iristool 0.1.2

iristool

Load pandas DataFrames into InterSystems IRIS and query them with Python.

iristool is a small Python library for moving data between pandas and InterSystems IRIS. Create a SQL table from a DataFrame, run parameterized queries, and manage rows, tables, views, and indexes through one connection object.

Python runs in your own environment and connects to an existing IRIS server through the official intersystems-irispython DB API. Install the library on your laptop, in a notebook, or in a Python application; no Python packages need to be installed inside IRIS.

Features · Installation · Quick start · Examples · API · Testing · Publishing

---

Features

• DataFrame to SQL: create a table, infer column types, and insert rows in batches.
• SQL to DataFrame: retrieve query results with optional bound parameters.
• Data handling: normalize column names and convert pandas missing values to SQL NULL.
• Import control: choose whether to fail, skip, or replace an existing table; supply explicit types, a primary key, constraints, and indexes.
• SQL helpers: insert and update rows, inspect metadata, and manage tables, columns, views, and indexes.
• Small dependency list: pandas and the official InterSystems Python SDK.

The library handles the IRIS part of a data workflow. Read a CSV with pandas, download data with another library, or construct a DataFrame yourself, then pass it to df_to_table().

Installation

Requirements

• Python 3.10 or newer, on a platform supported by the InterSystems SDK.
• A reachable InterSystems IRIS 2022.1 or newer server supporting the Python DB API.
• An IRIS account with SQL permissions for the operations you need.

With pip

Once the first release is published on PyPI:

python -m pip install iristool

First release status: this repository is prepared for PyPI publication. Until the first upload succeeds, install from the source checkout instead.

From the folder containing pyproject.toml:

python -m pip install .

For development, use python -m pip install -e .. Both installation methods install the runtime dependencies automatically. Import the class with:

from iristool import IRIStool

Quick start

Set your connection details. These example credentials are for the included local Docker development server:

export IRIS_HOST="localhost"
export IRIS_PORT="9091"
export IRIS_NAMESPACE="USER"
export IRIS_USER="_SYSTEM"
export IRIS_PASSWORD="SYS"

For another server, use its credentials and SuperServer port, usually 1972.

import os
import pandas as pd
from iristool import IRIStool

connection = {
    "host": os.getenv("IRIS_HOST", "localhost"),
    "port": int(os.getenv("IRIS_PORT", "1972")),
    "namespace": os.getenv("IRIS_NAMESPACE", "USER"),
    "username": os.environ["IRIS_USER"],
    "password": os.environ["IRIS_PASSWORD"],
}

people = pd.DataFrame({
    "Name": ["Ada", "Grace"],
    "Age": [36, 40],
})

with IRIStool(**connection) as db:
    db.df_to_table(people, "People")
    result = db.fetch("SELECT Name, Age FROM SQLUser.People ORDER BY Age")
    print(result)

This creates SQLUser.People and inserts two rows. The connection closes when the with block ends. A second run raises an error because the table exists; choose an existing-table policy below to make repeated imports explicit.

USER is the IRIS namespace selected when connecting. SQLUser is the default SQL schema used by the table helpers. Pass table_schema separately from the table name.

Examples

The following examples reuse the connection dictionary from the quick start.

Import a CSV

with IRIStool(**connection) as db:
    db.df_to_table(pd.read_csv("people.csv"), "PeopleCSV", batch_size=1000)

Pandas reads the file; iristool creates the table and inserts its contents. The entire DataFrame must fit in memory. batch_size limits the number of rows sent to IRIS per batch, rather than streaming the input file.

Control types and keys

sales = pd.DataFrame({
    "Order Number": [1, 2],
    "Customer": ["Ada", "Grace"],
    "Total": [19.50, 42.00],
})

with IRIStool(**connection) as db:
    db.df_to_table(
        sales,
        "Sales",
        column_types={
            "Order_Number": "INT",
            "Customer": "VARCHAR(100)",
            "Total": "DECIMAL(12,2)",
        },
        primary_key="Order_Number",
        indices=[{"column": "Customer", "type": "index"}],
    )

When supplied, column_types must cover every DataFrame column. Its keys can use the original or normalized names. Use explicit SQL declarations when your schema needs fixed lengths, decimal precision, or other types that inference cannot decide for you.

