Core - Cendat

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# Core API Reference

This module contains the main classes for interacting with the Census Bureau API.

## CenDatHelper

### `cendat.CenDatHelper`

#### `CenDatHelper`

A helper for exploring and retrieving data from the US Census Bureau API.

This class provides a chainable, stateful interface to list, select, and combine datasets, geographies, and variables to build and execute API calls.

Attributes:

Name | Type | Description
--- | --- | ---
`years` | `List[int]` | The primary year or years of interest for data queries.
`products` | `List[Dict]` | The currently selected data product details.
`geos` | `List[Dict]` | The currently selected geographies.
`groups` | `List[Dict]` | The currently selected variable groups.
`variables` | `List[Dict]` | The currently selected variables.
`params` | `List[Dict]` | The combined geo/variable parameters for API calls.
`n_calls` | `int` | The number of API calls that will be made by get_data().

Source code in `src/cendat/CenDatHelper.py`

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    class CenDatHelper:
        """
        A helper for exploring and retrieving data from the US Census Bureau API.

        This class provides a chainable, stateful interface to list, select, and
        combine datasets, geographies, and variables to build and execute API calls.

        Attributes:
            years (List[int]): The primary year or years of interest for data queries.
            products (List[Dict]): The currently selected data product details.
            geos (List[Dict]): The currently selected geographies.
            groups (List[Dict]): The currently selected variable groups.
            variables (List[Dict]): The currently selected variables.
            params (List[Dict]): The combined geo/variable parameters for API calls.
            n_calls (int): The number of API calls that will be made by get_data().
        """

        def __init__(
            self, years: Optional[Union[int, List[int]]] = None, key: Optional[str] = None
        ):
            """
            Initializes the CenDatHelper object.

            Args:
                years (Union[int, List[int]], optional): The year or years of
                    interest. If provided, they are set upon initialization.
                    Defaults to None.
                key (str, optional): A Census Bureau API key to load upon
                    initialization. Defaults to None.
            """
            self.years: Optional[List[int]] = None
            self.products: List[Dict] = []
            self.geos: List[Dict] = []
            self.groups: List[Dict] = []
            self.variables: List[Dict] = []
            self.params: List[Dict] = []
            self.__key: Optional[str] = None
            self._products_cache: Optional[List[Dict[str, str]]] = None
            self._filtered_products_cache: Optional[List[Dict]] = None
            self._filtered_geos_cache: Optional[List[Dict]] = None
            self._filtered_groups_cache: Optional[List[Dict]] = None
            self._filtered_variables_cache: Optional[List[Dict]] = None
            self.n_calls: Optional[int] = None
            self.call_type: Optional[str] = None

            if years is not None:
                self.set_years(years)
            if key is not None:
                self.load_key(key)

        def __getitem__(self, key: str) -> Union[List[Dict], Optional[int]]:
            """
            Allows dictionary-style access to key attributes.

            Args:
                key (str): The attribute to access. One of 'products', 'geos',
                           'groups', 'variables', 'params', or 'n_calls'.

            Returns:
                The value of the requested attribute.

            Raises:
                KeyError: If the key is not a valid attribute name.
            """
            if key == "products":
                return self.products
            elif key == "geos":
                return self.geos
            elif key == "groups":
                return self.groups
            elif key == "variables":
                return self.variables
            elif key == "params":
                return self.params
            elif key == "n_calls":
                return self.n_calls
            else:
                raise KeyError(
                    f"'{key}' is not a valid key. Available keys are: 'products', 'geos', 'groups', 'variables', 'params', 'n_calls'"
                )

        def set_years(self, years: Union[int, List[int]]):
            """
            Sets the object's active years for filtering API metadata.

            Args:
                years (Union[int, List[int]]): The year or list of years to set.

            Raises:
                TypeError: If `years` is not an integer or a list of integers.
            """
            if isinstance(years, int):
                self.years = [years]
            elif isinstance(years, list) and all(isinstance(y, int) for y in years):
                self.years = sorted(list(set(years)))
            else:
                raise TypeError("'years' must be an integer or a list of integers.")
            print(f"✅ Years set to: {self.years}")

        def load_key(self, key: Optional[str] = None):
            """
            Loads a Census API key for authenticated requests.

            Using a key is recommended to avoid stricter rate limits on anonymous
            requests.

            Args:
                key (str, optional): The API key string. Defaults to None.
            """
            if key:
                self.__key = key
                print("✅ API key loaded successfully.")
            else:
                print("⚠️ No API key provided. API requests may have stricter rate limits.")

        def _get_json_from_url(
            self, url: str, params: Optional[Dict] = None, timeout: int = 30
        ) -> Optional[List[List[str]]]:
            """
            Internal helper to fetch and parse JSON from a URL with error handling.

            Args:
                url (str): The URL to fetch.
                params (Dict, optional): Dictionary of query parameters.
                timeout (int): Request timeout in seconds.

            Returns:
                Optional[List[List[str]]]: The parsed JSON data (typically a list of lists), or None if an error occurs.
            """
            if not params:
                params = {}
            if self.__key:
                params["key"] = self.__key

            try:
                response = requests.get(url, params=params, timeout=timeout)
                response.raise_for_status()
                return response.json()
            # The Census API can return a 200 OK with an error message in the body
            # that is not valid JSON. This is common when a requested geography
            # does not exist within a parent geography (e.g., a tract in the wrong county).
            except requests.exceptions.JSONDecodeError as e:
                print(f"❌ Failed to decode JSON from {url}. Server response: {e}")
                params_minus = {key: value for key, value in params.items() if key != "key"}
                print(f"Query parameters: {params_minus}")
                print(
                    "Note: this may be the result of the 'in' geography being a special case "
                    "in which the 'for' summary level does not exist. All valid parent geographies "
                    "are queried without regard for whether or not the requested summary level exists "
                    "within them. If this is the case, your results will still be valid (barring other "
                    "errors)."
                )
            except requests.exceptions.RequestException as e:
                error_message = str(e)
                if e.response is not None:
                    api_error = e.response.text.strip()
                    if api_error:
                        error_message += f" - API Message: {api_error}"
                print(
                    f"❌ Error fetching data from {url} with params {params}: {error_message}"
                )
            return None

        def _get_json_from_url_with_status(
            self, url: str, params: Optional[Dict] = None, timeout: int = 30
        ) -> Tuple[Optional[List[List[str]]], Optional[int]]:
            """
            Internal helper to fetch and parse JSON from a URL, returning status code.

            This variant of _get_json_from_url returns a tuple of (data, status_code)
            to enable retry logic to distinguish between retryable server errors
            (429, 5xx) and non-retryable client errors (4xx).

            Args:
                url (str): The URL to fetch.
                params (Dict, optional): Dictionary of query parameters.
                timeout (int): Request timeout in seconds.

            Returns:
                Tuple[Optional[List[List[str]]], Optional[int]]: A tuple of (parsed JSON data, HTTP status code). Returns (None, status_code) on error, or (None, None) on connection/timeout errors.
            """
            if not params:
                params = {}
            if self.__key:
                params["key"] = self.__key

            try:
                response = requests.get(url, params=params, timeout=timeout)
                status_code = response.status_code
                response.raise_for_status()
                return response.json(), status_code
            except requests.exceptions.JSONDecodeError:
                # Server returned 200 but invalid JSON - treat as success with no data
                return None, response.status_code if 'response' in dir() else None
            except requests.exceptions.RequestException as e:
                status_code = e.response.status_code if e.response is not None else None
                return None, status_code

        def _parse_vintage(self, vintage_input: Union[str, int]) -> List[int]:
            """
            Internal helper to parse a vintage value, which can be a single year
            (e.g., 2022) or a multi-year range (e.g., "2018-2022").

            Robustly parses a vintage value which can be a single year or a range.

            Args:
                vintage_input (Union[str, int]): The vintage string or integer
                                                 (e.g., 2020, "2010-2014").

            Returns:
                List[int]: A list of integer years.
            """
            if not vintage_input:
                return []
            vintage_str = str(vintage_input)
            try:
                if "-" in vintage_str:
                    start, end = map(int, vintage_str.split("-"))
                    return list(range(start, end + 1))
                return [int(vintage_str)]
            except (ValueError, TypeError):
                return []

        def list_products(
            self,
            years: Optional[Union[int, List[int]]] = None,
            patterns: Optional[Union[str, List[str]]] = None,
            to_dicts: bool = True,
            logic: Callable[[iter], bool] = all,
            match_in: str = "title",
        ) -> Union[List[str], List[Dict[str, str]]]:
            """
            Lists available data products, with options for filtering.

            Fetches all available datasets from the main Census API endpoint and
            filters them based on year and string patterns. Results are cached
            for subsequent calls.

            Args:
                years (Union[int, List[int]], optional): Filter products available
                    for this year or list of years. Defaults to years set on the object.
                patterns (Union[str, List[str]], optional): A regex pattern or list
                    of patterns to search for in the product metadata.
                to_dicts (bool): If True (default), returns a list of dictionaries
                    with full product details. If False, returns a list of titles.
                logic (Callable): The function to apply when multiple `patterns` are
                    provided. Use `all` (default) for AND logic or `any` for OR logic.
                match_in (str): The metadata field to search within. Must be 'title'
                    (default) or 'desc'.

            Returns:
                A list of product dictionaries or a list of product titles.
            """
            # Strategy: Fetch all products from the main data.json endpoint once and cache them.
            # Subsequent calls will use the cache and apply filters.
            if not self._products_cache:
                data = self._get_json_from_url("https://api.census.gov/data.json")
                if not data or "dataset" not in data:
                    return []
                products = []
                for d in data["dataset"]:
                    is_micro = str(d.get("c_isMicrodata", "false")).lower() == "true"
                    is_agg = str(d.get("c_isAggregate", "false")).lower() == "true"
                    # We only support aggregate and microdata products, not timeseries or other types.
                    if not is_micro and not is_agg:
                        continue

                    access_url = next(
                        (
                            dist.get("accessURL")
                            for dist in d.get("distribution", [])
                            if "api.census.gov/data" in dist.get("accessURL", "")
                        ),
                        None,
                    )
                    if not access_url:
                        continue
                    c_dataset_val = d.get("c_dataset")
                    dataset_type = "N/A"
                    if isinstance(c_dataset_val, list) and len(c_dataset_val) > 1:
                        dataset_type = "/".join(c_dataset_val)
                    elif isinstance(c_dataset_val, str):
                        dataset_type = c_dataset_val

                    # Create a more descriptive and unique title by appending the API path fragment.
                    # This helps distinguish between similarly named products (e.g., ACS1 vs ACS5).
                    title = d.get("title")
                    title = (
                        f"{title} ({re.sub(r'http://api.census.gov/data/','', access_url)})"
                    )

                    products.append(
                        {
                            "title": title,
                            "desc": d.get("description"),
                            "vintage": self._parse_vintage(d.get("c_vintage")),
                            "type": dataset_type,
                            "url": access_url,
                            "is_microdata": is_micro,
                            "is_aggregate": is_agg,
                        }
                    )
                self._products_cache = products

            # Apply filters based on the provided arguments or the object's state.
            target_years = self.years
            if years is not None:
                target_years = [years] if isinstance(years, int) else list(years)

            filtered = self._products_cache
            if target_years:
                target_set = set(target_years)
                filtered = [
                    p
                    for p in filtered
                    if p.get("vintage") and target_set.intersection(p["vintage"])
                ]

            if patterns:
                if match_in not in ["title", "desc"]:
                    print("❌ Error: `match_in` must be either 'title' or 'desc'.")
                    return []
                pattern_list = [patterns] if isinstance(patterns, str) else patterns
                try:
                    regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                    filtered = [
                        p
                        for p in filtered
                        if p.get(match_in)
                        and logic(regex.search(p[match_in]) for regex in regexes)
                    ]
                except re.error as e:
                    print(f"❌ Invalid regex pattern: {e}")
                    return []

            self._filtered_products_cache = filtered
            return filtered if to_dicts else [p["title"] for p in filtered]

        def set_products(self, titles: Optional[Union[str, List[str]]] = None):
            """
            Sets the active data products for subsequent method calls.

            Args:
                titles (Union[str, List[str]], optional): The title or list of
                    titles of the products to set. If None, sets all products from
                    the last `list_products` call.
            """
            prods_to_set = []
            if titles is None:
                if not self._filtered_products_cache:
                    print("❌ Error: No products to set. Run `list_products` first.")
                    return
                prods_to_set = self._filtered_products_cache
            else:
                title_list = [titles] if isinstance(titles, str) else titles
                all_prods = self.list_products(to_dicts=True, years=self.years or [])
                for title in title_list:
                    matching_products = [p for p in all_prods if p.get("title") == title]
                    if not matching_products:
                        print(
                            f"⚠️ Warning: No product with the title '{title}' found. Skipping."
                        )
                        continue
                    prods_to_set.extend(matching_products)

            if not prods_to_set:
                print("❌ Error: No valid products were found to set.")
                return

            self.products = []
            self.groups = []
            self.variables = []
            self.geos = []
            for product in prods_to_set:
                product["base_url"] = product.get("url", "")
                self.products.append(product)
                print(
                    f"✅ Product set: '{product['title']}' (Vintage: {product.get('vintage')})"
                )

        def list_geos(
            self,
            to_dicts: bool = False,
            patterns: Optional[Union[str, List[str]]] = None,
            logic: Callable[[iter], bool] = all,
        ) -> Union[List[str], List[Dict[str, str]]]:
            """
            Lists available geographies for the currently set products.

            Args:
                to_dicts (bool): If True, returns a list of dictionaries with full
                    geography details. If False (default), returns a sorted list of
                    unique summary level names ('sumlev').
                patterns (Union[str, List[str]], optional): A regex pattern or list
                    of patterns to search for in the geography description.
                logic (Callable): The function to apply when multiple `patterns` are
                    provided. Use `all` (default) for AND logic or `any` for OR logic.

            Returns:
                A list of geography dictionaries or a list of summary level strings.
            """
            # Strategy: Iterate through each set product, fetch its specific geography.json,
            # and aggregate all available geographies into a single flat list.
            if not self.products:
                print("❌ Error: Products must be set first via `set_products()`.")
                return []
            flat_geo_list = []
            for product in self.products:
                url = f"{product['base_url']}/geography.json"
                data = self._get_json_from_url(url)
                if not data or "fips" not in data:
                    continue
                for geo_info in data["fips"]:
                    sumlev = geo_info.get("geoLevelDisplay")
                    if not sumlev:
                        continue
                    # Capture all relevant metadata for each geography, including the keys
                    # needed to determine API call structure (requires, wildcard, etc.).
                    flat_geo_list.append(
                        {
                            "sumlev": sumlev,
                            "desc": geo_info.get("name"),
                            "product": product["title"],
                            "vintage": product["vintage"],
                            "requires": geo_info.get("requires"),
                            "wildcard": geo_info.get("wildcard"),
                            "optionalWithWCFor": geo_info.get("optionalWithWCFor"),
                            "url": product["url"],
                        }
                    )
            result_list = flat_geo_list
            if patterns:
                pattern_list = [patterns] if isinstance(patterns, str) else patterns
                try:
                    regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                    result_list = [
                        g
                        for g in result_list
                        if g.get("desc")
                        and logic(regex.search(g["desc"]) for regex in regexes)
                    ]
                except re.error as e:
                    print(f"❌ Invalid regex pattern: {e}")
                    return []
            self._filtered_geos_cache = result_list
            return (
                result_list
                if to_dicts
                else sorted(list(set([g["sumlev"] for g in result_list])))
            )

        def set_geos(
            self,
            values: Optional[Union[str, List[str]]] = None,
            by: str = "sumlev",
        ):
            """
            Sets the active geographies for data retrieval.

            Args:
                values (Union[str, List[str]], optional): The geography values to set.
                    If None, sets all geos from the last `list_geos` call.
                by (str): The key to use for matching `values`. Must be either
                    'sumlev' (default) or 'desc'.
            """
            if by not in ["sumlev", "desc"]:
                print("❌ Error: `by` must be either 'sumlev' or 'desc'.")
                return

            geos_to_set = []
            if values is None:
                if not self._filtered_geos_cache:
                    print("❌ Error: No geos to set. Run `list_geos` first.")
                    return
                geos_to_set = self._filtered_geos_cache
            else:
                value_list = [values] if isinstance(values, str) else values
                all_geos = self.list_geos(to_dicts=True)
                geos_to_set = [g for g in all_geos if g.get(by) in value_list]

            if not geos_to_set:
                print("❌ Error: No valid geographies were found to set.")
                return

            # Microdata products have a constraint: you can only query for one type of
            # geography at a time (e.g., PUMAs within states).
            is_microdata_present = any(
                p.get("is_microdata")
                for p in self.products
                if p["title"] in [g["product"] for g in geos_to_set]
            )

            unique_geos = set(g["desc"] for g in geos_to_set)
            if is_microdata_present and len(unique_geos) > 1:
                print(
                    "❌ Error: Only a single geography type (e.g., 'public use microdata area') can be set when working with microdata products."
                )
                return

            self.geos = geos_to_set
            # Create a user-friendly message summarizing the requirements for the set geos.
            # This helps the user know what to provide in the `get_data` `within` clause.
            messages = {}
            for geo in self.geos:
                desc = geo["desc"]
                reqs = geo.get("requires") or []
                if desc not in messages:
                    messages[desc] = set(reqs)
                else:
                    messages[desc].update(reqs)
            message_parts = []
            for desc, reqs in messages.items():
                if reqs:
                    message_parts.append(
                        f"'{desc}' (requires `within` for: {', '.join(sorted(list(reqs)))})"
                    )
                else:
                    message_parts.append(f"'{desc}'")
            print(f"✅ Geographies set: {', '.join(message_parts)}")

        def list_groups(
            self,
            to_dicts: bool = True,
            patterns: Optional[Union[str, List[str]]] = None,
            logic: Callable[[iter], bool] = all,
            match_in: str = "description",
        ) -> Union[List[str], List[Dict[str, str]]]:
            """
            Lists available variable groups for the currently set products.

            Args:
                to_dicts (bool): If True (default), returns a list of dictionaries
                    with full group details. If False, returns a sorted list of
                    unique group names.
                patterns (Union[str, List[str]], optional): A regex pattern or list
                    of patterns to search for in the group metadata.
                logic (Callable): The function to apply when multiple `patterns` are
                    provided. Use `all` (default) for AND logic or `any` for OR logic.
                match_in (str): The metadata field to search within. Must be
                    'description' (default) or 'name'.

            Returns:
                A list of group dictionaries or a list of group name strings.
            """
            # Strategy: Similar to list_geos, iterate through each set product,
            # fetch its groups.json, and aggregate the results.
            if not self.products:
                print("❌ Error: Products must be set first via `set_products()`.")
                return []

            flat_group_list = []
            for product in self.products:
                url = f"{product['base_url']}/groups.json"
                data = self._get_json_from_url(url)
                if not data or "groups" not in data:
                    continue
                for group_details in data["groups"]:
                    flat_group_list.append(
                        {
                            "name": group_details.get("name", "N/A"),
                            "description": group_details.get("description", "N/A"),
                            "product": product["title"],
                            "vintage": product["vintage"],
                            "url": product["url"],
                        }
                    )
            result_list = flat_group_list

            if match_in not in ["description", "name"]:
                print("❌ Error: `match_in` must be either 'description' or 'name'.")
                return []

            if patterns:
                pattern_list = [patterns] if isinstance(patterns, str) else patterns
                try:
                    regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                    result_list = [
                        g
                        for g in result_list
                        if g.get(match_in)
                        and logic(regex.search(g[match_in]) for regex in regexes)
                    ]
                except re.error as e:
                    print(f"❌ Invalid regex pattern: {e}")
                    return []

            self._filtered_groups_cache = result_list
            return (
                result_list
                if to_dicts
                else sorted(list(set([g["name"] for g in result_list])))
            )

        def set_groups(self, names: Optional[Union[str, List[str]]] = None):
            """
            Sets the active variable groups for subsequent method calls.

            Args:
                names (Union[str, List[str]], optional): The name or list of names
                    of the groups to set. If None, sets all groups from the
                    last `list_groups` call.
            """
            groups_to_set = []
            if names is None:
                if not self._filtered_groups_cache:
                    print("❌ Error: No groups to set. Run `list_groups` first.")
                    return
                groups_to_set = self._filtered_groups_cache
            else:
                name_list = [names] if isinstance(names, str) else names
                all_groups = self.list_groups(to_dicts=True)
                groups_to_set = [g for g in all_groups if g.get("name") in name_list]

            if not groups_to_set:
                print("❌ Error: No valid groups were found to set.")
                return

            self.groups = groups_to_set
            self.variables = []
            unique_names = sorted(list(set(g["name"] for g in self.groups)))
            print(f"✅ Groups set: {', '.join(unique_names)}")

        def describe_groups(self, groups: Optional[Union[str, List[str]]] = None):
            """
            Displays the variables within specified groups in a formatted, indented list.

