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  "headline": "PFD Toolkit: Your All‑in‑One Solution for Turning PFD Reports into Structured Insights",
  "description": "Discover how PFD Toolkit automates the collection, filtering, summarization, theme discovery, and tabulation of PFD (Prevention of Future Deaths) reports in seconds, empowering researchers, journalists, and public health analysts with actionable data.",
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  "datePublished": "2025-06-19",
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Introduction

“

Reader: “What is a PFD report?”
Author: “A PFD (Prevention of Future Deaths) report is an official document issued by a coroner that outlines recommendations aimed at preventing similar deaths in the future. PFD Toolkit is a software package designed to automate the tedious process of gathering, reading, categorizing, and extracting themes from these reports.”

If you work in social science research, investigative journalism, or public health analysis, you know how laborious it can be to manually collect dozens—if not hundreds—of PFD reports, read through lengthy texts, tag key concerns, and discover common themes. PFD Toolkit transforms that multi‑hour manual workflow into a streamlined pipeline:

1. Load — fetch hundreds of reports in a single line of code
2. Filter — select relevant cases by keywords or topics
3. Summarize — distill long narratives into concise overviews
4. Discover — apply topic modeling to reveal hidden themes
5. Tabulate — generate structured tables ready for visualization

In this post, you’ll learn how to install, upgrade, and leverage PFD Toolkit’s core modules—with code examples, tables, step‑by‑step guides, and a detailed FAQ. By the end, you’ll be able to spin up an automated PFD analysis pipeline in under five minutes.

What Is PFD Toolkit?

Imagine spending days manually downloading each PFD report PDF from a government website, then copying and pasting sections into spreadsheets to compare concerns. PFD Toolkit replaces that grind with a Python library that:

• Fetches PFD reports by date range
• Loads them into a pandas DataFrame for instant analysis
• Lets you filter by topics (road safety, medical error, etc.)
• Generates summaries of the investigation and circumstances
• Runs topic modeling (e.g., LDA) to surface common themes
• Outputs clean tables ready for charts or further statistical work

Whether you’re a seasoned analyst or new to Python, PFD Toolkit gives you an end‑to‑end solution for turning raw coroner reports into structured insights.

Ideal users include:

• Social Science Researchers looking for data‑driven insights
• Investigative Journalists uncovering systemic risks
• Public Health Analysts identifying emerging safety issues

Key Features Overview

Installation & Upgrade

Installation Steps

1. Open your terminal or command prompt.

2. Run the install command:

   pip install pfd_toolkit
   

3. Once installation completes, you can import pfd_toolkit in any Python environment.

Upgrade to the Latest Reports

“

Reader: “How do I make sure I’m always working with the newest PFD data?”
Author: “Just upgrade the toolkit—new reports are published every week.”

pip install --upgrade pfd_toolkit

That’s it! The next time you invoke data‑loading functions, you’ll pull in the most recent PFD reports.

Quick Start: Loading Report Data (in Seconds)

Follow these three simple steps to load PFD reports into a pandas DataFrame:

1. Import the loader in your Python script or REPL:

   from pfd_toolkit import load_reports
   

2. Specify your date range (format YYYY-MM-DD):

   reports = load_reports(
       start_date="2024-01-01",
       end_date="2025-01-01"
   )
   

3. Explore your DataFrame:

   • Each row represents one PFD report.
   • Columns correspond to key sections of the report:

   Column	Example Content
   url	Link to the full PFD report
   date	Date published
   coroner	Name of the coroner
   area	Jurisdiction
   receiver	Recipient organization or person
   investigation	Detailed narrative of the investigation
   circumstances	Sequence of events leading to death
   concerns	Core safety concerns raised

Now you have a fully searchable, filterable dataset at your fingertips.

In‑Depth Feature Guide

1. Report Screening (screen_reports)

When you need to focus on a specific topic—say, “road safety” or “hospital procedures”—use the screening module:

from pfd_toolkit import screen_reports

# Assuming `reports` is already loaded
road_safety_reports = screen_reports(
    reports,
    keywords=["road", "safety"]
)

• Input: The full DataFrame and a list of keywords
• Output: A subset DataFrame containing only the matching reports

You can chain multiple keywords, apply boolean logic filters, or even filter by date and jurisdiction.

