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Collector

Collect internet search volumes from the Google Trends timeline for health endpoint.

See the schema for more about the API. Only the getTimelinesForHealth endpoint is used here.

Parameters:

Name Type Description Default
scope_dir str

Directory containing the terms.txt and locations.txt files. See Specification.

 'scope'
key_dir str

Directory containing a .env file, to extract the GOOGLE_API_KEY variable from, if it is not already in the environment.

 '.'
terms_per_batch int

Maximum terms to include in each collection batch. Theoretically 30 is the API's max, but more than 1 seems to not work.

 1
wait_time float

Seconds to wait between each batch.

 0.1
version str

Version of the service API.

 'v1beta'
Specification

To process in batches, search terms and locations must be specified in separate files (terms.txt and locations.txt), stored in the scope_dir directory. These should contain 1 term / location code per line.

Collection Process

Initializing this class retrieves the Google API service, stores the developer key, and points to the scope directory.

The process_batches() method reads in the terms and locations, and collects them in batches over the specified time frame.

Results from each batch are stored in the batches property, which can be pulled from in case the process_batches process does not complete (such as if the daily rate limit is reached).

The collect() method collects a single batch, and can be used on its own.

Examples:

from gtrends_collection import Collector

# initialize the collector
collector = Collector()
Source code in gtrends_collection/collector.py 
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class Collector:
    """
    Collect internet search volumes from the Google Trends timeline for health endpoint.

    See the [schema](https://trends.googleapis.com/$discovery/rest?version=v1beta)
    for more about the API. Only the `getTimelinesForHealth` endpoint is used here.

    Args:
        scope_dir (str): Directory containing the `terms.txt` and `locations.txt` files.
            See Specification.
        key_dir (str): Directory containing a `.env` file, to extract the
            `GOOGLE_API_KEY` variable from, if it is not already in the environment.
        terms_per_batch (int): Maximum terms to include in each collection batch.
            Theoretically 30 is the API's max, but more than 1 seems to not work.
        wait_time (float): Seconds to wait between each batch.
        version (str): Version of the service API.

    Specification:
        To process in batches, search terms and locations must be specified in separate
        files (`terms.txt` and `locations.txt`), stored in the `scope_dir` directory.
        These should contain 1 term / location code per line.

    Collection Process:
        Initializing this class retrieves the Google API service, stores the
        developer key, and points to the scope directory.

        The `process_batches()` method reads in the terms and locations,
        and collects them in batches over the specified time frame.

        Results from each batch are stored in the `batches` property,
        which can be pulled from in case the `process_batches` process does not complete
        (such as if the daily rate limit is reached).

        The `collect()` method collects a single batch, and
        can be used on its own.

    Examples:
        ```python
        from gtrends_collection import Collector

        # initialize the collector
        collector = Collector()
        ```
    """

    # time to wait between requests
    _regular_wait_time = 0.1
    # time to wait after a `rateLimitExceeded` error
    _fallback_wait_time = 2
    batches: ClassVar[List[DataFrame]] = []

    scope_dir = "scope"
    max_terms = 1

    def __init__(
        self,
        scope_dir: str = "scope",
        key_dir: str = ".",
        terms_per_batch: int = 1,
        wait_time: float = 0.1,
        version: str = "v1beta",
    ):
        self._regular_wait_time = wait_time
        self.scope_dir = scope_dir
        self.max_terms = terms_per_batch

        key = getenv("GOOGLE_API_KEY")
        if not key and isfile(f"{key_dir}/.env"):
            with open(f"{key_dir}/.env", encoding="utf-8") as content:
                for pair in content.read().split("\n"):
                    name, value = pair.split("=")
                    if name.startswith("GOOGLE_API_KEY"):
                        key = value.strip()
                        break
        if not key:
            msg = "no API key found (GOOGLE_API_KEY environment variable)"
            raise RuntimeError(msg)

        self.service = discovery.build(
            "trends",
            version,
            discoveryServiceUrl=f"https://trends.googleapis.com/$discovery/rest?version={version}",
            developerKey=key,
        )

    def process_batches(
        self,
        start: Union[str, None] = None,
        end: Union[str, None] = None,
        resolution: str = "week",
        override_terms: Union[List[str], None] = None,
        override_location: Union[List[str], None] = None,
    ) -> DataFrame:
        """
        Processes collection batches from scope.

