Integrations · Programming languages

Load job postings data into pandas

Load job posts into a pandas DataFrame with flattened company fields, then save as Parquet for analysis or modeling.

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Steps

You need: Python 3.9+, pip install requests pandas pyarrow and a jobdata API key.

Set your API key and filters.
json_normalize flattens nested objects, so the company name becomes company_name.
Save to Parquet (or CSV) and analyze, chart or feed it into a model.

pandas · python

import requests

API_KEY = "YOUR_API_KEY"


def fetch_jobs(**params):
    """Yield every job for the given filters, following pagination."""
    url = "https://jobdataapi.com/api/jobs/"
    params = {"page_size": 1000, **params}
    headers = {"Authorization": f"Api-Key {API_KEY}"}
    while url:
        resp = requests.get(url, headers=headers, params=params, timeout=60)
        resp.raise_for_status()
        data = resp.json()
        yield from data["results"]
        url, params = data["next"], None  # "next" already carries the filters


import pandas as pd

jobs = list(fetch_jobs(max_age=30, country_code="US", title="data engineer"))
df = pd.json_normalize(jobs, sep="_")
df["published"] = pd.to_datetime(df["published"])
df.to_parquet("jobs.parquet", index=False)

print(df.groupby("company_name").size().sort_values(ascending=False).head(10))

Notes

Add description_md=true for Markdown descriptions, or turn descriptions off with description_off=true for smaller, faster responses.
Run it daily with max_age=1 to pick up new job posts; on access lite, drop max_age - the feed covers the past 90 days and upserting by id keeps it free of duplicates.

Ready to load real data?

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