Charting PagerDuty Incidents over Time (using pandas)

Brian ONeillOctober 7th, 2018Last Updated: October 5th, 2018

2 33 1 minute read

We churn out charts for board meetings to show the health of our system (uptime, etc.). Historically, we did that once per quarter, manually. Recently, I endeavored to create a live dashboard for the same information, starting with production incidents over time. We use PagerDuty to alert on-call staff. Each incident is stored in PagerDuty, which is queryable via the PagerDuty API. From there, it is easy enough to transform that JSON into a matplotlib chart using pandas:

First, we grab the data:

from datetime import datetime, timedelta
import requests

%matplotlib inline

api_url = "https://api.pagerduty.com/incidents"
headers = {
    'Accept': 'application/vnd.pagerduty+json;version=2',
    'Authorization': 'Token token=YOUR_TOKEN'
}


today = datetime.today()
until = today.replace(day=1)

def get_month(since, until):
    current_date = since
    total_incidents = 0
    while current_date < until:
        next_date = current_date + timedelta(days=7)
        if (next_date > until):
            next_date = until
        url = api_url + "?since={}&until={}&time_zone=UTC&limit=100".format(current_date.date(), next_date.date())
        response = requests.get(url, headers=headers)
        incidents = response.json()['incidents']
        total_incidents = total_incidents + len(incidents)
        current_date = next_date
    return total_incidents
        

# Lookback over twelve months
incidents_per_month = {}
for delta in range(1,12):
    since = (until - timedelta(days=1)).replace(day=1)
    num_incidents = get_month(since, until)
    incidents_per_month[str(since.date())] = num_incidents
    print "{} - {} [{}]".format(since.date(), until.date(), num_incidents)
    until = since

At this point,

incidents_per_month = { 2018-07-01": 13, "2018-08-01":5 ... }

From there, it is just a matter of plotting the values using pandas:

import pandas as pd
import numpy as np
import matplotlib
import seaborn as sns

data = pd.DataFrame(incidents_per_month.items(), columns=['Month', 'Incidents'])
data = data.sort_values(by=["Month"], ascending=True)
data.plot(kind='bar', x='Month', y='Incidents', color="lightblue")

And voila, there you have it:

Published on Java Code Geeks with permission by Brian ONeill, partner at our JCG program. See the original article here: Charting PagerDuty Incidents over Time (using pandas)

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