---
title: "How to Export Datadog Metrics for Exploration in Jupyter Notebooks"
description: "Datadog dashboards can constrain metric analysis because they offer a limited set of visualizations and lack tooling for complex work such as statistical modeling. They also aggregate data over wider time ranges: the examples contrast one-second metrics across 15 minutes with two-hour intervals across 30 days, potentially hiding interesting events. The guide uses Datadog’s REST API and Python in Jupyter notebooks, requiring an API key, an APP key, and a metric query such as CPU utilization over time. It defines a time range, splits it into buckets whose width is controlled by time_delta, requests each window in a loop, appends the results, and converts them into a dataframe. The resulting data can be examined with tools such as seaborn; a KDE plot is used to inspect the distribution of system CPU utilization, with the exported data offering greater granularity than the dashboard example."
---

# How to Export Datadog Metrics for Exploration in Jupyter Notebooks

[Shopify](https://yomu.fyi/company/shopify) · 2023-10-18 · Nov 29, 2022

**Type:** Tutorial

## Summary

Datadog dashboards can constrain metric analysis because they offer a limited set of visualizations and lack tooling for complex work such as statistical modeling. They also aggregate data over wider time ranges: the examples contrast one-second metrics across 15 minutes with two-hour intervals across 30 days, potentially hiding interesting events. The guide uses Datadog’s REST API and Python in Jupyter notebooks, requiring an API key, an APP key, and a metric query such as CPU utilization over time. It defines a time range, splits it into buckets whose width is controlled by time\_delta, requests each window in a loop, appends the results, and converts them into a dataframe. The resulting data can be examined with tools such as seaborn; a KDE plot is used to inspect the distribution of system CPU utilization, with the exported data offering greater granularity than the dashboard example.

## Context

Datadog provides limited visualization options and lacks tooling for complex analysis such as statistical modeling. Its fixed-width dashboards also show less granular data over longer time ranges, where aggregation can smooth or hide interesting events.

## Approach / What changed

Use Datadog’s REST API from Python in a Jupyter notebook. Provide an API key, an APP key, and a metric query; divide the requested time range into buckets controlled by time\_delta, query each bucket, collect the responses, and convert them into a dataframe for local analysis and visualization.

## Takeaways

- Datadog dashboard examples show one-second metrics over 15 minutes but two-hour intervals over 30 days, reducing granularity across the longer window.
- The extraction process queries bucketed time windows and appends the returned data before converting the collected lists into a dataframe.
- Seaborn KDE plots can be applied to the exported data to examine the distribution of system CPU utilization.

**Tags:** [Monitoring](https://yomu.fyi/topic/monitoring), [Python](https://yomu.fyi/topic/python), [REST APIs](https://yomu.fyi/topic/rest-api)

- Source: [Shopify](https://shopify.engineering/export-datadog-metrics-in-jupyter-notebooks)
- Source URL: https://shopify.engineering/export-datadog-metrics-in-jupyter-notebooks
- Ingested by Yomu: 2026-08-30T13:37:20.901Z

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