dataramp 1.0.1.dev171

A Data science library for data science / data analysis teams

Dataramp

Dataramp is a Python library designed to streamline data science and data analysis workflows. It offers a collection of utility functions and tools tailored to assist data science teams in various aspects of their projects.

Key Features

1. Project Management

• Simplify project setup with a single function call to generate a standardized project directory structure.
• Organize datasets, model outputs, scripts, notebooks, and more in predefined folders for better project management.

2. Model Saving and Loading

• Save and load trained machine learning models effortlessly.
• Supports multiple formats including joblib, pickle, and keras for compatibility with diverse model types.

3. Data Exploration and Visualization

• Explore datasets and generate summary statistics with ease.
• Visualize feature distributions and missing data patterns to gain insights into your data.

4. Feature Engineering

• Handle missing data and outliers effectively.
• Drop missing columns based on user-defined thresholds and detect outliers using Tukey's Interquartile Range (IQR) method.

5. Model Evaluation and Cross-Validation

• Evaluate model performance with comprehensive metrics such as accuracy, F1-score, precision, and recall.
• Generate classification reports and support cross-validation for robust model evaluation.

6. Scaling and Normalization

• Scale and normalize data using min-max scaling and z-score normalization techniques.
• Bring features to a common scale for improved model performance.

By providing a range of functionalities, Dataramp aims to enhance productivity and efficiency in data science projects, empowering teams to focus on deriving meaningful insights from their data.

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 dr  # import the dataramp library
import pandas as pd

# To create your project with help of dataramp
from dataramp.create_project import create_project



df = pd.read_csv("data/iris.csv")  # load iris dataset

df.head() #  Snapshot of your df


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!