            This method fetches all variables for the currently set products and
            filters them to show only those belonging to the specified groups. The
            output is formatted to reflect the hierarchical structure of the variables
            as indicated by their labels.

            Args:
                groups (Union[str, List[str]], optional): A group name or list of
                    names to describe. If None, it will use the groups previously
                    set on the helper object via `set_groups()`.
            """
            if not self.products:
                print("❌ Error: Products must be set first via `set_products()`.")
                return

            # Determine which groups to filter by
            groups_to_filter = None
            if groups is not None:
                groups_to_filter = groups
            elif self.groups:
                groups_to_filter = [g["name"] for g in self.groups]

            if not groups_to_filter:
                print(
                    "❌ Error: No groups specified or set. Use `set_groups()` or the 'groups' parameter."
                )
                return

            if isinstance(groups_to_filter, str):
                groups_to_filter = [groups_to_filter]

            # A set is used for efficient lookup of whether a variable's group
            # is in the list of groups to be described.
            group_set = set(groups_to_filter)

            # Fetch all variables and group descriptions
            all_vars = self.list_variables(to_dicts=True)
            all_groups_details = self.list_groups(to_dicts=True)

            # Create a lookup for group descriptions
            group_descriptions = {g["name"]: g["description"] for g in all_groups_details}

            # Filter variables that belong to the selected groups
            group_vars = [v for v in all_vars if v.get("group") in group_set]

            if not group_vars:
                print(
                    f"ℹ️ No variables found for the specified group(s): {', '.join(group_set)}"
                )
                return

            # Organize variables by group and product/vintage for structured printing
            vars_by_group_product = {}
            for var in group_vars:
                key = (var["group"], var["product"], var["vintage"][0])
                if key not in vars_by_group_product:
                    vars_by_group_product[key] = []
                vars_by_group_product[key].append(var)

            # Print the formatted output
            last_group_printed = None
            for key in sorted(vars_by_group_product.keys()):
                group_name, product_title, vintage = key

                if group_name != last_group_printed:
                    group_desc = group_descriptions.get(
                        group_name, "No description available."
                    )
                    # Print a header for each new group.
                    print(f"\n--- Group: {group_name} ({group_desc}) ---")
                    last_group_printed = group_name

                print(f"\n  Product: {product_title} (Vintage: {vintage})")

                sorted_vars = sorted(vars_by_group_product[key], key=lambda x: x["name"])

                for var in sorted_vars:
                    label = var.get("label", "")

                    # The Census API uses "!!" in variable labels to denote hierarchy.
                    # We can use the count of this delimiter to determine indentation depth.
                    depth = label.count("!!")
                    indent = "  " * depth

                    # Get the last part of the label after splitting by '!!'
                    final_label_part = label.split("!!")[-1]

                    print(f"    {indent}{var['name']}: {final_label_part.strip()}")

        def list_variables(
            self,
            to_dicts: bool = True,
            patterns: Optional[Union[str, List[str]]] = None,
            logic: Callable[[iter], bool] = all,
            match_in: str = "label",
            groups: Optional[Union[str, List[str]]] = None,
        ) -> Union[List[str], List[Dict[str, str]]]:
            """
            Lists available variables for the currently set products.

            Args:
                to_dicts (bool): If True (default), returns a list of dictionaries
                    with full variable details. If False, returns a sorted list of
                    unique variable names.
                patterns (Union[str, List[str]], optional): A regex pattern or list
                    of patterns to search for in the variable metadata.
                logic (Callable): The function to apply when multiple `patterns` are
                    provided. Use `all` (default) for AND logic or `any` for OR logic.
                match_in (str): The metadata field to search within. Must be 'label'
                    (default), 'name', or 'concept'.
                groups (Union[str, List[str]], optional): A group name or list of
                    names to filter variables by. If provided, only variables
                    belonging to these groups will be returned.

            Returns:
                A list of variable dictionaries or a list of variable name strings.
            """
            # Strategy: Iterate through each set product, fetch its variables.json,
            # and aggregate all available variables into a single flat list.
            if not self.products:
                print("❌ Error: Products must be set first via `set_products()`.")
                return []
            flat_variable_list = []
            for product in self.products:
                url = f"{product['base_url']}/variables.json"
                data = self._get_json_from_url(url)
                if not data or "variables" not in data:
                    continue
                for name, details in data["variables"].items():
                    # Exclude reserved names used by the API for query parameters.
                    if name in ["GEO_ID", "for", "in", "ucgid"]:
                        continue
                    flat_variable_list.append(
                        {
                            "name": name,
                            "label": details.get("label", "N/A"),
                            "concept": details.get("concept", "N/A"),
                            "group": details.get("group", "N/A"),
                            "values": details.get("values", "N/A"),
                            "type": details.get("predicateType", "N/A"),
                            "attributes": details.get("attributes", "N/A"),
                            "sugg_wgt": details.get("suggested-weight", "N/A"),
                            "product": product["title"],
                            "vintage": product["vintage"],
                            "url": product["url"],
                        }
                    )
            result_list = flat_variable_list

            # Determine which groups to filter by: use the 'groups' parameter if
            # provided, otherwise fall back to the groups set on the object.
            groups_to_filter = None
            if groups is not None:
                groups_to_filter = groups
            elif self.groups:
                # Extract group names from the list of group dictionaries
                groups_to_filter = [g["name"] for g in self.groups]

            # Apply the group filter if there are any groups to filter by
            if groups_to_filter:
                # Ensure groups_to_filter is a list for set creation
                if isinstance(groups_to_filter, str):
                    groups_to_filter = [groups_to_filter]
                group_set = set(groups_to_filter)
                result_list = [v for v in result_list if v.get("group") in group_set]

            if match_in not in ["label", "name", "concept"]:
                print("❌ Error: `match_in` must be either 'label', 'name', or 'concept'.")
                return []

            if patterns:
                pattern_list = [patterns] if isinstance(patterns, str) else patterns
                try:
                    regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                    result_list = [
                        v
                        for v in result_list
                        if v.get(match_in)
                        and logic(regex.search(v[match_in]) for regex in regexes)
                    ]
                except re.error as e:
                    print(f"❌ Invalid regex pattern: {e}")
                    return []

            self._filtered_variables_cache = result_list
            return (
                result_list
                if to_dicts
                else sorted(list(set([v["name"] for v in result_list])))
            )

        def set_variables(
            self,
            names: Optional[Union[str, List[str]]] = None,
        ):
            """
            Sets the active variables for data retrieval.

            Args:
                names (Union[str, List[str]], optional): The name or list of names
                    of the variables to set. If None, sets all variables from the
                    last `list_variables` call.
            """
            vars_to_set = []
            if names is None:
                if not self._filtered_variables_cache:
                    print("❌ Error: No variables to set. Run `list_variables` first.")
                    return
                vars_to_set = self._filtered_variables_cache
            else:
                name_list = [names] if isinstance(names, str) else names
                all_vars = self.list_variables(to_dicts=True, patterns=None)
                vars_to_set = [v for v in all_vars if v.get("name") in name_list]
            if not vars_to_set:
                print("❌ Error: No valid variables were found to set.")
                return
            # Collapse the list of variables by product and vintage. This is a crucial
            # step to group all variables that can be requested in a single API call,
            # as each call is specific to one product/vintage.
            collapsed_vars = {}
            for var_info in vars_to_set:
                key = (var_info["product"], tuple(var_info["vintage"]), var_info["url"])
                if key not in collapsed_vars:
                    collapsed_vars[key] = {
                        "product": var_info["product"],
                        "vintage": var_info["vintage"],
                        "url": var_info["url"],
                        "names": [],
                        "labels": [],
                        "values": [],
                        "types": [],
                        "attributes": [],
                        "sugg_wgts": [],
                    }
                for collapsed, granular in zip(
                    ["names", "labels", "values", "types", "attributes", "sugg_wgts"],
                    ["name", "label", "values", "type", "attributes", "sugg_wgt"],
                ):
                    collapsed_vars[key][collapsed].append(var_info[granular])
            self.variables = list(collapsed_vars.values())
            self.call_type = "variable"
            print("✅ Variables set:")
            for var_group in self.variables:
                print(
                    f"  - Product: {var_group['product']} (Vintage: {var_group['vintage']})"
                )
                print(f"    Variables: {', '.join(var_group['names'])}")

        def _create_params(self):
            """
            Internal method to combine set geos, variables, and/or groups into API parameters.

            This method joins the user-selected geographies with either selected
            variables or a single selected group, based on matching product and vintage.
            This creates the final parameter sets for `get_data`.

            The logic handles two main scenarios:
            1. The user has explicitly set variables via `set_variables()`.
            2. The user has set groups via `set_groups()` and expects the library to
               fetch all variables within those groups. This is only valid if exactly
               one group is specified per product/vintage combination.
            This creates the final parameter sets for `get_data`.
            """
            if not self.geos:
                print("❌ Error: Geographies must be set before creating parameters.")
                return

            self.params = []  # Reset params

            micro_indicators = {
                p["url"]: p.get("is_microdata", False) for p in self.products
            }

            # Case 1: Variables are set (standard behavior)
            if self.variables and not self.call_type == "group":
                for geo in self.geos:
                    for var_group in self.variables:
                        if (
                            geo["product"] == var_group["product"]
                            and geo["vintage"] == var_group["vintage"]
                            and geo["url"] == var_group["url"]
                        ):
                            # Combine the geography and variable information into a single
                            # parameter dictionary.
                            self.params.append(
                                {
                                    "product": geo["product"],
                                    "vintage": geo["vintage"],
                                    "sumlev": geo["sumlev"],
                                    "desc": geo["desc"],
                                    "requires": geo.get("requires"),
                                    "wildcard": geo.get("wildcard"),
                                    "optionalWithWCFor": geo.get("optionalWithWCFor"),
                                    "names": var_group["names"],
                                    "labels": var_group["labels"],
                                    "values": var_group["values"],
                                    "types": var_group["types"],
                                    "attributes": var_group["attributes"],
                                    "url": geo["url"],
                                    "is_microdata": micro_indicators[geo["url"]],
                                }
                            )

            # Case 2: No variables, but exactly one group is set per vintage * geo
            elif self.groups:
                # First, verify that exactly one group is set for each product URL.
                # The Census API's `group()` parameter can only handle one group at a time.
                groups_by_url = defaultdict(set)
                for group in self.groups:
                    groups_by_url[group["url"]].add(group["name"])

                # Check if any product/vintage has more than one group selected.
                # This is invalid because we can only request one 'group()' per API call.
                if any(len(names) > 1 for names in groups_by_url.values()):
                    print(
                        "❌ Error: You must set variables if any set product has more than 1 set group."
                    )
                    return

                _ = self.list_variables()
                self.set_variables()
                self.call_type = "group"

                for geo in self.geos:
                    for i, group in enumerate(self.groups):
                        if (
                            geo["product"] == group["product"]
                            and geo["vintage"] == group["vintage"]
                            and geo["url"] == group["url"]
                        ):
                            self.params.append(
                                {
                                    "product": geo["product"],
                                    "vintage": geo["vintage"],
                                    "sumlev": geo["sumlev"],
                                    "desc": geo["desc"],
                                    "requires": geo.get("requires"),
                                    "wildcard": geo.get("wildcard"),
                                    "optionalWithWCFor": geo.get("optionalWithWCFor"),
                                    "group_name": group["name"],
                                    "names": self.variables[i]["names"],
                                    "labels": self.variables[i]["labels"],
                                    "values": self.variables[i]["values"],
                                    "types": self.variables[i]["types"],
                                    "attributes": self.variables[i]["attributes"],
                                    "url": geo["url"],
                                    "is_microdata": micro_indicators[geo["url"]],
                                }
                            )

            # Case 3: Invalid state
            else:
                print(
                    "❌ Error: You must set variables (using `set_variables`) OR set exactly one group (using `set_groups`) before getting data."
                )
                return  # self.params is already empty

            if not self.params:
                print(
                    "⚠️ Warning: No matching product-vintage combinations found between set geos and variables/groups."
                )
            else:
                print(
                    f"✅ Parameters created for {len(self.params)} geo-variable/group combinations."
                )

        def _get_parent_geo_combinations(
            self,
            base_url: str,
            required_geos: List[str],
            current_in_clause: Dict = {},
            timeout: int = 30,
            max_workers: Optional[int] = None,
        ) -> List[Dict]:
            """
            Recursively fetches all valid combinations of parent geographies.

            For aggregate data, if a geography requires parent geos (e.g., a county
            requires a state), this method fetches all possible parent FIPS codes
            to build the necessary `in` clauses for the final data query.

            Args:
                base_url (str): The base API URL for the product.
                required_geos (List[str]): A list of parent geo levels to fetch.
                current_in_clause (Dict): The `in` clause built so far in the recursion.
                timeout (int): Request timeout in seconds.
                max_workers (int, optional): Max concurrent threads for fetching.

            Returns:
                List[Dict]: A list of dictionaries, where each dict is a valid `in` clause for a data request.

            Strategy: This is a recursive function.
            - Base Case: If there are no more required geographies to fetch, return the
              `in` clause that has been built up so far.
            - Recursive Step: Fetch all FIPS codes for the current level of geography.
              Then, for each FIPS code, make a recursive call to fetch the next level down.
            """
            if not required_geos:
                return [current_in_clause]
            level_to_fetch = required_geos[0]
            remaining_levels = required_geos[1:]
            params = {"get": "NAME", "for": f"{level_to_fetch}:*"}
            if current_in_clause:
                in_parts = []
                for k, v in current_in_clause.items():
                    if isinstance(v, list):
                        in_parts.append(f"{k}:{','.join(v)}")
                    else:
                        in_parts.append(f"{k}:{v}")
                params["in"] = " ".join(in_parts)
            data = self._get_json_from_url(base_url, params, timeout=timeout)
            if not data or len(data) < 2:
                return []
            try:
                fips_index = data[0].index(level_to_fetch)
            except ValueError:
                print(
                    f"❌ Could not find FIPS column for '{level_to_fetch}' in API response."
                )
                return []
            all_combinations = []
            # Use a ThreadPoolExecutor to fetch combinations for the next level in parallel,
            # significantly speeding up discovery for deep geographic hierarchies.
            with ThreadPoolExecutor(max_workers=max_workers) as executor:
                future_to_fips = {
                    executor.submit(
                        self._get_parent_geo_combinations,
                        base_url,
                        remaining_levels,
                        {**current_in_clause, level_to_fetch: row[fips_index]},
                        timeout=timeout,
                        max_workers=max_workers,
                    ): row[fips_index]
                    for row in data[1:]
                }
                for future in as_completed(future_to_fips):
                    all_combinations.extend(future.result())
            return all_combinations

        def _get_gdf_from_url(
            self,
            layer_id: int,
            where_clause: str,
            service: str = "TIGERweb/tigerWMS_Current",
            timeout: int = None,
            offset: int = None,
            n_records: int = None,
            count_only: bool = False,
        ) -> Union["gpd.GeoDataFrame", int]:
            """
            Fetches geographic polygons or a feature count from the US Census TIGERweb REST API.

            Args:
                layer_id (int): The numeric ID for the desired geography layer.
                where_clause (str): An SQL-like clause to filter the geographies.
                service (str): The name of the TIGERweb map service to query.
                timeout (int, optional): Request timeout in seconds.
                offset (int, optional): The starting record offset for pagination.
                n_records (int, optional): The number of records to return per page.
                count_only (bool): If True, returns only the count of matching records.

            Returns:
                If count_only is True, returns an integer count. Otherwise, returns a GeoDataFrame with the requested geometries.
            """
            import geopandas as gpd

            API_URL = f"https://tigerweb.geo.census.gov/arcgis/rest/services/{service}/MapServer/{layer_id}/query"

            params = {
                "where": where_clause,
                "outFields": "GEOID,NAME",
                "outSR": "4326",
                "f": "geojson",
                "returnGeometry": "true" if not count_only else "false",
                "returnCountOnly": str(count_only).lower(),
                "resultOffset": offset,
                "resultRecordCount": n_records,
                "timeout": timeout,
            }
            # Filter out None values before making the request
            params = {k: v for k, v in params.items() if v is not None}

            try:
                response = requests.get(API_URL, params=params)
                response.raise_for_status()
                json_response = response.json()

                if count_only:
                    return json_response.get("count", 0)

                return gpd.GeoDataFrame.from_features(json_response["features"])

            except requests.exceptions.RequestException as e:
                print(f"❌ HTTP Request failed: {e}")
            except (KeyError, ValueError) as e:
                print(f"❌ Failed to parse response JSON: {e}")
                print(f"   Server Response: {response.text[:200]}...")

            return gpd.GeoDataFrame() if not count_only else 0

        def _get_gdf_from_url_with_status(
            self,
            layer_id: int,
            where_clause: str,
            service: str = "TIGERweb/tigerWMS_Current",
            timeout: int = None,
            offset: int = None,
            n_records: int = None,
            count_only: bool = False,
        ) -> Tuple[Union["gpd.GeoDataFrame", int], Optional[int]]:
            """
            Fetches geographic data from TIGERweb, returning status code for retry logic.

            This variant of _get_gdf_from_url returns a tuple of (result, status_code)
            to enable retry logic to distinguish between retryable server errors
            (429, 5xx) and non-retryable client errors (4xx). Connection errors
            return status_code=None, which should also be treated as retryable.

            Args:
                layer_id (int): The numeric ID for the desired geography layer.
                where_clause (str): An SQL-like clause to filter the geographies.
                service (str): The name of the TIGERweb map service to query.
                timeout (int, optional): Request timeout in seconds.
                offset (int, optional): The starting record offset for pagination.
                n_records (int, optional): The number of records to return per page.
                count_only (bool): If True, returns only the count of matching records.

            Returns:
                Tuple of (result, status_code). Result is either an int (count) or GeoDataFrame. status_code is None for connection errors.
            """
            import geopandas as gpd

            API_URL = f"https://tigerweb.geo.census.gov/arcgis/rest/services/{service}/MapServer/{layer_id}/query"

            params = {
                "where": where_clause,
                "outFields": "GEOID,NAME",
                "outSR": "4326",
                "f": "geojson",
                "returnGeometry": "true" if not count_only else "false",
                "returnCountOnly": str(count_only).lower(),
                "resultOffset": offset,
                "resultRecordCount": n_records,
                "timeout": timeout,
            }
            params = {k: v for k, v in params.items() if v is not None}

            try:
                response = requests.get(API_URL, params=params, timeout=timeout or 30)
                status_code = response.status_code
                response.raise_for_status()
                json_response = response.json()

                if count_only:
                    return json_response.get("count", 0), status_code

                return gpd.GeoDataFrame.from_features(json_response["features"]), status_code

            except requests.exceptions.RequestException as e:
                status_code = e.response.status_code if e.response is not None else None
                return (gpd.GeoDataFrame() if not count_only else 0), status_code
            except (KeyError, ValueError):
                # JSON parse error - treat as non-retryable (likely bad response format)
                return (gpd.GeoDataFrame() if not count_only else 0), 200

        def _data_fetching(
            self,
            tasks: List[Tuple[str, Dict, Dict]] = None,
            max_workers: Optional[int] = None,
            timeout: Optional[int] = None,
            auto_retry: bool = True,
            max_retries: int = 3,
            min_workers: int = 5,
        ):
            """
            Takes data API tasks and runs them in a thread pool with adaptive retry.

