saslite 0.4.0

Lightweight SAS language interpreter with statistical analysis capabilities

SASLite

SASLite is a lightweight local interpreter for a practical subset of the SAS language, implemented in Python on top of pandas. It is intended for local data checks, small automation workflows, SAS-like examples, and migration/testing work where a full SAS runtime is not available.

SASLite is an independent project. It is not affiliated with, endorsed by, or supported by SAS Institute Inc.

Status

Version 0.4.0 - Beta Release

SASLite is now in Beta. It supports a comprehensive subset of SAS syntax including advanced statistical analysis. The core features are stable and production-ready for many use cases. Validate results carefully before using them for regulated, clinical, financial, or other high-stakes work.

New in v0.4.0:

• 🎉 PROC REG - Linear regression analysis
• 🎉 PROC LOGISTIC - Logistic regression with full CLASS and ODDSRATIO support
• ⚡ 25-30% performance improvement
• 📚 8 comprehensive examples with automated validation
• ✅ 540 tests passing (100% pass rate)

See CHANGELOG.md for complete release notes.

Installation

pip install saslite

Optional extras:

pip install "saslite[excel]"
pip install "saslite[gui]"

For local development from a checkout:

pip install -e ".[excel,gui]"

Command Line

Run a SAS program:

saslite path/to/program.sas

Run one statement:

saslite -e "data demo; x = 1; output; run;"

Start the interactive prompt:

saslite --interactive

Use a persistent working directory for local datasets:

saslite --workdir ./work path/to/program.sas

GUI

Install the GUI extra:

pip install "saslite[gui]"

Start the browser-based GUI:

saslite-gui

This starts a local Flask server at http://127.0.0.1:5000 and opens it in the default browser. To keep it from opening a browser tab automatically:

saslite-gui --no-browser

Start the desktop wrapper, powered by pywebview:

saslite-desktop

On Windows, the desktop wrapper may require Microsoft Edge WebView2 Runtime. The browser-based saslite-gui command is the simpler fallback.

Python API

from saslite import SasInterpreter

sas = SasInterpreter()

result = sas.execute(
    """
    data work.employees;
        input name $ age salary;
        datalines;
    Alice 30 50000
    Bob 25 40000
    ;
    run;

    proc print data=work.employees;
    run;
    """
)

print(result.success)
df = sas.get_dataset("WORK", "EMPLOYEES")
print(df)

Create a SASLite dataset from pandas:

import pandas as pd
from saslite import SasInterpreter

sas = SasInterpreter()
source = pd.DataFrame({"id": [1, 2, 3], "value": [10, 20, 30]})
sas.create_dataset("source", source)

sas.execute(
    """
    data work.result;
        set work.source;
        doubled = value * 2;
    run;
    """
)

result = sas.get_dataset("WORK", "RESULT")

Supported Features

SASLite includes support for:

Data Processing

• DATA step: DATA, SET, MERGE, INPUT, DATALINES, INFILE, OUTPUT, KEEP, DROP, RENAME, WHERE, IF/THEN/ELSE, DO/END, DO WHILE/UNTIL, BY, FIRST. and LAST., ARRAY, RETAIN, LENGTH, ATTRIB
• Column-mode INPUT: Fixed-width data reading with position specifications
• INFILE options: DLM=, DSD, TRUNCOVER, FIRSTOBS, OBS

SQL & Queries

• PROC SQL: SELECT, CREATE TABLE, INSERT, UPDATE, DELETE
• Joins: INNER, LEFT, RIGHT, FULL OUTER, CROSS
• Advanced: Subqueries, GROUP BY, HAVING, ORDER BY, DISTINCT
• Window functions (new in 0.4.0): ROW_NUMBER(), RANK(), LAG(), LEAD(), SUM() OVER, AVG() OVER with PARTITION BY and ORDER BY
• Operators: CASE, LIKE, BETWEEN, IN, IS NULL, EXISTS
• Aggregate functions: SUM, AVG, COUNT, MIN, MAX, STD, VAR

Statistical Analysis

• PROC MEANS/SUMMARY: Descriptive statistics with CLASS, BY, VAR, OUTPUT
• PROC FREQ: Frequency tables, cross-tabulations, chi-square tests
• PROC CORR (new in 0.3.0): Correlation analysis (Pearson, Spearman, Kendall)
• PROC TTEST (new in 0.3.0): T-tests for comparing means
• PROC REG (new in 0.4.0): Linear regression analysis
  • Simple and multiple regression
  • R², Adjusted R², F-statistic, ANOVA tables
  • Residual analysis, VIF for multicollinearity
  • Standardized coefficients
• PROC LOGISTIC (new in 0.4.0): Logistic regression
  • Binary logistic regression
  • CLASS statement for categorical variables
  • ODDSRATIO statement with confidence intervals
  • Model fit statistics (AIC, BIC, -2 Log L)
  • Predicted probabilities

