Module dynamicio.core

Implements the DynamicDataIO class which provides functionality for data: loading; sinking, and; schema validation.

"""Implements the DynamicDataIO class which provides functionality for data: loading; sinking, and; schema validation."""
# pylint: disable=no-member
__all__ = ["DynamicDataIO", "SCHEMA_FROM_FILE"]

import asyncio
import inspect
import re
from concurrent.futures import ThreadPoolExecutor
from typing import Any, List, Mapping, MutableMapping, Optional, Tuple

import pandas as pd  # type: ignore
import pydantic
from magic_logger import logger

from dynamicio import validations
from dynamicio.config.pydantic import DataframeSchema, IOEnvironment, SchemaColumn
from dynamicio.errors import CASTING_WARNING_MSG, ColumnsDataTypeError, NOTICE_MSG, SchemaNotFoundError, SchemaValidationError
from dynamicio.metrics import get_metric

SCHEMA_FROM_FILE = {"schema": object()}

pool = ThreadPoolExecutor()


class DynamicDataIO:
    """Given a `src.utils.dynamicio.config.IOConfig` object, it generates an object with access to a series of methods for cloud I/O operations and data validations.

    Example:
       >>> input_sources_config = IOConfig(
       >>>     "path_to/input.yaml",
       >>>     os.getenv("ENVIRONMENT",default="LOCAL")
       >>> )
       >>>
       >>> class IO(WithS3File, WithLocal, DynamicDataIO):
       >>>     schema = S
       >>>
       >>> my_dataset_local_mapping = input_config.get(source_key="MY_DATASET")
       >>> my_dataset_io = IO(my_dataset_local_mapping)
       >>> my_dataset_df = my_dataset_io.read()
    """

    schema: DataframeSchema
    sources_config: IOEnvironment

    def __init__(
        self,
        source_config: IOEnvironment,
        apply_schema_validations: bool = False,
        log_schema_metrics: bool = False,
        show_casting_warnings: bool = False,
        **options: MutableMapping[str, Any],
    ):
        """Class constructor.

        Args:
            source_config: Configuration to use when reading/writing data from/to a source
            apply_schema_validations: Applies schema validations on either read() or write()
            log_schema_metrics: Logs schema metrics on either read() or write()
            show_casting_warnings: Logs casting warnings on either read() or write() if set to True
            options: Any additional kwargs that may be used throughout the lifecycle of the object
        """
        if type(self) is DynamicDataIO:  # pylint: disable=unidiomatic-typecheck
            raise TypeError("Abstract class DynamicDataIO cannot be used to instantiate an object...")

        self.sources_config = source_config
        self.name = self._transform_class_name_to_dataset_name(self.__class__.__name__)
        self.apply_schema_validations = apply_schema_validations
        self.log_schema_metrics = log_schema_metrics
        self.show_casting_warnings = show_casting_warnings
        self.options = self._get_options(options, source_config.options)
        source_name = self.sources_config.data_backend_type
        if self.schema is SCHEMA_FROM_FILE:
            active_schema = self.sources_config.dynamicio_schema
        else:
            active_schema = self._schema_from_obj(self)

        if not active_schema:
            raise SchemaNotFoundError()

        assert isinstance(active_schema, DataframeSchema)
        self.schema = active_schema
        self.name = self.schema.name.upper()
        self.schema_validations = self.schema.validations
        self.schema_metrics = self.schema.metrics

        assert hasattr(self, f"_read_from_{source_name}") or hasattr(
            self, f"_write_to_{source_name}"
        ), f"No method '_read_from_{source_name}' or '_write_to_{source_name}'. Have you registered a mixin for {source_name}?"

    @staticmethod
    def _schema_from_obj(target) -> DataframeSchema:
        """Construct `DataframeSchema` from an object.

