https://github.com/codehariom/data-cleaning-in-python-of-airbnb
Data Cleaning In Python
https://github.com/codehariom/data-cleaning-in-python-of-airbnb
Last synced: 10 months ago
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Data Cleaning In Python
- Host: GitHub
- URL: https://github.com/codehariom/data-cleaning-in-python-of-airbnb
- Owner: codehariom
- License: gpl-3.0
- Created: 2023-12-21T04:57:11.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-21T05:05:41.000Z (about 2 years ago)
- Last Synced: 2023-12-21T07:50:44.370Z (about 2 years ago)
- Language: Jupyter Notebook
- Size: 3.11 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# Data Cleaning in Python
## Overview
This repository provides a collection of Python scripts and notebooks for data cleaning tasks. Data cleaning is a crucial step in the data preprocessing pipeline, ensuring that datasets are accurate, consistent, and ready for analysis or machine learning. This collection covers common data cleaning techniques using Python and popular libraries such as Pandas.
## Table of Contents
1. [Installation](#installation)
2. [Usage](#usage)
3. [Examples](#examples)
4. [Contributing](#contributing)
5. [License](#license)
## Installation
Clone the repository to your local machine:
```bash
git clone https://github.com/your-username/data-cleaning-python.git
cd data-cleaning-python
```
Create a virtual environment and install the required dependencies:
```bash
python -m venv venv
source venv/bin/activate # On Windows, use 'venv\Scripts\activate'
pip install -r requirements.txt
```
## Usage
Navigate to the `scripts` or `notebooks` directory to find various data cleaning scripts and Jupyter notebooks. Each script or notebook focuses on a specific data cleaning task and provides explanations along with code comments.
To run a script, use the following command:
```bash
python script_name.py
```
To open a Jupyter notebook, use:
```bash
jupyter notebook notebook_name.ipynb
```
Make sure to adjust paths and filenames according to your dataset.
## Examples
### 1. Handling Missing Values
The script `handle_missing_values.py` demonstrates techniques for handling missing values in a dataset using Pandas. It covers methods such as dropping missing values, imputing values, and more.
### 2. Removing Duplicates
The notebook `remove_duplicates.ipynb` provides a step-by-step guide on identifying and removing duplicate rows from a dataset using Pandas.
### 3. Data Type Conversion
In the script `convert_data_types.py`, you will find examples of converting data types of columns to the appropriate format using Pandas.
## Contributing
Contributions are welcome! If you have additional data cleaning techniques, scripts, or notebooks to share, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature: `git checkout -b feature-name`.
3. Commit your changes: `git commit -m 'Add a new feature'`.
4. Push to the branch: `git push origin feature-name`.
5. Open a pull request.
Please make sure to follow the existing coding style and include comments to explain the logic of your code.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.