IRS Private Foundation Data Extraction

@sohandas

I can extract and normalize IRS 990-PF data with Python into a clean Excel file, including a data dictionary and reproducible code.

@Giovanni704

Hi there! As I have read, I see you need help extracting and cleaning IRS private foundation data into a structured Excel dataset, and I could help you! I'm a professional Software Engineer with more than 10 years of experience and more than 400 projects done here on this platform. I offer unlimited revisions and I don't require any payment in front. I always meet delivery deadlines working until I get the desired results. Send me a message so I can show you my previous work and we can discuss more. Best Regards, Giovanni.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need a complete IRS private-foundation dataset built from 990-PF filings and related schedules, with every available variable cleaned, normalized, and delivered in Excel with a data dictionary and reproducible code. I’ve worked with Python data extraction, structured document processing, XML/PDF parsing, data normalization, and large datasets where validation and consistent schemas are critical. I’d follow your existing instructions, automate the source collection, preserve the IRS field naming, validate numeric fields and missing values, and cross-check samples against the original filings before delivery. I have a few questions for clarification: Q1 – Do you already have the detailed extraction instructions and target IRS schema ready to share? Q2 – What filing years and approximate number of private foundations should the dataset cover? Q3 – Should the final workbook contain one consolidated dataset, or separate sheets for returns and schedules? Looking forward to hearing from you.

@ayazakhtar75

Hello, This is a strong fit for my Python and data-processing experience. I can follow your existing extraction instructions, collect the IRS 990-PF source data, normalize it into a consistent schema, and deliver an analysis-ready Excel workbook rather than a raw data dump. I’ll preserve the IRS field structure, keep numeric values genuinely numeric, flag missing data, handle duplicates/amended filings according to your rules, and validate samples back against the original XML/PDF records. The first tab will contain a clear data dictionary, and I’ll provide the reproducible Python code/notebook used for extraction and cleaning. Given the potential size of the dataset, I’d first review your instructions and a representative sample, then confirm the most reliable workflow and delivery timeline. Could you share the extraction instructions and confirm the filing years and approximate foundation population required for the first delivery? Best regards, Ayaz Akhtar

@JustinJcob

I got you! I can follow your IRS private foundation data instructions, pull the 990-PF/XML/PDF source data, clean and normalize every released field, and deliver a spot-check-ready Excel file with a first-tab data dictionary and reproducible code/notebook. I’m ready to handle the full workflow carefully: consistent IRS-style column names, numeric formatting, missing-value flags, OCR/parsing fixes, and validation against raw filings. I’m young, a fast learner, and available 24/7. Get the demo first before you pay. Two quick questions: What filing years and foundation universe should be included, all private foundations or a filtered list/EIN set? Do you prefer the reproducible workflow in Python notebook, R, or SQL-based scripts? Let’s chat so I can confirm the scope and start with a demo sample. Kind regards, Haroon Z

@techplusintl

Hi, I will start by reviewing the step-by-step instructions you provided to ensure I understand the data extraction process thoroughly. I will then set up a Python script to scrape the IRS data for the 990-PF filings, ensuring I capture all required fields. After downloading the source files, I will clean and normalize the data, paying close attention to the IRS schema for consistent column naming. I will implement checks to flag missing values and correct any OCR or parsing errors I encounter. Numeric fields will be stored as numbers to facilitate immediate analysis. Once the data is cleaned, I will compile it into a comprehensive Excel spreadsheet, including a brief data dictionary on the first tab for clarity. I will also prepare the reproducible code or notebook used during the process. To get started, I will need access to the specific IRS data sources you want me to scrape and any additional guidelines you may have regarding the data cleaning process. Thanks!

@Hubready

Hello!, I am a US-based senior software engineer, and this project is exactly the kind of detail-heavy work I handle well. The main challenge here is not just extracting IRS private foundation data, but making sure every field is captured accurately, validated, and delivered in a format you can use without manual cleanup. Since you already have step-by-step instructions, I’d break this into phases: confirm the target fields, build the extraction workflow, add validation for missing or inconsistent values, and deliver clean Excel/CSV/SQL-ready output. I’ve built data pipelines and extraction workflows where accuracy mattered more than speed, and I’m very comfortable with Python, Excel, SQL, and structured scraping. I also pay close attention to the small details most people miss, which is usually what makes these projects succeed. A few relevant examples of work: - IRS filing data cleanup and extraction workflow for a compliance team - Foundation/grant database normalization and reporting pipeline - Python-based document parsing tool with Excel output Could you please clarify the following questions to help me better understand the project? 1. What is the final deliverable format you want most: Excel, CSV, SQL database, or all three? 2. Do you already have the exact source pages or URLs for each data element, or should I help map those too? 3. Are there any fields that must be cross-checked or flagged when missing, so the final dataset is audit-friendly? James Zappi

