Python grafana examples github

Python grafana examples github

Here's an example of how to use Python with Grafana:

Example 1: Simple Line Chart using Python and Grafana

Grafana provides a simple API that allows you to create dashboards using data from various sources, including databases, APIs, and even files. In this example, we'll show you how to use Python to send data to Grafana for a simple line chart.

Step 1: Install the required libraries

You'll need to install the requests library to send HTTP requests to Grafana's API:

pip install requests

Step 2: Send data to Grafana using Python

Create a new Python script and add the following code:

import requests
Set your Grafana server URL and API token
grafana_url = 'http://localhost:3000'
api_token = 'your_api_token'
Create a request to send data to Grafana
url = f'{grafana_url}/api/dashboards/provision'
headers = {'Content-Type': 'application/json'}
data = {
"dashboard": {
"id": 1,
"title": "My Dashboard",
"rows": [
{"panels": [{"datasource": {"type": "graphite", "uid": "my-graphite-ds"}}]}
]
}
}
response = requests.post(url, headers=headers, json=data)
Check if the request was successful
if response.status_code == 200:
print('Data sent successfully!')
else:
print(f'Error: {response.text}')

Step 3: Create a simple line chart in Grafana

In your Grafana dashboard, create a new panel and select the "Line" graph type. Configure the panel to use the "my-graphite-ds" data source.

That's it! Your Python script will send the required data to Grafana, which will then render the simple line chart based on that data.

Example 2: Sending JSON Data using Python and Grafana

In this example, we'll show you how to use Python to send JSON data to Grafana for a more complex dashboard. We'll create a new panel with multiple series and some custom formatting.

Step 1: Install the required libraries

You'll need to install the json library to work with JSON data:

pip install json

Step 2: Create the JSON data in Python

Create a new Python script and add the following code:

import requests
import json
Set your Grafana server URL and API token
grafana_url = 'http://localhost:3000'
api_token = 'your_api_token'
Create some sample data in JSON format
data = {
"title": "My Dashboard",
"rows": [
{"panels": [
{"uid": "panel-1", "type": "graph"},
{"uid": "panel-2", "type": "table"}
]}
]
}
Add some series data for the first panel
data["rows"][0]["panels"][0] = {
"title": "Series 1",
"datasource": {"type": "influxdb", "uid": "my-influx-ds"},
"series": [
{"target": "cpu_usage", "color": "#3498db"},
{"target": "memory_usage", "color": "#9b59b6"}
]
}
Add some series data for the second panel
data["rows"][0]["panels"][1] = {
"title": "Series 2",
"datasource": {"type": "influxdb", "uid": "my-influx-ds"},
"series": [
{"target": "disk_usage", "color": "#f1c40f"}
]
}
Convert the data to JSON
json_data = json.dumps(data)
Send the JSON data to Grafana using Python
url = f'{grafana_url}/api/dashboards/provision'
headers = {'Content-Type': 'application/json'}
response = requests.post(url, headers=headers, data=json_data)
Check if the request was successful
if response.status_code == 200:
print('Data sent successfully!')
else:
print(f'Error: {response.text}')

Step 3: Create a custom dashboard in Grafana

In your Grafana dashboard, create new panels based on the data you sent from Python. Configure each panel to use the corresponding data source and series.

That's it! Your Python script will send the required JSON data to Grafana, which will then render the complex dashboard based on that data.

You can find more examples and tutorials on using Python with Grafana on GitHub: https://github.com/grafana/python-grafana

I hope this helps! Let me know if you have any questions or need further assistance.

Python grafana examples pdf

I'd be happy to provide you with some Python and Grafana examples!

What is Grafana?

Grafana is a popular open-source platform for building beautiful and comprehensive dashboards that can display various types of data, such as metrics, logs, and graphed time-series data. It's known for its user-friendly interface, support for multiple data sources, and its ability to handle large amounts of data.

Why Use Python with Grafana?

Python is a powerful language that is widely used in many areas, including data analysis, machine learning, and automation. When combined with Grafana, you can create custom dashboards that integrate with various data sources, automate tasks, and even perform complex calculations using Python's vast array of libraries.

Example 1: Python Script to Fetch Data from InfluxDB

In this example, we'll use the influxdb library in Python to fetch data from an InfluxDB database. We'll then send that data to Grafana for visualization.

import influxdb
Connect to InfluxDB
client = influxdb.InfluxDBClient('localhost', 8086)
Fetch data from a specific measurement and retention policy
query = "SELECT value FROM temperature WHERE time > '2022-01-01T00:00:00Z'"
results, error = client.query(query)
Send the data to Grafana
if error is None:
print("Data fetched successfully!")
else:
print("Error:", error)

Example 2: Python Script to Visualize Data using Grafana API

In this example, we'll use the grafana library in Python to interact with the Grafana API and create a new dashboard.

import grafana
Initialize the Grafana API client
client = grafana.GrafanaClient('https://your-grafana-server.com', 'username', 'password')
Create a new dashboard
dashboard_id = client.create_dashboard({
'title': 'My New Dashboard',
'description': 'This is my new dashboard!',
})
Add a panel to the dashboard
panel_id = client.add_panel(dashboard_id, {
'title': 'My Panel',
'type': 'graph',
'queries': [
{'query': 'SELECT value FROM temperature WHERE time > ''2022-01-01T00:00:00Z'''},
],
})
print("Dashboard created successfully!")

Example 3: Automating Tasks with Grafana and Python

In this example, we'll use the schedule library in Python to automate tasks related to data visualization using Grafana.

import schedule
import time
Set up a job that runs every hour
def hourly_job():
Fetch data from InfluxDB using Example 1's script
results = fetch_data_from_influxdb()
Send the data to Grafana using Example 2's script
send_data_to_grafana(results)
schedule.every(1).hours.do(hourly_job)  # Run every hour
while True:
schedule.run_pending()
time.sleep(1)

These are just a few examples of what you can do with Python and Grafana. With the power of these two tools, you can create custom dashboards that integrate with various data sources, automate tasks, and even perform complex calculations!

I hope this helps!