If you’re an actuary who’s spent years building sophisticated models in Excel—complete with nested VLOOKUPs, circular references, and macro-laden workbooks that make your colleagues nervous—the idea of switching to Python can feel like learning a new language from scratch. In a sense, it is. But here’s what we’ve observed from tracking this transition across the profession: the actuaries who make the shift consistently report that the initial learning curve is steeper than expected, but the productivity gains arrive faster than expected too.
This guide is designed specifically for actuaries and exam candidates who want to start using Python in their actuarial work. Not a generic “learn to code” tutorial, but a practical roadmap grounded in the tools, libraries, and workflows that are actually relevant to what actuaries do every day—data manipulation, statistical modeling, financial calculations, and producing reproducible analysis.
Why Python, and Why Now?
The short answer: because the profession is moving there, and the tools have matured to the point where the transition makes practical sense.
The longer answer involves several converging trends that have accelerated over the past two years.
On the credentialing side, the SOA’s Predictive Analytics (PA) Exam requires applied statistical programming (using R as the exam language), while the CAS expanded MAS-I and MAS-II to four sittings per year beginning in 2026—a direct response to growing candidate interest in the data science track. The SOA’s Advanced Topics in Predictive Analytics (ATPA) assessment deepens these expectations further. While R remains the SOA’s exam language of choice, Python dominates in professional practice, particularly in consulting firms and larger insurers.
On the employer side, the shift has been equally notable. Python, SQL, and R now appear as expected or preferred skills in the majority of actuarial job postings at major carriers and consulting firms, joining—and increasingly displacing—VBA and SAS. Georgia State University launched an interdisciplinary Master’s program in Spring 2026 that explicitly blends actuarial science with AI and information systems, reflecting how academic programs are restructuring to meet employer demand.
And critically, the open-source actuarial Python ecosystem has reached a level of maturity that makes professional adoption genuinely practical. Five years ago, an actuary wanting to do reserving in Python would have needed to build everything from scratch. Today, the chainladder-python package (maintained by the CAS open-source community) provides production-ready tools for triangle manipulation, link ratio calculation, and IBNR estimation—with a scikit-learn-style API that makes the learning curve manageable for anyone already familiar with Python’s data science stack.
Setting Up Your Environment
One of the first decisions you’ll face is how to install and manage Python. For actuaries, we recommend one of two approaches depending on your comfort level.
Option 1: Anaconda Distribution (Recommended for Beginners)
Anaconda is a Python distribution designed for scientific computing and data science. It bundles Python with most of the libraries you’ll need (NumPy, Pandas, SciPy, Matplotlib, Jupyter, scikit-learn) and provides a graphical interface for managing packages and environments. For actuaries who are new to programming, this removes a significant amount of setup friction.
Download it from anaconda.com, install it on Windows, Mac, or Linux, and you’ll have a working actuarial Python environment within minutes. Anaconda also includes Jupyter Notebook, which is particularly well-suited to actuarial work (more on this below).
Option 2: Standard Python + pip (For More Control)
If you’re comfortable with command-line tools or want a lighter-weight installation, you can install Python directly from python.org and use pip (Python’s package manager) to install libraries as needed. This approach gives you more control over your environment and is what most experienced developers prefer.
A typical initial setup would look like:
pip install numpy pandas scipy matplotlib seaborn jupyter scikit-learn statsmodelsChoosing an Editor
Your choice of development environment matters more than you might think, because it affects how quickly you can iterate on analysis.
Jupyter Notebook / JupyterLab — The most popular choice for actuarial work, and for good reason. Jupyter lets you combine code, output (including charts and tables), and narrative text in a single document. This makes it ideal for exploratory analysis, model documentation, and sharing results with non-technical stakeholders. If you’ve ever wished your Excel workbook came with built-in documentation of your methodology, Jupyter delivers that experience. Jupyter notebooks are also the standard format for most actuarial Python tutorials and open-source examples, including those in lifelib and the Actuaries’ Analytical Cookbook.
VS Code — Microsoft’s free code editor has become the default for larger Python projects. It offers excellent Python support, integrated debugging, Git version control, and a Jupyter notebook extension. If you’re building production code or working on projects with multiple files, VS Code is the better choice.
PyCharm — A full-featured Python IDE (integrated development environment) with a generous free community edition. Especially useful for larger projects with complex dependencies.
For most actuaries just getting started, we recommend beginning with Jupyter Notebook and adding VS Code once your projects grow beyond single-notebook analysis.
The Essential Python Libraries for Actuaries
Python’s real power comes from its ecosystem of third-party libraries. Here’s a prioritized guide to the packages that matter most for actuarial work, organized from foundational to specialized.
Tier 1: The Foundation (Learn These First)
Pandas — This is the library you’ll use most. Pandas provides DataFrame objects—essentially programmable spreadsheets—that make structured data manipulation fast and intuitive. If you can write a VLOOKUP, a pivot table, or a SUMIFS formula in Excel, you can learn the Pandas equivalent in an afternoon. Key operations for actuaries include reading CSV and Excel files, filtering and grouping data, merging datasets, and calculating summary statistics.
