From 1f4c3766088b3822666eb8c3f88777ccce3a0161 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Sun, 18 Jan 2026 19:34:42 +0100 Subject: [PATCH 01/13] feat(api)!: replace pandas backend with narwhals wip for now --- pyproject.toml | 8 +- tab_err/_error_model.py | 11 +- tab_err/_utils.py | 73 +- tab_err/api/high_level.py | 54 +- tab_err/api/low_level.py | 29 +- tab_err/api/mid_level.py | 43 +- tab_err/error_mechanism/_ear.py | 59 +- tab_err/error_mechanism/_ecar.py | 46 +- tab_err/error_mechanism/_enar.py | 53 +- tab_err/error_mechanism/_error_mechanism.py | 34 +- tab_err/error_type/_add_delta.py | 56 +- tab_err/error_type/_category_swap.py | 78 +- tab_err/error_type/_error_type.py | 27 +- tab_err/error_type/_extraneous.py | 38 +- tab_err/error_type/_missing.py | 55 +- tab_err/error_type/_mistype.py | 80 +- tab_err/error_type/_mojibake.py | 44 +- tab_err/error_type/_outlier.py | 82 +- tab_err/error_type/_permutate.py | 62 +- tab_err/error_type/_replace.py | 49 +- tab_err/error_type/_typo.py | 40 +- tab_err/error_type/_wrong_unit.py | 38 +- uv.lock | 1873 ++++++++++++++----- 23 files changed, 2093 insertions(+), 839 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d983f89..695ac4d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,10 +24,9 @@ classifiers = [ "Operating System :: Microsoft :: Windows", ] -requires-python = ">=3.10,<3.15" +requires-python = ">=3.9,<3.15" dependencies = [ - "numpy>=2.2.6,<2.5.0", - "pandas>=2.3.3,<2.4.0", + "narwhals>=1.30.0", ] [project.urls] @@ -41,6 +40,9 @@ ci = [ "tomli>=2.2.1", ] dev = [ + "numpy>=1.24.0", + "pandas>=2.0.0", + "polars>=1.0.0", "pytest>=8.3.5,<9.0.0", "ruff>=0.9.8", "pre-commit>=4.0.1,<5.0.0", diff --git a/tab_err/_error_model.py b/tab_err/_error_model.py index 4908e1c..b74179e 100644 --- a/tab_err/_error_model.py +++ b/tab_err/_error_model.py @@ -6,7 +6,7 @@ from tab_err.api import low_level if TYPE_CHECKING: - import pandas as pd + from narwhals.typing import IntoDataFrame from tab_err import ErrorMechanism, ErrorType @@ -25,17 +25,18 @@ class ErrorModel: error_type: ErrorType error_rate: float - def apply(self: ErrorModel, data: pd.DataFrame, column: str | int) -> tuple[pd.DataFrame, pd.DataFrame]: - """Applies the defined ErrorModel to the given column of a pandas DataFrame. + def apply(self: ErrorModel, data: IntoDataFrame, column: str | int) -> tuple[IntoDataFrame, IntoDataFrame]: + """Applies the defined ErrorModel to the given column of a DataFrame. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and other narwhals-compatible backends. column (str | int): The column to create errors in. Returns: - tuple[pd.DataFrame, pd.DataFrame]: + tuple[IntoDataFrame, IntoDataFrame]: - The first element is a copy of 'data' with errors. - The second element is the associated error mask. + Both are returned in the same format as the input data. """ data_with_errors, error_mask = low_level.create_errors( data=data, column=column, error_rate=self.error_rate, error_mechanism=self.error_mechanism, error_type=self.error_type diff --git a/tab_err/_utils.py b/tab_err/_utils.py index 629d274..19d3277 100644 --- a/tab_err/_utils.py +++ b/tab_err/_utils.py @@ -1,26 +1,21 @@ from __future__ import annotations import random -from typing import TYPE_CHECKING +import narwhals as nw import numpy as np -if TYPE_CHECKING: - import pandas as pd - -def set_column(data: pd.DataFrame, column: int | str, series: pd.Series) -> None: +def set_column(data: nw.DataFrame, column: int | str, series: nw.Series) -> nw.DataFrame: """Replaces a column in the given DataFrame with the given Series. - Mutates data and changes the dtype of the original data to that of the series, - which, depending on the error type, might change. + Returns a new DataFrame with the column replaced. """ - col = data.columns[column] if isinstance(column, int) else column - data[col] = data[col].astype(series.dtype) - data[col] = series + col_name = get_column_str(data, column) + return data.with_columns(series.alias(col_name)) -def get_column_str(data: pd.DataFrame, column: int | str) -> str: +def get_column_str(data: nw.DataFrame, column: int | str) -> str: """Return column's name of the given DataFrame, where column can be defined as name or index.""" if isinstance(column, int): col = data.columns[column] @@ -33,7 +28,7 @@ def get_column_str(data: pd.DataFrame, column: int | str) -> str: return col -def get_column(data: pd.DataFrame, column: int | str) -> pd.Series: +def get_column(data: nw.DataFrame, column: int | str) -> nw.Series: """Selects a column from the given DataFrame and returns it as a Series.""" return data[get_column_str(data, column)] @@ -56,8 +51,58 @@ def check_error_rate(error_rate: float) -> None: raise ValueError(msg) -def check_data_emptiness(data: pd.DataFrame) -> None: +def check_data_emptiness(data: nw.DataFrame) -> None: """Check that the dataset is not empty, raise a ValueError otherwise.""" - if data.empty: + if data.is_empty(): msg = "The dataframe