iristool

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.

Installation · Quick start · Dataset imports · CLI · Export · Examples · API · Testing

Authentication · Adding data sources · Publishing

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Features

What iristool does

• DataFrame to SQL: create a table, infer column types, and insert rows in batches.
• SQL to DataFrame: retrieve query results with optional bound parameters.
• Table export: save a table as CSV, XLSX, or JSON through pandas.
• 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.

Optional dataset integrations download Kaggle and Hugging Face datasets and import them through the same DataFrame API. Source libraries are installed only when requested.

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().

Supported import and export formats

Operation	Supported input or output	How it works
Core import	pandas DataFrame	db.df_to_table(df, "Table"); the core receives a DataFrame, not a file path
Local file import through pandas	CSV, TSV, XLSX, JSON, Parquet, and other formats pandas can read	Read the file with pandas first, then pass the resulting DataFrame to df_to_table(); some readers require additional dependencies
Kaggle import	CSV (.csv) and TSV (.tsv) only	import_kaggle() / iristool-kaggle; other file types in a downloaded dataset are ignored
Hugging Face import	Dataset splits loadable by datasets.load_dataset(), including tabular CSV, JSON, and Parquet sources	import_huggingface() / iristool-hf; the split must convert to a DataFrame with SQL-compatible scalar columns
Core export	CSV (.csv), Excel (.xlsx), JSON (.json)	db.export_table("Table", "output.csv"); the extension selects the pandas writer

CSV/JSON export needs only the base installation. XLSX import through pandas and XLSX export require the optional excel extra (openpyxl). Parquet import through pandas requires a compatible engine such as pyarrow, installed separately or provided by the Hugging Face dependencies.

Nested JSON objects, lists, images, and audio need preprocessing into scalar columns before SQL import and are not directly supported. JSON export defaults to an array of row objects.

Direct export_table() output is limited to the three extensions above: .tsv, .xls, and .parquet are not accepted. For other export formats, retrieve a DataFrame with db.fetch(...) and use pandas directly.

See dataset integrations and export examples.

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

python -m pip install iristool

With uv

Standalone environment (no pyproject.toml required)

To install from PyPI in a folder used for testing or scripts:

uv venv
source .venv/bin/activate
uv pip install iristool
# Or install the core plus both dataset sources:
# uv pip install 'iristool[integrations]>=0.3.1'

Application project (with pyproject.toml)

In a separate uv project that depends on iristool:

uv add iristool

This records the dependency in the application's pyproject.toml and lockfile. If starting a new application project, run uv init first.

Local iristool source development

If you cloned this repository, inside the cloned iristool source folder run:

uv sync                 # core only; creates or updates the local .venv
# uv sync --all-extras  # include both dataset sources and Excel support

This installs the local source for development, rather than testing the PyPI release. Do not run uv add iristool in this folder: a project cannot depend on itself.

See DEV.md for source-development instructions.

For Kaggle or Hugging Face support, select an extra as shown in dataset integrations. Both pip and uv install the
required dependencies automatically.

Import the class with:

from iristool import IRIStool

Quick start

Configure and load .env

Create a .env file in the folder where you run your scripts or CLI commands. If you cloned this repository, copy the template once:

cp .env.example .env

If you installed from PyPI, create .env yourself with the same settings:

IRIS_HOST=localhost
IRIS_PORT=9091
IRIS_NAMESPACE=USER
IRIS_USER=_SYSTEM
IRIS_PASSWORD=SYS
# Optional source tokens; uncomment only when needed:
KAGGLE_API_TOKEN='your-kaggle-token'
HF_TOKEN='your-hugging-face-read-token'

These connection settings match the repository's Docker development server.

For your own IRIS server, change the credentials, namespace, and SQL port.

See authentication for obtaining data source tokens.

Load .env before running any example or import in this terminal (bash/zsh):

set -a
source .env
set +a

This exports the settings to Python and CLI processes, including any uncommented source tokens. Repeat the loading step in each new terminal and after editing .env. iristool does not read the file automatically.