            When server errors (429, 5xx) are detected, this method automatically:
            1. Waits with exponential backoff
            2. Reduces worker count by 50%
            3. Retries failed requests

            Args:
                tasks: List of (url, params, context) tuples for API calls.
                max_workers: Maximum concurrent threads.
                timeout: Request timeout in seconds.
                auto_retry: If True, automatically retry on server errors.
                max_retries: Maximum retry attempts before giving up.
                min_workers: Floor for worker reduction (won't go below this).
            """
            results_aggregator = {
                i: {"schema": None, "data": []} for i in range(len(self.params))
            }

            pending_tasks = list(tasks)
            current_workers = max_workers
            retry_count = 0

            # Status codes that indicate server overload - these are retryable
            RETRYABLE_STATUS_CODES = {429, 500, 502, 503, 504}

            while pending_tasks:
                failed_tasks = []
                server_error_count = 0

                with ThreadPoolExecutor(max_workers=current_workers) as executor:
                    future_to_task = {
                        executor.submit(
                            self._get_json_from_url_with_status, url, params, timeout
                        ): (url, params, context)
                        for url, params, context in pending_tasks
                    }

                    for future in as_completed(future_to_task):
                        url, params, context = future_to_task[future]
                        param_index = context["param_index"]

                        try:
                            data, status_code = future.result()

                            # Check if this is a retryable server error
                            # Also retry on connection errors (status_code is None)
                            if status_code in RETRYABLE_STATUS_CODES or status_code is None:
                                server_error_count += 1
                                failed_tasks.append((url, params, context))
                            elif data and len(data) > 1:
                                # Success - aggregate the data
                                if results_aggregator[param_index]["schema"] is None:
                                    results_aggregator[param_index]["schema"] = data[0]
                                results_aggregator[param_index]["data"].extend(data[1:])
                            # else: request succeeded but returned no/empty data - that's OK

                        except Exception as exc:
                            print(f"❌ Task for {context} generated an exception: {exc}")

                # Decide whether to retry
                if failed_tasks and auto_retry and retry_count < max_retries:
                    # Calculate error rate
                    error_rate = server_error_count / len(pending_tasks)

                    if error_rate > 0.1:  # More than 10% failed with server errors
                        retry_count += 1
                        # Reduce workers by 50%, but don't go below minimum
                        current_workers = max(min_workers, current_workers // 2)
                        # Exponential backoff
                        backoff_time = 2 ** retry_count

                        print(
                            f"⚠️ {server_error_count}/{len(pending_tasks)} requests failed with server errors. "
                            f"Retry {retry_count}/{max_retries}: reducing workers to {current_workers} "
                            f"and waiting {backoff_time}s..."
                        )
                        time.sleep(backoff_time)
                        pending_tasks = failed_tasks
                    else:
                        # Low error rate - probably not a rate limiting issue
                        if server_error_count > 0:
                            print(
                                f"⚠️ {server_error_count} requests failed but error rate is low. Not retrying."
                            )
                        pending_tasks = []
                elif failed_tasks and retry_count >= max_retries:
                    print(
                        f"❌ Maximum retries ({max_retries}) exceeded. "
                        f"{len(failed_tasks)} requests could not be completed."
                    )
                    pending_tasks = []
                else:
                    pending_tasks = []

            # After all tasks are complete, attach the aggregated data to self.params
            print("✅ Data fetching complete. Stacking results.")
            for i, param in enumerate(self.params):
                aggregated_result = results_aggregator[i]
                if aggregated_result["schema"]:
                    param["schema"] = aggregated_result["schema"]
                    param["data"] = aggregated_result["data"]
                else:
                    # Ensure 'data' key exists, even if empty, for downstream consistency.
                    param["data"] = []

        def _geometry_fetching(
            self,
            tasks: List[Dict],
            max_workers: Optional[int] = None,
            timeout: Optional[int] = None,
            verbose: bool = False,
            auto_retry: bool = True,
            max_retries: int = 3,
            min_workers: int = 5,
        ):
            """
            Takes TIGERweb tasks, handles pagination, and fetches geometries in a thread pool.

            This method uses an iterative approach for pagination to avoid Python's
            recursion depth limits, which is safer for queries that could return
            many thousands of features.

            When server errors (429, 5xx) or connection errors are detected, this method
            automatically:
            1. Waits with exponential backoff
            2. Reduces worker count by 50%
            3. Retries failed requests

            Strategy:
            1. For each initial task, make a pre-flight request to get the total count
               of geometries. These requests are run concurrently.
            2. Based on the count, calculate how many paginated requests are needed.
            3. Create a list of all sub-tasks (one for each page).
            4. Execute all sub-tasks concurrently in a thread pool with retry logic.
            5. Aggregate the resulting GeoDataFrames and attach them to the corresponding
               item in `self.params`.
            """
            # This will hold all the individual page-fetching tasks
            paginated_tasks = []

            RECORDS_PER_PAGE = 1000
            RETRYABLE_STATUS_CODES = {429, 500, 502, 503, 504}

            if not tasks:
                return

            import pandas as pd
            import geopandas as gpd

            if verbose:
                print("ℹ️ Pre-querying for geometry counts to determine pagination...")

            # Step 1 & 2: Concurrently pre-flight requests to get counts and create paginated tasks
            # Pre-flight requests also use retry logic
            pending_preflight = list(tasks)
            current_workers = max_workers
            retry_count = 0

            while pending_preflight:
                failed_preflight = []
                server_error_count = 0

                with ThreadPoolExecutor(max_workers=current_workers) as executor:
                    future_to_task = {
                        executor.submit(
                            self._get_gdf_from_url_with_status,
                            layer_id=task["layer_id"],
                            where_clause=task["where_clause"],
                            service=task["map_server"],
                            timeout=timeout,
                            count_only=True,
                        ): task
                        for task in pending_preflight
                    }

                    for future in as_completed(future_to_task):
                        task = future_to_task[future]
                        where_clause = task["where_clause"]
                        try:
                            total_records, status_code = future.result()

                            # Check if this is a retryable error
                            if status_code in RETRYABLE_STATUS_CODES or status_code is None:
                                server_error_count += 1
                                failed_preflight.append(task)
                                continue

                            if total_records == 0:
                                if verbose:
                                    print(
                                        f"  - No geometries found for WHERE '{where_clause[:60]}...'. Skipping."
                                    )
                                continue

                            if verbose:
                                print(
                                    f"  - Found {total_records} geometries for WHERE '{where_clause[:60]}...'. Building paginated tasks."
                                )

                            # Step 3: Create sub-tasks for each page
                            for offset in range(0, total_records, RECORDS_PER_PAGE):
                                paginated_tasks.append(
                                    {
                                        "param_index": task["param_index"],
                                        "layer_id": task["layer_id"],
                                        "where_clause": where_clause,
                                        "service": task["map_server"],
                                        "offset": offset,
                                        "n_records": RECORDS_PER_PAGE,
                                    }
                                )
                        except Exception as exc:
                            print(
                                f"❌ Failed during geometry count pre-flight for {where_clause}: {exc}"
                            )

                # Decide whether to retry pre-flight
                if failed_preflight and auto_retry and retry_count < max_retries:
                    error_rate = server_error_count / len(pending_preflight)
                    if error_rate > 0.1:
                        retry_count += 1
                        current_workers = max(min_workers, current_workers // 2)
                        backoff_time = 2 ** retry_count
                        print(
                            f"⚠️ {server_error_count}/{len(pending_preflight)} pre-flight requests failed. "
                            f"Retry {retry_count}/{max_retries}: reducing workers to {current_workers} "
                            f"and waiting {backoff_time}s..."
                        )
                        time.sleep(backoff_time)
                        pending_preflight = failed_preflight
                    else:
                        pending_preflight = []
                elif failed_preflight and retry_count >= max_retries:
                    print(
                        f"❌ Maximum retries ({max_retries}) exceeded for pre-flight. "
                        f"{len(failed_preflight)} requests could not be completed."
                    )
                    pending_preflight = []
                else:
                    pending_preflight = []

            if not paginated_tasks:
                if verbose:
                    print("ℹ️ No paginated geometry tasks to execute.")
                return

            # Dictionary to aggregate GDFs for each original param
            results_aggregator = defaultdict(list)

            # Step 4: Execute all paginated tasks concurrently with retry logic
            if verbose:
                print(
                    f"ℹ️ Fetching geometries across {len(paginated_tasks)} paginated calls..."
                )

            pending_tasks = list(paginated_tasks)
            current_workers = max_workers  # Reset workers for this phase
            retry_count = 0

            while pending_tasks:
                failed_tasks = []
                server_error_count = 0

                with ThreadPoolExecutor(max_workers=current_workers) as executor:
                    future_to_context = {
                        executor.submit(
                            self._get_gdf_from_url_with_status,
                            layer_id=p_task["layer_id"],
                            where_clause=p_task["where_clause"],
                            service=p_task["service"],
                            timeout=timeout,
                            offset=p_task["offset"],
                            n_records=p_task["n_records"],
                        ): p_task
                        for p_task in pending_tasks
                    }

                    for future in as_completed(future_to_context):
                        context = future_to_context[future]
                        param_index = context["param_index"]
                        try:
                            gdf, status_code = future.result()

                            # Check if this is a retryable error
                            if status_code in RETRYABLE_STATUS_CODES or status_code is None:
                                server_error_count += 1
                                failed_tasks.append(context)
                            elif not gdf.empty:
                                results_aggregator[param_index].append(gdf)
                            # else: request succeeded but returned no data - that's OK

                        except Exception as exc:
                            print(f"❌ Geometry fetch task failed for {context}: {exc}")

                # Decide whether to retry
                if failed_tasks and auto_retry and retry_count < max_retries:
                    error_rate = server_error_count / len(pending_tasks)
                    if error_rate > 0.1:
                        retry_count += 1
                        current_workers = max(min_workers, current_workers // 2)
                        backoff_time = 2 ** retry_count
                        print(
                            f"⚠️ {server_error_count}/{len(pending_tasks)} geometry requests failed. "
                            f"Retry {retry_count}/{max_retries}: reducing workers to {current_workers} "
                            f"and waiting {backoff_time}s..."
                        )
                        time.sleep(backoff_time)
                        pending_tasks = failed_tasks
                    else:
                        if server_error_count > 0:
                            print(
                                f"⚠️ {server_error_count} geometry requests failed but error rate is low. Not retrying."
                            )
                        pending_tasks = []
                elif failed_tasks and retry_count >= max_retries:
                    print(
                        f"❌ Maximum retries ({max_retries}) exceeded for geometry fetching. "
                        f"{len(failed_tasks)} requests could not be completed."
                    )
                    pending_tasks = []
                else:
                    pending_tasks = []

            # Step 5: Concatenate GDFs and attach to self.params
            print("✅ Geometry fetching complete. Stacking results.")
            for i, param in enumerate(self.params):
                if i in results_aggregator:
                    # Concatenate all the GeoDataFrame pages into a single one
                    combined_gdf = pd.concat(results_aggregator[i], ignore_index=True)
                    param["geometry"] = gpd.GeoDataFrame(combined_gdf)
                else:
                    param["geometry"] = gpd.GeoDataFrame()

        def get_data(
            self,
            within: Union[str, Dict, List[Dict]] = "us",
            max_workers: Optional[int] = 25,
            timeout: int = 30,
            preview_only: bool = False,
            include_names: bool = False,
            include_geoids: bool = False,
            include_attributes: bool = False,
            include_geometry: bool = False,
            in_place: bool = False,
            verbose: bool = False,
            auto_retry: bool = True,
            max_retries: int = 3,
            min_workers: int = 5,
        ) -> "CenDatResponse":
            """
            Retrieves data from the Census API based on the set parameters.

            This is the final method in the chain. It constructs and executes all
            necessary API calls in parallel, aggregates the results, and returns
            a CenDatResponse object for further processing.

            Args:
                within (Union[str, Dict, List[Dict]]): Specifies the geographic
                    scope. Can be "us" (default), or a dictionary defining parent
                    geographies (e.g., `{'state': '06'}` for California), or a list
                    of such dictionaries for multiple scopes.
                max_workers (int, optional): The maximum number of concurrent
                    threads to use for API calls. Defaults to 25.
                timeout (int): Request timeout in seconds for each API call.
                    Defaults to 30.
                preview_only (bool): If True, builds the list of API calls but does
                    not execute them. Useful for debugging. Defaults to False.
                auto_retry (bool): If True (default), automatically retry failed
                    requests caused by server overload (429, 5xx errors) with
                    reduced worker count and exponential backoff.
                max_retries (int): Maximum number of retry attempts when
                    auto_retry is enabled. Defaults to 3.
                min_workers (int): Minimum number of workers to use when
                    retrying. Worker count won't be reduced below this. Defaults to 5.

            Returns:
                CenDatResponse: An object containing the aggregated data from all successful API calls.

            High-level Strategy:
            1. Generate parameter sets by combining set products, geos, and variables/groups.
            2. For each parameter set, determine the necessary API calls based on the `within` clause.
            3. This involves handling microdata vs. aggregate data, and for aggregate data, determining if parent geography discovery or wildcards are needed.
            4. Build a list of all API call tasks (URL + parameters).
            5. If not in preview mode, execute all tasks concurrently using a thread pool.
            6. Aggregate the results and return them in a `CenDatResponse` object.
            """
            self._create_params()

            if not self.params:
                print(
                    "❌ Error: Could not create parameters. Please set geos and variables."
                )
                return CenDatResponse([])

            if include_geometry:
                if find_spec("geopandas") is None or find_spec("pandas") is None:
                    print(
                        "❌ GeoPandas and/or Pandas are not installed. Please install them with 'pip install geopandas pandas'"
                    )
                    return CenDatResponse([])

                include_geoids = True

                valid_sumlevs_geometry = {
                    "020": ["Census Regions"],
                    "030": ["Census Divisions"],
                    "040": ["States"],
                    "050": ["Counties"],
                    "060": ["County Subdivisions"],
                    "140": ["Census Tracts"],
                    "150": ["Census Block Groups"],
                    "160": ["Incorporated Places", "Census Designated Places"],
                }

                desc_map = {
                    "region": "REGION",
                    "division": "DIVISION",
                    "state": "STATE",
                    "county": "COUNTY",
                    "county subdivision": "COUSUB",
                    "tract": "TRACT",
                    "block group": "BLKGRP",
                    "place": "PLACE",
                }

                state_codes = {
                    "WA": "53",
                    "DE": "10",
                    "DC": "11",
                    "WI": "55",
                    "WV": "54",
                    "HI": "15",
                    "FL": "12",
                    "WY": "56",
                    "PR": "72",
                    "NJ": "34",
                    "NM": "35",
                    "TX": "48",
                    "LA": "22",
                    "NC": "37",
                    "ND": "38",
                    "NE": "31",
                    "TN": "47",
                    "NY": "36",
                    "PA": "42",
                    "AK": "02",
                    "NV": "32",
                    "NH": "33",
                    "VA": "51",
                    "CO": "08",
                    "CA": "06",
                    "AL": "01",
                    "AR": "05",
                    "VT": "50",
                    "IL": "17",
                    "GA": "13",
                    "IN": "18",
                    "IA": "19",
                    "MA": "25",
                    "AZ": "04",
                    "ID": "16",
                    "CT": "09",
                    "ME": "23",
                    "MD": "24",
                    "OK": "40",
                    "OH": "39",
                    "UT": "49",
                    "MO": "29",
                    "MN": "27",
                    "MI": "26",
                    "RI": "44",
                    "KS": "20",
                    "MT": "30",
                    "MS": "28",
                    "SC": "45",
                    "KY": "21",
                    "OR": "41",
                    "SD": "46",
                }

                url = (
                    "https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb?f=pjson"
                )

                try:
                    response = requests.get(url)
                    response.raise_for_status()
                    map_servers = [
                        item["name"]
                        for item in response.json()["services"]
                        if re.search(r"ACS|Census|Current", item["name"])
                    ]
                    if verbose:
                        print("✅ Successfully fetched map servers.")

                except requests.exceptions.RequestException as e:
                    print(f"❌ HTTP Request failed: {e}")

                # Helper function to correctly format TIGERweb SQL WHERE clauses,
                # handling both single values and lists of values.
                def format_sql_in_clause(key, value):
                    # Map the user-facing geo name (e.g., 'state') to the TIGERweb field name (e.g., 'STATE')
                    field = desc_map[key]
                    if isinstance(value, list):
                        # For a list, create a comma-separated, quoted string: "'val1','val2'"
                        values_str = ",".join(f"'{item}'" for item in value)
                        return f"{field} IN ({values_str})"
                    else:
                        # For a single value, just wrap it in quotes
                        return f"{field} IN ('{value}')"

            data_tasks = []
            geo_tasks = []

            raw_within_clauses = within if isinstance(within, list) else [within]
            if (
                include_geometry
                and raw_within_clauses in [[], ["us"]]
                and any(param["sumlev"] == "040" for param in self.params)
            ):
                raw_within_clauses = [{"state": list(state_codes.values())}]

            # Expand the `within` clauses. If a user provides a list of codes for a
            # geography (e.g., `{'state': '08', 'county': ['001', '005']}`), this
            # logic expands it into separate clauses for each combination.
            expanded_within_clauses = []
            for clause in raw_within_clauses:
                # Use builtins.dict to prevent shadowing errors with local variables
                if not isinstance(clause, builtins.dict):
                    expanded_within_clauses.append(clause)
                    continue

                # Separate keys with list values from those with single values
                list_items = {k: v for k, v in clause.items() if isinstance(v, list)}
                single_items = {k: v for k, v in clause.items() if not isinstance(v, list)}

                if not list_items:
                    expanded_within_clauses.append(clause)
                    continue

                # Create all combinations of the list values
                keys, values = zip(*list_items.items())
                for v_combination in itertools.product(*values):
                    new_clause = single_items.copy()
                    new_clause.update(builtins.dict(zip(keys, v_combination)))
                    expanded_within_clauses.append(new_clause)

            # Iterate through each parameter set (a unique product/vintage/geo combination)
            # and each `within` clause to build the full list of API calls.
            for i, param in enumerate(self.params):
                product_info = next(
                    (p for p in self.products if p["title"] == param["product"]), None
                )
                if not product_info:
                    continue

                if include_geometry:
                    skip_geometry = False
                    if (
                        product_info["type"].split("/")[0] == "dec"
                        and product_info["vintage"][0] >= 2010
                    ):
                        map_server = f"TIGERweb/tigerWMS_Census{product_info['vintage'][0]}"
                    elif product_info["type"].split("/")[0] == "acs":
                        if (
                            product_info["vintage"][0] >= 2012
                            and product_info["vintage"][0] != 2020
                        ):
                            map_server = (
                                f"TIGERweb/tigerWMS_ACS{product_info['vintage'][0]}"
                            )
                        elif product_info["vintage"][0] >= 2012:
                            map_server = "TIGERweb/tigerWMS_Census2020"
                        else:
                            map_server = "TIGERweb/tigerWMS_Census2010"
                    else:
                        map_server = "TIGERweb/tigerWMS_Current"

                    if map_server not in map_servers:
                        skip_geometry = True
                        print(
                            f"❌ Error: the requested map server '{map_server}' is not available."
                        )
                    if param["sumlev"] not in valid_sumlevs_geometry.keys():
                        skip_geometry = True
                        print(
                            f"❌ Error: the requested summary level ({param['sumlev']}) is currently supported for geometry."
                        )
                    if not skip_geometry:
                        url = f"https://tigerweb.geo.census.gov/arcgis/rest/services/{map_server}/MapServer?f=pjson"

                        try:
                            response = requests.get(url)
                            response.raise_for_status()

                            map_server_layers = [
                                item["id"]
                                for item in response.json()["layers"]
                                if item["name"] in valid_sumlevs_geometry[param["sumlev"]]
                            ]
                            if verbose:
                                print("✅ Successfully fetched map server layers.")

                        except requests.exceptions.RequestException as e:
                            print(f"❌ HTTP Request failed: {e}")

                # Conditionally build the 'get' parameter string based on call type
                if "group_name" in param:  # This is a group-based call, not variable-based
                    vars_to_get = [f"group({param['group_name']})"]
                    if include_geoids and param["is_microdata"]:
                        print("ℹ️ GEO_ID not valid for microdata - request ignored.")
                    elif include_geoids:
                        vars_to_get.insert(0, "GEO_ID")
                    if include_names and param["is_microdata"]:
                        print("ℹ️ NAME not valid for microdata - request ignored.")
                    elif include_names:
                        vars_to_get.insert(0, "NAME")
                    # `include_attributes` is ignored for group calls as it's not applicable.
                else:
                    vars_to_get = param["names"].copy()
                    if include_geoids and param["is_microdata"]:
                        print("ℹ️ GEO_ID not valid for microdata - request ignored.")
                    elif include_geoids:
                        vars_to_get.insert(0, "GEO_ID")
                    if include_names and param["is_microdata"]:
                        print("ℹ️ NAME not valid for microdata - request ignored.")
                    elif include_names:
                        vars_to_get.insert(0, "NAME")
                    if include_attributes:
                        all_attributes = set()
                        # Iterate through the list of attribute strings for the selected variables
                        for attr_string in param.get("attributes", []):
                            # Check if the string is valid and not the "N/A" placeholder
                            if attr_string and attr_string != "N/A":
                                # The 'attributes' key contains a comma-separated string of variable names.
                                # We split this string and add the names to our set.
                                all_attributes.update(attr_string.split(","))

                        # Add the unique, valid attributes to the list of variables to request.
                        if all_attributes:
                            vars_to_get.extend(list(all_attributes))

                variable_names = ",".join(vars_to_get)
                target_geo = param["desc"]
                vintage_url = param["url"]
                context = {"param_index": i}

                for within_clause in expanded_within_clauses:
                    # --- Microdata Path ---
                    # Microdata requests are simpler: they require a dictionary specifying
                    # the target geography and its codes, plus any parent geographies.
                    if product_info.get("is_microdata"):
                        if not isinstance(within_clause, builtins.dict):
                            print(
                                "❌ Error: A `within` dictionary or list of dictionaries is required for microdata requests."
                            )
                            continue

                        if include_geometry:
                            print("ℹ️ Geometry not valid for microdata - request ignored.")