2. Text Summarization (summarise_text)

Reading dozens of investigation narratives can be overwhelming. Summarize key sections with:

from pfd_toolkit import summarise_text

first_summary = summarise_text(
    text=reports.loc[0, "investigation"],
    max_length=120  # Maximum number of words or characters
)
print(first_summary)

• Use Case: Quickly skim through many reports to spot relevant cases
• Tip: Always manually review the AI‑generated summary to ensure no critical detail is lost.

3. Theme Discovery (discover_themes)

To identify overarching topics across your dataset, apply topic modeling:

from pfd_toolkit import discover_themes

themes = discover_themes(
    texts=reports["circumstances"].tolist(),
    num_themes=5
)
for i, theme in enumerate(themes, start=1):
    print(f"Theme {i}: {theme}")

• Input: List of text snippets (e.g., all circumstances entries)
• Output: A list of themes, each represented by its top keywords

Use the resulting themes to guide deeper qualitative analysis or to tag reports automatically.

4. Tabulation (tabulate_reports)

Once themes are discovered, convert your data into a structured table:

from pfd_toolkit import tabulate_reports

theme_table = tabulate_reports(
    reports=reports,
    themes=themes
)

• Output: A pandas DataFrame where each report row is labeled with its dominant theme(s)
• Next Steps: Export this table to CSV, feed into visualization libraries, or run quantitative analyses.

HowTo: Building an Automated PFD Analysis Pipeline

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      "text": "Run `pip install pfd_toolkit` in your terminal"
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      "@type": "HowToStep",
      "name": "Load Report Data",
      "text": "Call `load_reports(start_date, end_date)` to fetch PFDs"
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      "name": "Filter Relevant Reports",
      "text": "Use `screen_reports(reports, keywords)` to focus your dataset"
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    {
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      "name": "Generate Summaries",
      "text": "Invoke `summarise_text` on key sections for quick reading"
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    {
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      "name": "Discover Themes",
      "text": "Run `discover_themes` to reveal common topics"
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      "text": "Use `tabulate_reports` to create structured tables for export"
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Follow these steps in sequence, and you’ll have an end‑to‑end pipeline that:

1. Automatically fetches new PFD reports weekly
2. Filters and tags them by topic of interest
3. Creates concise summaries for rapid review
4. Discovers and labels themes at scale
5. Outputs clean tables ready for reporting or visualization

Frequently Asked Questions

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      "@type": "Question",
      "name": "Which Python versions are supported?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "PFD Toolkit supports Python 3.7 and above. Check the GitHub repo for exact dependency requirements."
      }
    },
    {
      "@type": "Question",
      "name": "How do I sync the latest PFD reports?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Run `pip install --upgrade pfd_toolkit` to fetch the newest reports automatically."
      }
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      "@type": "Question",
      "name": "How can I set multiple keywords when screening reports?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Pass a list of strings to the `keywords` parameter; the toolkit will match any report containing at least one keyword."
      }
    },
    {
      "@type": "Question",
      "name": "What algorithm powers theme discovery?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The toolkit uses Latent Dirichlet Allocation (LDA) under the hood to extract topic keywords."
      }
    },
    {
      "@type": "Question",
      "name": "Can I export the tabulated results?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Yes—simply call `theme_table.to_csv('output.csv', index=False)` to save your table for use in spreadsheets or BI tools."
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Putting It All Together

With PFD Toolkit, tasks that once took hours or days can now be completed in mere seconds:

• Streamlined Installation: One command to get started.
• Instant Data Loading: DataFrame format for immediate analysis.
• Modular Workflow: Pick and choose the functions you need—filter, summarize, discover, tabulate.
• Regular Updates: Weekly sync ensures you never miss a new PFD report.

Whether you’re drafting a research paper, producing a newsroom exposé, or monitoring public health trends, PFD Toolkit lets you focus on interpretation and action—leaving repetitive data‑gathering to the code.

Ready to revolutionize your PFD analysis? Install PFD Toolkit today and unlock structured insights at the speed of Python.