@rezamahi

I can handle the full IRS private-foundation dataset workflow end-to-end. I’ll follow your existing extraction instructions, collect the available 990-PF filings and related schedules, normalize the IRS fields, correct obvious parsing/OCR issues, and preserve the original IRS schema/column naming wherever applicable. Deliverables: • Comprehensive Excel workbook with all available IRS variables • Data dictionary on the first tab • Clean numeric/date fields ready for analysis • Missing-value flags • Deduplication and consistency checks • Foundation/year identifiers for easy filtering • Validation against source XML/PDF returns • Reproducible Python/R code or notebook used for the extraction and cleaning I’ll also structure the workbook so you can easily analyse assets, grants, investment income, compensation, foundation size, program activity and year-over-year trends. I’ll prioritize completeness and traceability over simply producing a large spreadsheet, and I’ll document any IRS fields that cannot be reliably extracted rather than silently filling or guessing values. I can start with the provided instructions/source material and produce a validated sample first, then scale the same reproducible pipeline across the full dataset.

@mayr81

With my solid background in data analysis, extraction, and proficiency across multiple tools including excel, python, SQL and Visual Basic , I am confident in my ability to meet and exceed your project requirements. I have successfully completed several projects involving data extraction from complex sources, and with your thorough instructions in place, delivering meticulous end results has always been a priority. A keystone of my work is ensuring the highest data quality by paying meticulous attention to detail and executing comprehensive data validations. This entails not only pulling data according to predefined logics but also rectifying any inherent errors beforehand while keeping the column names consistent with IRS schema. I will aim to go above and beyond these project demands by flagging any missing values and correcting any OCR or parsing mistakes throughout this process. Lastly, my ability to analyze complex data and condense it into accessible insights fits perfectly with your aim to extract valuable trends from the IRS filings. I guarantee focused dedication to this project, resulting in a comprehensively clean Excel file that covers each variable you require for future dimension slicing along with a reproducible code/notebook that replicates my work

@yeisonvelez11

Hi, I can handle this project using **Node.js** to automate the collection, parsing, cleaning, and normalization of the IRS data, including the 990-PF filings and related schedules. I understand that the important part here is not simply downloading the returns, but building a reliable dataset where the IRS fields remain consistent and useful for analysis. I can structure the Excel file with the IRS schema, include a data dictionary in the first tab, flag missing values, correct obvious parsing/OCR issues, and make sure numerical fields are stored correctly for filtering, calculations, and charts. I can also provide the **reproducible Node.js code** used to generate the dataset, so the process can be reviewed or reused later. Before you award the project, I’m happy to do a **free demo using a small sample of the IRS data**. This will let you check the extraction quality, Excel structure, field naming, and overall approach before committing to the full project. I’m focused on accuracy and clean, usable data rather than simply delivering a large spreadsheet. I can also make the workflow efficient enough to handle the volume of filings required for a comprehensive dataset. I’m ready to start and can adapt the workflow to the step-by-step instructions you already prepared. Best regards, Yeison

@mobasherreza10

With my extensive experience in data analysis, extraction, and visualization using tools like Python, SQL, and Excel, I am well-positioned to handle your project of IRS Private Foundation Data Extraction efficiently and accurately. Having successfully completed 56 projects with a 4.9★ rating and a 96% on-time delivery record demonstrate my commitment to quality- an essential aspect you seek for in this task. As a member of Aesthetic Logic, my diverse skill set spans various areas of technology including AI, Engineering, CMS, E-commerce, designs, and Data management. This unique expertise allows me to approach your project with a holistic perspective that addresses your specific needs while ensuring compatibility with the IRS schema. At the core of my process lies an unwavering commitment to accuracy. I will not only follow your instructions but also thoroughly spot-check the raw return PDFs or XML against the comprehensive Excel spreadsheet for consistency. My deliverable will include not only the cleaned data but also the reproducible code or notebook for transparency. Let's connect and transform IRS datasets into actionable insights together!

@Georgeoustar

Hi there! Extracting and normalizing IRS 990-PF data for comprehensive sector analysis is a task I’m very familiar with. With years of experience in Python, data processing, and Excel automation, I understand the importance of meticulous data quality, consistent schemas, and reproducibility. I will strictly follow your step-by-step instructions to download, parse, and clean all available private foundation data fields—ensuring numeric columns are ready for analysis, missing data is clearly flagged, and column names match IRS conventions. The final deliverable will be a structured Excel file with a user-friendly data dictionary, along with all code and documentation for full transparency. Having handled similar datasets, I pay special attention to error correction, normalization, and making data ready for slicing by any dimension you need. I’m committed to producing a result that not only passes your spot-checks, but also sets a strong foundation for further analysis and visualization. To better tailor my approach: Are there any specific years or foundation types you want prioritized? Are there frequent parsing challenges or edge cases you’ve encountered with certain IRS filings? Best Regards, George

