dsr-data-tools 1.1.0

Generic data handling utilities including data splitting and analysis.

dsr-data-tools

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

Version 1.1.0: This release adds new functionality and bug fixes while remaining compatible with 1.0.0.

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)

Datetime Conversion Recommendation

generate_recommendations() detects object/string columns that are likely datetimes and recommends converting them to a proper datetime dtype.

import pandas as pd
from dsr_data_tools.analysis import generate_recommendations
from dsr_data_tools.recommendations import apply_recommendations

# Example column with mostly valid date strings
df = pd.DataFrame({
	'date_str': [
		'2025-01-01', '2025-01-02', '2025-01-03',
		'2025-01-04', 'invalid',  # one invalid value
	] * 10  # scale up rows
})

recs = generate_recommendations(df)

# If detected, apply the datetime conversion recommendation
if 'date_str' in recs and 'datetime_conversion' in recs['date_str']:
	df_converted = apply_recommendations(df, {
		'date_str': recs['date_str']['datetime_conversion']
	})
	# Column is now datetime64; invalid entries coerced to NaT
	print(df_converted['date_str'].dtype)  # datetime64[ns]

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 >= 1.0.0

License

MIT License - see LICENSE file for details