Query with parameters

with IRIStool(**connection) as db:
    adults = db.fetch(
        "SELECT Name, Age FROM SQLUser.People WHERE Age >= ?",
        parameters=[18],
    )
    print(adults)

Use ? placeholders for values. fetch() returns a pandas DataFrame; a query with no rows returns an empty DataFrame.

Insert and update rows

with IRIStool(**connection) as db:
    db.insert_row("People", {"Name": "Linus", "Age": 28})
    inserted = db.insert_many("People", [
        {"Name": "Margaret", "Age": 32},
        {"Name": "Barbara", "Age": 35},
    ])
    updated = db.update("People", {"Age": 37}, filters={"Name": "Ada"})
    print(inserted, updated)

insert_many() and update() return affected row counts. Each mutating helper commits its own successful operation and rolls back on failure; the connection context manager closes the connection and does not group multiple helper calls into one transaction.

Inspect a table

with IRIStool(**connection) as db:
    print(db.table_exists("People"))
    print(db.describe_table("People"))
    print(db.show_namespace_tables(table_schema="SQLUser"))

Import behavior

Existing tables

Options passed to df_to_table()	Behavior when the table exists
Default options	Raise an error
exist_ok=True	Skip the import and keep existing data
exist_ok=True, drop_if_exists=True	Drop the table, recreate it, and import the new data

Replacement deletes the previous table and its data. Set drop_related_views=True only when you also want dependent views removed. To add rows to an existing table, use insert_row() or insert_many(); df_to_table() does not append.

Inferred SQL types

DataFrame values or dtype	Inferred IRIS type
Boolean	BIT
Integers within the signed 32-bit range	INT
Larger integers within the signed 64-bit range	BIGINT
Integers outside the signed 64-bit range	DECIMAL(20,0)
Floating point	DOUBLE
Datetimes	DATETIME; midnight-only values become DATE
Datetimes all dated January 1, 1970	TIME, unless the midnight rule applies
Python date / time values	DATE / TIME
Text up to 255 characters	VARCHAR(255)
Longer text	CLOB
All-null object column	VARCHAR(255)

Nullable numeric and Boolean columns retain their dtype during inference. None, pd.NA, NaN, and NaT are inserted as SQL NULL. An empty DataFrame with named columns creates an empty table.

For object columns, inference recognizes dates/times or falls back to text length. Convert mixed values and structured objects before insertion. Normalize timezone-aware timestamps to the intended timezone and remove timezone information before importing.

Names and transactions

• DataFrame column names are trimmed; spaces and dots become underscores: Total Sales → Total_Sales.
• Identifiers must start with an ASCII letter and contain only ASCII letters, digits, or underscores. Column names that collide after normalization, including case differences, are rejected.
• Pass "People" and table_schema="SQLUser" separately, rather than "SQLUser.People" as a table name. Avoid SQL reserved words for table/schema names.
• All insertion batches from a DataFrame share one insertion transaction. If any row fails, the inserted rows are rolled back.
• Table creation and replacement are committed before insertion. A failed import can leave an empty new table; replacement is not atomic.
• SQL queries, type declarations, and constraints are SQL supplied by the caller. Row values use bound parameters.

fetch() uses pandas' normal dtype inference. A result containing large integers and nulls may become a floating-point column; select or cast appropriately when exact integer representation in the returned DataFrame matters.

API reference

Most table helpers default to table_schema="SQLUser". See the method docstrings for complete signatures and options.

Method	Purpose
df_to_table(df, table_name, ...)	Create a table and import a DataFrame
fetch(sql, parameters=None)	Return query results as a DataFrame
insert_row(table_name, values)	Insert one dictionary of values
insert_many(table_name, rows)	Insert dictionaries in a batch
update(table_name, new_values, filters)	Update matching rows
update_many(table_name, updates)	Apply a list of (new_values, filters) pairs
create_table(table_name, columns, ...)	Create a table from SQL type declarations
add_columns(table_name, new_columns)	Add columns to an existing table
drop_table(table_name, ...)	Drop a table, or a view with view_or_table="view"
table_exists(table_name)	Check whether a table or view exists
describe_table(table_name)	Return column and index metadata
create_index(index_name, table_name, column_name, ...)	Create an index
index_exists(table_name, index_name)	Check index metadata
create_view(view_name, sql, ...)	Create a SQL view
views_using_table(table_name)	List dependent views
show_namespace_tables(...)	List tables, with optional name/schema filters
show_namespace_schemas()	List schemas containing base tables
close()	Close the connection explicitly

Development and testing

Use uv with Python 3.10 or newer. uv run prepares the project environment and runs Python; unittest is the test runner. No virtual-environment activation is needed.

uv run python -m unittest discover -s tests -v -b

The regular suite runs unit tests and skips live database tests. -v shows each test and its result. -b buffers output from passing tests, hiding expected error messages; if a test fails, its captured output is displayed.

