Writing data to csv

You may want a local copy of data instead of querying the API again. Reasons include analyzing it later or using a different tool outside of Python.

This guide shows how to write measurements data to a .csv file for later use using the OpenAQ SDK.

Getting measurements data

As an example, the code below fetches all hourly PM_2.5 meausrement values for location ID 2178 reported in December of 2025. See Querying measurements for a closer look at how this workflow finds sensor IDs and queries measurements.

from openaq import OpenAQ
from datetime import datetime

client = OpenAQ(api_key="replace-with-valid-openaq-api-key")
sensors_resp = client.locations.sensors(2178)
pm25_sensors_id = 0

for sensor in sensors_resp.results:
    if "pm25" in sensor.name:
        pm25_sensors_id = sensor.id
        break

response = client.measurements.list(pm25_sensors_id,
                                    data="hours",
                                    datetime_from=datetime(2025, 12, 1),
                                    datetime_to=datetime(2025, 12, 31)
                                    )

measurements = response.results

Caution

This example uses a placeholder API key `replace-me-with-a-valid-key` and will not work as-is. Replace this value with a valid OpenAQ API key.

Writing to csv

With the measurements data fetched, write it to a .csv file using either Python’s built-in csv module or Pandas.

csv module

The Python csv module ships with the standard library, so the OpenAQ Python SDK is the only dependency required.

    import csv

    # Assumes `measurements` is the results list from a prior client.measurements.list() call

    # Choose column names in .csv output
    headers = ["timestamp", "parameter", "unit", "value"]

    # Write each measurement data into .csv
    with open("csv_pm25_2178_2025_12.csv", "w", newline="") as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        writer.writeheader()

        for item in measurements:
            row = {
                "timestamp": item.period.datetime_to.local,
                "parameter": item.parameter.name,
                "unit": item.parameter.units,
                "value": item.value
            }

            writer.writerow(row)

Pandas

This approach requires Pandas and converting the MeasurementsResponse object into a Pandas DataFrame. Because Pandas flattens nested results automatically, columns can be selected or filtered for export instead of extracting fields manually.

import pandas as pd

# Assumes `response` is the MeasurementsResponse object from a prior client.measurements.list() call

data = response.dict()
df = pd.json_normalize(data["results"])

# Rename columns as desired
df = df.rename(columns={
    "period.datetime_to.local": "timestamp",
    "parameter.name": "parameter",
    "parameter.units": "unit"
    })

# Filter columns to write to .csv
df[["timestamp", "parameter", "unit", "value"]].to_csv(
    "pd_pm25_2178_2025_12.csv", index=False
    )

# Close the connection when done
client.close()

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