pyxxdi 0.3.0

Modern bibliometric analytics package for x-index, xd-index, ranking, benchmarking, and research evaluation.

pyxxdi

Modern Python package for bibliometric analytics, research evaluation, and expertise intelligence.

pyxxdi is the Python successor to the earlier R package xxdi, designed for scalable, transparent, and reproducible scientometric workflows.

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Overview

pyxxdi provides production-grade tools for evaluating research strength, thematic expertise, institutional diversity, and scholarly performance using modern Python workflows.

The package is built for:

• researchers
• universities
• policy analysts
• ranking teams
• scientometricians
• research intelligence units

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Core Focus Areas

• x-index
• xc-index
• xd-index
• xo-index
• xx-index
• xxd-index
• h-index
• g-index
• expertise diversity analytics
• institutional benchmarking
• ranking workflows
• portfolio intelligence
• reproducible research evaluation

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Why pyxxdi?

Modern research analytics requires tools that are:

• transparent
• programmable
• scalable
• auditable
• publication-ready
• open-source

pyxxdi aims to provide:

• clean pandas-first APIs
• robust metric implementations
• reproducible workflows
• extensible package architecture
• research-grade outputs
• modern Python tooling

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Installation

Basic

pip install pyxxdi

Development

uv sync --extra dev --extra docs --extra viz --extra network

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Quick Start

import pyxxdi as px
import pandas as pd

df = pd.read_csv("data.csv")

px.h_index(df, unit="institution")
px.g_index(df, unit="institution")

px.x_index(df)
px.xd_index(df)
px.xo_index(df)

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Example Outputs

px.xd_index(df, top_n=10)

unit	records	xd_index	rank
Inst A	240	18	1
Inst B	221	16	2

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Available Metrics

Traditional Citation Metrics

• h_index()
• g_index()

Expertise Metrics

• x_index()
• xc_index()
• xd_index()
• xo_index()

Nested / Policy Metrics

• xx_index()
• xxd_index()

xd-index Variants

• xd_fractional_index()
• xd_field_normalized_index()
• xd_ivw_index()

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Important Methodological Note

Fractional, field-normalized, and inverse-variance-weighted variants are recommended for xd-index, not x-index.

Reason:

• x-index operates on fine-grained keywords
• keywords often lack stable field-wide baselines
• broad categories are more suitable for normalization

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Planned Modules

• pyxxdi.io
• pyxxdi.cleaning
• pyxxdi.classify
• pyxxdi.metrics
• pyxxdi.ranking
• pyxxdi.trends
• pyxxdi.portfolio
• pyxxdi.network
• pyxxdi.viz
• pyxxdi.report
• pyxxdi.utils

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Development Status

Current Release

v0.2.x beta

Status

• Core metrics implemented
• Tests passing
• Ongoing optimization
• Documentation expansion in progress

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Roadmap

Phase 1

Foundation package architecture

Phase 2

Metric engine and expertise indices

Phase 3

Data ingestion, cleaning, classification

Phase 4

Network analytics and collaboration intelligence

Phase 5

Visual dashboards and reporting

Phase 6

Research paper + software citation release

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Reproducibility

pyxxdi is built with reproducible research principles:

• versioned outputs
• deterministic calculations
• transparent formulas
• test coverage
• open-source workflows

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Contributing

Contributions, bug reports, feature requests, and academic collaborations are welcome.

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Citation

A formal CITATION.cff file will be included in the repository.

If using pyxxdi in academic work, please cite the software and related methodological publications.

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License

MIT License

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Author

Abhirup Nandy

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