# datahelp
datahelp is a Python library designed to assist data science and data analysis teams in their workflow. It provides various utility functions and tools to streamline common data science tasks.
## Features
datahelp offers the following key features:
1. **Project Management:** datahelp simplifies the creation of standard data science project structures. With a single function call, you can generate a well-organized project directory with predefined folders for datasets, processed data, raw data, outputs, models, scripts, notebooks, and more.
2. **Model Saving and Loading:** datahelp provides easy-to-use functions for saving and loading trained machine learning models. It supports various formats such as joblib, pickle, and keras, enabling seamless integration with different model types.
3. **Data Exploration and Visualization:** The library includes functions for data exploration, summary statistics, and visualization. You can quickly generate feature vi plots and visualize missing data to gain insights into your datasets.
4. **Feature Engineering:** datahelp includes methods for handling missing data and noise in your datasets. It offers functions for dropping missing columns based on a specified threshold and detecting outliers using Tukey's Interquartile Range (IQR) method.
5. **Model Evaluation and Cross-Validation:** datahelp provides tools to evaluate model performance, including functions to calculate accuracy, F1-score, precision, recall, and generate classification reports. It also supports cross-validation for model evaluation.
6. **Scaling and Normalization:** The library offers functions for min-max scaling and z-score normalization of data to bring features to a common scale.
## Quickstart
To use datahelp in your data science projects, you can install it via pip:
```bash
pip install datahelp
```
Once installed, you can import the library and explore its functionality:
```python
import datahelp as dh # import the datahelp library
df = pd.read_csv("data/iris.csv") # load iris dataset
df.head()
cats = dh.eda.get_cat_vars(df)
print(cats)
num_var = dh.eda.get_num_vars(df)
print(num_var)
cat_count = dh.eda.get_cat_counts(df)
cat_count
missing = dh.eda.display_missing(df)
missing
```
## Lins
Project: https://github.com/kimxons/datahelp
PyPi: https://pypi.python.org/pypi/dataehlp/
## Documentation
For detailed usage instructions and API reference, please refer to the official documentation at [https://datahelp-docs.example.com](https://datahelp-docs.example.com)
## Contribution
datahelp is an open-source project, and we welcome contributions from the data science community. If you find a bug, have a feature request, or want to contribute improvements, please open an issue or submit a pull request on our GitHub repository at [https://github.com/datahelp/datahelp](https://github.com/datahelp/datahelp).
## License
datahelp is licensed under the MIT License. See the [LICENSE](https://github.com/datahelp/datahelp/blob/main/LICENSE) file for more details.
## Contact
If you have any questions or feedback, feel free to reach out to our support team at dev.kitonga@gmail.com or join our community forum at [https://community.datahelp.com](https://community.datahelp.com). We are here to assist you in making your data science journey smooth and successful!
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"description": "# datahelp\n\ndatahelp is a Python library designed to assist data science and data analysis teams in their workflow. It provides various utility functions and tools to streamline common data science tasks.\n\n## Features\n\ndatahelp offers the following key features:\n\n1. **Project Management:** datahelp simplifies the creation of standard data science project structures. With a single function call, you can generate a well-organized project directory with predefined folders for datasets, processed data, raw data, outputs, models, scripts, notebooks, and more.\n\n2. **Model Saving and Loading:** datahelp provides easy-to-use functions for saving and loading trained machine learning models. It supports various formats such as joblib, pickle, and keras, enabling seamless integration with different model types.\n\n3. **Data Exploration and Visualization:** The library includes functions for data exploration, summary statistics, and visualization. You can quickly generate feature vi plots and visualize missing data to gain insights into your datasets.\n\n4. **Feature Engineering:** datahelp includes methods for handling missing data and noise in your datasets. It offers functions for dropping missing columns based on a specified threshold and detecting outliers using Tukey's Interquartile Range (IQR) method.\n\n5. **Model Evaluation and Cross-Validation:** datahelp provides tools to evaluate model performance, including functions to calculate accuracy, F1-score, precision, recall, and generate classification reports. It also supports cross-validation for model evaluation.\n\n6. **Scaling and Normalization:** The library offers functions for min-max scaling and z-score normalization of data to bring features to a common scale.\n\n## Quickstart\n\nTo use datahelp in your data science projects, you can install it via pip:\n\n```bash\npip install datahelp\n```\n\nOnce installed, you can import the library and explore its functionality:\n\n```python\nimport datahelp as dh # import the datahelp library\n\ndf = pd.read_csv(\"data/iris.csv\") # load iris dataset\n\ndf.head()\n\ncats = dh.eda.get_cat_vars(df)\nprint(cats)\n\nnum_var = dh.eda.get_num_vars(df)\nprint(num_var)\n\ncat_count = dh.eda.get_cat_counts(df)\ncat_count\n\nmissing = dh.eda.display_missing(df)\nmissing\n```\n## Lins\nProject: https://github.com/kimxons/datahelp\nPyPi: https://pypi.python.org/pypi/dataehlp/\n\n## Documentation\n\nFor detailed usage instructions and API reference, please refer to the official documentation at [https://datahelp-docs.example.com](https://datahelp-docs.example.com)\n\n## Contribution\n\ndatahelp is an open-source project, and we welcome contributions from the data science community. If you find a bug, have a feature request, or want to contribute improvements, please open an issue or submit a pull request on our GitHub repository at [https://github.com/datahelp/datahelp](https://github.com/datahelp/datahelp).\n\n## License\n\ndatahelp is licensed under the MIT License. See the [LICENSE](https://github.com/datahelp/datahelp/blob/main/LICENSE) file for more details.\n\n## Contact\n\nIf you have any questions or feedback, feel free to reach out to our support team at dev.kitonga@gmail.com or join our community forum at [https://community.datahelp.com](https://community.datahelp.com). We are here to assist you in making your data science journey smooth and successful!\n",
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