                        within_copy = within_clause.copy()
                        target_geo_codes = within_copy.pop(target_geo, None)

                        if target_geo_codes is None:
                            print(
                                f"❌ Error: `within` dictionary must contain the target geography: '{target_geo}'"
                            )
                            continue

                        codes_str = (
                            target_geo_codes
                            if isinstance(target_geo_codes, str)
                            else ",".join(target_geo_codes)
                        )

                        api_params = {
                            "get": variable_names,
                            "for": f"{target_geo}:{codes_str}",
                        }
                        if within_copy:
                            api_params["in"] = " ".join(
                                [f"{k}:{v}" for k, v in within_copy.items()]
                            )
                        data_tasks.append((vintage_url, api_params, context))

                    # --- Aggregate Data Path ---
                    # This path is more complex as it needs to handle geographic hierarchies.
                    elif product_info.get("is_aggregate"):
                        required_geos = param.get("requires") or []
                        provided_parent_geos = {}
                        target_geo_codes = None
                        # If `within` is a dictionary, parse out the target geography codes
                        # and any provided parent geographies.
                        if isinstance(within_clause, builtins.dict):
                            within_copy = within_clause.copy()
                            target_geo_codes = within_copy.pop(target_geo, None)
                            provided_parent_geos = {
                                k: v for k, v in within_copy.items() if k in required_geos
                            }

                        # Case A: The target geography itself is specified in `within`.
                        # No discovery or wildcards needed; we can build the call directly.
                        if target_geo_codes:
                            codes_str = (
                                target_geo_codes
                                if isinstance(target_geo_codes, str)
                                else ",".join(map(str, target_geo_codes))
                            )
                            api_params = {
                                "get": variable_names,
                                "for": f"{target_geo}:{codes_str}",
                            }
                            if provided_parent_geos:
                                api_params["in"] = " ".join(
                                    [f"{k}:{v}" for k, v in provided_parent_geos.items()]
                                )
                            data_tasks.append((vintage_url, api_params, context))
                            if include_geometry and not skip_geometry:
                                geo_params = [
                                    {
                                        "param_index": i,
                                        "map_server": map_server,
                                        "layer_id": map_server_layer,
                                        "where_clause": " AND ".join(
                                            [
                                                format_sql_in_clause(k, v)
                                                for k, v in within_clause.items()
                                            ]
                                        ),
                                    }
                                    for map_server_layer in map_server_layers
                                ]
                                geo_tasks.extend(geo_params)
                            continue

                        # Case B: Target geography is not specified. We need to figure out
                        # the `for` and `in` clauses using wildcards or discovery.
                        # `final_in_clause` will store the components of the `in` parameter.
                        # A value of `None` indicates a level that needs discovery.
                        final_in_clause = {}
                        if include_geometry:
                            param.pop("optionalWithWCFor", None)
                            if "wildcard" in param and isinstance(param["wildcard"], list):
                                param["wildcard"] = param["wildcard"][1:]
                        if required_geos:
                            for geo in required_geos:
                                if geo in provided_parent_geos:
                                    final_in_clause[geo] = provided_parent_geos[geo]
                                elif param.get("wildcard") and geo in param["wildcard"]:
                                    final_in_clause[geo] = "*"
                                else:
                                    final_in_clause[geo] = None  # Needs discovery

                        # Some geographies have an optional parent for wildcards.
                        # If that optional parent isn't provided, we should not include it
                        # in the `in` clause at all.

                        optional_level = param.get("optionalWithWCFor")
                        if optional_level and optional_level not in provided_parent_geos:
                            final_in_clause.pop(optional_level, None)

                        geos_to_fetch = [
                            geo for geo, code in final_in_clause.items() if code is None
                        ]

                        # If there are levels that need discovery, call the recursive helper.
                        combinations = []
                        if geos_to_fetch:
                            if verbose:
                                print(
                                    f"ℹ️ Discovering parent geographies for: {geos_to_fetch}"
                                )
                            resolved_parents = {
                                k: v
                                for k, v in final_in_clause.items()
                                if v is not None and v != "*"
                            }
                            combinations = self._get_parent_geo_combinations(
                                vintage_url,
                                geos_to_fetch,
                                resolved_parents,
                                timeout=timeout,
                                max_workers=max_workers,
                            )
                        else:
                            combinations = [final_in_clause]

                        if combinations and verbose:
                            print(
                                f"✅ Found {len(combinations)} combinations. Building API queries..."
                            )

                        # Build an API task for each discovered parent geography combination.
                        for combo in combinations:
                            call_in_clause = final_in_clause.copy()
                            call_in_clause.update(combo)
                            call_in_clause = {
                                k: v for k, v in call_in_clause.items() if v is not None
                            }

                            api_params = {"get": variable_names, "for": f"{target_geo}:*"}
                            if call_in_clause:
                                api_params["in"] = " ".join(
                                    [f"{k}:{v}" for k, v in call_in_clause.items()]
                                )
                            data_tasks.append((vintage_url, api_params, context))

                            # Corrected logic for geometry task creation
                            if include_geometry and not skip_geometry:
                                # Build the WHERE clause conditions from the call_in_clause dictionary.
                                where_conditions = [
                                    format_sql_in_clause(k, v)
                                    for k, v in call_in_clause.items()
                                    if v != "*"
                                ]

                                # If there are no conditions (e.g., a nationwide query for "us"),
                                # use '1=1' as a universal "select all" filter. Otherwise, join them.
                                final_where_clause = (
                                    " AND ".join(where_conditions)
                                    if where_conditions
                                    else "1=1"
                                )

                                geo_params = [
                                    {
                                        "param_index": i,
                                        "map_server": map_server,
                                        "layer_id": map_server_layer,
                                        "where_clause": final_where_clause,
                                    }
                                    for map_server_layer in map_server_layers
                                ]
                                geo_tasks.extend(geo_params)

            if not data_tasks:
                print("❌ Error: Could not determine any API calls to make.")
                return CenDatResponse([])

            self.n_calls = len(data_tasks)

            # If in preview mode, print the first few planned calls and exit.
            if preview_only:
                print(f"ℹ️ Preview: this will yield {self.n_calls} API call(s).")
                for i, (url, params, _) in enumerate(data_tasks[:5]):
                    print(
                        f"  - Call {i+1}: {url}?get={params.get('get')}&for={params.get('for')}&in={params.get('in','')}"
                    )
                if len(data_tasks) > 5:
                    print(f"  ... and {len(data_tasks) - 5} more.")
                return CenDatResponse([])

            else:
                if verbose:
                    print(f"ℹ️ Making {self.n_calls} API call(s)...")
                # Execute all API calls concurrently.
                try:
                    with ThreadPoolExecutor(max_workers=2) as executor:
                        # Submit the two master jobs for data and geometry fetching.
                        future_geo = executor.submit(
                            self._geometry_fetching,
                            geo_tasks,
                            max_workers,
                            timeout,
                            verbose,
                            auto_retry,
                            max_retries,
                            min_workers,
                        )
                        future_data = executor.submit(
                            self._data_fetching,
                            data_tasks,
                            max_workers,
                            timeout,
                            auto_retry,
                            max_retries,
                            min_workers,
                        )

                        # Wait for both futures to complete. The .result() call will
                        # re-raise any exceptions that occurred in the thread.
                        future_data.result()
                        future_geo.result()

                except Exception as exc:
                    print(f"❌ A master fetching task failed: {exc}")
                    # Return an empty response if a master task fails
                    return CenDatResponse([])

                if in_place is False:
                    params_copy = copy.deepcopy(self.params)
                    self.params = []
                    return CenDatResponse(params_copy)

##### `__init__(years=None, key=None)`

Initializes the CenDatHelper object.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`years` | `Union[int, List[int]]` | The year or years of interest. If provided, they are set upon initialization. Defaults to None. | `None`
`key` | `str` | A Census Bureau API key to load upon initialization. Defaults to None. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def __init__(
        self, years: Optional[Union[int, List[int]]] = None, key: Optional[str] = None
    ):
        """
        Initializes the CenDatHelper object.

        Args:
            years (Union[int, List[int]], optional): The year or years of
                interest. If provided, they are set upon initialization.
                Defaults to None.
            key (str, optional): A Census Bureau API key to load upon
                initialization. Defaults to None.
        """
        self.years: Optional[List[int]] = None
        self.products: List[Dict] = []
        self.geos: List[Dict] = []
        self.groups: List[Dict] = []
        self.variables: List[Dict] = []
        self.params: List[Dict] = []
        self.__key: Optional[str] = None
        self._products_cache: Optional[List[Dict[str, str]]] = None
        self._filtered_products_cache: Optional[List[Dict]] = None
        self._filtered_geos_cache: Optional[List[Dict]] = None
        self._filtered_groups_cache: Optional[List[Dict]] = None
        self._filtered_variables_cache: Optional[List[Dict]] = None
        self.n_calls: Optional[int] = None
        self.call_type: Optional[str] = None

        if years is not None:
            self.set_years(years)
        if key is not None:
            self.load_key(key)

##### `__getitem__(key)`

Allows dictionary-style access to key attributes.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`key` | `str` | The attribute to access. One of 'products', 'geos', 'groups', 'variables', 'params', or 'n_calls'. | required

Returns:

Type | Description
--- | ---
`Union[List[Dict], Optional[int]]` | The value of the requested attribute.

Raises:

Type | Description
--- | ---
`KeyError` | If the key is not a valid attribute name.

Source code in `src/cendat/CenDatHelper.py`

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    def __getitem__(self, key: str) -> Union[List[Dict], Optional[int]]:
        """
        Allows dictionary-style access to key attributes.

        Args:
            key (str): The attribute to access. One of 'products', 'geos',
                       'groups', 'variables', 'params', or 'n_calls'.

        Returns:
            The value of the requested attribute.

        Raises:
            KeyError: If the key is not a valid attribute name.
        """
        if key == "products":
            return self.products
        elif key == "geos":
            return self.geos
        elif key == "groups":
            return self.groups
        elif key == "variables":
            return self.variables
        elif key == "params":
            return self.params
        elif key == "n_calls":
            return self.n_calls
        else:
            raise KeyError(
                f"'{key}' is not a valid key. Available keys are: 'products', 'geos', 'groups', 'variables', 'params', 'n_calls'"
            )

##### `set_years(years)`

Sets the object's active years for filtering API metadata.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`years` | `Union[int, List[int]]` | The year or list of years to set. | required

Raises:

Type | Description
--- | ---
`TypeError` | If `years` is not an integer or a list of integers.

Source code in `src/cendat/CenDatHelper.py`

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    def set_years(self, years: Union[int, List[int]]):
        """
        Sets the object's active years for filtering API metadata.

        Args:
            years (Union[int, List[int]]): The year or list of years to set.

        Raises:
            TypeError: If `years` is not an integer or a list of integers.
        """
        if isinstance(years, int):
            self.years = [years]
        elif isinstance(years, list) and all(isinstance(y, int) for y in years):
            self.years = sorted(list(set(years)))
        else:
            raise TypeError("'years' must be an integer or a list of integers.")
        print(f"✅ Years set to: {self.years}")

##### `load_key(key=None)`

Loads a Census API key for authenticated requests.

Using a key is recommended to avoid stricter rate limits on anonymous requests.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`key` | `str` | The API key string. Defaults to None. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def load_key(self, key: Optional[str] = None):
        """
        Loads a Census API key for authenticated requests.

        Using a key is recommended to avoid stricter rate limits on anonymous
        requests.

        Args:
            key (str, optional): The API key string. Defaults to None.
        """
        if key:
            self.__key = key
            print("✅ API key loaded successfully.")
        else:
            print("⚠️ No API key provided. API requests may have stricter rate limits.")

##### `list_products(years=None, patterns=None, to_dicts=True, logic=all, match_in='title')`

Lists available data products, with options for filtering.

Fetches all available datasets from the main Census API endpoint and filters them based on year and string patterns. Results are cached for subsequent calls.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`years` | `Union[int, List[int]]` | Filter products available for this year or list of years. Defaults to years set on the object. | `None`
`patterns` | `Union[str, List[str]]` | A regex pattern or list of patterns to search for in the product metadata. | `None`
`to_dicts` | `bool` | If True (default), returns a list of dictionaries with full product details. If False, returns a list of titles. | `True`
`logic` | `Callable` | The function to apply when multiple `patterns` are provided. Use `all` (default) for AND logic or `any` for OR logic. | `all`
`match_in` | `str` | The metadata field to search within. Must be 'title' (default) or 'desc'. | `'title'`

Returns:

Type | Description
--- | ---
`Union[List[str], List[Dict[str, str]]]` | A list of product dictionaries or a list of product titles.

Source code in `src/cendat/CenDatHelper.py`

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    def list_products(
        self,
        years: Optional[Union[int, List[int]]] = None,
        patterns: Optional[Union[str, List[str]]] = None,
        to_dicts: bool = True,
        logic: Callable[[iter], bool] = all,
        match_in: str = "title",
    ) -> Union[List[str], List[Dict[str, str]]]:
        """
        Lists available data products, with options for filtering.

        Fetches all available datasets from the main Census API endpoint and
        filters them based on year and string patterns. Results are cached
        for subsequent calls.

        Args:
            years (Union[int, List[int]], optional): Filter products available
                for this year or list of years. Defaults to years set on the object.
            patterns (Union[str, List[str]], optional): A regex pattern or list
                of patterns to search for in the product metadata.
            to_dicts (bool): If True (default), returns a list of dictionaries
                with full product details. If False, returns a list of titles.
            logic (Callable): The function to apply when multiple `patterns` are
                provided. Use `all` (default) for AND logic or `any` for OR logic.
            match_in (str): The metadata field to search within. Must be 'title'
                (default) or 'desc'.

        Returns:
            A list of product dictionaries or a list of product titles.
        """
        # Strategy: Fetch all products from the main data.json endpoint once and cache them.
        # Subsequent calls will use the cache and apply filters.
        if not self._products_cache:
            data = self._get_json_from_url("https://api.census.gov/data.json")
            if not data or "dataset" not in data:
                return []
            products = []
            for d in data["dataset"]:
                is_micro = str(d.get("c_isMicrodata", "false")).lower() == "true"
                is_agg = str(d.get("c_isAggregate", "false")).lower() == "true"
                # We only support aggregate and microdata products, not timeseries or other types.
                if not is_micro and not is_agg:
                    continue

                access_url = next(
                    (
                        dist.get("accessURL")
                        for dist in d.get("distribution", [])
                        if "api.census.gov/data" in dist.get("accessURL", "")
                    ),
                    None,
                )
                if not access_url:
                    continue
                c_dataset_val = d.get("c_dataset")
                dataset_type = "N/A"
                if isinstance(c_dataset_val, list) and len(c_dataset_val) > 1:
                    dataset_type = "/".join(c_dataset_val)
                elif isinstance(c_dataset_val, str):
                    dataset_type = c_dataset_val

                # Create a more descriptive and unique title by appending the API path fragment.
                # This helps distinguish between similarly named products (e.g., ACS1 vs ACS5).
                title = d.get("title")
                title = (
                    f"{title} ({re.sub(r'http://api.census.gov/data/','', access_url)})"
                )

                products.append(
                    {
                        "title": title,
                        "desc": d.get("description"),
                        "vintage": self._parse_vintage(d.get("c_vintage")),
                        "type": dataset_type,
                        "url": access_url,
                        "is_microdata": is_micro,
                        "is_aggregate": is_agg,
                    }
                )
            self._products_cache = products

        # Apply filters based on the provided arguments or the object's state.
        target_years = self.years
        if years is not None:
            target_years = [years] if isinstance(years, int) else list(years)

        filtered = self._products_cache
        if target_years:
            target_set = set(target_years)
            filtered = [
                p
                for p in filtered
                if p.get("vintage") and target_set.intersection(p["vintage"])
            ]

        if patterns:
            if match_in not in ["title", "desc"]:
                print("❌ Error: `match_in` must be either 'title' or 'desc'.")
                return []
            pattern_list = [patterns] if isinstance(patterns, str) else patterns
            try:
                regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                filtered = [
                    p
                    for p in filtered
                    if p.get(match_in)
                    and logic(regex.search(p[match_in]) for regex in regexes)
                ]
            except re.error as e:
                print(f"❌ Invalid regex pattern: {e}")
                return []

        self._filtered_products_cache = filtered
        return filtered if to_dicts else [p["title"] for p in filtered]

##### `set_products(titles=None)`

Sets the active data products for subsequent method calls.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`titles` | `Union[str, List[str]]` | The title or list of titles of the products to set. If None, sets all products from the last `list_products` call. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def set_products(self, titles: Optional[Union[str, List[str]]] = None):
        """
        Sets the active data products for subsequent method calls.

        Args:
            titles (Union[str, List[str]], optional): The title or list of
                titles of the products to set. If None, sets all products from
                the last `list_products` call.
        """
        prods_to_set = []
        if titles is None:
            if not self._filtered_products_cache:
                print("❌ Error: No products to set. Run `list_products` first.")
                return
            prods_to_set = self._filtered_products_cache
        else:
            title_list = [titles] if isinstance(titles, str) else titles
            all_prods = self.list_products(to_dicts=True, years=self.years or [])
            for title in title_list:
                matching_products = [p for p in all_prods if p.get("title") == title]
                if not matching_products:
                    print(
                        f"⚠️ Warning: No product with the title '{title}' found. Skipping."
                    )
                    continue
                prods_to_set.extend(matching_products)

        if not prods_to_set:
            print("❌ Error: No valid products were found to set.")
            return

        self.products = []
        self.groups = []
        self.variables = []
        self.geos = []
        for product in prods_to_set:
            product["base_url"] = product.get("url", "")
            self.products.append(product)
            print(
                f"✅ Product set: '{product['title']}' (Vintage: {product.get('vintage')})"
            )

##### `list_geos(to_dicts=False, patterns=None, logic=all)`

Lists available geographies for the currently set products.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`to_dicts` | `bool` | If True, returns a list of dictionaries with full geography details. If False (default), returns a sorted list of unique summary level names ('sumlev'). | `False`
`patterns` | `Union[str, List[str]]` | A regex pattern or list of patterns to search for in the geography description. | `None`
`logic` | `Callable` | The function to apply when multiple `patterns` are provided. Use `all` (default) for AND logic or `any` for OR logic. | `all`

Returns:

Type | Description
--- | ---
`Union[List[str], List[Dict[str, str]]]` | A list of geography dictionaries or a list of summary level strings.

Source code in `src/cendat/CenDatHelper.py`

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    def list_geos(
        self,
        to_dicts: bool = False,
        patterns: Optional[Union[str, List[str]]] = None,
        logic: Callable[[iter], bool] = all,
    ) -> Union[List[str], List[Dict[str, str]]]:
        """
        Lists available geographies for the currently set products.

        Args:
            to_dicts (bool): If True, returns a list of dictionaries with full
                geography details. If False (default), returns a sorted list of
                unique summary level names ('sumlev').
            patterns (Union[str, List[str]], optional): A regex pattern or list
                of patterns to search for in the geography description.
            logic (Callable): The function to apply when multiple `patterns` are
                provided. Use `all` (default) for AND logic or `any` for OR logic.