Run against Docker IRIS

docker compose up -d --build
docker compose logs --tail 20
# Wait until IRIS reports that it has started.
IRIS_INTEGRATION=1 uv run python -m unittest discover -s tests -v -b

The included development server uses:

Setting	Value
SQL connection	localhost:9091
Management Portal	localhost:9092
Namespace	USER
Development account	_SYSTEM / SYS
Durable database files	storage/

Live tests read the IRIS_HOST, IRIS_PORT, IRIS_NAMESPACE, IRIS_USER, and IRIS_PASSWORD variables, with the above defaults. They create uniquely named test tables/views and remove them after each test. The startup script unexpires development passwords.

On PowerShell, set $env:IRIS_INTEGRATION = "1", then run the same uv run command without the IRIS_INTEGRATION=1 prefix.

Expected result: Ran 22 tests followed by OK, with no skipped tests. Some tests deliberately attempt a duplicate table or primary key to verify that errors propagate and inserted rows roll back. Their error messages are expected, not failed tests. Unit-test names such as SQLUser.Data refer to mocked operations and do not create real tables.

Inspect sample data in the IRIS Portal

The automated tests clean up their tables, so they leave nothing to browse. To see a lasting example, keep IRIS running and execute:

uv run python examples/portal_demo.py

This imports three rows, verifies names, nulls, and Unicode, prints the result, and leaves SQLUser.IRISTool_Demo in IRIS. Each run replaces that demo table and its previous data. It uses the same connection environment variables and defaults as the tests.

Open the Management Portal, navigate to System Explorer → SQL, select namespace USER (or your configured IRIS_NAMESPACE), and execute:

SELECT Sample_Number, Name, Score, Active, Note
FROM SQLUser.IRISTool_Demo
ORDER BY Sample_Number;

You should see Ada, Grace, and Linus, including a missing score and a missing Boolean value. To remove the demo, execute DROP TABLE SQLUser.IRISTool_Demo; in the SQL page. When finished, run docker compose stop; the demo data persists in storage/ when IRIS is restarted.

Coverage includes mixed DataFrame types, nulls, Unicode and long text, large integers, batching, empty imports, replacement, row helpers, metadata, ordinary indexes/views, and rollback after constraint failures.

Troubleshooting

Symptom	What to check
pip install iristool finds no package	Before the first PyPI release, install the source checkout with python -m pip install .
Connection refused	Check that IRIS is running and the SuperServer port is reachable; this Compose setup maps it to 9091
Authentication failure	Check the account, password, namespace access, and password expiration
Table already exists	Choose skip/replacement explicitly, or use row helpers to insert into it
Invalid identifier or colliding columns	Rename columns before importing; follow the naming rules above
Constraint failure in a batch	Correct the offending data; inserted rows roll back, but the created table remains
Timezone-aware datetime rejected	Convert to the desired timezone, then remove timezone information

Project layout

.
├── src/iristool/
│   ├── __init__.py         Public IRIStool import
│   └── core.py             DataFrame and SQL helpers
├── tests/
│   ├── test_iristool.py    Unit tests
│   └── test_iris_integration.py  Opt-in live IRIS tests
├── docs/
│   └── publish.md          PyPI release instructions using uv
├── examples/
│   └── portal_demo.py      Persistent sample table for Portal inspection
├── pyproject.toml         Package metadata and build configuration
├── requirements.txt       Runtime dependencies for pip
├── Dockerfile             Development IRIS image
├── docker-compose.yml     Local test server and port mappings
├── docker-entrypoint.sh   IRIS startup wrapper
├── iris.script            Development password initialization
├── .dockerignore          Docker build exclusions
├── .gitignore             Git exclusions
├── README.md
└── LICENSE

The Python wheel includes the library, metadata, and license. Docker files and durable database data are not part of the installed library.

Credits and license

Created by Pietro Di Leo, based on the Python utilities from iris_tool_and_data_manager.

Released under the MIT License. Copyright © 2025–2026 Pietro Di Leo.

See LICENSE for the full license text. A copy is included in both the Python wheel and source distribution.