        The object:
            - MUST have `schema` attribute that is a dictionary specifying columns and datatypes
            - CAN have `schema_validations` and `schema_metrics` attributes
        """
        col_info = {}
        for (col_name, dtype) in target.schema.items():
            col_validations = {}
            col_metrics = []
            try:
                col_validations = target.schema_validations[col_name]
            except (KeyError, AttributeError):
                pass
            try:
                col_metrics = target.schema_metrics[col_name]
            except (KeyError, AttributeError):
                pass
            col_info[col_name] = {
                "type": dtype,
                "validations": col_validations,
                "metrics": col_metrics,
            }
        try:
            out = DataframeSchema(name=target.name, columns=col_info)
        except pydantic.ValidationError:
            logger.exception(f"Error parsing {target.name=!r} {col_info=!r}")
            raise
        return out

    def __init_subclass__(cls):
        """Ensure that all subclasses have a `schema` attribute and a `validate` method.

        Raises:
            AssertionError: If either of the attributes is not implemented
        """
        if not inspect.getmodule(cls).__name__.startswith("dynamicio"):
            assert "schema" in cls.__dict__

            if cls.schema is None or (cls.schema is not SCHEMA_FROM_FILE and len(cls.schema) == 0):
                raise ValueError(f"schema for class {cls} cannot be None or empty...")

    async def async_read(self):
        """Allows the use of asyncio to concurrently read files in memory.

        Returns:
            A pandas dataframe or an iterable.
        """
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(pool, self.read)

    def read(self) -> pd.DataFrame:
        """Reads data source and returns a schema validated dataframe (by means of _apply_schema).

        Returns:
            A pandas dataframe or an iterable.
        """
        source_name = self.sources_config.data_backend_type
        df = getattr(self, f"_read_from_{source_name}")()

        df = self._apply_schema(df)
        if self.apply_schema_validations:
            self.validate_from_schema(df)
        if self.log_schema_metrics:
            self.log_metrics_from_schema(df)

        return df

    async def async_write(self, df: pd.DataFrame):
        """Allows the use of asyncio to concurrently write files out.

        Args:
            df: The data to be written
        """
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(pool, self.write, df)

    def write(self, df: pd.DataFrame):
        """Sink data to a given source based on the sources_config.

        Args:
            df: The data to be written
        """
        source_name = self.sources_config.data_backend_type
        if set(df.columns) != self.schema.column_names:  # pylint: disable=E1101
            columns = [column for column in df.columns.to_list() if column in self.schema.column_names]
            df = df[columns]

        if self.apply_schema_validations:
            self.validate_from_schema(df)
        if self.log_schema_metrics:
            self.log_metrics_from_schema(df)

        getattr(self, f"_write_to_{source_name}")(self._apply_schema(df))

    def validate_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
        """Validates a dataframe based on the validations present in its schema definition.

        All validations are checked and if any of them fails, a `SchemaValidationError` is raised.

        Args:
            df:

        Returns:
             self (to allow for method chaining).

        Raises:
            SchemaValidationError: if any of the validations failed. The `message` attribute of
                the exception object is a `List[str]`, where each element is the name of a
                validation that failed.
        """

        failed_validations = {}
        for column in self.schema_validations.keys():
            col_validations = self.schema_validations[column]
            for validation in col_validations:
                if validation.apply:
                    validator = validations.ALL_VALIDATORS[validation.name]
                    validation_result = validator(self.name, df, column, **validation.options)
                    if not validation_result.valid:
                        failed_validations[validation.name] = validation_result.message

        if len(failed_validations) > 0:
            raise SchemaValidationError(failed_validations)

        return self

    def log_metrics_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
        """Calculates and logs metrics based on the metrics present in its schema definition.

        Args:
            df: A dataframe for which metrics are generated and logged

        Returns:
             self (to allow for method chaining).
        """

        for column in self.schema_metrics.keys():
            for metric in self.schema_metrics[column]:
                get_metric(metric)(self.name, df, column)()  # type: ignore

        return self

    def _apply_schema(self, df: pd.DataFrame) -> pd.DataFrame:
        """Called by the `self.read()` and the `self._write_to_local()` methods.

        Contrasts a dataframe's read from a given source against the class's schema dictionary,
        checking that columns are the same (by means of _has_columns and _has_valid_dtypes). Then,
        check if the columns are fine, it further validates if the types of columns conform to the
        expected schema. Finally, if schema types are different, then it attempts to apply schema;
        if possible then the schema validation is successful.