        Returns:
            A list of geography dictionaries or a list of summary level strings.
        """
        # Strategy: Iterate through each set product, fetch its specific geography.json,
        # and aggregate all available geographies into a single flat list.
        if not self.products:
            print("❌ Error: Products must be set first via `set_products()`.")
            return []
        flat_geo_list = []
        for product in self.products:
            url = f"{product['base_url']}/geography.json"
            data = self._get_json_from_url(url)
            if not data or "fips" not in data:
                continue
            for geo_info in data["fips"]:
                sumlev = geo_info.get("geoLevelDisplay")
                if not sumlev:
                    continue
                # Capture all relevant metadata for each geography, including the keys
                # needed to determine API call structure (requires, wildcard, etc.).
                flat_geo_list.append(
                    {
                        "sumlev": sumlev,
                        "desc": geo_info.get("name"),
                        "product": product["title"],
                        "vintage": product["vintage"],
                        "requires": geo_info.get("requires"),
                        "wildcard": geo_info.get("wildcard"),
                        "optionalWithWCFor": geo_info.get("optionalWithWCFor"),
                        "url": product["url"],
                    }
                )
        result_list = flat_geo_list
        if patterns:
            pattern_list = [patterns] if isinstance(patterns, str) else patterns
            try:
                regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                result_list = [
                    g
                    for g in result_list
                    if g.get("desc")
                    and logic(regex.search(g["desc"]) for regex in regexes)
                ]
            except re.error as e:
                print(f"❌ Invalid regex pattern: {e}")
                return []
        self._filtered_geos_cache = result_list
        return (
            result_list
            if to_dicts
            else sorted(list(set([g["sumlev"] for g in result_list])))
        )

##### `set_geos(values=None, by='sumlev')`

Sets the active geographies for data retrieval.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`values` | `Union[str, List[str]]` | The geography values to set. If None, sets all geos from the last `list_geos` call. | `None`
`by` | `str` | The key to use for matching `values`. Must be either 'sumlev' (default) or 'desc'. | `'sumlev'`

Source code in `src/cendat/CenDatHelper.py`

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    def set_geos(
        self,
        values: Optional[Union[str, List[str]]] = None,
        by: str = "sumlev",
    ):
        """
        Sets the active geographies for data retrieval.

        Args:
            values (Union[str, List[str]], optional): The geography values to set.
                If None, sets all geos from the last `list_geos` call.
            by (str): The key to use for matching `values`. Must be either
                'sumlev' (default) or 'desc'.
        """
        if by not in ["sumlev", "desc"]:
            print("❌ Error: `by` must be either 'sumlev' or 'desc'.")
            return

        geos_to_set = []
        if values is None:
            if not self._filtered_geos_cache:
                print("❌ Error: No geos to set. Run `list_geos` first.")
                return
            geos_to_set = self._filtered_geos_cache
        else:
            value_list = [values] if isinstance(values, str) else values
            all_geos = self.list_geos(to_dicts=True)
            geos_to_set = [g for g in all_geos if g.get(by) in value_list]

        if not geos_to_set:
            print("❌ Error: No valid geographies were found to set.")
            return

        # Microdata products have a constraint: you can only query for one type of
        # geography at a time (e.g., PUMAs within states).
        is_microdata_present = any(
            p.get("is_microdata")
            for p in self.products
            if p["title"] in [g["product"] for g in geos_to_set]
        )

        unique_geos = set(g["desc"] for g in geos_to_set)
        if is_microdata_present and len(unique_geos) > 1:
            print(
                "❌ Error: Only a single geography type (e.g., 'public use microdata area') can be set when working with microdata products."
            )
            return

        self.geos = geos_to_set
        # Create a user-friendly message summarizing the requirements for the set geos.
        # This helps the user know what to provide in the `get_data` `within` clause.
        messages = {}
        for geo in self.geos:
            desc = geo["desc"]
            reqs = geo.get("requires") or []
            if desc not in messages:
                messages[desc] = set(reqs)
            else:
                messages[desc].update(reqs)
        message_parts = []
        for desc, reqs in messages.items():
            if reqs:
                message_parts.append(
                    f"'{desc}' (requires `within` for: {', '.join(sorted(list(reqs)))})"
                )
            else:
                message_parts.append(f"'{desc}'")
        print(f"✅ Geographies set: {', '.join(message_parts)}")

##### `list_groups(to_dicts=True, patterns=None, logic=all, match_in='description')`

Lists available variable groups for the currently set products.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`to_dicts` | `bool` | If True (default), returns a list of dictionaries with full group details. If False, returns a sorted list of unique group names. | `True`
`patterns` | `Union[str, List[str]]` | A regex pattern or list of patterns to search for in the group metadata. | `None`
`logic` | `Callable` | The function to apply when multiple `patterns` are provided. Use `all` (default) for AND logic or `any` for OR logic. | `all`
`match_in` | `str` | The metadata field to search within. Must be 'description' (default) or 'name'. | `'description'`

Returns:

Type | Description
--- | ---
`Union[List[str], List[Dict[str, str]]]` | A list of group dictionaries or a list of group name strings.

Source code in `src/cendat/CenDatHelper.py`

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    def list_groups(
        self,
        to_dicts: bool = True,
        patterns: Optional[Union[str, List[str]]] = None,
        logic: Callable[[iter], bool] = all,
        match_in: str = "description",
    ) -> Union[List[str], List[Dict[str, str]]]:
        """
        Lists available variable groups for the currently set products.

        Args:
            to_dicts (bool): If True (default), returns a list of dictionaries
                with full group details. If False, returns a sorted list of
                unique group names.
            patterns (Union[str, List[str]], optional): A regex pattern or list
                of patterns to search for in the group metadata.
            logic (Callable): The function to apply when multiple `patterns` are
                provided. Use `all` (default) for AND logic or `any` for OR logic.
            match_in (str): The metadata field to search within. Must be
                'description' (default) or 'name'.

        Returns:
            A list of group dictionaries or a list of group name strings.
        """
        # Strategy: Similar to list_geos, iterate through each set product,
        # fetch its groups.json, and aggregate the results.
        if not self.products:
            print("❌ Error: Products must be set first via `set_products()`.")
            return []

        flat_group_list = []
        for product in self.products:
            url = f"{product['base_url']}/groups.json"
            data = self._get_json_from_url(url)
            if not data or "groups" not in data:
                continue
            for group_details in data["groups"]:
                flat_group_list.append(
                    {
                        "name": group_details.get("name", "N/A"),
                        "description": group_details.get("description", "N/A"),
                        "product": product["title"],
                        "vintage": product["vintage"],
                        "url": product["url"],
                    }
                )
        result_list = flat_group_list

        if match_in not in ["description", "name"]:
            print("❌ Error: `match_in` must be either 'description' or 'name'.")
            return []

        if patterns:
            pattern_list = [patterns] if isinstance(patterns, str) else patterns
            try:
                regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                result_list = [
                    g
                    for g in result_list
                    if g.get(match_in)
                    and logic(regex.search(g[match_in]) for regex in regexes)
                ]
            except re.error as e:
                print(f"❌ Invalid regex pattern: {e}")
                return []

        self._filtered_groups_cache = result_list
        return (
            result_list
            if to_dicts
            else sorted(list(set([g["name"] for g in result_list])))
        )

##### `set_groups(names=None)`

Sets the active variable groups for subsequent method calls.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`names` | `Union[str, List[str]]` | The name or list of names of the groups to set. If None, sets all groups from the last `list_groups` call. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def set_groups(self, names: Optional[Union[str, List[str]]] = None):
        """
        Sets the active variable groups for subsequent method calls.

        Args:
            names (Union[str, List[str]], optional): The name or list of names
                of the groups to set. If None, sets all groups from the
                last `list_groups` call.
        """
        groups_to_set = []
        if names is None:
            if not self._filtered_groups_cache:
                print("❌ Error: No groups to set. Run `list_groups` first.")
                return
            groups_to_set = self._filtered_groups_cache
        else:
            name_list = [names] if isinstance(names, str) else names
            all_groups = self.list_groups(to_dicts=True)
            groups_to_set = [g for g in all_groups if g.get("name") in name_list]

        if not groups_to_set:
            print("❌ Error: No valid groups were found to set.")
            return

        self.groups = groups_to_set
        self.variables = []
        unique_names = sorted(list(set(g["name"] for g in self.groups)))
        print(f"✅ Groups set: {', '.join(unique_names)}")

##### `describe_groups(groups=None)`

Displays the variables within specified groups in a formatted, indented list.

This method fetches all variables for the currently set products and filters them to show only those belonging to the specified groups. The output is formatted to reflect the hierarchical structure of the variables as indicated by their labels.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`groups` | `Union[str, List[str]]` | A group name or list of names to describe. If None, it will use the groups previously set on the helper object via `set_groups()`. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def describe_groups(self, groups: Optional[Union[str, List[str]]] = None):
        """
        Displays the variables within specified groups in a formatted, indented list.

        This method fetches all variables for the currently set products and
        filters them to show only those belonging to the specified groups. The
        output is formatted to reflect the hierarchical structure of the variables
        as indicated by their labels.

        Args:
            groups (Union[str, List[str]], optional): A group name or list of
                names to describe. If None, it will use the groups previously
                set on the helper object via `set_groups()`.
        """
        if not self.products:
            print("❌ Error: Products must be set first via `set_products()`.")
            return

        # Determine which groups to filter by
        groups_to_filter = None
        if groups is not None:
            groups_to_filter = groups
        elif self.groups:
            groups_to_filter = [g["name"] for g in self.groups]

        if not groups_to_filter:
            print(
                "❌ Error: No groups specified or set. Use `set_groups()` or the 'groups' parameter."
            )
            return

        if isinstance(groups_to_filter, str):
            groups_to_filter = [groups_to_filter]

        # A set is used for efficient lookup of whether a variable's group
        # is in the list of groups to be described.
        group_set = set(groups_to_filter)

        # Fetch all variables and group descriptions
        all_vars = self.list_variables(to_dicts=True)
        all_groups_details = self.list_groups(to_dicts=True)

        # Create a lookup for group descriptions
        group_descriptions = {g["name"]: g["description"] for g in all_groups_details}

        # Filter variables that belong to the selected groups
        group_vars = [v for v in all_vars if v.get("group") in group_set]

        if not group_vars:
            print(
                f"ℹ️ No variables found for the specified group(s): {', '.join(group_set)}"
            )
            return

        # Organize variables by group and product/vintage for structured printing
        vars_by_group_product = {}
        for var in group_vars:
            key = (var["group"], var["product"], var["vintage"][0])
            if key not in vars_by_group_product:
                vars_by_group_product[key] = []
            vars_by_group_product[key].append(var)

        # Print the formatted output
        last_group_printed = None
        for key in sorted(vars_by_group_product.keys()):
            group_name, product_title, vintage = key

            if group_name != last_group_printed:
                group_desc = group_descriptions.get(
                    group_name, "No description available."
                )
                # Print a header for each new group.
                print(f"\n--- Group: {group_name} ({group_desc}) ---")
                last_group_printed = group_name

            print(f"\n  Product: {product_title} (Vintage: {vintage})")

            sorted_vars = sorted(vars_by_group_product[key], key=lambda x: x["name"])

            for var in sorted_vars:
                label = var.get("label", "")

                # The Census API uses "!!" in variable labels to denote hierarchy.
                # We can use the count of this delimiter to determine indentation depth.
                depth = label.count("!!")
                indent = "  " * depth

                # Get the last part of the label after splitting by '!!'
                final_label_part = label.split("!!")[-1]

                print(f"    {indent}{var['name']}: {final_label_part.strip()}")

##### `list_variables(to_dicts=True, patterns=None, logic=all, match_in='label', groups=None)`

Lists available variables for the currently set products.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`to_dicts` | `bool` | If True (default), returns a list of dictionaries with full variable details. If False, returns a sorted list of unique variable names. | `True`
`patterns` | `Union[str, List[str]]` | A regex pattern or list of patterns to search for in the variable metadata. | `None`
`logic` | `Callable` | The function to apply when multiple `patterns` are provided. Use `all` (default) for AND logic or `any` for OR logic. | `all`
`match_in` | `str` | The metadata field to search within. Must be 'label' (default), 'name', or 'concept'. | `'label'`
`groups` | `Union[str, List[str]]` | A group name or list of names to filter variables by. If provided, only variables belonging to these groups will be returned. | `None`

Returns:

Type | Description
--- | ---
`Union[List[str], List[Dict[str, str]]]` | A list of variable dictionaries or a list of variable name strings.

Source code in `src/cendat/CenDatHelper.py`

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    def list_variables(
        self,
        to_dicts: bool = True,
        patterns: Optional[Union[str, List[str]]] = None,
        logic: Callable[[iter], bool] = all,
        match_in: str = "label",
        groups: Optional[Union[str, List[str]]] = None,
    ) -> Union[List[str], List[Dict[str, str]]]:
        """
        Lists available variables for the currently set products.

        Args:
            to_dicts (bool): If True (default), returns a list of dictionaries
                with full variable details. If False, returns a sorted list of
                unique variable names.
            patterns (Union[str, List[str]], optional): A regex pattern or list
                of patterns to search for in the variable metadata.
            logic (Callable): The function to apply when multiple `patterns` are
                provided. Use `all` (default) for AND logic or `any` for OR logic.
            match_in (str): The metadata field to search within. Must be 'label'
                (default), 'name', or 'concept'.
            groups (Union[str, List[str]], optional): A group name or list of
                names to filter variables by. If provided, only variables
                belonging to these groups will be returned.

        Returns:
            A list of variable dictionaries or a list of variable name strings.
        """
        # Strategy: Iterate through each set product, fetch its variables.json,
        # and aggregate all available variables into a single flat list.
        if not self.products:
            print("❌ Error: Products must be set first via `set_products()`.")
            return []
        flat_variable_list = []
        for product in self.products:
            url = f"{product['base_url']}/variables.json"
            data = self._get_json_from_url(url)
            if not data or "variables" not in data:
                continue
            for name, details in data["variables"].items():
                # Exclude reserved names used by the API for query parameters.
                if name in ["GEO_ID", "for", "in", "ucgid"]:
                    continue
                flat_variable_list.append(
                    {
                        "name": name,
                        "label": details.get("label", "N/A"),
                        "concept": details.get("concept", "N/A"),
                        "group": details.get("group", "N/A"),
                        "values": details.get("values", "N/A"),
                        "type": details.get("predicateType", "N/A"),
                        "attributes": details.get("attributes", "N/A"),
                        "sugg_wgt": details.get("suggested-weight", "N/A"),
                        "product": product["title"],
                        "vintage": product["vintage"],
                        "url": product["url"],
                    }
                )
        result_list = flat_variable_list

        # Determine which groups to filter by: use the 'groups' parameter if
        # provided, otherwise fall back to the groups set on the object.
        groups_to_filter = None
        if groups is not None:
            groups_to_filter = groups
        elif self.groups:
            # Extract group names from the list of group dictionaries
            groups_to_filter = [g["name"] for g in self.groups]

        # Apply the group filter if there are any groups to filter by
        if groups_to_filter:
            # Ensure groups_to_filter is a list for set creation
            if isinstance(groups_to_filter, str):
                groups_to_filter = [groups_to_filter]
            group_set = set(groups_to_filter)
            result_list = [v for v in result_list if v.get("group") in group_set]

        if match_in not in ["label", "name", "concept"]:
            print("❌ Error: `match_in` must be either 'label', 'name', or 'concept'.")
            return []

        if patterns:
            pattern_list = [patterns] if isinstance(patterns, str) else patterns
            try:
                regexes = [re.compile(p, re.IGNORECASE) for p in pattern_list]
                result_list = [
                    v
                    for v in result_list
                    if v.get(match_in)
                    and logic(regex.search(v[match_in]) for regex in regexes)
                ]
            except re.error as e:
                print(f"❌ Invalid regex pattern: {e}")
                return []

        self._filtered_variables_cache = result_list
        return (
            result_list
            if to_dicts
            else sorted(list(set([v["name"] for v in result_list])))
        )

##### `set_variables(names=None)`

Sets the active variables for data retrieval.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`names` | `Union[str, List[str]]` | The name or list of names of the variables to set. If None, sets all variables from the last `list_variables` call. | `None`

Source code in `src/cendat/CenDatHelper.py`

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    def set_variables(
        self,
        names: Optional[Union[str, List[str]]] = None,
    ):
        """
        Sets the active variables for data retrieval.

        Args:
            names (Union[str, List[str]], optional): The name or list of names
                of the variables to set. If None, sets all variables from the
                last `list_variables` call.
        """
        vars_to_set = []
        if names is None:
            if not self._filtered_variables_cache:
                print("❌ Error: No variables to set. Run `list_variables` first.")
                return
            vars_to_set = self._filtered_variables_cache
        else:
            name_list = [names] if isinstance(names, str) else names
            all_vars = self.list_variables(to_dicts=True, patterns=None)
            vars_to_set = [v for v in all_vars if v.get("name") in name_list]
        if not vars_to_set:
            print("❌ Error: No valid variables were found to set.")
            return
        # Collapse the list of variables by product and vintage. This is a crucial
        # step to group all variables that can be requested in a single API call,
        # as each call is specific to one product/vintage.
        collapsed_vars = {}
        for var_info in vars_to_set:
            key = (var_info["product"], tuple(var_info["vintage"]), var_info["url"])
            if key not in collapsed_vars:
                collapsed_vars[key] = {
                    "product": var_info["product"],
                    "vintage": var_info["vintage"],
                    "url": var_info["url"],
                    "names": [],
                    "labels": [],
                    "values": [],
                    "types": [],
                    "attributes": [],
                    "sugg_wgts": [],
                }
            for collapsed, granular in zip(
                ["names", "labels", "values", "types", "attributes", "sugg_wgts"],
                ["name", "label", "values", "type", "attributes", "sugg_wgt"],
            ):
                collapsed_vars[key][collapsed].append(var_info[granular])
        self.variables = list(collapsed_vars.values())
        self.call_type = "variable"
        print("✅ Variables set:")
        for var_group in self.variables:
            print(
                f"  - Product: {var_group['product']} (Vintage: {var_group['vintage']})"
            )
            print(f"    Variables: {', '.join(var_group['names'])}")

##### `get_data(within='us', max_workers=25, timeout=30, preview_only=False, include_names=False, include_geoids=False, include_attributes=False, include_geometry=False, in_place=False, verbose=False, auto_retry=True, max_retries=3, min_workers=5)`

Retrieves data from the Census API based on the set parameters.

This is the final method in the chain. It constructs and executes all necessary API calls in parallel, aggregates the results, and returns a CenDatResponse object for further processing.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`within` | `Union[str, Dict, List[Dict]]` | Specifies the geographic scope. Can be "us" (default), or a dictionary defining parent geographies (e.g., `{'state': '06'}` for California), or a list of such dictionaries for multiple scopes. | `'us'`
`max_workers` | `int` | The maximum number of concurrent threads to use for API calls. Defaults to 25. | `25`
`timeout` | `int` | Request timeout in seconds for each API call. Defaults to 30. | `30`
`preview_only` | `bool` | If True, builds the list of API calls but does not execute them. Useful for debugging. Defaults to False. | `False`
`auto_retry` | `bool` | If True (default), automatically retry failed requests caused by server overload (429, 5xx errors) with reduced worker count and exponential backoff. | `True`
`max_retries` | `int` | Maximum number of retry attempts when auto_retry is enabled. Defaults to 3. | `3`
`min_workers` | `int` | Minimum number of workers to use when retrying. Worker count won't be reduced below this. Defaults to 5. | `5`

Returns:

Name | Type | Description
--- | --- | ---
`CenDatResponse` | `CenDatResponse` | An object containing the aggregated data from all successful API calls.

High-level Strategy: 1. Generate parameter sets by combining set products, geos, and variables/groups. 2. For each parameter set, determine the necessary API calls based on the `within` clause. 3. This involves handling microdata vs. aggregate data, and for aggregate data, determining if parent geography discovery or wildcards are needed. 4. Build a list of all API call tasks (URL + parameters). 5. If not in preview mode, execute all tasks concurrently using a thread pool. 6. Aggregate the results and return them in a `CenDatResponse` object.

Source code in `src/cendat/CenDatHelper.py`

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    def get_data(
        self,
        within: Union[str, Dict, List[Dict]] = "us",
        max_workers: Optional[int] = 25,
        timeout: int = 30,
        preview_only: bool = False,
        include_names: bool = False,
        include_geoids: bool = False,
        include_attributes: bool = False,
        include_geometry: bool = False,
        in_place: bool = False,
        verbose: bool = False,
        auto_retry: bool = True,
        max_retries: int = 3,
        min_workers: int = 5,
    ) -> "CenDatResponse":
        """
        Retrieves data from the Census API based on the set parameters.

        This is the final method in the chain. It constructs and executes all
        necessary API calls in parallel, aggregates the results, and returns
        a CenDatResponse object for further processing.

