dataramp 1.0.1.dev169

A Data science library for data science / data analysis teams

dataramp

dataramp 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

dataramp offers the following key features:

1. Project Management: 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: 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: Includes functions for data exploration, summary statistics, and visualization. Quickly generate feature vi plots and visualize missing data to gain insights into your datasets.

4. Feature Engineering: Methods for handling missing data and noise in your datasets. 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: Provides tools to evaluate model performance, including functions to calculate accuracy, F1-score, precision, recall, and generate classification reports. Also supports cross-validation for model evaluation.

6. Scaling and Normalization: Offers functions for min-max scaling and z-score normalization of data to bring features to a common scale.

Quickstart

To use dataramp in your data science projects, you can install it via pip:

pip install dataramp

Once installed, you can import the library and explore its functionality:

import dataramp as dh  # import the dataramp 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/dataramp PyPi: https://pypi.org/project/dataramp/

Documentation

For detailed usage instructions and API reference, please refer to the official documentation at https://dataramp-docs.example.com

We use SemVer for versioning

Contribution

dataramp 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/kimxons/dataramp.

License

dataramp is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions or feedback, feel free to reach out to our support team at [e-mail] or join our community forum at https://community.dataramp.com. We are here to assist you in making your data science journey smooth and successful!