        Args:
            df: A pandas dataframe.

        Returns:
            A schema validated dataframe.
        """
        if not self._has_valid_dtypes(df):
            raise ColumnsDataTypeError()
        return df

    @staticmethod
    def _transform_class_name_to_dataset_name(string_to_transform: str) -> str:
        """Called by the init function to fetch dataset names from class name.

        Used to create dataset name from class name, turns camel case into upper snake case.
        For example: 'ThisNameABC' -> 'THIS_NAME_ABC'.
        """
        words = re.findall(r"\d[A-Z]+|[A-Z]?[a-z\d]+|[A-Z]{2,}(?=[A-Z][a-z]|\d|\W|$)|\d+|[A-Z]{2,}|[A-Z]", string_to_transform)
        return "_".join(map(str.lower, words)).upper()

    def _has_valid_dtypes(self, df: pd.DataFrame) -> bool:
        """Checks if `df` has the expected dtypes defined in `schema`.

        Schema is a dictionary object where keys are column names and values are dtypes in string format as returned by e.g.
        `df[column].dtype.name`.

        This function issues `error` level logs describing the first column that caused the check to fail.

        It is assumed that `df` only has the columns defined in `schema`.

        Args:
            df:

        Returns:
            bool - `True` if `df` has the given dtypes, `False` otherwise
        """
        dtypes = df.dtypes

        cols_allowed, cols_not_allowed = self._split_columns_to_validate_between_allowed_and_not(columns_to_validate=self.options.get("columns", self.schema.column_names))
        if cols_not_allowed:
            logger.exception(f"The columns passed: {cols_not_allowed} do not belong to the schema")
            return False

        for col_info in cols_allowed:
            column_name = col_info.name
            expected_dtype = col_info.data_type
            found_dtype = dtypes[column_name].name
            if found_dtype != expected_dtype:
                if self.show_casting_warnings:
                    logger.info(f"Expected: '{expected_dtype}' dtype for {self.name}['{column_name}]', found '{found_dtype}'")
                try:
                    if len(set(type(v) for v in df[column_name].values)) > 1:  # pylint: disable=consider-using-set-comprehension
                        logger.warning(CASTING_WARNING_MSG.format(column_name, expected_dtype, found_dtype))  # pylint: disable=logging-format-interpolation
                        logger.info(NOTICE_MSG.format(column_name))  # pylint: disable=logging-format-interpolation
                    df[column_name] = df[column_name].astype(self.schema.columns[column_name].data_type)
                except (ValueError, TypeError):
                    logger.exception(f"ValueError: Tried casting column {self.name}['{column_name}'] to '{expected_dtype}' from '{found_dtype}', but failed")
                    return False
        return True

    def _split_columns_to_validate_between_allowed_and_not(self, columns_to_validate: List[str]) -> Tuple[List[SchemaColumn], List[str]]:
        not_allowed_cols, allowed_cols = [], []
        for col in columns_to_validate:
            col_from_schema = self.schema.columns.get(col)
            if col_from_schema:
                allowed_cols.append(col_from_schema)
            else:
                not_allowed_cols.append(col)
        return allowed_cols, not_allowed_cols

    @staticmethod
    def _get_options(options_from_code: MutableMapping[str, Any], options_from_resource_definition: Optional[Mapping[str, Any]]) -> MutableMapping[str, Any]:
        """Retrieves options either from code or from a resource-definition.

        Options are merged if they are provided by both sources, while in the case of conflicts, the options from the code
        take precedence.

        Args:
            options_from_code (Optional[Mapping])
            options_from_resource_definition (Optional[Mapping])

        Returns:
            [Optional[Mapping]]: options that are going to be used
        """
        if options_from_resource_definition:
            return {**options_from_resource_definition, **options_from_code}
        return options_from_code

Classes

class DynamicDataIO (source_config: IOEnvironment, apply_schema_validations: bool = False, log_schema_metrics: bool = False, show_casting_warnings: bool = False, **options: MutableMapping[str, Any])

Given a src.utils.dynamicio.config.IOConfig object, it generates an object with access to a series of methods for cloud I/O operations and data validations.

