addressed by specific similar functionality examples an…

…d make high-level clarification

README.md

@@ -2,26 +2,18 @@
 
 [![Documentation Status](https://readthedocs.org/projects/datamop/badge/?version=latest)](https://datamop.readthedocs.io/en/latest/)[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/release/python-360/)[![codecov](https://codecov.io/gh/UBC-MDS/DataMop_package_group14/graph/badge.svg?token=F32xo7rWCj)](https://codecov.io/gh/UBC-MDS/DataMop_package_group14)[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)![version](https://img.shields.io/github/v/release/UBC-MDS/DataMop_package_group14)
 
-`datamop` is a data cleaning and wrangling package designed to streamline the preprocessing of datasets. Whether you meet missing values, inconsistent categorical columns or need scaling for numeric columns when dealing with data. `datamop` provides a simple and consistent solution to automate and simplify these repetitive tasks. 
+`datamop` is a data cleaning and wrangling python package designed to streamline the preprocessing of datasets. Whether you meet missing values, inconsistent categorical columns or need scaling for numeric columns when dealing with data. `datamop` provides a simple and consistent solution to automate and simplify these repetitive tasks. 
+
+## Functions
+
 The following are core functions of this package:
 
-* `sweep_nulls()`: Handle missing values such as imputation or removal, based on user preference.
+* `sweep_nulls()`: Handles missing values such as imputation or removal, based on user preference.
 
 * `column_encoder()`: Encodes categorical columns using either one-hot encoding or ordinal encoding, based on user preference.
 
 * `column_scaler()`: Scales numerical columns, including Min-Max scaling and Z-score standardization, based on user preference.
 
-`datamop` fits into Python data preprocessing ecosystem by offering a more lightweight and user-friendly alternative to complex libraries like `pandas`, `scikit-learn`. `datamop` focuses specifically on handling missing values, encoding categorical columns and normalizing numerical columns. `datamop` changes `scikit-learn` tasks performed by modules like `SimpleImputer`, `OneHotEncoder`, `OrdinalEncoder` and `StandardScaler` with fewer steps and easier customization.
-Similar functionality can be found in:
-
-* **pandas** (`fillna()`, etc.): [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)
-
-* **scikit-learn** (`SimpleImputer`, `OneHotEncoder`, `LabelEncoder`, `MinMaxScaler`, etc.): [scikit-learn preprocessing](https://scikit-learn.org/stable/modules/preprocessing.html)
-
-## Contributors
-
-The authors of this project are Sepehr Heydarian, Ximin Xu, and Essie Zhang.
-
 ## Installation
 
 ```bash
@@ -30,6 +22,9 @@ $ pip install datamop
 
 ## Usage
 
+All functions in `datamop` take a pandas DataFrame as input. Each function returns a modified pandas DataFrame as output, with the specified transformations applied.
+For a quick glimpse into what `datamop` can do, here are a few simple examples:
+
 `datamop` can be used to encode columns in a DataFrame using one-hot or ordinal encoding as follows:
 
 ```
@@ -65,11 +60,25 @@ df = pd.DataFrame({"price": [25, 50, 75]})
 df_scaled = datamop.column_scaler(df, column = 'price', method='minmax', new_min=0, new_max=1)
 ```
 
+## Python Ecosystem
+
+`datamop` fits into Python data preprocessing ecosystem by offering a more lightweight and user-friendly alternative to complex libraries like `pandas`, `scikit-learn`. `datamop` focuses specifically on handling missing values, encoding categorical columns and normalizing numerical columns. `datamop` changes `scikit-learn` tasks performed by modules like `SimpleImputer`, `OneHotEncoder`, `OrdinalEncoder` and `StandardScaler` with fewer steps and easier customization.
+Similar functionality can be found in:
+
+* **pandas** (Functions like `fillna()` for handling missing values, `get_dummies()` for one-hot encoding, and `replace()` for categorical encoding): [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)
+
+* **scikit-learn** (Modules like `SimpleImputer` for missing value imputation, `OneHotEncoder` for one-hot encoding, `OrdinalEncoder` for ordinal encoding, and `MinMaxScaler` or `StandardScaler` for scaling numerical data.): [scikit-learn preprocessing](https://scikit-learn.org/stable/modules/preprocessing.html)
+
+
 ## Contributing
 
 Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
 See CONTRIBUTING file [here](CONTRIBUTING.md).
 
+## Contributors
+
+The authors of this project are Sepehr Heydarian, Ximin Xu, and Essie Zhang.
+
 ## License
 
 `datamop` was created by Sepehr Heydarian, Ximin Xu, Essie Zhang. It is licensed under the terms of the MIT license.