        Args:
            start (str | None): First date to collect from; `YYYY-MM-DD`.
            end (str | None): Last date to collect from; `YYYY-MM-DD`.
            resolution (str): Collection resolution; `day`, `week`, `month`, or `year`.
            override_terms (str): List of terms to collect instead of those in scope.
                Useful for changing collection order or filling out select terms.
            override_location (str): List of locations to collect from instead of those in scope.

        Examples:
            ```python
            # collect across all scope-defined terms and locations in 2024
            data = collector.process_batches("2024-01-01", "2024-12-31")
            ```

        Returns:
            A `pandas.DataFrame` of the combined results.
        """

        params: Dict[str, Union[List[str], str]] = {"timelineResolution": resolution}
        if start:
            params["time_startDate"] = start
        if end:
            params["time_endDate"] = end

        terms = override_terms if override_terms else read_scope(self.scope_dir, "terms")
        locations = override_location if override_location else read_scope(self.scope_dir, "locations")
        locations = {loc if len(loc) < 9 else loc.split("-")[2] for loc in locations}

        for term_set in range(0, len(terms), self.max_terms):
            for location in locations:
                batch_params = {
                    "terms": terms[term_set : (term_set + self.max_terms)],
                    **params,
                }
                batch_params[_location_type(location)] = location
                batch = self.collect(location, batch_params)
                self.batches.append(batch)
                sleep(self._regular_wait_time)

        data = concat(self.batches)
        return data

    def collect(
        self,
        location: str,
        params: Dict[str, Union[List[str], str]],
    ) -> DataFrame:
        """
        Collect a single batch.

        Args:
            location (str): Country (e.g., `US`), region (state; e.g., `US-AL`),
                or DMA (metro area; e.g., `US-AL-630` or `630`) code.
            params (dict[str, list[str] | str]): A dictionary with the following entries:

                * `terms` (list[str]): List of terms to collect.
                * `timelineResolution` (str): Collection resolution; `day`, `week`, `month`, or `year`.
                * `time_startDate` (str): First date to collect from; `YYYY-MM-DD`.
                * `time_endDate` (str): First date to collect from; `YYYY-MM-DD`.

        Examples:
            ```python
            # collect a small, custom sample
            data = collector.collect(
                "US-NY",
                {
                    "terms": ["cough", "/m/01b_21"],
                    "timelineResolution": "month",
                    "time_startDate": "2014-01-01",
                    "time_endDate": "2024-01-01",
                },
            )
            ```

        Returns:
            A `pandas.DataFrame` of the prepared results, with these columns:

                * `value`: Number indicating search volume.
                * `date`: Date the searches were recorded on.
                * `location`: Location code in which searches were recorded from.
                * `term`: The search term.
                * `retrieved`: Date retrived from the API.
        """

        try:
            # pylint: disable=E1101
            response = self.service.getTimelinesForHealth(**params).execute()
        except errors.HttpError as e:
            if e.status_code == 429:
                sleep(self._fallback_wait_time)
                return self.collect(location, params)
            raise e
        today = (datetime.datetime.now(datetime.timezone.utc)).strftime("%Y-%m-%d")
        data = []
        for line in response["lines"]:
            points = json_normalize(line["points"])
            points["date"] = to_datetime(points["date"], format="mixed").dt.strftime("%Y-%m-%d")
            points["location"] = location
            points["term"] = line["term"]
            points["retrieved"] = today
            data.append(points)
        return concat(data)

collect(location, params)

Collect a single batch.

Parameters:

Name Type Description Default
location str

Country (e.g., US), region (state; e.g., US-AL), or DMA (metro area; e.g., US-AL-630 or 630) code.

 required
params dict[str, list[str] | str]

A dictionary with the following entries:

  • terms (list[str]): List of terms to collect.
  • timelineResolution (str): Collection resolution; day, week, month, or year.
  • time_startDate (str): First date to collect from; YYYY-MM-DD.
  • time_endDate (str): First date to collect from; YYYY-MM-DD.
 required

Examples:

# collect a small, custom sample
data = collector.collect(
    "US-NY",
    {
        "terms": ["cough", "/m/01b_21"],
        "timelineResolution": "month",
        "time_startDate": "2014-01-01",
        "time_endDate": "2024-01-01",
    },
)

Returns:

Type Description
DataFrame

A pandas.DataFrame of the prepared results, with these columns:

  • value: Number indicating search volume.
  • date: Date the searches were recorded on.
  • location: Location code in which searches were recorded from.
  • term: The search term.
  • retrieved: Date retrived from the API.
Source code in gtrends_collection/collector.py 
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def collect(
    self,
    location: str,
    params: Dict[str, Union[List[str], str]],
) -> DataFrame:
    """
    Collect a single batch.