Example

>>> input_sources_config = IOConfig(
>>>     "path_to/input.yaml",
>>>     os.getenv("ENVIRONMENT",default="LOCAL")
>>> )
>>>
>>> class IO(WithS3File, WithLocal, DynamicDataIO):
>>>     schema = S
>>>
>>> my_dataset_local_mapping = input_config.get(source_key="MY_DATASET")
>>> my_dataset_io = IO(my_dataset_local_mapping)
>>> my_dataset_df = my_dataset_io.read()

Class constructor.

Args

source_config

Configuration to use when reading/writing data from/to a source

apply_schema_validations

Applies schema validations on either read() or write()

log_schema_metrics

Logs schema metrics on either read() or write()

show_casting_warnings

Logs casting warnings on either read() or write() if set to True

options

Any additional kwargs that may be used throughout the lifecycle of the object

Expand source code

class DynamicDataIO:
    """Given a `src.utils.dynamicio.config.IOConfig` object, it generates an object with access to a series of methods for cloud I/O operations and data validations.

    Example:
       >>> input_sources_config = IOConfig(
       >>>     "path_to/input.yaml",
       >>>     os.getenv("ENVIRONMENT",default="LOCAL")
       >>> )
       >>>
       >>> class IO(WithS3File, WithLocal, DynamicDataIO):
       >>>     schema = S
       >>>
       >>> my_dataset_local_mapping = input_config.get(source_key="MY_DATASET")
       >>> my_dataset_io = IO(my_dataset_local_mapping)
       >>> my_dataset_df = my_dataset_io.read()
    """

    schema: DataframeSchema
    sources_config: IOEnvironment

    def __init__(
        self,
        source_config: IOEnvironment,
        apply_schema_validations: bool = False,
        log_schema_metrics: bool = False,
        show_casting_warnings: bool = False,
        **options: MutableMapping[str, Any],
    ):
        """Class constructor.

        Args:
            source_config: Configuration to use when reading/writing data from/to a source
            apply_schema_validations: Applies schema validations on either read() or write()
            log_schema_metrics: Logs schema metrics on either read() or write()
            show_casting_warnings: Logs casting warnings on either read() or write() if set to True
            options: Any additional kwargs that may be used throughout the lifecycle of the object
        """
        if type(self) is DynamicDataIO:  # pylint: disable=unidiomatic-typecheck
            raise TypeError("Abstract class DynamicDataIO cannot be used to instantiate an object...")

        self.sources_config = source_config
        self.name = self._transform_class_name_to_dataset_name(self.__class__.__name__)
        self.apply_schema_validations = apply_schema_validations
        self.log_schema_metrics = log_schema_metrics
        self.show_casting_warnings = show_casting_warnings
        self.options = self._get_options(options, source_config.options)
        source_name = self.sources_config.data_backend_type
        if self.schema is SCHEMA_FROM_FILE:
            active_schema = self.sources_config.dynamicio_schema
        else:
            active_schema = self._schema_from_obj(self)

        if not active_schema:
            raise SchemaNotFoundError()

        assert isinstance(active_schema, DataframeSchema)
        self.schema = active_schema
        self.name = self.schema.name.upper()
        self.schema_validations = self.schema.validations
        self.schema_metrics = self.schema.metrics

        assert hasattr(self, f"_read_from_{source_name}") or hasattr(
            self, f"_write_to_{source_name}"
        ), f"No method '_read_from_{source_name}' or '_write_to_{source_name}'. Have you registered a mixin for {source_name}?"

    @staticmethod
    def _schema_from_obj(target) -> DataframeSchema:
        """Construct `DataframeSchema` from an object.