Import a DataFrame

After loading .env, run:

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.

Dataset integrations

Download datasets directly from external sources like Kaggle or Hugging Face and save them using the same IRIStool.df_to_table() implementation as a normal DataFrame import.

The integrations live in iristool.integrations.

Install optional support

Follow Installation, replacing iristool with the package specification for the sources you need:

Sources	Package specification
Kaggle	iristool[kaggle]
Hugging Face	iristool[huggingface]
All data sources	iristool[integrations]

For example: uv pip install 'iristool[integrations]>=0.3.1' or python -m pip install 'iristool[integrations]>=0.3.1'. Every extra includes the core library.

Datasets used in the examples

Dataset	Source	IRIS table names used below
Iris — uciml/iris (version 2)	Kaggle	KaggleIris, Flowers, FlowersTextID; default Iris
Iris — scikit-learn/iris	Hugging Face	HFIris, HFSample, IrisSample; default HF_iris_train
SST-2 — stanfordnlp/sst2	Hugging Face	Sentiment
Brazilian E-Commerce — olistbr/brazilian-ecommerce	Kaggle	One table per CSV, including olist_orders_dataset and olist_customers_dataset

These are the source dataset pages. SQL table names are local names chosen by iristool or by --table.

Import datasets from Python

This example downloads the Iris dataset from Kaggle (uciml/iris) and Hugging Face (scikit-learn/iris), importing each into its own IRIS table.

Use a running IRIS server and configure and load .env in the terminal where you launch Python. For the repository's development server, start IRIS with docker compose up -d --build first. Then run:

import os
from iristool import IRIStool
from iristool.integrations import import_kaggle, import_huggingface

with IRIStool(
    host=os.environ["IRIS_HOST"],
    port=int(os.environ["IRIS_PORT"]),
    namespace=os.environ["IRIS_NAMESPACE"],
    username=os.environ["IRIS_USER"],
    password=os.environ["IRIS_PASSWORD"],
) as db:
    import_kaggle(db, "uciml/iris", table="KaggleIris")
    import_huggingface(db, "scikit-learn/iris", table="HFIris")
    print(db.fetch("SELECT COUNT(*) AS RowsLoaded FROM SQLUser.HFIris"))
    print(db.describe_table("HFIris"))

Each example imports 150 rows. The helpers return a mapping such as {"SQLUser.HFIris": 150} and print the imported table and row count. The caller owns the connection: the integrations neither open nor close it. Use the same db afterwards for queries, metadata, indexes, or other imports.

You can also import from iristool.integrations.kaggle or iristool.integrations.huggingface; each module exposes import_dataset(db, ...).

Import datasets from the command line (CLI)

The CLI lets you download a dataset and save it to IRIS from your terminal, without writing a Python script.

iristool-kaggle imports Kaggle datasets; iristool-hf imports Hugging Face datasets.

Both use iristool's DataFrame import and SQL type inference.

1. Install the source support using the pip or uv commands under Install optional support.

2. Configure and load .env. Follow the setup instructions to create the file with your IRIS settings and optional source tokens. Make sure IRIS is running; for the repository's Docker server, use docker compose up -d --build and wait for startup. In the terminal where you will run the imports, load .env (bash/zsh):

set -a
source .env
set +a

The CLI reads IRIS_* from the environment. KaggleHub reads KAGGLE_API_TOKEN and Hugging Face reads HF_TOKEN when those tokens are set. If IRIS settings are omitted, the CLI defaults to localhost:1972, namespace USER, and _SYSTEM / SYS.

3. Run the import. After activating the environment installed with pip or uv pip install, use the CLI directly:

iristool-kaggle uciml/iris --file Iris.csv --table KaggleIris
iristool-hf scikit-learn/iris --table HFIris

If you installed with uv add in your own project, run:

uv run iristool-kaggle uciml/iris --file Iris.csv --table KaggleIris
uv run iristool-hf scikit-learn/iris --table HFIris

Each command downloads the data, creates its target table in SQLUser, and prints the imported row count. These examples normally need no source login; for private or gated data, follow the authentication guide.