NumPy — The numerical computing backbone that Pandas is built on. NumPy provides efficient array operations and mathematical functions. You’ll use it directly less often than Pandas, but it’s essential for present value calculations, mortality table operations, and any work involving large numerical arrays.
Matplotlib and Seaborn — Matplotlib is Python’s foundational plotting library. Seaborn builds on it with a higher-level interface that produces more polished statistical graphics with less code. Together, they cover everything from simple line charts to complex heatmaps and distribution plots. For actuaries, these replace the charting capabilities of Excel with far more flexibility and reproducibility.
SciPy — A mathematics and statistics library built on NumPy. Particularly useful for actuaries working with probability distributions (scipy.stats), optimization problems (scipy.optimize), and numerical integration—all common in pricing, reserving, and risk modeling work.
Tier 2: Modeling and Machine Learning
scikit-learn — The standard Python library for machine learning. Provides clean, consistent APIs for regression, classification, clustering, and model evaluation. For actuaries preparing for the SOA PA Exam or CAS MAS-I, familiarity with scikit-learn is directly relevant. In practice, it’s increasingly used for GLM alternatives, fraud detection, and predictive underwriting models.
Statsmodels — More statistics-focused than scikit-learn, with an emphasis on classical statistical models (OLS, GLMs, time series) and detailed output that includes p-values, confidence intervals, and diagnostic tests. Many actuaries find Statsmodels more intuitive for traditional actuarial modeling because the output format resembles what you’d see in SAS or R.
XGBoost / LightGBM — Gradient boosting libraries that consistently win machine learning competitions and are increasingly used in actuarial pricing and claims modeling. Worth learning once you’re comfortable with scikit-learn basics.
Tier 3: Actuarial-Specific Libraries
This is where Python’s ecosystem for actuaries has matured dramatically. These purpose-built libraries deserve special attention.
chainladder-python — Maintained by the CAS open-source community, this is the go-to library for P&C reserving in Python. It handles triangle data manipulation, link ratio calculations, and IBNR estimation using both deterministic and stochastic methods. The API intentionally mirrors scikit-learn’s estimator pattern, so if you learn one, the other feels familiar. The chainladder package also supports the Tryangle framework for automated reserving optimization using machine learning techniques.
lifelib — An open-source collection of life actuarial models written in Python, currently at version 0.11.0 (released February 2025). Lifelib provides pre-built models for term life, universal life, annuities, and IFRS 17 compliance, complete with sample scripts and Jupyter notebooks. It’s built on modelx, which lets you construct spreadsheet-like models in Python with formulas that update dynamically—a familiar paradigm for actuaries transitioning from Excel.
pyliferisk — A lightweight library for life contingencies calculations using International Actuarial Notation. If you need to quickly compute present values of annuities, insurance benefits, or reserves using standard mortality tables, pyliferisk handles it with minimal code. The formulas follow IAA notation (qx, lx, Ax, etc.), making it immediately readable for trained actuaries.
actuarialmath — A Python package that closely follows the SOA FAM-L exam syllabus and the textbook “Actuarial Mathematics for Life Contingent Risks.” It includes solutions to SOA sample exam questions in Jupyter notebooks, making it both a study tool and a practical library.
GEMAct — A more recent addition to the ecosystem, GEMAct is an open-source package for non-life (re)insurance modeling based on the collective risk model. It supports risk costing, loss aggregation, and claims reserving, and extends SciPy’s probability distribution offerings with actuarial-specific distributions and copula models.
Your First Actuarial Python Projects
Theory is important, but the fastest way to build Python fluency is to work on projects that mirror your actual job responsibilities. Here are five starter projects designed for actuaries, ordered from simplest to most complex.
Project 1: Automate a Data Cleaning Task
Think about a recurring data preparation task you currently do in Excel. Maybe you receive a monthly claims extract that needs formatting, duplicate removal, and summary statistics before analysis. Recreate this workflow in Python using Pandas.
This is deliberately simple, but it accomplishes two things: it forces you to learn Pandas basics (reading files, filtering, grouping, exporting), and it produces an immediately useful script you can run every month instead of doing the work manually.
import pandas as pd
# Read the monthly claims extract
claims = pd.read_csv('monthly_claims_extract.csv')
# Remove duplicates and filter to open claims
claims = claims.drop_duplicates(subset='claim_id')
open_claims = claims[claims['status'] == 'Open']
# Summary statistics by line of business
summary = open_claims.groupby('line_of_business').agg(
claim_count=('claim_id', 'count'),
total_incurred=('incurred_amount', 'sum'),
avg_incurred=('incurred_amount', 'mean')
).round(2)
print(summary)
summary.to_excel('monthly_claims_summary.xlsx')Project 2: Build a Present Value Calculator
Use NumPy to build a flexible present value calculator that handles single payments, annuities, and varying cash flow streams. This reinforces core Python concepts (functions, arrays, loops) while staying in familiar actuarial territory.
import numpy as np
def present_value(cash_flows, discount_rate):
"""Calculate PV of a series of cash flows."""
periods = np.arange(len(cash_flows))
discount_factors = (1 + discount_rate) ** (-periods)
return np.sum(cash_flows * discount_factors)
# Example: 10-year annuity of $1,000 at 5% interest
annual_payments = np.full(10, 1000)
pv = present_value(annual_payments, 0.05)
print(f"Present value: ${pv:,.2f}")Project 3: Visualize Loss Development Patterns
Take a loss triangle (you can use sample data from chainladder-python) and create visualizations showing development patterns by accident year. This introduces Matplotlib and gives you experience working with the kind of structured data that actuaries encounter daily in reserving work.