        The object:
            - MUST have `schema` attribute that is a dictionary specifying columns and datatypes
            - CAN have `schema_validations` and `schema_metrics` attributes
        """
        col_info = {}
        for (col_name, dtype) in target.schema.items():
            col_validations = {}
            col_metrics = []
            try:
                col_validations = target.schema_validations[col_name]
            except (KeyError, AttributeError):
                pass
            try:
                col_metrics = target.schema_metrics[col_name]
            except (KeyError, AttributeError):
                pass
            col_info[col_name] = {
                "type": dtype,
                "validations": col_validations,
                "metrics": col_metrics,
            }
        try:
            out = DataframeSchema(name=target.name, columns=col_info)
        except pydantic.ValidationError:
            logger.exception(f"Error parsing {target.name=!r} {col_info=!r}")
            raise
        return out

    def __init_subclass__(cls):
        """Ensure that all subclasses have a `schema` attribute and a `validate` method.

        Raises:
            AssertionError: If either of the attributes is not implemented
        """
        if not inspect.getmodule(cls).__name__.startswith("dynamicio"):
            assert "schema" in cls.__dict__

            if cls.schema is None or (cls.schema is not SCHEMA_FROM_FILE and len(cls.schema) == 0):
                raise ValueError(f"schema for class {cls} cannot be None or empty...")

    async def async_read(self):
        """Allows the use of asyncio to concurrently read files in memory.

        Returns:
            A pandas dataframe or an iterable.
        """
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(pool, self.read)

    def read(self) -> pd.DataFrame:
        """Reads data source and returns a schema validated dataframe (by means of _apply_schema).

        Returns:
            A pandas dataframe or an iterable.
        """
        source_name = self.sources_config.data_backend_type
        df = getattr(self, f"_read_from_{source_name}")()

        df = self._apply_schema(df)
        if self.apply_schema_validations:
            self.validate_from_schema(df)
        if self.log_schema_metrics:
            self.log_metrics_from_schema(df)

        return df

    async def async_write(self, df: pd.DataFrame):
        """Allows the use of asyncio to concurrently write files out.

        Args:
            df: The data to be written
        """
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(pool, self.write, df)

    def write(self, df: pd.DataFrame):
        """Sink data to a given source based on the sources_config.

        Args:
            df: The data to be written
        """
        source_name = self.sources_config.data_backend_type
        if set(df.columns) != self.schema.column_names:  # pylint: disable=E1101
            columns = [column for column in df.columns.to_list() if column in self.schema.column_names]
            df = df[columns]

        if self.apply_schema_validations:
            self.validate_from_schema(df)
        if self.log_schema_metrics:
            self.log_metrics_from_schema(df)

        getattr(self, f"_write_to_{source_name}")(self._apply_schema(df))

    def validate_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
        """Validates a dataframe based on the validations present in its schema definition.

        All validations are checked and if any of them fails, a `SchemaValidationError` is raised.

        Args:
            df:

        Returns:
             self (to allow for method chaining).

        Raises:
            SchemaValidationError: if any of the validations failed. The `message` attribute of
                the exception object is a `List[str]`, where each element is the name of a
                validation that failed.
        """

        failed_validations = {}
        for column in self.schema_validations.keys():
            col_validations = self.schema_validations[column]
            for validation in col_validations:
                if validation.apply:
                    validator = validations.ALL_VALIDATORS[validation.name]
                    validation_result = validator(self.name, df, column, **validation.options)
                    if not validation_result.valid:
                        failed_validations[validation.name] = validation_result.message

        if len(failed_validations) > 0:
            raise SchemaValidationError(failed_validations)

        return self

    def log_metrics_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
        """Calculates and logs metrics based on the metrics present in its schema definition.

        Args:
            df: A dataframe for which metrics are generated and logged

        Returns:
             self (to allow for method chaining).
        """

        for column in self.schema_metrics.keys():
            for metric in self.schema_metrics[column]:
                get_metric(metric)(self.name, df, column)()  # type: ignore

        return self

    def _apply_schema(self, df: pd.DataFrame) -> pd.DataFrame:
        """Called by the `self.read()` and the `self._write_to_local()` methods.

        Contrasts a dataframe's read from a given source against the class's schema dictionary,
        checking that columns are the same (by means of _has_columns and _has_valid_dtypes). Then,
        check if the columns are fine, it further validates if the types of columns conform to the
        expected schema. Finally, if schema types are different, then it attempts to apply schema;
        if possible then the schema validation is successful.