An existing table causes an error. Add --replace only when you want to delete the existing table and its data:

iristool-kaggle uciml/iris --file Iris.csv --table KaggleIris --replace
iristool-hf scikit-learn/iris --table HFIris --replace

Show all available options without importing data:

iristool-kaggle --help
iristool-hf --help

Kaggle datasets

Browse Kaggle datasets, open a dataset, and copy its owner/dataset ID or its https://www.kaggle.com/datasets/... page URL. The integration downloads and extracts the data automatically through KaggleHub. Public datasets such as uciml/iris usually work without login. For private or consent-required data, follow the Kaggle authentication guide.

Every CSV/TSV is imported into its own table; other file types are ignored. Default table names come from file stems (Iris.csv → SQLUser.Iris). Select one file by its case-sensitive path from Kaggle's Data Explorer:

iristool-kaggle uciml/iris --file Iris.csv --table Flowers
iristool-kaggle uciml/iris/versions/2 --file Iris.csv --table Flowers --replace

--table requires one file. Without --file, KaggleHub downloads the whole dataset; with it, only the requested file is downloaded. Downloads are cached in ~/.cache/kagglehub; the environment variable KAGGLEHUB_CACHE changes the cache location (for example, export KAGGLEHUB_CACHE="$HOME/dataset-cache").

Example: import every CSV in a Kaggle dataset

The Olist dataset contains several CSV files. Load .env, then omit both --file and --table:

iristool-kaggle olistbr/brazilian-ecommerce

iristool imports every CSV/TSV into its own table in SQLUser, including:

Downloaded file	Target table
olist_orders_dataset.csv	SQLUser.olist_orders_dataset
olist_customers_dataset.csv	SQLUser.olist_customers_dataset
olist_order_items_dataset.csv	SQLUser.olist_order_items_dataset

The remaining CSVs are imported too; non-CSV/TSV files are ignored.

The Python equivalent, using the quick-start connection, is:

with IRIStool(**connection) as db:
    imported = import_kaggle(db, "olistbr/brazilian-ecommerce")
    print(imported)  # {"SQLUser.olist_customers_dataset": row_count, ...}

To import only orders, use iristool-kaggle olistbr/brazilian-ecommerce --file olist_orders_dataset.csv --table Orders. Existing targets cause an error; --replace deletes and recreates them. Imports across several tables are not atomic: if a later file fails, earlier imports remain. Each CSV is loaded into memory separately. No foreign keys are inferred between these tables.

For CSV parsing control in Python, pass pandas options directly:

with IRIStool(**connection) as db:
    import_kaggle(db, "olistbr/brazilian-ecommerce", file="olist_orders_dataset.csv", read_csv_kwargs={
        "dtype": {"customer_id": "string"},
        "parse_dates": ["order_purchase_timestamp"],
        "encoding": "utf-8",
    })

Here connection is the dictionary from the quick start. Do not pass pandas iterator/chunk options: this version imports complete DataFrames.

Hugging Face datasets

Browse Hugging Face datasets, open one, and copy its repository ID or https://huggingface.co/datasets/... page URL. The integration uses datasets.load_dataset(...).to_pandas(), then calls db.df_to_table(). It imports one split per call; the default split is train.

The datasets library handles supported underlying formats such as CSV, JSON, and Parquet. No manual file download or unzipping is needed.

Public, ungated datasets such as scikit-learn/iris work without login. For private or gated data, follow the Hugging Face authentication guide.

