# 🧹 DataCleaner
A simple and flexible Python utility for **data cleaning and preprocessing**.
It helps you handle missing values, remove duplicates, treat outliers, reduce features, and encode categorical data — all in one place.
---
## ✨ Features
- **Missing Values Handling**
- Drop, Mean, Median, Mode, KNN Imputation
- **Remove Duplicates**
- **Outlier Detection & Removal**
- IQR, Z-score, Isolation Forest
- **Feature Reduction**
- Low variance feature removal
- High correlation feature removal
- **Encoding Categorical Data**
- Label Encoding
- One-Hot Encoding
- Frequency Encoding
- Auto Encoding (chooses based on cardinality)
- **Report Generation** – keeps track of all cleaning steps applied
---
## 📦 Installation
Clone the repo:
```bash
git clone https://github.com/ck-ahmad/datacleaner.git
```
## Install dependencies:
```bash
pip install -r requirements.txt
```
## 🚀 Usage Example
``` python
import pandas as pd
from cleaner import DataCleaner
# Sample dataset
df = pd.DataFrame({
"Name": ["Ali", "Sara", "John", "Ali", None],
"Age": [25, None, 30, 25, 22],
"Salary": [50000, 60000, None, 50000, 45000],
"City": ["Lahore", "Karachi", "Karachi", "Lahore", "Islamabad"]
})
# Initialize cleaner
cleaner = DataCleaner(df)
# Apply cleaning steps
cleaned_df = (
cleaner
.handle_missing(strategy="mean") # fill missing values
.remove_duplicates() # remove duplicate rows
.handle_outliers(method="iqr") # handle outliers
.remove_low_variance(threshold=0.01)
.remove_high_correlation(corr_threshold=0.9)
.encode(method="auto") # encode categorical columns
.get_data()
)
# Get report of cleaning steps
report = cleaner.get_report()
print("✅ Cleaned Data:")
print(cleaned_df)
print("\n📝 Cleaning Report:")
print(report)
```
## 📋 Example Output
# Cleaned Data
``` bash
Copy code
Age Salary City_Karachi City_Lahore City_Islamabad
0 25 50000.0 0 1 0
1 25 50000.0 1 0 0
2 22 45000.0 0 0 1
```
# Cleaning Report
``` sql
Step Action
1 Filled missing Age with mean
2 Filled missing Salary with mean
3 Dropped rows with missing in Name
4 Removed 1 duplicate rows
5 Removed outliers in Age using IQR
6 Applied Auto Encoding
```
## 🛠️ Requirements
pandas
numpy
scikit-learn
```bash
Copy code
pip install pandas numpy scikit-learn
```
## 📖 License
This project is licensed under the MIT License – feel free to use and Open To Collabrate For Contibutions in it.
## 👨💻 Author
Ahmad Abdullah
🌐 LinkedIn: linkedin.com/in/ahmad0763
💻 GitHub: github.com/ck-ahmad
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"description": "# \ud83e\uddf9 DataCleaner\r\n\r\nA simple and flexible Python utility for **data cleaning and preprocessing**. \r\nIt helps you handle missing values, remove duplicates, treat outliers, reduce features, and encode categorical data \u2014 all in one place. \r\n\r\n---\r\n\r\n## \u2728 Features\r\n- **Missing Values Handling** \r\n - Drop, Mean, Median, Mode, KNN Imputation \r\n- **Remove Duplicates** \r\n- **Outlier Detection & Removal** \r\n - IQR, Z-score, Isolation Forest \r\n- **Feature Reduction** \r\n - Low variance feature removal \r\n - High correlation feature removal \r\n- **Encoding Categorical Data** \r\n - Label Encoding \r\n - One-Hot Encoding \r\n - Frequency Encoding \r\n - Auto Encoding (chooses based on cardinality) \r\n- **Report Generation** \u2013 keeps track of all cleaning steps applied \r\n\r\n---\r\n\r\n## \ud83d\udce6 Installation\r\n\r\nClone the repo:\r\n\r\n```bash\r\ngit clone https://github.com/ck-ahmad/datacleaner.git\r\n```\r\n## Install dependencies:\r\n\r\n```bash\r\npip install -r requirements.txt\r\n```\r\n\r\n## \ud83d\ude80 Usage Example\r\n\r\n``` python\r\nimport pandas as pd\r\nfrom cleaner import DataCleaner\r\n\r\n# Sample dataset\r\ndf = pd.DataFrame({\r\n \"Name\": [\"Ali\", \"Sara\", \"John\", \"Ali\", None],\r\n \"Age\": [25, None, 30, 25, 22],\r\n \"Salary\": [50000, 60000, None, 50000, 45000],\r\n \"City\": [\"Lahore\", \"Karachi\", \"Karachi\", \"Lahore\", \"Islamabad\"]\r\n})\r\n\r\n# Initialize cleaner\r\ncleaner = DataCleaner(df)\r\n\r\n# Apply cleaning steps\r\ncleaned_df = (\r\n cleaner\r\n .handle_missing(strategy=\"mean\") # fill missing values\r\n .remove_duplicates() # remove duplicate rows\r\n .handle_outliers(method=\"iqr\") # handle outliers\r\n .remove_low_variance(threshold=0.01)\r\n .remove_high_correlation(corr_threshold=0.9)\r\n .encode(method=\"auto\") # encode categorical columns\r\n .get_data()\r\n)\r\n\r\n# Get report of cleaning steps\r\nreport = cleaner.get_report()\r\n\r\nprint(\"\u2705 Cleaned Data:\")\r\nprint(cleaned_df)\r\n\r\nprint(\"\\n\ud83d\udcdd Cleaning Report:\")\r\nprint(report)\r\n```\r\n## \ud83d\udccb Example Output\r\n# Cleaned Data\r\n\r\n``` bash\r\nCopy code\r\n Age Salary City_Karachi City_Lahore City_Islamabad\r\n0 25 50000.0 0 1 0\r\n1 25 50000.0 1 0 0\r\n2 22 45000.0 0 0 1\r\n```\r\n# Cleaning Report\r\n\r\n``` sql\r\n Step Action\r\n 1 Filled missing Age with mean\r\n 2 Filled missing Salary with mean\r\n 3 Dropped rows with missing in Name\r\n 4 Removed 1 duplicate rows\r\n 5 Removed outliers in Age using IQR\r\n 6 Applied Auto Encoding\r\n```\r\n## \ud83d\udee0\ufe0f Requirements\r\npandas\r\nnumpy\r\nscikit-learn\r\n\r\n```bash\r\nCopy code\r\npip install pandas numpy scikit-learn\r\n```\r\n## \ud83d\udcd6 License\r\nThis project is licensed under the MIT License \u2013 feel free to use and Open To Collabrate For Contibutions in it.\r\n\r\n## \ud83d\udc68\u200d\ud83d\udcbb Author\r\nAhmad Abdullah\r\n\ud83c\udf10 LinkedIn: linkedin.com/in/ahmad0763\r\n\ud83d\udcbb GitHub: github.com/ck-ahmad\r\n",
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