Other Procedures

• PROC SORT: Multi-key sorting with BY, DESCENDING, NODUPKEY
• PROC PRINT: Data display with VAR, WHERE, BY
• PROC CONTENTS: Dataset metadata
• PROC DATASETS: Library management, dataset operations
• PROC APPEND: Append datasets
• PROC IMPORT/EXPORT: CSV and delimited file I/O

Macro System

• Macro variables: %LET, %PUT, macro variable resolution
• Macro functions: %MACRO, %MEND, %DO, %IF/%THEN/%ELSE
• %SYSFUNC (new in 0.3.0): Call DATA step functions in macro code
• %INCLUDE: Compose programs from multiple files
• Conditional logic: %IF, %DO WHILE, %DO UNTIL

Built-in Functions (82 functions)

• Character: STRIP, UPCASE, LOWCASE, SUBSTR, INDEX, SCAN, CAT, CATX, TRIM, LEFT, RIGHT, COMPRESS, TRANSLATE, REVERSE, REPEAT, COMPARE, COMPBL, QUOTE, DEQUOTE, COUNTC, COUNTW
• Numeric: SUM, MEAN, MIN, MAX, ROUND, CEIL, FLOOR, ABS, SQRT, EXP, LOG, LOG10, MOD, SIGN, INT, RANUNI, RANNOR
• Date/Time: TODAY, DATE, DATETIME, DATEPART, TIMEPART, YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, WEEKDAY, QTR, MDY, INTNX, INTCK, DHMS, HMS, YYQ, WEEK
• Conversion: INPUT, PUT, INPUTN, INPUTC, PUTN, PUTC
• Missing values: NMISS, CMISS, MISSING, COALESCE, COALESCEC
• Utility: N, DIM, IFN, IFERROR, LAG, DIF
• Statistical: PROBNORM, PROBT, PROBCHI, PROBF

Libraries & I/O

• LIBNAME: Local work areas, reference libraries
• CSV support: PROC IMPORT/EXPORT for CSV files
• Python API: Create datasets from pandas, export to pandas
• Excel support (optional): With saslite[excel] extra

See DOCS.md and the examples/ directory for detailed usage.

Examples

Basic DATA Step and SQL

data work.sales;
    input region $ product $ amount;
    datalines;
East Widget 100
West Gadget 200
East Widget 150
;
run;

proc sql;
    create table work.summary as
    select region, sum(amount) as total_sales
    from work.sales
    group by region
    order by total_sales desc;
quit;

Statistical Analysis - Linear Regression

data work.study;
    input hours_studied score;
    datalines;
2 65
4 75
6 85
8 92
;
run;

proc reg data=work.study;
    model score = hours_studied;
    output out=work.predicted predicted=pred_score residual=resid;
run;

Logistic Regression with Categorical Variables

data work.patients;
    input age treatment $ outcome;
    datalines;
30 A 1
40 B 0
50 A 1
;
run;

proc logistic data=work.patients;
    class treatment;
    model outcome = age treatment;
    oddsratio age;
    oddsratio treatment;
run;

More Examples

See the examples/ directory for 8 comprehensive examples:

• 01_hello_world.sas - Basic DATA Step
• 02_proc_sql.sas - SQL with window functions
• 03_macro_programming.sas - Macros and %SYSFUNC
• 04_statistical_analysis.sas - MEANS, FREQ, CORR, TTEST
• 05_linear_regression.sas - PROC REG
• 06_logistic_regression.sas - PROC LOGISTIC
• 07_advanced_data_manipulation.sas - Functions and arrays
• 08_import_export.sas - CSV I/O

Run examples with:

saslite examples/05_linear_regression.sas

Validate all examples:

cd examples
python validate_examples.py

Known Limits

SASLite intentionally implements a practical subset of SAS. Some advanced or environment-specific SAS features are not currently supported:

Not Implemented:

• Advanced macro features (MACRO PROC, compiled macros)
• Remote libraries and server integration
• Complete format/informat catalog system
• ODS (Output Delivery System)
• Graphics procedures (SGPLOT, GPLOT, etc.)
• Some specialized PROCs (IML, REPORT, TABULATE, etc.)
• BY-group processing in all contexts
• Full index support
• Hash objects and data structures

Partial Support:

• PROC PRINT does not support TITLE statements
• ARRAY syntax: use full variable lists instead of score1-score4 shorthand
• Some advanced SQL features (recursive CTEs, complex subqueries)
• Informats and formats (common ones supported)

When SASLite cannot parse or execute a program exactly, simplify the program to the supported subset or use the Python API to prepare input datasets directly.

Performance Note: SASLite is optimized for datasets up to ~1M rows. For larger datasets, consider using SAS itself or chunking strategies with the Python API.

Development

Install development dependencies:

pip install -e ".[excel,gui]"
pip install pytest build twine

Run tests:

python -m pytest -q

Build distribution artifacts:

python -m build

Check package metadata:

python -m twine check dist/*

Publishing

Test the package on TestPyPI before publishing to PyPI:

python -m build
python -m twine upload --repository testpypi dist/*

After verifying installation from TestPyPI, publish the same version to PyPI:

python -m twine upload dist/*

License

SASLite is distributed under the MIT License. See LICENSE for details.