@mohamedahmedmoha

With over a decade of experience in financial accounting and data analysis, I have honed strong expertise in the very tasks and tools required for this project. I am intimately familiar with Excel, which is crucial for your goal of tracking overall trends and slicing data by various dimensions. My proficiency in using diverse tools such as Python, R, OpenRefine, or SQL only amplifies my ability to provide you with a cleaned Excel file that meets your specific requirements while maintaining the integrity of every data element you need. What distinguishes me from others is my meticulous attention to detail, a trait paramount to ensure data accuracy, especially when dealing with complex information like IRS-related filings. My skills in bank reconciliation and financial statement preparation have trained me always to identify and correct OCR or parsing errors, flag missing values, and store numeric fields properly for immediate analysis. By choosing me for this project, you are not just acquiring a data collector; you will gain a collaborative partner invested in your goal. I offer unlimited support after project completion because I aim not only to hand over an excellent deliverable but also to ensure your long-term satisfaction. Plus, with the appropriate NDA arrangements, the confidentiality of your sensitive data is a non-negotiable guarantee. Let's get started on extracting your private foundation data today!

@Srashtasoft

Hi, A quick note before diving into approach: for 990-PF data at this scale, I'd actually recommend pulling from the IRS's bulk e-file XML index on AWS S3 (the public Form 990 dataset the IRS publishes there) rather than scraping their search UI page-by-page — it's faster, more reliable, and avoids missing filings due to search-tool pagination or rate limits. I'll cross-check a sample against source PDFs either way to make sure nothing's missed. Approach: - Extraction: Pull filings via the IRS bulk XML index where available, falling back to direct scraping for any gaps, following your existing field instructions. - Normalization: Map every field to consistent, IRS-schema-aligned column names — asset balances, grant totals, officer compensation, investment income, and the rest of the released variables. - Quality control: Numeric fields stored as true numbers (summable/chartable immediately), missing values explicitly flagged rather than left blank, and any XML-parsing or OCR errors corrected against the source filing. - Deliverables: Clean Excel workbook with a data dictionary tab, plus the reproducible Python code/notebook so you can re-run or extend it yourself. Timeline (8 days): - Days 1-2: confirm field list/instructions, build extraction pipeline against the bulk XML source - Days 3-5: full extraction + normalization - Days 6-7: QA pass — spot-check against raw filings, flag missing values, fix parsing errors - Day 8: final formatting, data dictionary, delivery Happy to start with a small sample batch (say, 20-30 foundations) first so you can confirm the column mapping before I run the full set. 111 reviews, 4.9 star overall on Freelancer — happy to share examples of similar data-cleaning/extraction deliverables if useful. Best, Srashtasoft

@hassanmustafa01

Hello!⚡ Thank you for posting your IRS Private Foundation Data Extraction project. Turning large IRS filing datasets into clean, analysis-ready spreadsheets is exactly the kind of data work I do. I recently parsed several years of IRS 990 e-file XML with Python into a normalized dataset, validating fields against the IRS schema and spot-checking against original returns. How I'd approach yours: 1. Follow your instructions to pull 990-PF e-file XML, SOI extracts and related schedules 2. Parse every released variable with Python, keeping IRS schema field names 3. Normalize years and form versions so columns line up across all filings 4. Store numbers as numbers, flag missing values and fix parsing or OCR errors 5. Excel workbook with a data dictionary tab and year, size and program-area fields for slicing 6. Reproducible Jupyter notebook plus a spot-check log against source PDFs and XML I'll deliver accurate, analysis-ready data, and make this a success with you. Thanks. Hassan

@TidewaterDev

Hello, Adam from New Jersey here. You need every public 990-PF data element pulled, cleaned and delivered as one consistent Excel workbook you can slice by year, size or program area, with a data dictionary and reproducible code. The main challenge is consistency: IRS XML schemas change between years, and repeating items like grants and officers do not fit one row per return, so they need linked tabs keyed by EIN and tax year. I build complete data pipelines end to end, and I make every step scripted so any figure can be traced back to its source return. This is my plan to complete your project. 1. Follow your instructions to download IRS 990-PF e-file XML and SOI extracts for the target years. 2. Parse with Python using a version-aware mapping to IRS schema field names, with core, grants and officers tables linked by EIN and year. 3. Store numbers as numbers, flag missing values, and run validation checks (totals, ranges, duplicates) to catch parsing errors. 4. Deliver the Excel workbook with a data dictionary tab, a validation report, and a documented Jupyter notebook. Happy to discuss details and suggest the best technical approach. Thanks, Adam.