        Args:
            within (Union[str, Dict, List[Dict]]): Specifies the geographic
                scope. Can be "us" (default), or a dictionary defining parent
                geographies (e.g., `{'state': '06'}` for California), or a list
                of such dictionaries for multiple scopes.
            max_workers (int, optional): The maximum number of concurrent
                threads to use for API calls. Defaults to 25.
            timeout (int): Request timeout in seconds for each API call.
                Defaults to 30.
            preview_only (bool): If True, builds the list of API calls but does
                not execute them. Useful for debugging. Defaults to False.
            auto_retry (bool): If True (default), automatically retry failed
                requests caused by server overload (429, 5xx errors) with
                reduced worker count and exponential backoff.
            max_retries (int): Maximum number of retry attempts when
                auto_retry is enabled. Defaults to 3.
            min_workers (int): Minimum number of workers to use when
                retrying. Worker count won't be reduced below this. Defaults to 5.

        Returns:
            CenDatResponse: An object containing the aggregated data from all successful API calls.

        High-level Strategy:
        1. Generate parameter sets by combining set products, geos, and variables/groups.
        2. For each parameter set, determine the necessary API calls based on the `within` clause.
        3. This involves handling microdata vs. aggregate data, and for aggregate data, determining if parent geography discovery or wildcards are needed.
        4. Build a list of all API call tasks (URL + parameters).
        5. If not in preview mode, execute all tasks concurrently using a thread pool.
        6. Aggregate the results and return them in a `CenDatResponse` object.
        """
        self._create_params()

        if not self.params:
            print(
                "❌ Error: Could not create parameters. Please set geos and variables."
            )
            return CenDatResponse([])

        if include_geometry:
            if find_spec("geopandas") is None or find_spec("pandas") is None:
                print(
                    "❌ GeoPandas and/or Pandas are not installed. Please install them with 'pip install geopandas pandas'"
                )
                return CenDatResponse([])

            include_geoids = True

            valid_sumlevs_geometry = {
                "020": ["Census Regions"],
                "030": ["Census Divisions"],
                "040": ["States"],
                "050": ["Counties"],
                "060": ["County Subdivisions"],
                "140": ["Census Tracts"],
                "150": ["Census Block Groups"],
                "160": ["Incorporated Places", "Census Designated Places"],
            }

            desc_map = {
                "region": "REGION",
                "division": "DIVISION",
                "state": "STATE",
                "county": "COUNTY",
                "county subdivision": "COUSUB",
                "tract": "TRACT",
                "block group": "BLKGRP",
                "place": "PLACE",
            }

            state_codes = {
                "WA": "53",
                "DE": "10",
                "DC": "11",
                "WI": "55",
                "WV": "54",
                "HI": "15",
                "FL": "12",
                "WY": "56",
                "PR": "72",
                "NJ": "34",
                "NM": "35",
                "TX": "48",
                "LA": "22",
                "NC": "37",
                "ND": "38",
                "NE": "31",
                "TN": "47",
                "NY": "36",
                "PA": "42",
                "AK": "02",
                "NV": "32",
                "NH": "33",
                "VA": "51",
                "CO": "08",
                "CA": "06",
                "AL": "01",
                "AR": "05",
                "VT": "50",
                "IL": "17",
                "GA": "13",
                "IN": "18",
                "IA": "19",
                "MA": "25",
                "AZ": "04",
                "ID": "16",
                "CT": "09",
                "ME": "23",
                "MD": "24",
                "OK": "40",
                "OH": "39",
                "UT": "49",
                "MO": "29",
                "MN": "27",
                "MI": "26",
                "RI": "44",
                "KS": "20",
                "MT": "30",
                "MS": "28",
                "SC": "45",
                "KY": "21",
                "OR": "41",
                "SD": "46",
            }

            url = (
                "https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb?f=pjson"
            )

            try:
                response = requests.get(url)
                response.raise_for_status()
                map_servers = [
                    item["name"]
                    for item in response.json()["services"]
                    if re.search(r"ACS|Census|Current", item["name"])
                ]
                if verbose:
                    print("✅ Successfully fetched map servers.")

            except requests.exceptions.RequestException as e:
                print(f"❌ HTTP Request failed: {e}")

            # Helper function to correctly format TIGERweb SQL WHERE clauses,
            # handling both single values and lists of values.
            def format_sql_in_clause(key, value):
                # Map the user-facing geo name (e.g., 'state') to the TIGERweb field name (e.g., 'STATE')
                field = desc_map[key]
                if isinstance(value, list):
                    # For a list, create a comma-separated, quoted string: "'val1','val2'"
                    values_str = ",".join(f"'{item}'" for item in value)
                    return f"{field} IN ({values_str})"
                else:
                    # For a single value, just wrap it in quotes
                    return f"{field} IN ('{value}')"

        data_tasks = []
        geo_tasks = []

        raw_within_clauses = within if isinstance(within, list) else [within]
        if (
            include_geometry
            and raw_within_clauses in [[], ["us"]]
            and any(param["sumlev"] == "040" for param in self.params)
        ):
            raw_within_clauses = [{"state": list(state_codes.values())}]

        # Expand the `within` clauses. If a user provides a list of codes for a
        # geography (e.g., `{'state': '08', 'county': ['001', '005']}`), this
        # logic expands it into separate clauses for each combination.
        expanded_within_clauses = []
        for clause in raw_within_clauses:
            # Use builtins.dict to prevent shadowing errors with local variables
            if not isinstance(clause, builtins.dict):
                expanded_within_clauses.append(clause)
                continue

            # Separate keys with list values from those with single values
            list_items = {k: v for k, v in clause.items() if isinstance(v, list)}
            single_items = {k: v for k, v in clause.items() if not isinstance(v, list)}

            if not list_items:
                expanded_within_clauses.append(clause)
                continue

            # Create all combinations of the list values
            keys, values = zip(*list_items.items())
            for v_combination in itertools.product(*values):
                new_clause = single_items.copy()
                new_clause.update(builtins.dict(zip(keys, v_combination)))
                expanded_within_clauses.append(new_clause)

        # Iterate through each parameter set (a unique product/vintage/geo combination)
        # and each `within` clause to build the full list of API calls.
        for i, param in enumerate(self.params):
            product_info = next(
                (p for p in self.products if p["title"] == param["product"]), None
            )
            if not product_info:
                continue

            if include_geometry:
                skip_geometry = False
                if (
                    product_info["type"].split("/")[0] == "dec"
                    and product_info["vintage"][0] >= 2010
                ):
                    map_server = f"TIGERweb/tigerWMS_Census{product_info['vintage'][0]}"
                elif product_info["type"].split("/")[0] == "acs":
                    if (
                        product_info["vintage"][0] >= 2012
                        and product_info["vintage"][0] != 2020
                    ):
                        map_server = (
                            f"TIGERweb/tigerWMS_ACS{product_info['vintage'][0]}"
                        )
                    elif product_info["vintage"][0] >= 2012:
                        map_server = "TIGERweb/tigerWMS_Census2020"
                    else:
                        map_server = "TIGERweb/tigerWMS_Census2010"
                else:
                    map_server = "TIGERweb/tigerWMS_Current"

                if map_server not in map_servers:
                    skip_geometry = True
                    print(
                        f"❌ Error: the requested map server '{map_server}' is not available."
                    )
                if param["sumlev"] not in valid_sumlevs_geometry.keys():
                    skip_geometry = True
                    print(
                        f"❌ Error: the requested summary level ({param['sumlev']}) is currently supported for geometry."
                    )
                if not skip_geometry:
                    url = f"https://tigerweb.geo.census.gov/arcgis/rest/services/{map_server}/MapServer?f=pjson"

                    try:
                        response = requests.get(url)
                        response.raise_for_status()

                        map_server_layers = [
                            item["id"]
                            for item in response.json()["layers"]
                            if item["name"] in valid_sumlevs_geometry[param["sumlev"]]
                        ]
                        if verbose:
                            print("✅ Successfully fetched map server layers.")

                    except requests.exceptions.RequestException as e:
                        print(f"❌ HTTP Request failed: {e}")

            # Conditionally build the 'get' parameter string based on call type
            if "group_name" in param:  # This is a group-based call, not variable-based
                vars_to_get = [f"group({param['group_name']})"]
                if include_geoids and param["is_microdata"]:
                    print("ℹ️ GEO_ID not valid for microdata - request ignored.")
                elif include_geoids:
                    vars_to_get.insert(0, "GEO_ID")
                if include_names and param["is_microdata"]:
                    print("ℹ️ NAME not valid for microdata - request ignored.")
                elif include_names:
                    vars_to_get.insert(0, "NAME")
                # `include_attributes` is ignored for group calls as it's not applicable.
            else:
                vars_to_get = param["names"].copy()
                if include_geoids and param["is_microdata"]:
                    print("ℹ️ GEO_ID not valid for microdata - request ignored.")
                elif include_geoids:
                    vars_to_get.insert(0, "GEO_ID")
                if include_names and param["is_microdata"]:
                    print("ℹ️ NAME not valid for microdata - request ignored.")
                elif include_names:
                    vars_to_get.insert(0, "NAME")
                if include_attributes:
                    all_attributes = set()
                    # Iterate through the list of attribute strings for the selected variables
                    for attr_string in param.get("attributes", []):
                        # Check if the string is valid and not the "N/A" placeholder
                        if attr_string and attr_string != "N/A":
                            # The 'attributes' key contains a comma-separated string of variable names.
                            # We split this string and add the names to our set.
                            all_attributes.update(attr_string.split(","))

                    # Add the unique, valid attributes to the list of variables to request.
                    if all_attributes:
                        vars_to_get.extend(list(all_attributes))

            variable_names = ",".join(vars_to_get)
            target_geo = param["desc"]
            vintage_url = param["url"]
            context = {"param_index": i}

            for within_clause in expanded_within_clauses:
                # --- Microdata Path ---
                # Microdata requests are simpler: they require a dictionary specifying
                # the target geography and its codes, plus any parent geographies.
                if product_info.get("is_microdata"):
                    if not isinstance(within_clause, builtins.dict):
                        print(
                            "❌ Error: A `within` dictionary or list of dictionaries is required for microdata requests."
                        )
                        continue

                    if include_geometry:
                        print("ℹ️ Geometry not valid for microdata - request ignored.")

                    within_copy = within_clause.copy()
                    target_geo_codes = within_copy.pop(target_geo, None)

                    if target_geo_codes is None:
                        print(
                            f"❌ Error: `within` dictionary must contain the target geography: '{target_geo}'"
                        )
                        continue

                    codes_str = (
                        target_geo_codes
                        if isinstance(target_geo_codes, str)
                        else ",".join(target_geo_codes)
                    )

                    api_params = {
                        "get": variable_names,
                        "for": f"{target_geo}:{codes_str}",
                    }
                    if within_copy:
                        api_params["in"] = " ".join(
                            [f"{k}:{v}" for k, v in within_copy.items()]
                        )
                    data_tasks.append((vintage_url, api_params, context))

                # --- Aggregate Data Path ---
                # This path is more complex as it needs to handle geographic hierarchies.
                elif product_info.get("is_aggregate"):
                    required_geos = param.get("requires") or []
                    provided_parent_geos = {}
                    target_geo_codes = None
                    # If `within` is a dictionary, parse out the target geography codes
                    # and any provided parent geographies.
                    if isinstance(within_clause, builtins.dict):
                        within_copy = within_clause.copy()
                        target_geo_codes = within_copy.pop(target_geo, None)
                        provided_parent_geos = {
                            k: v for k, v in within_copy.items() if k in required_geos
                        }

                    # Case A: The target geography itself is specified in `within`.
                    # No discovery or wildcards needed; we can build the call directly.
                    if target_geo_codes:
                        codes_str = (
                            target_geo_codes
                            if isinstance(target_geo_codes, str)
                            else ",".join(map(str, target_geo_codes))
                        )
                        api_params = {
                            "get": variable_names,
                            "for": f"{target_geo}:{codes_str}",
                        }
                        if provided_parent_geos:
                            api_params["in"] = " ".join(
                                [f"{k}:{v}" for k, v in provided_parent_geos.items()]
                            )
                        data_tasks.append((vintage_url, api_params, context))
                        if include_geometry and not skip_geometry:
                            geo_params = [
                                {
                                    "param_index": i,
                                    "map_server": map_server,
                                    "layer_id": map_server_layer,
                                    "where_clause": " AND ".join(
                                        [
                                            format_sql_in_clause(k, v)
                                            for k, v in within_clause.items()
                                        ]
                                    ),
                                }
                                for map_server_layer in map_server_layers
                            ]
                            geo_tasks.extend(geo_params)
                        continue

                    # Case B: Target geography is not specified. We need to figure out
                    # the `for` and `in` clauses using wildcards or discovery.
                    # `final_in_clause` will store the components of the `in` parameter.
                    # A value of `None` indicates a level that needs discovery.
                    final_in_clause = {}
                    if include_geometry:
                        param.pop("optionalWithWCFor", None)
                        if "wildcard" in param and isinstance(param["wildcard"], list):
                            param["wildcard"] = param["wildcard"][1:]
                    if required_geos:
                        for geo in required_geos:
                            if geo in provided_parent_geos:
                                final_in_clause[geo] = provided_parent_geos[geo]
                            elif param.get("wildcard") and geo in param["wildcard"]:
                                final_in_clause[geo] = "*"
                            else:
                                final_in_clause[geo] = None  # Needs discovery

                    # Some geographies have an optional parent for wildcards.
                    # If that optional parent isn't provided, we should not include it
                    # in the `in` clause at all.

                    optional_level = param.get("optionalWithWCFor")
                    if optional_level and optional_level not in provided_parent_geos:
                        final_in_clause.pop(optional_level, None)

                    geos_to_fetch = [
                        geo for geo, code in final_in_clause.items() if code is None
                    ]

                    # If there are levels that need discovery, call the recursive helper.
                    combinations = []
                    if geos_to_fetch:
                        if verbose:
                            print(
                                f"ℹ️ Discovering parent geographies for: {geos_to_fetch}"
                            )
                        resolved_parents = {
                            k: v
                            for k, v in final_in_clause.items()
                            if v is not None and v != "*"
                        }
                        combinations = self._get_parent_geo_combinations(
                            vintage_url,
                            geos_to_fetch,
                            resolved_parents,
                            timeout=timeout,
                            max_workers=max_workers,
                        )
                    else:
                        combinations = [final_in_clause]

                    if combinations and verbose:
                        print(
                            f"✅ Found {len(combinations)} combinations. Building API queries..."
                        )

                    # Build an API task for each discovered parent geography combination.
                    for combo in combinations:
                        call_in_clause = final_in_clause.copy()
                        call_in_clause.update(combo)
                        call_in_clause = {
                            k: v for k, v in call_in_clause.items() if v is not None
                        }

                        api_params = {"get": variable_names, "for": f"{target_geo}:*"}
                        if call_in_clause:
                            api_params["in"] = " ".join(
                                [f"{k}:{v}" for k, v in call_in_clause.items()]
                            )
                        data_tasks.append((vintage_url, api_params, context))

                        # Corrected logic for geometry task creation
                        if include_geometry and not skip_geometry:
                            # Build the WHERE clause conditions from the call_in_clause dictionary.
                            where_conditions = [
                                format_sql_in_clause(k, v)
                                for k, v in call_in_clause.items()
                                if v != "*"
                            ]

                            # If there are no conditions (e.g., a nationwide query for "us"),
                            # use '1=1' as a universal "select all" filter. Otherwise, join them.
                            final_where_clause = (
                                " AND ".join(where_conditions)
                                if where_conditions
                                else "1=1"
                            )

                            geo_params = [
                                {
                                    "param_index": i,
                                    "map_server": map_server,
                                    "layer_id": map_server_layer,
                                    "where_clause": final_where_clause,
                                }
                                for map_server_layer in map_server_layers
                            ]
                            geo_tasks.extend(geo_params)

        if not data_tasks:
            print("❌ Error: Could not determine any API calls to make.")
            return CenDatResponse([])

        self.n_calls = len(data_tasks)

        # If in preview mode, print the first few planned calls and exit.
        if preview_only:
            print(f"ℹ️ Preview: this will yield {self.n_calls} API call(s).")
            for i, (url, params, _) in enumerate(data_tasks[:5]):
                print(
                    f"  - Call {i+1}: {url}?get={params.get('get')}&for={params.get('for')}&in={params.get('in','')}"
                )
            if len(data_tasks) > 5:
                print(f"  ... and {len(data_tasks) - 5} more.")
            return CenDatResponse([])

        else:
            if verbose:
                print(f"ℹ️ Making {self.n_calls} API call(s)...")
            # Execute all API calls concurrently.
            try:
                with ThreadPoolExecutor(max_workers=2) as executor:
                    # Submit the two master jobs for data and geometry fetching.
                    future_geo = executor.submit(
                        self._geometry_fetching,
                        geo_tasks,
                        max_workers,
                        timeout,
                        verbose,
                        auto_retry,
                        max_retries,
                        min_workers,
                    )
                    future_data = executor.submit(
                        self._data_fetching,
                        data_tasks,
                        max_workers,
                        timeout,
                        auto_retry,
                        max_retries,
                        min_workers,
                    )

                    # Wait for both futures to complete. The .result() call will
                    # re-raise any exceptions that occurred in the thread.
                    future_data.result()
                    future_geo.result()

            except Exception as exc:
                print(f"❌ A master fetching task failed: {exc}")
                # Return an empty response if a master task fails
                return CenDatResponse([])

            if in_place is False:
                params_copy = copy.deepcopy(self.params)
                self.params = []
                return CenDatResponse(params_copy)

## CenDatResponse

### `cendat.CenDatResponse`

#### `CenDatResponse`

A container for data returned by CenDatHelper.get_data().

This class holds the raw JSON response from the Census API and provides methods to easily filter, tabulate, and convert the data into Polars or Pandas DataFrames for analysis.

Attributes:

Name | Type | Description
--- | --- | ---
`_data` | `List[Dict]` | The raw data structure from the API calls.
`all_columns` | `set` | A set of all unique column names found in the data.

Source code in `src/cendat/CenDatResponse.py`

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    class CenDatResponse:
        """
        A container for data returned by CenDatHelper.get_data().

        This class holds the raw JSON response from the Census API and provides
        methods to easily filter, tabulate, and convert the data into Polars or
        Pandas DataFrames for analysis.

        Attributes:
            _data (List[Dict]): The raw data structure from the API calls.
            all_columns (set): A set of all unique column names found in the data.
        """

        def __init__(self, data: List[Dict]):
            """
            Initializes the CenDatResponse object.

            Args:
                data (List[Dict]): The list of dictionaries representing the
                                     API response data, typically from CenDatHelper.
            """
            self._data = data
            self.OPERATOR_MAP = {
                ">": operator.gt,
                "<": operator.lt,
                ">=": operator.ge,
                "<=": operator.le,
                "==": operator.eq,
                "!=": operator.ne,
                "in": lambda a, b: a in b,
                "not in": lambda a, b: a not in b,
            }
            self.ALLOWED_OPERATORS = set(self.OPERATOR_MAP.keys())
            self.all_columns = set(
                col for item in self._data for col in item.get("schema", [])
            )

        def _build_safe_checker(self, condition_string: str) -> Callable:
            """
            Parses a condition string and returns a function to check it.

            This internal method uses regex to safely parse a condition like
            "AGE > 50" or "10 in MY_VAR", validates the column name and operator,
            and returns a callable function that can be applied to a data row (dict).

            Args:
                condition_string (str): The condition to parse (e.g., "POP >= 1000").

            Returns:
                Callable: A function that takes a dictionary row and returns True or False.

            Raises:
                ValueError: If the condition string format, column name, or value
                            is invalid.
            """

            if not self.all_columns:
                # Fallback or handle case with no data/names
                all_columns_pattern = ""
            else:
                # FIX: Add re.escape() to handle column names with special regex characters.
                all_columns_pattern = "|".join(re.escape(col) for col in self.all_columns)

            sorted_operators = sorted(self.ALLOWED_OPERATORS, key=len, reverse=True)
            operators_pattern = "|".join(re.escape(op) for op in sorted_operators)

            patternL = re.compile(
                r"^\s*("
                + all_columns_pattern
                + r")\s*("
                + operators_pattern
                + r")\s*(.+?)\s*$"
            )
            patternR = re.compile(
                r"^\s*(.+?)\s*("
                + operators_pattern
                + r")\s*("
                + all_columns_pattern
                + r")\s*$"
            )
            patternFrac = re.compile(
                r"^\s*(("
                + all_columns_pattern
                + r")\s*/\s*("
                + all_columns_pattern
                + r"))\s*("
                + operators_pattern
                + r")\s*(.+?)\s*$"
            )
            matchL = patternL.match(condition_string)
            matchR = patternR.match(condition_string)
            matchFrac = patternFrac.match(condition_string)

            if not (matchL or matchR or matchFrac):
                raise ValueError(f"Invalid condition format: '{condition_string}'")

            if matchL:
                variable, op_string, value_string = matchL.groups()
            elif matchR:
                value_string, op_string, variable = matchR.groups()
            else:
                discard, numerator, denominator, op_string, value_string = (
                    matchFrac.groups()
                )

            if (matchL or matchR) and variable not in self.all_columns:
                raise ValueError(f"Invalid column name: '{variable}'")
            elif matchFrac and numerator not in self.all_columns:
                raise ValueError(f"Invalid column name: '{numerator}'")
            elif matchFrac and denominator not in self.all_columns:
                raise ValueError(f"Invalid column name: '{denominator}'")

            op_func = self.OPERATOR_MAP[op_string]

            try:
                value = ast.literal_eval(value_string)
            except (ValueError, SyntaxError):
                raise ValueError(f"Invalid value format: '{value_string}'")

            if matchL:
                return lambda row: (op_func(row[variable], value))
            elif matchR:
                return lambda row: (op_func(value, row[variable]))
            else:
                # Safely handle division by zero. If the denominator is 0, the condition is False.
                return lambda row: (
                    op_func(row[numerator] / row[denominator], value)
                    if row[denominator] != 0
                    else False
                )

        def _prepare_dataframe_data(self, destring: bool, _data: Optional[List[Dict]]):
            """
            Prepares and yields data for DataFrame conversion.

