def fetch_data()

in jobs/desktop-mobile-mau-2020/desktop_mau/desktop_mau_dau.py [0:0]
36 lines of code
2 McCabe index (conditional complexity)

def fetch_data(project):
    bq_client = bigquery.Client(project=project)
    bq_storage_client = bigquery_storage_v1beta1.BigQueryStorageClient()

    IMG_DIR.mkdir(exist_ok=True)

    desktop_data = (
        bq_client.query(DESKTOP_QUERY)
        .result()
        .to_dataframe(bqstorage_client=bq_storage_client)
    )
    desktop_user_state_data = (
        bq_client.query(DESKTOP_USER_STATE_QUERY)
        .result()
        .to_dataframe(bqstorage_client=bq_storage_client)
    )

    desktop_data = pd.merge(
        desktop_data, desktop_user_state_data, on=["date", "country"]
    )
    desktop_data["year"] = pd.DatetimeIndex(desktop_data["date"]).year
    desktop_data["doy"] = pd.DatetimeIndex(desktop_data["date"]).dayofyear
    desktop_data["fakedate"] = [
        pd.to_datetime("20170101") + timedelta(days=x) for x in desktop_data["doy"]
    ]
    desktop_data["DAU_MA7d"] = desktop_data.groupby("country")["DAU"].transform(
        lambda x: x.rolling(window=7).mean()
    )
    desktop_data["MAU_base"] = desktop_data.groupby(["country", "year"]).MAU.transform(
        "first"
    )
    desktop_data["DAU_MA7d_base"] = desktop_data.groupby(
        ["country", "year"]
    ).DAU_MA7d.transform("first")
    desktop_data["dau_pcnt_Jan01"] = (
        desktop_data["DAU_MA7d"] / desktop_data["DAU_MA7d_base"]
    )
    desktop_data["mau_pcnt_Jan01"] = desktop_data["MAU"] / desktop_data["MAU_base"]

    return desktop_data