Loading Amplitude Events Data into Pandas DataFrame

Amplitude allows you to export the events data from their platform using their Export API. Let’s see how we can get that exported data loaded into a Pandas dataframe.

First, get the Amplitude API credentials for your project.

API_KEY = "xxxx"
SECRET_KEY = "yyyy"

The data is exported based on a date range, but there are limitations on the size of the data which can be exported in a single API call. So depending on the size of the data, we might need to make multiple API calls.

For this post, I’m assuming a day’s worth of data falls within this limit. Let’s export the data for the past 7 days.

from datetime import date, timedelta

end_date = date.today()
start_date = end_date - timedelta(days=NUM_DAYS)

Since the API response is a zip file, let’s create a temp directory to store them.

from pathlib import Path

raw_files_path = Path.cwd().joinpath("./temp/amplitude")
Path.mkdir(raw_files_path, parents=True, exist_ok=True)

As mentioned before, we’re splitting the specified date range into multiple days, and making an API request for each day. The date range for the API request is specified using the start and end query parameters formatted in the YYYYMMDDTHH format. We use the API_KEY and SECRET_KEY from before, encode them and send them along as the Authorization header. The response is then written to a ZIP file in the temp directory.

import requests
from base64 import b64encode
from datetime import timedelta

url = "https://amplitude.com/api/2/export"
token = b64encode(f"{API_KEY}:{SECRET_KEY}".encode("utf-8")).decode("utf-8")
headers = {"Authorization": f"Basic {token}"}

for idx in range((end_date - start_date).days):
    current_date = end_date - timedelta(days=idx)
    current_date = current_date.strftime("%Y%m%d")
    params = {"start": f"{current_date}T00", "end": f"{current_date}T23"}
    res = requests.get(url, params=params, headers=headers)
    open(raw_files_path.joinpath(f"{current_date}.zip"), "wb").write(res.content)

Once we’re done downloading the ZIP files, we iterate through them and extract the contents.

import shutil

for path in list(raw_files_path.iterdir()):
    if path.suffix != ".zip":
    shutil.unpack_archive(path, raw_files_path)

Finally, we iterate through the compressed JSON files and load them into dataframe df.

import pandas as pd

df = None
for path in list(raw_files_path.iterdir()):
    if not path.is_dir():
    for file_path in list(path.iterdir()):
        if not file_path.name.endswith(".json.gz"):
        if df is None:
            df = pd.read_json(file_path, lines=True)
            new_df = pd.read_json(file_path, lines=True)
            df = pd.concat([df, new_df])