        Args:
            df: A pandas dataframe.

        Returns:
            A schema validated dataframe.
        """
        if not self._has_valid_dtypes(df):
            raise ColumnsDataTypeError()
        return df

    @staticmethod
    def _transform_class_name_to_dataset_name(string_to_transform: str) -> str:
        """Called by the init function to fetch dataset names from class name.

        Used to create dataset name from class name, turns camel case into upper snake case.
        For example: 'ThisNameABC' -> 'THIS_NAME_ABC'.
        """
        words = re.findall(r"\d[A-Z]+|[A-Z]?[a-z\d]+|[A-Z]{2,}(?=[A-Z][a-z]|\d|\W|$)|\d+|[A-Z]{2,}|[A-Z]", string_to_transform)
        return "_".join(map(str.lower, words)).upper()

    def _has_valid_dtypes(self, df: pd.DataFrame) -> bool:
        """Checks if `df` has the expected dtypes defined in `schema`.

        Schema is a dictionary object where keys are column names and values are dtypes in string format as returned by e.g.
        `df[column].dtype.name`.

        This function issues `error` level logs describing the first column that caused the check to fail.

        It is assumed that `df` only has the columns defined in `schema`.

        Args:
            df:

        Returns:
            bool - `True` if `df` has the given dtypes, `False` otherwise
        """
        dtypes = df.dtypes

        cols_allowed, cols_not_allowed = self._split_columns_to_validate_between_allowed_and_not(columns_to_validate=self.options.get("columns", self.schema.column_names))
        if cols_not_allowed:
            logger.exception(f"The columns passed: {cols_not_allowed} do not belong to the schema")
            return False

        for col_info in cols_allowed:
            column_name = col_info.name
            expected_dtype = col_info.data_type
            found_dtype = dtypes[column_name].name
            if found_dtype != expected_dtype:
                if self.show_casting_warnings:
                    logger.info(f"Expected: '{expected_dtype}' dtype for {self.name}['{column_name}]', found '{found_dtype}'")
                try:
                    if len(set(type(v) for v in df[column_name].values)) > 1:  # pylint: disable=consider-using-set-comprehension
                        logger.warning(CASTING_WARNING_MSG.format(column_name, expected_dtype, found_dtype))  # pylint: disable=logging-format-interpolation
                        logger.info(NOTICE_MSG.format(column_name))  # pylint: disable=logging-format-interpolation
                    df[column_name] = df[column_name].astype(self.schema.columns[column_name].data_type)
                except (ValueError, TypeError):
                    logger.exception(f"ValueError: Tried casting column {self.name}['{column_name}'] to '{expected_dtype}' from '{found_dtype}', but failed")
                    return False
        return True

    def _split_columns_to_validate_between_allowed_and_not(self, columns_to_validate: List[str]) -> Tuple[List[SchemaColumn], List[str]]:
        not_allowed_cols, allowed_cols = [], []
        for col in columns_to_validate:
            col_from_schema = self.schema.columns.get(col)
            if col_from_schema:
                allowed_cols.append(col_from_schema)
            else:
                not_allowed_cols.append(col)
        return allowed_cols, not_allowed_cols

    @staticmethod
    def _get_options(options_from_code: MutableMapping[str, Any], options_from_resource_definition: Optional[Mapping[str, Any]]) -> MutableMapping[str, Any]:
        """Retrieves options either from code or from a resource-definition.

        Options are merged if they are provided by both sources, while in the case of conflicts, the options from the code
        take precedence.

        Args:
            options_from_code (Optional[Mapping])
            options_from_resource_definition (Optional[Mapping])

        Returns:
            [Optional[Mapping]]: options that are going to be used
        """
        if options_from_resource_definition:
            return {**options_from_resource_definition, **options_from_code}
        return options_from_code

Subclasses

• UnifiedIO

Class variables

var schema : DataframeSchema

var sources_config : IOEnvironment

Methods

async def async_read(self)

Allows the use of asyncio to concurrently read files in memory.

