azure-monitor-query-py

name	azure-monitor-query-py
description	Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.
type	skill
created	2026-02-27T00:00:00.000Z
domain	cloud-infrastructure
category	azure
risk	unknown
source	community
tags	["skill","cloud-infrastructure","azure","monitor","query"]

Azure Monitor Query SDK for Python

Query logs and metrics from Azure Monitor and Log Analytics workspaces.

Installation

pip install azure-monitor-query

Environment Variables

# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>

# Metrics
AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>

Authentication

from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

Logs Query Client

Basic Query

from azure.monitor.query import LogsQueryClient
from datetime import timedelta

client = LogsQueryClient(credential)

query = """
AppRequests
| where TimeGenerated > ago(1h)
| summarize count() by bin(TimeGenerated, 5m), ResultCode
| order by TimeGenerated desc
"""

response = client.query_workspace(
    workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
    query=query,
    timespan=timedelta(hours=1)
)

for table in response.tables:
    for row in table.rows:
        print(row)

Query with Time Range

from datetime import datetime, timezone

response = client.query_workspace(
    workspace_id=workspace_id,
    query="AppRequests | take 10",
    timespan=(
        datetime(2024, 1, 1, tzinfo=timezone.utc),
        datetime(2024, 1, 2, tzinfo=timezone.utc)
    )
)

Convert to DataFrame

import pandas as pd

response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=))

 response.tables:
    table = response.tables[]
    df = pd.DataFrame(data=table.rows, columns=[col.name  col  table.columns])
    (df.head())