            This internal generator iterates through the data source, handles the
            'destringing' of values (converting string numbers to numeric types),
            and yields the processed data in a format suitable for DataFrame
            constructors.

            Args:
                destring (bool): If True, attempts to convert string representations
                                 of numbers into native numeric types.
                _data (Optional[List[Dict]]): An optional alternative data source to
                                               process, used internally by `tabulate`.

            Yields:
                tuple: A tuple containing the source item (dict), the processed data
                       (list of lists or list of dicts), and the orientation
                       ("row" or "dicts").
            """
            data_source = _data if _data is not None else self._data

            for item in data_source:
                if not item.get("data"):
                    continue  # Skip if no data was returned for this parameter set

                # Fix for potential duplication of NAME and GEO_ID if user accepts entire group
                index_map = defaultdict(list)
                for index, name in enumerate(item["schema"]):
                    index_map[name].append(index)

                removals = set()
                for indexes in index_map.values():
                    if len(indexes) > 1:
                        removals.update(indexes[1:])

                item["schema"] = [
                    var for i, var in enumerate(item["schema"]) if i not in removals
                ]
                item["data"] = [
                    [datum for i, datum in enumerate(row) if i not in removals]
                    for row in item["data"]
                ]

                if not destring:
                    yield item, item["data"], "row"
                else:
                    # Create a list of dictionaries and evaluate string values to native types
                    processed_data = []
                    for row in item["data"]:
                        row_dict = {}
                        # Use schema to ensure all columns are included in the dict
                        for k, v in zip(item["schema"], row):
                            # Check if the column is a variable that should be destringed
                            if isinstance(v, str) and (
                                k in item.get("names", [])
                                or k
                                in [
                                    var
                                    for sub in item.get("attributes", [])
                                    for var in sub.split(",")
                                ]
                                or item.get("group_name", "N/A") in k
                            ):
                                try:
                                    row_dict[k] = ast.literal_eval(v)
                                except (ValueError, SyntaxError):
                                    row_dict[k] = v  # Keep as string if eval fails

                            else:
                                row_dict[k] = v
                        processed_data.append(row_dict)
                    yield item, processed_data, "dicts"

        def to_polars(
            self,
            schema_overrides: Optional[Dict] = None,
            concat: bool = False,
            destring: bool = False,
            *,
            _data=None,
        ) -> Union[List["pl.DataFrame"], "pl.DataFrame"]:
            """
            Converts the response data into Polars DataFrames.

            Each distinct API call result is converted into its own DataFrame.
            Contextual columns (product, vintage, etc.) are added automatically.

            Args:
                schema_overrides (dict, optional): A dictionary to override inferred
                    Polars schema types. Passed directly to pl.DataFrame().
                    Example: {'POP': pl.Int64, 'GEO_ID': pl.Utf8}.
                concat (bool): If True, concatenates all resulting DataFrames into a
                    single DataFrame. Defaults to False.
                destring (bool): If True, attempts to convert string representations
                    of numbers into native numeric types. Defaults to False.
                _data: For internal use by other methods. Do not set manually.

            Returns:
                Union[List[pl.DataFrame], pl.DataFrame]: A list of Polars DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Polars is not installed or no data is available.
            """
            try:
                import polars as pl
            except ImportError:
                print(
                    "❌ Polars is not installed. Please install it with 'pip install polars'"
                )
                return []

            dataframes = []
            for item, processed_data, orient in self._prepare_dataframe_data(
                destring, _data
            ):
                df = pl.DataFrame(
                    processed_data,
                    schema=item["schema"],
                    orient=orient,
                    schema_overrides=schema_overrides,
                    infer_schema_length=None,
                )

                # Add context columns
                df = df.with_columns(
                    [
                        pl.lit(item["product"]).alias("product"),
                        pl.lit(item["vintage"][0]).cast(str).alias("vintage"),
                        pl.lit(item["sumlev"]).alias("sumlev"),
                        pl.lit(item["desc"]).alias("desc"),
                    ]
                )
                dataframes.append(df)

            if not dataframes:
                return []

            return pl.concat(dataframes, how="diagonal") if concat else dataframes

        def to_pandas(
            self,
            dtypes: Optional[Dict] = None,
            concat: bool = False,
            destring: bool = False,
            *,
            _data=None,
        ) -> Union[List["pd.DataFrame"], "pd.DataFrame"]:
            """
            Converts the response data into Pandas DataFrames.

            Each distinct API call result is converted into its own DataFrame.
            Contextual columns (product, vintage, etc.) are added automatically.

            Args:
                dtypes (dict, optional): A dictionary of column names to data types,
                    passed to the pandas.DataFrame.astype() method.
                    Example: {'POP': 'int64', 'GEO_ID': 'str'}.
                concat (bool): If True, concatenates all resulting DataFrames into a
                    single DataFrame. Defaults to False.
                destring (bool): If True, attempts to convert string representations
                    of numbers into native numeric types. Defaults to False.
                _data: For internal use by other methods. Do not set manually.

            Returns:
                Union[List[pd.DataFrame], pd.DataFrame]: A list of Pandas DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Pandas is not installed or no data is available.
            """
            try:
                import pandas as pd
            except ImportError:
                print(
                    "❌ Pandas is not installed. Please install it with 'pip install pandas'"
                )
                return []

            dataframes = []
            for item, processed_data, orient in self._prepare_dataframe_data(
                destring, _data
            ):
                # Pandas DataFrame constructor can handle both orientations
                df = pd.DataFrame(
                    processed_data, columns=item["schema"] if orient == "row" else None
                )

                if dtypes:
                    df = df.astype(dtypes, errors="ignore")

                # Add context columns
                df["product"] = item["product"]
                df["vintage"] = item["vintage"][0]
                df["vintage"] = df["vintage"].astype("string")
                df["sumlev"] = item["sumlev"]
                df["desc"] = item["desc"]
                dataframes.append(df)

            if not dataframes:
                return []

            return pd.concat(dataframes, ignore_index=True) if concat else dataframes

        def to_gpd(
            self,
            dtypes: Optional[Dict] = None,
            destring: bool = False,
            join_strategy: str = "left",
        ) -> "gpd.GeoDataFrame":
            """
            Converts the response data into a GeoPandas GeoDataFrame with geometries.

            This method first converts the tabular data to Pandas DataFrames, then
            joins them with the corresponding geometry data fetched via the
            `include_geometry=True` flag in `CenDatHelper.get_data()`.

            Args:
                destring (bool): If True, attempts to convert string representations
                    of numbers into native numeric types. Passed to `to_pandas`.
                    Defaults to False.
                join_strategy (str): The type of join to perform between the data
                    and the geometries. Must be 'left' (default) or 'inner'.
                    - 'left': Keeps all records from the data, adding geometry where available.
                    - 'inner': Keeps only records that exist in both data and geometry sets.

            Returns:
                gpd.GeoDataFrame: A single, concatenated GeoDataFrame containing both the tabular data and the geographic shapes. Returns an empty GeoDataFrame if GeoPandas is not installed or no data is available.
            """
            try:
                import geopandas as gpd
                import pandas as pd
            except ImportError:
                print(
                    "❌ GeoPandas and/or Pandas are not installed. Please install them with 'pip install geopandas pandas'"
                )
                return []

            if join_strategy not in ["left", "inner"]:
                raise ValueError("`join_strategy` must be either 'left' or 'inner'")

            geodataframes = []
            for item, processed_data, orient in self._prepare_dataframe_data(
                destring, _data=None
            ):
                # Create the base pandas DataFrame
                df = pd.DataFrame(
                    processed_data, columns=item["schema"] if orient == "row" else None
                )
                df["GEOID"] = df["GEO_ID"].str[9:]

                if dtypes:
                    df = df.astype(dtypes, errors="ignore")

                geometry_gdf = item.get("geometry")

                # Proceed only if geometry data is available for this item
                if geometry_gdf is not None and not geometry_gdf.empty:
                    if "GEO_ID" not in df.columns or "GEOID" not in geometry_gdf.columns:
                        print(
                            f"⚠️ Warning: 'GEO_ID' (for data) or 'GEOID' (for geometry) column not found for product '{item['product']}'. Cannot join. "
                            "Try re-running get_data() with include_geoids=True."
                        )
                        continue

                    # To prevent column clashes (e.g., NAME_x, NAME_y), only use essential columns from the geometry GDF
                    geo_subset = geometry_gdf[["GEOID", "geometry"]]

                    # Merge the tabular data with the geometry data, specifying the different key names
                    merged_df = df.merge(geo_subset, on="GEOID", how=join_strategy)

                    # Convert the merged result into a GeoDataFrame
                    gdf = gpd.GeoDataFrame(merged_df, geometry="geometry")

                    # Add context columns
                    gdf["product"] = item["product"]
                    gdf["vintage"] = item["vintage"][0]
                    gdf["vintage"] = gdf["vintage"].astype("string")
                    gdf["sumlev"] = item["sumlev"]
                    gdf["desc"] = item["desc"]

                    geodataframes.append(gdf)
                else:
                    print(
                        f"ℹ️ No geometry found for product '{item['product']}'. Skipping this item for GeoDataFrame conversion."
                    )

            if not geodataframes:
                return gpd.GeoDataFrame()

            return pd.concat(geodataframes, ignore_index=True)

        def tabulate(
            self,
            *variables: str,
            strat_by: Optional[str] = None,
            weight_var: Optional[str] = None,
            weight_div: Optional[int] = None,
            where: Optional[Union[str, List[str]]] = None,
            logic: Callable = all,
            digits: int = 1,
        ):
            """
            Generates and prints a frequency table for specified variables.

            This method creates a crosstabulation, similar to Stata's `tab` command,
            calculating counts, percentages, and cumulative distributions. It can
            dynamically use either the Polars or Pandas library for data manipulation,
            whichever is available.

            Args:
                *variables (str): One or more column names to include in the tabulation.
                strat_by (Optional[str]): A column name to stratify the results by.
                    Percentages and cumulative stats will be calculated within each
                    stratum. Defaults to None.
                weight_var (Optional[str]): The name of the column to use for weighting.
                    If None, each row has a weight of 1. Defaults to None.
                weight_div (Optional[int]): A positive integer to divide the weight by,
                    useful for pooled tabulations across multiple product vintages.
                    `weight_var` must be provided if this is used. Defaults to None.
                where (Optional[Union[str, List[str]]]): A string or list of strings
                    representing conditions to filter the data before tabulation.
                    Each condition should be in a format like "variable operator value"
                    (e.g., "age > 30"). Defaults to None.
                logic (Callable): The function to apply when multiple `where` conditions
                    are provided. Use `all` for AND logic (default) or `any` for OR logic.
                digits (int): The number of decimal places to display for floating-point
                    numbers in the output table. Defaults to 1.
            """
            try:
                import polars as pl

                df_lib = "pl"
            except ImportError:
                try:
                    import pandas as pd

                    df_lib = "pd"
                except ImportError:
                    print(
                        "❌ Neither Polars nor Pandas are installed. Please install "
                        "whichever you prefer to proceed with tabulations"
                    )
                    return

            bad_vars = [
                variable
                for variable in variables
                if variable
                not in self.all_columns.union({"product", "vintage", "sumlev", "desc"})
            ]
            if bad_vars:
                print(
                    f"❌ Cross-tabulation variables {bad_vars} not found in available variables."
                )
                return

            if strat_by and strat_by not in self.all_columns.union(
                {"product", "vintage", "sumlev", "desc"}
            ):
                print(
                    f"❌ Stratification variable '{strat_by}' not found in available variables."
                )
                return

            if weight_var and weight_var not in self.all_columns.union(
                {"product", "vintage", "sumlev", "desc"}
            ):
                print(f"❌ Weight variable '{weight_var}' not found in set variables.")
                return

            if weight_div is not None:
                if not isinstance(weight_div, int) or weight_div <= 0:
                    print("❌ Error: `weight_div` must be a positive integer.")
                    return
                if not weight_var:
                    print("ℹ️ `weight_div` is only valid if `weight_var` is provided.")

            if where:
                where_list = [where] if isinstance(where, str) else where
                try:
                    checker_functions = [self._build_safe_checker(w) for w in where_list]

                    dat_filtered = []
                    # for item in self._data:
                    for item, processed_data, _ in self._prepare_dataframe_data(
                        destring=True, _data=None
                    ):
                        if not processed_data:
                            continue

                        filtered_rows = [
                            row
                            for row in processed_data
                            if logic(checker(row) for checker in checker_functions)
                        ]

                        if filtered_rows:
                            new_item = item.copy()
                            schema = new_item["schema"]
                            new_item["data"] = [
                                [row.get(col) for col in schema] for row in filtered_rows
                            ]

                            dat_filtered.append(new_item)

                except ValueError as e:
                    print(f"Error processing conditions: {e}")
                    return
            else:
                dat_filtered = self._data

            if not dat_filtered:
                print("ℹ️ No data to tabulate after filtering.")
                return

            table = None
            if df_lib == "pl":
                try:
                    if weight_var and weight_div:
                        wgt_agg = (pl.col(weight_var) / weight_div).sum()
                    elif weight_var:
                        wgt_agg = pl.col(weight_var).sum()
                    else:
                        wgt_agg = pl.len()

                    df = self.to_polars(
                        concat=True,
                        destring=True if not where else False,
                        _data=dat_filtered,
                    )

                    if df.height == 0:
                        print("ℹ️ DataFrame is empty, cannot tabulate.")
                        return

                    table = (
                        (
                            df.with_columns(wgt_agg.over(strat_by).alias("N"))
                            .group_by(strat_by, *variables)
                            .agg(
                                wgt_agg.alias("n"),
                                ((wgt_agg * 100) / pl.col("N").first()).alias("pct"),
                            )
                            .sort(strat_by, *variables)
                            .with_columns(
                                pl.col("n").cum_sum().over(strat_by).alias("cumn"),
                                pl.col("pct").cum_sum().over(strat_by).alias("cumpct"),
                            )
                        )
                        if strat_by
                        else (
                            df.with_columns(wgt_agg.alias("N"))
                            .group_by(*variables)
                            .agg(
                                wgt_agg.alias("n"),
                                ((wgt_agg * 100) / pl.col("N").first()).alias("pct"),
                            )
                            .sort(*variables)
                            .with_columns(
                                pl.col("n").cum_sum().alias("cumn"),
                                pl.col("pct").cum_sum().alias("cumpct"),
                            )
                        )
                    )

                except pl.exceptions.ColumnNotFoundError:
                    print(
                        f"❌ Error: The weight column '{weight_var}' was not found in the DataFrame."
                    )
                    return
                except TypeError:
                    print(
                        f"❌ Error: The weight column '{weight_var}' contains non-numeric values."
                    )
                    return
                except Exception as e:
                    print(f"❌ Polars tabulation failed: {e}")
                    return

            else:  # df_lib == "pd"
                try:
                    df = self.to_pandas(
                        concat=True,
                        destring=True if not where else False,
                        _data=dat_filtered,
                    )
                    if df.empty:
                        print("ℹ️ DataFrame is empty, cannot tabulate.")
                        return

                    group_cols = list(variables)
                    if strat_by:
                        group_cols.insert(0, strat_by)

                    # Determine the weight column and calculate n
                    if weight_var:
                        wgt_col = weight_var
                        if weight_div:
                            wgt_col = "_temp_wgt"
                            df[wgt_col] = df[weight_var] / weight_div
                        table = (
                            df.groupby(group_cols, observed=True)[wgt_col]
                            .sum()
                            .reset_index(name="n")
                        )
                    else:
                        table = (
                            df.groupby(group_cols, observed=True)
                            .size()
                            .reset_index(name="n")
                        )

                    # Calculate N (total per stratum or overall) and percentages
                    if strat_by:
                        if weight_var:
                            wgt_col_for_n = wgt_col  # Use temp col if it exists
                            stratum_totals = (
                                df.groupby(strat_by, observed=True)[wgt_col_for_n]
                                .sum()
                                .reset_index(name="N")
                            )
                        else:
                            stratum_totals = (
                                df.groupby(strat_by, observed=True)
                                .size()
                                .reset_index(name="N")
                            )
                        table = pd.merge(table, stratum_totals, on=strat_by)
                    else:
                        if weight_var:
                            wgt_col_for_n = wgt_col  # Use temp col if it exists
                            table["N"] = df[wgt_col_for_n].sum()
                        else:
                            table["N"] = len(df)

                    table["pct"] = (table["n"] * 100) / table["N"]
                    table = table.sort_values(by=group_cols)

                    # Calculate cumulative sums (within strata or overall)
                    if strat_by:
                        table["cumn"] = table.groupby(strat_by, observed=True)["n"].cumsum()
                        table["cumpct"] = table.groupby(strat_by, observed=True)[
                            "pct"
                        ].cumsum()
                    else:
                        table["cumn"] = table["n"].cumsum()
                        table["cumpct"] = table["pct"].cumsum()

                    # Cleanup
                    table.drop(columns=["N"], inplace=True)
                    if weight_var and weight_div:
                        df.drop(columns=["_temp_wgt"], inplace=True)

                except KeyError:
                    print(
                        f"❌ Error: A specified column (e.g., '{weight_var}' or '{strat_by}') was not found."
                    )
                    return
                except TypeError:
                    print(
                        f"❌ Error: The weight column '{weight_var}' contains non-numeric values."
                    )
                    return
                except Exception as e:
                    print(f"❌ Pandas tabulation failed: {e}")
                    return

            if table is None:
                return

            with (
                pl.Config(
                    float_precision=digits,
                    set_tbl_rows=-1,
                    set_tbl_cols=-1,
                    set_tbl_width_chars=-1,
                    set_thousands_separator=",",
                    set_tbl_hide_column_data_types=True,
                    set_tbl_cell_alignment="RIGHT",
                )
                if df_lib == "pl"
                else pd.option_context(
                    "display.float_format",
                    lambda x: f"{x:,.{digits}f}",
                    "display.max_rows",
                    None,
                    "display.max_columns",
                    None,
                    "display.max_colwidth",
                    None,
                    "styler.format.thousands",
                    ",",
                )
            ):
                print(table)

        def __repr__(self) -> str:
            """Provides a developer-friendly representation of the object."""
            return f"<CenDatResponse with {len(self._data)} result(s)>"

        def __getitem__(self, index: int) -> Dict:
            """Allows accessing individual raw result dictionaries by index."""
            return self._data[index]

##### `__init__(data)`

Initializes the CenDatResponse object.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data` | `List[Dict]` | The list of dictionaries representing the API response data, typically from CenDatHelper. | required

Source code in `src/cendat/CenDatResponse.py`

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    def __init__(self, data: List[Dict]):
        """
        Initializes the CenDatResponse object.

        Args:
            data (List[Dict]): The list of dictionaries representing the
                                 API response data, typically from CenDatHelper.
        """
        self._data = data
        self.OPERATOR_MAP = {
            ">": operator.gt,
            "<": operator.lt,
            ">=": operator.ge,
            "<=": operator.le,
            "==": operator.eq,
            "!=": operator.ne,
            "in": lambda a, b: a in b,
            "not in": lambda a, b: a not in b,
        }
        self.ALLOWED_OPERATORS = set(self.OPERATOR_MAP.keys())
        self.all_columns = set(
            col for item in self._data for col in item.get("schema", [])
        )

##### `to_polars(schema_overrides=None, concat=False, destring=False, *, _data=None)`

Converts the response data into Polars DataFrames.