Example of a dataset requiring browser confirmation

LMSYS-Chat-1M (lmsys/lmsys-chat-1m) requires you to sign in and accept its conditions on the dataset page. To see the access error with an account that has not received access (or without a token), load your IRIS connection settings and run:

iristool-hf lmsys/lmsys-chat-1m --split 'train[:1]' --table AccessCheck

The command exits with status 1 before importing any tables and reports:

Import failed: Dataset processing failed: access denied. Open https://huggingface.co/datasets/lmsys/lmsys-chat-1m in your browser, sign in and confirm the terms or request access. Wait for approval if required. Then set HF_TOKEN (or log in with the source SDK) using the approved account and repeat the same command. No dataset tables were imported.

See browser approval and token setup for the steps to resolve access errors.

This example demonstrates the access restriction. LMSYS contains nested conversation values, so approval alone does not make it ready for direct SQL import: flatten or serialize them before using db.df_to_table(). Once access is granted, even a one-row slice may download/prepare the full dataset. Use the Iris examples below for a small, directly importable dataset.

Choose the subset/configuration and split shown in the dataset viewer:

iristool-hf scikit-learn/iris --split 'train[:10]' --table HFSample
# For a dataset with named configurations:
iristool-hf stanfordnlp/sst2 --config default --split validation --table Sentiment

Use --revision TAG_OR_COMMIT to pin a dataset revision. A split slice limits rows imported into IRIS; the loader may still download/prepare the full source. Datasets without a train split require --split. Datasets with multiple configurations may require --config.

The default table is HF_<dataset>_[<config>_]<split>, with punctuation replaced by underscores. For scikit-learn/iris, this is SQLUser.HF_iris_train. Use --table to choose a short explicit name, particularly for sliced splits. Class-label features keep the values returned by to_pandas() (often integer codes); the integration does not convert them into human-readable label names.

Options and behavior

Option / Python argument	Kaggle	Hugging Face
--table / table	One file's target table	Target split's table
--schema / schema	SQL schema, default SQLUser	Same
--replace / replace=True	Drop and recreate existing targets	Same
--file / file	Relative CSV/TSV path	—
read_csv_kwargs (Python only)	Options passed to pandas	—
--split / split	—	Split/slice, default train
--config / config	—	Configuration/subset
--revision / revision	Version is part of dataset ID	Branch, tag, commit

CLI examples — activate your environment and load .env first.

Import one Kaggle file with an explicit schema and table name:

iristool-kaggle uciml/iris --file Iris.csv --schema SQLUser --table Flowers

Import only the first 10 rows of a Hugging Face split from its main revision:

iristool-hf scikit-learn/iris --split 'train[:10]' --revision main --table IrisSample

Choose a named Hugging Face configuration and its validation split:

iristool-hf stanfordnlp/sst2 --config default --split validation --table Sentiment

These create new tables. To replace an existing target, add --replace; this deletes its previous data. main follows the repository's branch; use a commit ID with --revision when you need a fixed revision.

Python example — reuse the connection dictionary from the quick start. Pass pandas CSV options through read_csv_kwargs; here, Id is read as text instead of being inferred as an integer:

from iristool import IRIStool
from iristool.integrations import import_kaggle

with IRIStool(**connection) as db:
    import_kaggle(
        db,
        "uciml/iris",
        file="Iris.csv",
        table="FlowersTextID",
        schema="SQLUser",
        read_csv_kwargs={"dtype": {"Id": "string"}},
    )

iristool uses the resulting pandas dtypes to infer SQL types. In Python, replace=True has the same destructive replacement behavior as --replace.

Export a table

Save a table as CSV, Excel, or JSON

Command line — load .env, then select the table, schema, and output file:

iristool-export KaggleIris iris.csv --schema SQLUser
iristool-export KaggleIris iris.xlsx --schema SQLUser
iristool-export KaggleIris iris.json --schema SQLUser

The schema defaults to SQLUser. The output extension selects the format. Connection settings come from IRIS_*, just like the import commands. Use --overwrite to replace an existing output file; the source table stays unchanged. Errors print Export failed: ... and exit with status 1.

iristool-export KaggleIris iris.csv --schema SQLUser --overwrite
iristool-export --help
# From this source checkout, install the new command and run it:
uv sync --all-extras
uv run --all-extras iristool-export KaggleIris iris.csv --schema SQLUser

