montecarlodata

library0.168.0pypypiunverified

The `montecarlodata` library provides a Python client for interacting with the Monte Carlo data observability platform. It allows users to programmatically manage metadata, incidents, and other aspects of their data environment. As of version 0.163.0, it offers a robust API wrapper. The library sees frequent updates, often multiple minor releases per week, indicating active development and continuous improvements.

devops data observability

pip install montecarlodata

package ↗homepage ↗

INSTALL

IMPORT

SIG · MONTECARLODATA

montecarlodata

devopspythonv0.168.0

Install

7.5s avg

Import

—

Disk

100MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.168.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.000s · 90.3MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 7.5s · import 0.000s · 109MB

100MB installed

● package 100MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

MonteCarloClient

✓ from montecarlodata import MonteCarloClient

✗ from montecarlodata import MonteCarloClient

This quickstart initializes the Monte Carlo client using API keys sourced from environment variables. It includes a basic check for missing credentials and demonstrates how to instantiate the `MonteCarloClient` to prepare for API interactions. Actual API calls are commented out as they require valid credentials and a configured Monte Carlo environment.

import os from montecarlodata.client import MonteCarloClient # It is highly recommended to store API keys securely in environment variables. # Example: export MONTE_CARLO_API_KEY="your_key" api_key = os.environ.get("MONTE_CARLO_API_KEY", "") api_secret = os.environ.get("MONTE_CARLO_API_SECRET", "") if not api_key or not api_secret: print("Warning: MONTE_CARLO_API_KEY and MONTE_CARLO_API_SECRET environment variables are not set.") print("Client initialization may fail without valid credentials.") # For a runnable quickstart, we proceed, but real applications would raise an error. try: client = MonteCarloClient(api_key=api_key, api_secret=api_secret) print("MonteCarloClient initialized successfully (credentials may be invalid).") # Example: Attempt to list some catalogs. This will likely fail with invalid/missing keys. # This demonstrates an API call but requires proper authentication and data setup. # catalog_info = client.metadata.list_catalogs() # print(f"Successfully fetched {len(catalog_info.items)} catalog entries.") except Exception as e: print(f"Error initializing MonteCarloClient or making an API call: {e}") print("Please ensure your MONTE_CARLO_API_KEY and MONTE_CARLO_API_SECRET are correctly set.")

Debug

Known issues

breakingThe client initialization method underwent significant changes around version `0.117.0`. Older versions might have used different parameter names or relied on implicit environment variables without direct passing. The current method requires `api_key` and `api_secret` to be passed explicitly to the `MonteCarloClient` constructor.

fix

Ensure you are passing `api_key` and `api_secret` directly as keyword arguments to `MonteCarloClient(api_key=..., api_secret=...)`.

affects: <0.117.0

gotchaIncorrect or missing API keys (`MONTE_CARLO_API_KEY`, `MONTE_CARLO_API_SECRET`) will prevent the client from authenticating and making successful API calls, leading to authentication errors.

fix

Verify that your Monte Carlo API key and secret are correctly set in your environment variables or passed directly to the client, and that they have the necessary permissions for the operations you are performing. Refer to Monte Carlo documentation for key generation.

affects: All versions

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Version history

0.168.0latest on PyPI · released May 29, 2026

Audit

Dependencies

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Agent activity

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Resources

packagehttps://pypi.org/project/montecarlodata/ ↗

homepagehttps://www.montecarlodata.com/ ↗