Each distinct API call result is converted into its own DataFrame. Contextual columns (product, vintage, etc.) are added automatically.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`schema_overrides` | `dict` | A dictionary to override inferred Polars schema types. Passed directly to pl.DataFrame(). Example: {'POP': pl.Int64, 'GEO_ID': pl.Utf8}. | `None`
`concat` | `bool` | If True, concatenates all resulting DataFrames into a single DataFrame. Defaults to False. | `False`
`destring` | `bool` | If True, attempts to convert string representations of numbers into native numeric types. Defaults to False. | `False`
`_data` |  | For internal use by other methods. Do not set manually. | `None`

Returns:

Type | Description
--- | ---
`Union[List[DataFrame], DataFrame]` | Union[List[pl.DataFrame], pl.DataFrame]: A list of Polars DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Polars is not installed or no data is available.

Source code in `src/cendat/CenDatResponse.py`

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    def to_polars(
        self,
        schema_overrides: Optional[Dict] = None,
        concat: bool = False,
        destring: bool = False,
        *,
        _data=None,
    ) -> Union[List["pl.DataFrame"], "pl.DataFrame"]:
        """
        Converts the response data into Polars DataFrames.

        Each distinct API call result is converted into its own DataFrame.
        Contextual columns (product, vintage, etc.) are added automatically.

        Args:
            schema_overrides (dict, optional): A dictionary to override inferred
                Polars schema types. Passed directly to pl.DataFrame().
                Example: {'POP': pl.Int64, 'GEO_ID': pl.Utf8}.
            concat (bool): If True, concatenates all resulting DataFrames into a
                single DataFrame. Defaults to False.
            destring (bool): If True, attempts to convert string representations
                of numbers into native numeric types. Defaults to False.
            _data: For internal use by other methods. Do not set manually.

        Returns:
            Union[List[pl.DataFrame], pl.DataFrame]: A list of Polars DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Polars is not installed or no data is available.
        """
        try:
            import polars as pl
        except ImportError:
            print(
                "❌ Polars is not installed. Please install it with 'pip install polars'"
            )
            return []

        dataframes = []
        for item, processed_data, orient in self._prepare_dataframe_data(
            destring, _data
        ):
            df = pl.DataFrame(
                processed_data,
                schema=item["schema"],
                orient=orient,
                schema_overrides=schema_overrides,
                infer_schema_length=None,
            )

            # Add context columns
            df = df.with_columns(
                [
                    pl.lit(item["product"]).alias("product"),
                    pl.lit(item["vintage"][0]).cast(str).alias("vintage"),
                    pl.lit(item["sumlev"]).alias("sumlev"),
                    pl.lit(item["desc"]).alias("desc"),
                ]
            )
            dataframes.append(df)

        if not dataframes:
            return []

        return pl.concat(dataframes, how="diagonal") if concat else dataframes

##### `to_pandas(dtypes=None, concat=False, destring=False, *, _data=None)`

Converts the response data into Pandas DataFrames.

Each distinct API call result is converted into its own DataFrame. Contextual columns (product, vintage, etc.) are added automatically.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`dtypes` | `dict` | A dictionary of column names to data types, passed to the pandas.DataFrame.astype() method. Example: {'POP': 'int64', 'GEO_ID': 'str'}. | `None`
`concat` | `bool` | If True, concatenates all resulting DataFrames into a single DataFrame. Defaults to False. | `False`
`destring` | `bool` | If True, attempts to convert string representations of numbers into native numeric types. Defaults to False. | `False`
`_data` |  | For internal use by other methods. Do not set manually. | `None`

Returns:

Type | Description
--- | ---
`Union[List[DataFrame], DataFrame]` | Union[List[pd.DataFrame], pd.DataFrame]: A list of Pandas DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Pandas is not installed or no data is available.

Source code in `src/cendat/CenDatResponse.py`

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    def to_pandas(
        self,
        dtypes: Optional[Dict] = None,
        concat: bool = False,
        destring: bool = False,
        *,
        _data=None,
    ) -> Union[List["pd.DataFrame"], "pd.DataFrame"]:
        """
        Converts the response data into Pandas DataFrames.

        Each distinct API call result is converted into its own DataFrame.
        Contextual columns (product, vintage, etc.) are added automatically.

        Args:
            dtypes (dict, optional): A dictionary of column names to data types,
                passed to the pandas.DataFrame.astype() method.
                Example: {'POP': 'int64', 'GEO_ID': 'str'}.
            concat (bool): If True, concatenates all resulting DataFrames into a
                single DataFrame. Defaults to False.
            destring (bool): If True, attempts to convert string representations
                of numbers into native numeric types. Defaults to False.
            _data: For internal use by other methods. Do not set manually.

        Returns:
            Union[List[pd.DataFrame], pd.DataFrame]: A list of Pandas DataFrames, or a single concatenated DataFrame if `concat=True`. Returns an empty list if Pandas is not installed or no data is available.
        """
        try:
            import pandas as pd
        except ImportError:
            print(
                "❌ Pandas is not installed. Please install it with 'pip install pandas'"
            )
            return []

        dataframes = []
        for item, processed_data, orient in self._prepare_dataframe_data(
            destring, _data
        ):
            # Pandas DataFrame constructor can handle both orientations
            df = pd.DataFrame(
                processed_data, columns=item["schema"] if orient == "row" else None
            )

            if dtypes:
                df = df.astype(dtypes, errors="ignore")

            # Add context columns
            df["product"] = item["product"]
            df["vintage"] = item["vintage"][0]
            df["vintage"] = df["vintage"].astype("string")
            df["sumlev"] = item["sumlev"]
            df["desc"] = item["desc"]
            dataframes.append(df)

        if not dataframes:
            return []

        return pd.concat(dataframes, ignore_index=True) if concat else dataframes

##### `to_gpd(dtypes=None, destring=False, join_strategy='left')`

Converts the response data into a GeoPandas GeoDataFrame with geometries.

This method first converts the tabular data to Pandas DataFrames, then joins them with the corresponding geometry data fetched via the `include_geometry=True` flag in `CenDatHelper.get_data()`.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`destring` | `bool` | If True, attempts to convert string representations of numbers into native numeric types. Passed to `to_pandas`. Defaults to False. | `False`
`join_strategy` | `str` | The type of join to perform between the data and the geometries. Must be 'left' (default) or 'inner'. - 'left': Keeps all records from the data, adding geometry where available. - 'inner': Keeps only records that exist in both data and geometry sets. | `'left'`

Returns:

Type | Description
--- | ---
`GeoDataFrame` | gpd.GeoDataFrame: A single, concatenated GeoDataFrame containing both the tabular data and the geographic shapes. Returns an empty GeoDataFrame if GeoPandas is not installed or no data is available.

Source code in `src/cendat/CenDatResponse.py`

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    def to_gpd(
        self,
        dtypes: Optional[Dict] = None,
        destring: bool = False,
        join_strategy: str = "left",
    ) -> "gpd.GeoDataFrame":
        """
        Converts the response data into a GeoPandas GeoDataFrame with geometries.

        This method first converts the tabular data to Pandas DataFrames, then
        joins them with the corresponding geometry data fetched via the
        `include_geometry=True` flag in `CenDatHelper.get_data()`.

        Args:
            destring (bool): If True, attempts to convert string representations
                of numbers into native numeric types. Passed to `to_pandas`.
                Defaults to False.
            join_strategy (str): The type of join to perform between the data
                and the geometries. Must be 'left' (default) or 'inner'.
                - 'left': Keeps all records from the data, adding geometry where available.
                - 'inner': Keeps only records that exist in both data and geometry sets.

        Returns:
            gpd.GeoDataFrame: A single, concatenated GeoDataFrame containing both the tabular data and the geographic shapes. Returns an empty GeoDataFrame if GeoPandas is not installed or no data is available.
        """
        try:
            import geopandas as gpd
            import pandas as pd
        except ImportError:
            print(
                "❌ GeoPandas and/or Pandas are not installed. Please install them with 'pip install geopandas pandas'"
            )
            return []

        if join_strategy not in ["left", "inner"]:
            raise ValueError("`join_strategy` must be either 'left' or 'inner'")

        geodataframes = []
        for item, processed_data, orient in self._prepare_dataframe_data(
            destring, _data=None
        ):
            # Create the base pandas DataFrame
            df = pd.DataFrame(
                processed_data, columns=item["schema"] if orient == "row" else None
            )
            df["GEOID"] = df["GEO_ID"].str[9:]

            if dtypes:
                df = df.astype(dtypes, errors="ignore")

            geometry_gdf = item.get("geometry")

            # Proceed only if geometry data is available for this item
            if geometry_gdf is not None and not geometry_gdf.empty:
                if "GEO_ID" not in df.columns or "GEOID" not in geometry_gdf.columns:
                    print(
                        f"⚠️ Warning: 'GEO_ID' (for data) or 'GEOID' (for geometry) column not found for product '{item['product']}'. Cannot join. "
                        "Try re-running get_data() with include_geoids=True."
                    )
                    continue

                # To prevent column clashes (e.g., NAME_x, NAME_y), only use essential columns from the geometry GDF
                geo_subset = geometry_gdf[["GEOID", "geometry"]]

                # Merge the tabular data with the geometry data, specifying the different key names
                merged_df = df.merge(geo_subset, on="GEOID", how=join_strategy)

                # Convert the merged result into a GeoDataFrame
                gdf = gpd.GeoDataFrame(merged_df, geometry="geometry")

                # Add context columns
                gdf["product"] = item["product"]
                gdf["vintage"] = item["vintage"][0]
                gdf["vintage"] = gdf["vintage"].astype("string")
                gdf["sumlev"] = item["sumlev"]
                gdf["desc"] = item["desc"]

                geodataframes.append(gdf)
            else:
                print(
                    f"ℹ️ No geometry found for product '{item['product']}'. Skipping this item for GeoDataFrame conversion."
                )

        if not geodataframes:
            return gpd.GeoDataFrame()

        return pd.concat(geodataframes, ignore_index=True)

##### `tabulate(*variables, strat_by=None, weight_var=None, weight_div=None, where=None, logic=all, digits=1)`

Generates and prints a frequency table for specified variables.

This method creates a crosstabulation, similar to Stata's `tab` command, calculating counts, percentages, and cumulative distributions. It can dynamically use either the Polars or Pandas library for data manipulation, whichever is available.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`*variables` | `str` | One or more column names to include in the tabulation. | `()`
`strat_by` | `Optional[str]` | A column name to stratify the results by. Percentages and cumulative stats will be calculated within each stratum. Defaults to None. | `None`
`weight_var` | `Optional[str]` | The name of the column to use for weighting. If None, each row has a weight of 1. Defaults to None. | `None`
`weight_div` | `Optional[int]` | A positive integer to divide the weight by, useful for pooled tabulations across multiple product vintages. `weight_var` must be provided if this is used. Defaults to None. | `None`
`where` | `Optional[Union[str, List[str]]]` | A string or list of strings representing conditions to filter the data before tabulation. Each condition should be in a format like "variable operator value" (e.g., "age > 30"). Defaults to None. | `None`
`logic` | `Callable` | The function to apply when multiple `where` conditions are provided. Use `all` for AND logic (default) or `any` for OR logic. | `all`
`digits` | `int` | The number of decimal places to display for floating-point numbers in the output table. Defaults to 1. | `1`

Source code in `src/cendat/CenDatResponse.py`

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    def tabulate(
        self,
        *variables: str,
        strat_by: Optional[str] = None,
        weight_var: Optional[str] = None,
        weight_div: Optional[int] = None,
        where: Optional[Union[str, List[str]]] = None,
        logic: Callable = all,
        digits: int = 1,
    ):
        """
        Generates and prints a frequency table for specified variables.

        This method creates a crosstabulation, similar to Stata's `tab` command,
        calculating counts, percentages, and cumulative distributions. It can
        dynamically use either the Polars or Pandas library for data manipulation,
        whichever is available.

        Args:
            *variables (str): One or more column names to include in the tabulation.
            strat_by (Optional[str]): A column name to stratify the results by.
                Percentages and cumulative stats will be calculated within each
                stratum. Defaults to None.
            weight_var (Optional[str]): The name of the column to use for weighting.
                If None, each row has a weight of 1. Defaults to None.
            weight_div (Optional[int]): A positive integer to divide the weight by,
                useful for pooled tabulations across multiple product vintages.
                `weight_var` must be provided if this is used. Defaults to None.
            where (Optional[Union[str, List[str]]]): A string or list of strings
                representing conditions to filter the data before tabulation.
                Each condition should be in a format like "variable operator value"
                (e.g., "age > 30"). Defaults to None.
            logic (Callable): The function to apply when multiple `where` conditions
                are provided. Use `all` for AND logic (default) or `any` for OR logic.
            digits (int): The number of decimal places to display for floating-point
                numbers in the output table. Defaults to 1.
        """
        try:
            import polars as pl

            df_lib = "pl"
        except ImportError:
            try:
                import pandas as pd

                df_lib = "pd"
            except ImportError:
                print(
                    "❌ Neither Polars nor Pandas are installed. Please install "
                    "whichever you prefer to proceed with tabulations"
                )
                return

        bad_vars = [
            variable
            for variable in variables
            if variable
            not in self.all_columns.union({"product", "vintage", "sumlev", "desc"})
        ]
        if bad_vars:
            print(
                f"❌ Cross-tabulation variables {bad_vars} not found in available variables."
            )
            return

        if strat_by and strat_by not in self.all_columns.union(
            {"product", "vintage", "sumlev", "desc"}
        ):
            print(
                f"❌ Stratification variable '{strat_by}' not found in available variables."
            )
            return

        if weight_var and weight_var not in self.all_columns.union(
            {"product", "vintage", "sumlev", "desc"}
        ):
            print(f"❌ Weight variable '{weight_var}' not found in set variables.")
            return

        if weight_div is not None:
            if not isinstance(weight_div, int) or weight_div <= 0:
                print("❌ Error: `weight_div` must be a positive integer.")
                return
            if not weight_var:
                print("ℹ️ `weight_div` is only valid if `weight_var` is provided.")

        if where:
            where_list = [where] if isinstance(where, str) else where
            try:
                checker_functions = [self._build_safe_checker(w) for w in where_list]

                dat_filtered = []
                # for item in self._data:
                for item, processed_data, _ in self._prepare_dataframe_data(
                    destring=True, _data=None
                ):
                    if not processed_data:
                        continue

                    filtered_rows = [
                        row
                        for row in processed_data
                        if logic(checker(row) for checker in checker_functions)
                    ]

                    if filtered_rows:
                        new_item = item.copy()
                        schema = new_item["schema"]
                        new_item["data"] = [
                            [row.get(col) for col in schema] for row in filtered_rows
                        ]

                        dat_filtered.append(new_item)

            except ValueError as e:
                print(f"Error processing conditions: {e}")
                return
        else:
            dat_filtered = self._data

        if not dat_filtered:
            print("ℹ️ No data to tabulate after filtering.")
            return

        table = None
        if df_lib == "pl":
            try:
                if weight_var and weight_div:
                    wgt_agg = (pl.col(weight_var) / weight_div).sum()
                elif weight_var:
                    wgt_agg = pl.col(weight_var).sum()
                else:
                    wgt_agg = pl.len()

                df = self.to_polars(
                    concat=True,
                    destring=True if not where else False,
                    _data=dat_filtered,
                )

                if df.height == 0:
                    print("ℹ️ DataFrame is empty, cannot tabulate.")
                    return

                table = (
                    (
                        df.with_columns(wgt_agg.over(strat_by).alias("N"))
                        .group_by(strat_by, *variables)
                        .agg(
                            wgt_agg.alias("n"),
                            ((wgt_agg * 100) / pl.col("N").first()).alias("pct"),
                        )
                        .sort(strat_by, *variables)
                        .with_columns(
                            pl.col("n").cum_sum().over(strat_by).alias("cumn"),
                            pl.col("pct").cum_sum().over(strat_by).alias("cumpct"),
                        )
                    )
                    if strat_by
                    else (
                        df.with_columns(wgt_agg.alias("N"))
                        .group_by(*variables)
                        .agg(
                            wgt_agg.alias("n"),
                            ((wgt_agg * 100) / pl.col("N").first()).alias("pct"),
                        )
                        .sort(*variables)
                        .with_columns(
                            pl.col("n").cum_sum().alias("cumn"),
                            pl.col("pct").cum_sum().alias("cumpct"),
                        )
                    )
                )

            except pl.exceptions.ColumnNotFoundError:
                print(
                    f"❌ Error: The weight column '{weight_var}' was not found in the DataFrame."
                )
                return
            except TypeError:
                print(
                    f"❌ Error: The weight column '{weight_var}' contains non-numeric values."
                )
                return
            except Exception as e:
                print(f"❌ Polars tabulation failed: {e}")
                return

        else:  # df_lib == "pd"
            try:
                df = self.to_pandas(
                    concat=True,
                    destring=True if not where else False,
                    _data=dat_filtered,
                )
                if df.empty:
                    print("ℹ️ DataFrame is empty, cannot tabulate.")
                    return

                group_cols = list(variables)
                if strat_by:
                    group_cols.insert(0, strat_by)

                # Determine the weight column and calculate n
                if weight_var:
                    wgt_col = weight_var
                    if weight_div:
                        wgt_col = "_temp_wgt"
                        df[wgt_col] = df[weight_var] / weight_div
                    table = (
                        df.groupby(group_cols, observed=True)[wgt_col]
                        .sum()
                        .reset_index(name="n")
                    )
                else:
                    table = (
                        df.groupby(group_cols, observed=True)
                        .size()
                        .reset_index(name="n")
                    )

                # Calculate N (total per stratum or overall) and percentages
                if strat_by:
                    if weight_var:
                        wgt_col_for_n = wgt_col  # Use temp col if it exists
                        stratum_totals = (
                            df.groupby(strat_by, observed=True)[wgt_col_for_n]
                            .sum()
                            .reset_index(name="N")
                        )
                    else:
                        stratum_totals = (
                            df.groupby(strat_by, observed=True)
                            .size()
                            .reset_index(name="N")
                        )
                    table = pd.merge(table, stratum_totals, on=strat_by)
                else:
                    if weight_var:
                        wgt_col_for_n = wgt_col  # Use temp col if it exists
                        table["N"] = df[wgt_col_for_n].sum()
                    else:
                        table["N"] = len(df)

                table["pct"] = (table["n"] * 100) / table["N"]
                table = table.sort_values(by=group_cols)

                # Calculate cumulative sums (within strata or overall)
                if strat_by:
                    table["cumn"] = table.groupby(strat_by, observed=True)["n"].cumsum()
                    table["cumpct"] = table.groupby(strat_by, observed=True)[
                        "pct"
                    ].cumsum()
                else:
                    table["cumn"] = table["n"].cumsum()
                    table["cumpct"] = table["pct"].cumsum()

                # Cleanup
                table.drop(columns=["N"], inplace=True)
                if weight_var and weight_div:
                    df.drop(columns=["_temp_wgt"], inplace=True)

            except KeyError:
                print(
                    f"❌ Error: A specified column (e.g., '{weight_var}' or '{strat_by}') was not found."
                )
                return
            except TypeError:
                print(
                    f"❌ Error: The weight column '{weight_var}' contains non-numeric values."
                )
                return
            except Exception as e:
                print(f"❌ Pandas tabulation failed: {e}")
                return

        if table is None:
            return

        with (
            pl.Config(
                float_precision=digits,
                set_tbl_rows=-1,
                set_tbl_cols=-1,
                set_tbl_width_chars=-1,
                set_thousands_separator=",",
                set_tbl_hide_column_data_types=True,
                set_tbl_cell_alignment="RIGHT",
            )
            if df_lib == "pl"
            else pd.option_context(
                "display.float_format",
                lambda x: f"{x:,.{digits}f}",
                "display.max_rows",
                None,
                "display.max_columns",
                None,
                "display.max_colwidth",
                None,
                "styler.format.thousands",
                ",",
            )
        ):
            print(table)

##### `__repr__()`

Provides a developer-friendly representation of the object.

Source code in `src/cendat/CenDatResponse.py`

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    def __repr__(self) -> str:
        """Provides a developer-friendly representation of the object."""
        return f"<CenDatResponse with {len(self._data)} result(s)>"

##### `__getitem__(index)`

Allows accessing individual raw result dictionaries by index.

Source code in `src/cendat/CenDatResponse.py`

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    def __getitem__(self, index: int) -> Dict:
        """Allows accessing individual raw result dictionaries by index."""
        return self._data[index]