dsr-data-tools 0.0.6

Generic data handling utilities including data splitting and analysis.

dsr-data-tools

Data analysis and exploration tools for exploratory data analysis (EDA).

Features

• Dataset Analysis: Comprehensive statistical summaries and data quality assessment
• Data Exploration: Tools for understanding data distributions, correlations, and patterns
• Quality Metrics: Missing value detection, data type analysis, and anomaly identification
• Statistically Guided Feature Interactions: Automatic discovery of meaningful feature interactions using Mutual Information and Pearson Correlation

Installation

pip install dsr-data-tools

Usage

import pandas as pd
from dsr_data_tools import analyze_dataset

# Load your data
df = pd.read_csv('data.csv')

# Perform comprehensive analysis
analyze_dataset(df)

Performance

This library is optimized for large-scale data processing using vectorized operations.

• Vectorized Integer Checks: Optimized from $O(N)$ Python-level application to vectorized modulo operations, resulting in a 5-6× speed increase.

• Cached Data Scans: Implemented caching for common operations like dropna() and unique() to ensure each data column is scanned as few times as possible, maintaining high efficiency for wide datasets.

Benchmarks

A benchmark script compares per-element apply(is_integer) against a vectorized modulo check for detecting integer-like floats. On large series, the vectorized approach is typically 5–6× faster.

• Script: scripts/benchmark_integer_checks.py

Run via Python:

python scripts/benchmark_integer_checks.py           # default size (2,000,000)
python scripts/benchmark_integer_checks.py 5000000  # custom size

Or via Makefile target:

make benchmark                # default N=2,000,000
make benchmark N=5000000      # custom size

Requirements

• Python >= 3.10
• pandas
• numpy
• scikit-learn
• dsr-utils

License

MIT License - see LICENSE file for details