Python

export_table() is part of the core. It reads the whole table into a pandas DataFrame and selects the writer from the output extension:

with IRIStool(**connection) as db:
    db.export_table("KaggleIris", "iris.csv")
    db.export_table("KaggleIris", "iris.xlsx", sheet_name="Iris")
    db.export_table("KaggleIris", "iris.json", indent=2)

Use the KaggleIris table imported above, or replace its name with your table. table_schema="SQLUser" is the default; pass another schema separately. The method returns a pathlib.Path. Output directories must already exist. CSV uses UTF-8, CSV/XLSX omit the pandas index, and JSON is an array of row objects with ISO dates and JSON null for missing values.

CSV and JSON need no additional dependency. For XLSX, install Excel support:

python -m pip install 'iristool[excel]'
# Or in an activated standalone uv environment:
uv pip install 'iristool[excel]'
# Local development before release:
uv sync --all-extras

Combine extras with 'iristool[integrations,excel]' to include both source SDKs and Excel. The integrations extra alone still includes only source SDKs.

An existing output file causes an error. To replace it explicitly:

with IRIStool(**connection) as db:
    db.export_table("KaggleIris", "iris.csv", overwrite=True, sep=";")

Extra keyword arguments go to pandas to_csv, to_excel, or to_json. The entire table must fit in memory; no row ordering is guaranteed. For a filtered or ordered export, use db.fetch(...) and the pandas writer directly. An output failure can leave a partial file; overwrite=True replaces the previous file once writing starts. This does not change the source table.

Examples

Python 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

Table policies, inferred SQL types, and transactions

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

Available methods

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
export_table(table_name, path, table_schema="SQLUser", overwrite=False, **kwargs)	Export a table to CSV/XLSX/JSON; refuse overwrites by default
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

The commands in this section test the local source checkout. To test the published package instead, use the isolated PyPI environment described in installation and the dataset import examples above.

Local setup, Docker, and test commands

To add another dataset source, follow the integration development guide.

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.

Source-specific live tests remain skipped unless enabled separately; for more information see dataset integration tests.

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.

Test the integrations

From this iristool folder, run the suite with both extras installed:

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

Unit tests use mocked downloads/connections and real pandas DataFrames. Tests also check missing optional dependencies and preservation of numeric/null values. Without extras, source-specific unit tests are skipped and core tests still run.

For all core and source integration tests against the bundled Docker IRIS:

docker compose up -d --build
# Wait until IRIS has started, then:
IRIS_PORT=9091 IRIS_INTEGRATION=1 KAGGLE_IRIS_INTEGRATION=1 HF_IRIS_INTEGRATION=1 \
  uv run --all-extras python -m unittest discover -s tests -v -b

The source tests require internet access, download the public Iris dataset from each provider, verify 150 rows and inferred integer/double/text SQL types, and remove their uniquely named test tables. No source token is normally needed. Enable only KAGGLE_IRIS_INTEGRATION=1 or HF_IRIS_INTEGRATION=1 to test one provider. On PowerShell, set the environment variables with $env:NAME = "value" before running the same Python test command without the inline assignments.

Troubleshooting

Common problems and solutions

Symptom	What to check
Integrations missing after pip install	Install iristool 0.3.1 or newer with the desired source extra
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

Repository files

.
├── src/iristool/
│   ├── __init__.py         Public IRIStool import
│   ├── core.py             DataFrame and SQL helpers
│   └── integrations/       Optional Kaggle / Hugging Face downloads and CLI
├── tests/
│   ├── test_iristool.py    Unit tests
│   └── test_iris_integration.py  Opt-in live IRIS tests
├── .github/workflows/publish.yml  Tests, build, and tag-triggered PyPI publishing
├── docs/
│   ├── authentication.md   Kaggle and Hugging Face login and tokens
│   ├── DEV.md              Guide to adding dataset sources
│   └── 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.