    Args:
        location (str): Country (e.g., `US`), region (state; e.g., `US-AL`),
            or DMA (metro area; e.g., `US-AL-630` or `630`) code.
        params (dict[str, list[str] | str]): A dictionary with the following entries:

            * `terms` (list[str]): List of terms to collect.
            * `timelineResolution` (str): Collection resolution; `day`, `week`, `month`, or `year`.
            * `time_startDate` (str): First date to collect from; `YYYY-MM-DD`.
            * `time_endDate` (str): First date to collect from; `YYYY-MM-DD`.

    Examples:
        ```python
        # collect a small, custom sample
        data = collector.collect(
            "US-NY",
            {
                "terms": ["cough", "/m/01b_21"],
                "timelineResolution": "month",
                "time_startDate": "2014-01-01",
                "time_endDate": "2024-01-01",
            },
        )
        ```

    Returns:
        A `pandas.DataFrame` of the prepared results, with these columns:

            * `value`: Number indicating search volume.
            * `date`: Date the searches were recorded on.
            * `location`: Location code in which searches were recorded from.
            * `term`: The search term.
            * `retrieved`: Date retrived from the API.
    """

    try:
        # pylint: disable=E1101
        response = self.service.getTimelinesForHealth(**params).execute()
    except errors.HttpError as e:
        if e.status_code == 429:
            sleep(self._fallback_wait_time)
            return self.collect(location, params)
        raise e
    today = (datetime.datetime.now(datetime.timezone.utc)).strftime("%Y-%m-%d")
    data = []
    for line in response["lines"]:
        points = json_normalize(line["points"])
        points["date"] = to_datetime(points["date"], format="mixed").dt.strftime("%Y-%m-%d")
        points["location"] = location
        points["term"] = line["term"]
        points["retrieved"] = today
        data.append(points)
    return concat(data)

process_batches(start=None, end=None, resolution='week', override_terms=None, override_location=None)

Processes collection batches from scope.

Parameters:

Name Type Description Default
start str | None

First date to collect from; YYYY-MM-DD.

 None
end str | None

Last date to collect from; YYYY-MM-DD.

 None
resolution str

Collection resolution; day, week, month, or year.

 'week'
override_terms str

List of terms to collect instead of those in scope. Useful for changing collection order or filling out select terms.

 None
override_location str

List of locations to collect from instead of those in scope.

 None

Examples:

# collect across all scope-defined terms and locations in 2024
data = collector.process_batches("2024-01-01", "2024-12-31")

Returns:

Type Description
DataFrame

A pandas.DataFrame of the combined results.
Source code in gtrends_collection/collector.py 
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def process_batches(
    self,
    start: Union[str, None] = None,
    end: Union[str, None] = None,
    resolution: str = "week",
    override_terms: Union[List[str], None] = None,
    override_location: Union[List[str], None] = None,
) -> DataFrame:
    """
    Processes collection batches from scope.

    Args:
        start (str | None): First date to collect from; `YYYY-MM-DD`.
        end (str | None): Last date to collect from; `YYYY-MM-DD`.
        resolution (str): Collection resolution; `day`, `week`, `month`, or `year`.
        override_terms (str): List of terms to collect instead of those in scope.
            Useful for changing collection order or filling out select terms.
        override_location (str): List of locations to collect from instead of those in scope.

    Examples:
        ```python
        # collect across all scope-defined terms and locations in 2024
        data = collector.process_batches("2024-01-01", "2024-12-31")
        ```

    Returns:
        A `pandas.DataFrame` of the combined results.
    """

    params: Dict[str, Union[List[str], str]] = {"timelineResolution": resolution}
    if start:
        params["time_startDate"] = start
    if end:
        params["time_endDate"] = end

    terms = override_terms if override_terms else read_scope(self.scope_dir, "terms")
    locations = override_location if override_location else read_scope(self.scope_dir, "locations")
    locations = {loc if len(loc) < 9 else loc.split("-")[2] for loc in locations}

    for term_set in range(0, len(terms), self.max_terms):
        for location in locations:
            batch_params = {
                "terms": terms[term_set : (term_set + self.max_terms)],
                **params,
            }
            batch_params[_location_type(location)] = location
            batch = self.collect(location, batch_params)
            self.batches.append(batch)
            sleep(self._regular_wait_time)

    data = concat(self.batches)
    return data