Returns

A pandas dataframe or an iterable.

Expand source code

async def async_read(self):
    """Allows the use of asyncio to concurrently read files in memory.

    Returns:
        A pandas dataframe or an iterable.
    """
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(pool, self.read)

async def async_write(self, df: pandas.core.frame.DataFrame)

Allows the use of asyncio to concurrently write files out.

Args

df

The data to be written

Expand source code

async def async_write(self, df: pd.DataFrame):
    """Allows the use of asyncio to concurrently write files out.

    Args:
        df: The data to be written
    """
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(pool, self.write, df)

def log_metrics_from_schema(self, df: pandas.core.frame.DataFrame) ‑> DynamicDataIO

Calculates and logs metrics based on the metrics present in its schema definition.

Args

df

A dataframe for which metrics are generated and logged

Returns

self (to allow for method chaining).

Expand source code

def log_metrics_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
    """Calculates and logs metrics based on the metrics present in its schema definition.

    Args:
        df: A dataframe for which metrics are generated and logged

    Returns:
         self (to allow for method chaining).
    """

    for column in self.schema_metrics.keys():
        for metric in self.schema_metrics[column]:
            get_metric(metric)(self.name, df, column)()  # type: ignore

    return self

def read(self) ‑> pandas.core.frame.DataFrame

Reads data source and returns a schema validated dataframe (by means of _apply_schema).

Returns

A pandas dataframe or an iterable.

Expand source code

def read(self) -> pd.DataFrame:
    """Reads data source and returns a schema validated dataframe (by means of _apply_schema).

    Returns:
        A pandas dataframe or an iterable.
    """
    source_name = self.sources_config.data_backend_type
    df = getattr(self, f"_read_from_{source_name}")()

    df = self._apply_schema(df)
    if self.apply_schema_validations:
        self.validate_from_schema(df)
    if self.log_schema_metrics:
        self.log_metrics_from_schema(df)

    return df

def validate_from_schema(self, df: pandas.core.frame.DataFrame) ‑> DynamicDataIO

Validates a dataframe based on the validations present in its schema definition.

All validations are checked and if any of them fails, a SchemaValidationError is raised.

Args

df:

Returns

self (to allow for method chaining).

Raises

SchemaValidationError

if any of the validations failed. The message attribute of the exception object is a List[str], where each element is the name of a validation that failed.

Expand source code

def validate_from_schema(self, df: pd.DataFrame) -> "DynamicDataIO":
    """Validates a dataframe based on the validations present in its schema definition.

    All validations are checked and if any of them fails, a `SchemaValidationError` is raised.

    Args:
        df:

    Returns:
         self (to allow for method chaining).

    Raises:
        SchemaValidationError: if any of the validations failed. The `message` attribute of
            the exception object is a `List[str]`, where each element is the name of a
            validation that failed.
    """

    failed_validations = {}
    for column in self.schema_validations.keys():
        col_validations = self.schema_validations[column]
        for validation in col_validations:
            if validation.apply:
                validator = validations.ALL_VALIDATORS[validation.name]
                validation_result = validator(self.name, df, column, **validation.options)
                if not validation_result.valid:
                    failed_validations[validation.name] = validation_result.message

    if len(failed_validations) > 0:
        raise SchemaValidationError(failed_validations)

    return self

def write(self, df: pandas.core.frame.DataFrame)

Sink data to a given source based on the sources_config.

Args

df

The data to be written

Expand source code

def write(self, df: pd.DataFrame):
    """Sink data to a given source based on the sources_config.

    Args:
        df: The data to be written
    """
    source_name = self.sources_config.data_backend_type
    if set(df.columns) != self.schema.column_names:  # pylint: disable=E1101
        columns = [column for column in df.columns.to_list() if column in self.schema.column_names]
        df = df[columns]

    if self.apply_schema_validations:
        self.validate_from_schema(df)
    if self.log_schema_metrics:
        self.log_metrics_from_schema(df)

    getattr(self, f"_write_to_{source_name}")(self._apply_schema(df))