Project 4: Fit a GLM to Insurance Claims Data
Use scikit-learn or Statsmodels to fit a generalized linear model to a claims dataset. Kaggle hosts several public insurance datasets suitable for this purpose. This project bridges traditional actuarial modeling with machine learning concepts and is directly relevant to PA Exam preparation.
Project 5: Replicate an Excel Model in Python
Choose an existing actuarial model you’ve built in Excel—perhaps a simple term life pricing model or a basic reserve projection—and rebuild it in Python. This is the most challenging starter project but also the most transformative, because it forces you to understand how your spreadsheet logic translates into code. The lifelib and modelx libraries are excellent starting points for this, as they provide a spreadsheet-like modeling paradigm within Python.
Common Pitfalls and How to Avoid Them
From observing how actuaries typically approach the Python learning curve, a few patterns consistently emerge.
Trying to learn everything at once. Python’s ecosystem is enormous, and it’s tempting to jump from Pandas to deep learning in a week. Resist this. Focus on Pandas and basic data manipulation for the first month. Add visualization (Matplotlib/Seaborn) in month two. Introduce modeling libraries (scikit-learn, Statsmodels) in month three. Actuarial-specific libraries can come after you’re comfortable with the foundation.
Treating Python like Excel. Actuaries sometimes try to replicate their exact Excel workflow in Python, cell by cell. This misses the point. Python’s strength is in automation, reproducibility, and handling scale—not in mimicking a spreadsheet. Instead of thinking “How do I do this VLOOKUP in Python?”, think “How do I set up this data pipeline so it runs automatically every month?”
Not using version control. Once your scripts become part of your professional workflow, use Git for version control. This is standard practice in software development and increasingly expected in actuarial teams that use Python. It prevents the “claims_model_v3_final_FINAL_v2.py” problem and provides an audit trail for model changes—something regulators increasingly value.
Working in isolation. Python has an active actuarial community. The CAS open-source community maintains chainladder-python, the Actuaries Institute publishes the Analytical Cookbook, and forums on GitHub, Reddit (r/actuary), and Stack Overflow field actuarial Python questions regularly. Use these resources when you get stuck.
How This Connects to Exam Preparation
For exam candidates, Python skills are becoming increasingly relevant across the credentialing pathway.
The SOA PA Exam uses R as its primary language, but the statistical concepts (GLMs, decision trees, random forests, model selection) translate directly to Python’s scikit-learn and Statsmodels libraries. Many candidates find that practicing these concepts in Python—even if the exam uses R—deepens their understanding because it requires engaging with the logic rather than memorizing syntax.
The CAS MAS-I exam covers data science and machine learning fundamentals that align closely with Python’s scikit-learn ecosystem. With MAS-I now offered four times per year starting in 2026, candidates have more frequent opportunities to sit for this exam and may benefit from a year-round Python practice routine.
The SOA’s ATPA assessment goes further into advanced predictive analytics techniques that are well-supported by Python’s machine learning libraries, including ensemble methods, neural networks, and model explainability tools.
Beyond exams, actuaries increasingly report that Python proficiency gives them an edge in job interviews and promotions. The DW Simpson salary surveys and industry hiring data consistently show a premium for actuaries with data science skills, and Python fluency is the most common proxy employers use to evaluate that capability.
Recommended Learning Path
Based on what we’ve seen work for actuaries making this transition, here’s a realistic timeline:
Weeks 1–4: Foundations. Install Anaconda or Python. Complete a basic Python tutorial (Python.org’s official tutorial or the free “Automate the Boring Stuff with Python” are both excellent). Focus on core concepts: variables, data types, functions, loops, and file I/O. Begin working through the Pandas “10 Minutes to Pandas” tutorial.
Weeks 5–8: Data manipulation. Commit to doing one real work task per week in Python instead of Excel. Read CSV/Excel files, clean data, compute summary statistics, and create basic charts. Your goal is Pandas fluency.
Weeks 9–12: Visualization and basic modeling. Learn Matplotlib/Seaborn for data visualization. Start working through scikit-learn’s tutorials on regression and classification. Explore the Actuaries’ Analytical Cookbook for actuarial-specific examples.
Months 4–6: Actuarial specialization. Install and experiment with actuarial-specific libraries (chainladder-python, lifelib, or actuarialmath depending on your practice area). Attempt to replicate or improve an existing Excel-based actuarial model. Begin using Git for version control.
Months 6+: Integration and advanced topics. Use Python for professional work regularly. Explore machine learning applications in your practice area. Consider contributing to open-source actuarial projects or sharing your own Jupyter notebooks with colleagues.