refactor: ♻️ check column data types

pyproject.toml

@@ -32,8 +32,6 @@ requests = "^2.32.3"
 platformdirs = "^4.3.2"
 jsonschema = "^4.23.0"
 dacite = "^1.8.1"
-xmlschema = "^3.4.3"
-pandera = {extras = ["polars"], version = "^0.22.1"}
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.3.2"

src/seedcase_sprout/core/sprout_checks/check_column_data_types.py

@@ -0,0 +1,216 @@
+import json
+
+import polars as pl
+
+from seedcase_sprout.core.map_data_types import FRICTIONLESS_TO_POLARS
+from seedcase_sprout.core.properties import FieldType
+
+# https://datapackage.org/standard/table-schema/#boolean
+BOOLEAN_VALUES = {"false", "False", "FALSE", "0", "true", "True", "TRUE", "1"}
+
+
+def check_is_boolean(column_name: str) -> pl.Expr:
+    """Checks if the column contains only boolean values.
+
+    Failed values are marked with False.
+
+    Args:
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return pl.col(column_name).is_in(BOOLEAN_VALUES)
+
+
+def check_is_type_by_conversion(column_name: str, data_type: FieldType) -> pl.Expr:
+    """Checks if the column contains only values of the given type.
+
+    The check is done by attempting to convert (cast) the column to the
+    appropriate Polars data type. If it fails, the values are marked with
+    False.
+
+    Args:
+        column_name: The name of the column to check.
+        data_type: The type of the column.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return (
+        pl.col(column_name)
+        # Strict is false means map to None if it fails rather than give an error.
+        .cast(FRICTIONLESS_TO_POLARS[data_type], strict=False)
+        .is_not_null()
+    )
+
+
+def check_is_yearmonth(column_name: str) -> pl.Expr:
+    """Checks if the column contains only yearmonth values.
+
+    Failed values are marked with False.
+
+    Args:
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return (
+        # Fail negative values starting with `-`.
+        pl.col(column_name).str.starts_with("-").not_()
+        & (pl.col(column_name) + "-01")
+        # Strict is false means map to None if it fails rather than give an error.
+        .str.to_date(format="%Y-%m-%d", strict=False)
+        .is_not_null()
+    )
+
+
+def check_is_datetime(data_frame: pl.DataFrame, column_name: str) -> pl.Expr:
+    """Checks if the column contains only datetime values.
+
+    Mixing values with and without timezone information is not allowed.
+    Mixing values with different timezones is allowed, as they will be standardised
+    before saving to Parquet.
+
+    Failed values are marked with False.
+
+    Args:
+        data_frame: The data frame being operated on.
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    first_datetime = (
+        data_frame.get_column(column_name)
+        .drop_nulls()
+        # Strict is false means map to None if it fails rather than give an error.
+        .str.to_datetime(strict=False)
+        .first()
+    )
+    has_timezone = bool(first_datetime.tzinfo) if first_datetime else False
+    datetime_format = "%Y-%m-%dT%H:%M:%S%.f" + ("%z" if has_timezone else "")
+
+    return (
+        # Fail negative values starting with `-`.
+        pl.col(column_name).str.starts_with("-").not_()
+        & pl.col(column_name)
+        .str.replace("Z", "+00:00")
+        .str.to_datetime(time_unit="ms", format=datetime_format, strict=False)
+        .dt.convert_time_zone(time_zone="UTC")
+        .is_not_null()
+    )
+
+
+def check_is_date(column_name: str) -> pl.Expr:
+    """Checks if the column contains only date values.
+
+    Failed values are marked with False.
+
+    Args:
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return (
+        # Fail negative values starting with `-`.
+        pl.col(column_name).str.starts_with("-").not_()
+        & pl.col(column_name).str.to_date(format="%Y-%m-%d", strict=False).is_not_null()
+    )
+
+
+def check_is_time(column_name: str) -> pl.Expr:
+    """Checks if the column contains only time values.
+
+    Failed values are marked with False.
+
+    Args:
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return (
+        pl.col(column_name)
+        .str.to_time(format="%H:%M:%S%.f", strict=False)
+        .is_not_null()
+    )
+
+
+# https://stackoverflow.com/a/18690202
+GEOPOINT_PATTERN = (
+    r"^(?:[-+]?(?:[1-8]?\d(?:\.\d+)?|90(?:\.0+)?)),\s*"
+    r"(?:[-+]?(?:180(?:\.0+)?|(?:1[0-7]\d|[1-9]?\d)(?:\.\d+)?))$"
+)
+
+
+def check_is_geopoint(column_name: str) -> pl.Expr:
+    """Checks if the column contains only geopoint values.
+
+    Failed values are marked with False.
+
+    Args:
+        column_name: The name of the column to check.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return pl.col(column_name).str.contains(GEOPOINT_PATTERN)
+
+
+def check_is_json(column_name: str, expected_type: type[list | dict]) -> pl.Expr:
+    """Checks if the column contains only JSON values.
+
+    Failed values are marked with False.
+
+    Warning: uses `map_elements` to check the formatting of each value and may run
+    slowly on large datasets.
+
+    Args:
+        column_name: The name of the column to check.
+        expected_type: The expected JSON type: an object or an array.
+
+    Returns:
+        A Polars expression for checking the column.
+    """
+    return pl.col(column_name).map_elements(
+        lambda value: check_value_is_json(value, expected_type),
+        return_dtype=pl.Boolean,
+    )
+
+
+def check_value_is_json(value: str, expected_type: type[list | dict]) -> bool:
+    """Checks if the `value` is correctly formatted as a JSON object or array.
+
+    Args:
+        value: The value to check.
+        expected_type: The expected JSON type: an object or an array.
+
+    Returns:
+        True if the value is a correct JSON type, False otherwise.
+    """
+    try:
+        return isinstance(json.loads(value), expected_type)
+    except json.JSONDecodeError:
+        return False
+
+
+FRICTIONLESS_TO_COLUMN_CHECK = {
+    "boolean": check_is_boolean,
+    "integer": lambda col_name: check_is_type_by_conversion(col_name, "integer"),
+    "number": lambda col_name: check_is_type_by_conversion(col_name, "number"),
+    "year": lambda col_name: check_is_type_by_conversion(col_name, "year"),
+    "yearmonth": check_is_yearmonth,
+    "datetime": check_is_datetime,
+    "date": check_is_date,
+    "time": check_is_time,
+    "geopoint": check_is_geopoint,
+    "array": lambda value: check_is_json(value, list),
+    "object": lambda value: check_is_json(value, dict),
+    "geojson": lambda value: check_is_json(value, dict),
+    "string": lambda _: pl.lit(True),
+    "any": lambda _: pl.lit(True),
+    "duration": lambda _: pl.lit(True),
+}