is empty, cannot introduce errors." raise ValueError(msg) + + +def is_string_dtype(series: nw.Series) -> bool: + """Check if a series has a string dtype.""" + return series.dtype == nw.String or series.dtype == nw.Object + + +def is_numeric_dtype(series: nw.Series) -> bool: + """Check if a series has a numeric dtype.""" + return series.dtype.is_numeric() + + +def is_integer_dtype(series: nw.Series) -> bool: + """Check if a series has an integer dtype.""" + return series.dtype.is_integer() + + +def is_datetime_dtype(series: nw.Series) -> bool: + """Check if a series has a datetime dtype.""" + return series.dtype == nw.Datetime + + +def select_string_columns(data: nw.DataFrame) -> list[str]: + """Select columns with string dtype.""" + return [col for col in data.columns if is_string_dtype(data[col])] + + +def select_numeric_columns(data: nw.DataFrame) -> list[str]: + """Select columns with numeric dtype.""" + return [col for col in data.columns if is_numeric_dtype(data[col])] + + +def select_datetime_columns(data: nw.DataFrame) -> list[str]: + """Select columns with datetime dtype.""" + return [col for col in data.columns if is_datetime_dtype(data[col])] + + +def select_numeric_or_datetime_columns(data: nw.DataFrame) -> list[str]: + """Select columns with numeric or datetime dtype.""" + return [col for col in data.columns if is_numeric_dtype(data[col]) or is_datetime_dtype(data[col])] + + +def create_empty_boolean_mask(data: nw.DataFrame) -> nw.DataFrame: + """Create an empty boolean mask DataFrame with the same shape as data.""" + n_rows = len(data) + mask_values = [False] * n_rows + return nw.from_dict( + dict.fromkeys(data.columns, mask_values), + backend=nw.get_native_namespace(data), + ) diff --git a/tab_err/api/high_level.py b/tab_err/api/high_level.py index 7570981..936d6a7 100644 --- a/tab_err/api/high_level.py +++ b/tab_err/api/high_level.py @@ -3,14 +3,15 @@ import warnings from typing import TYPE_CHECKING -import pandas as pd +import narwhals as nw from tab_err import ErrorMechanism, ErrorType, error_mechanism, error_type from tab_err._error_model import ErrorModel -from tab_err._utils import check_data_emptiness, check_error_rate, seed_randomness_and_get_generator +from tab_err._utils import check_data_emptiness, check_error_rate, create_empty_boolean_mask, seed_randomness_and_get_generator from tab_err.api import MidLevelConfig, mid_level if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame from numpy.random import Generator @@ -39,7 +40,7 @@ def _are_same_error_mechanism(error_mechanism1: ErrorMechanism, error_mechanism2 def _build_column_type_dictionary( - data: pd.DataFrame, + data: nw.DataFrame, random_generator: Generator, error_types_to_include: list[ErrorType] | None = None, error_types_to_exclude: list[ErrorType] | None = None, @@ -47,7 +48,7 @@ def _build_column_type_dictionary( """Creates a dictionary mapping from column names to the list of valid error types to apply to that column. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (nw.DataFrame): The DataFrame to create errors in. random_generator (Generator): Random Generator. Defaults to None. error_types_to_include (list[ErrorType] | None, optional): A list of the error types to be included when building error models. Defaults to None. error_types_to_exclude (list[ErrorType] | None, optional): A list of the error types to be excluded when building error models. Defaults to None. @@ -92,7 +93,7 @@ def _build_column_type_dictionary( # else: do nothing because the default behavior uses all error types if len(error_types_applied) == 0: - msg = "The list of error types to be applied cannot have length 0. Use the default or resturcture your input." + msg = "The list of error types to be applied cannot have length 0. Use the default or restructure your input." raise ValueError(msg) return { @@ -101,7 +102,7 @@ def _build_column_type_dictionary( def _build_column_mechanism_dictionary( - data: pd.DataFrame, + data: nw.DataFrame, random_generator: Generator, error_mechanisms_to_include: list[ErrorMechanism] | None = None, error_mechanisms_to_exclude: list[ErrorMechanism] | None = None, @@ -109,7 +110,7 @@ def _build_column_mechanism_dictionary( """Builds a dictionary mapping from column names to the list of valid error mechanisms to apply to that column. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (nw.DataFrame): The DataFrame to create errors in. random_generator (Generator): Random Generator. Defaults to None. error_mechanisms_to_include (list[ErrorMechanism] | None, optional): The error mechanisms (EAR, ECAR, ENAR) to include from the dictionary. Defaults to None. @@ -164,12 +165,12 @@ def _build_column_mechanism_dictionary( def _build_column_number_of_models_dictionary( - data: pd.DataFrame, column_types: dict[int | str, list[ErrorType]], column_mechanisms: dict[int | str, list[ErrorMechanism]] + data: nw.DataFrame, column_types: dict[int | str, list[ErrorType]], column_mechanisms: dict[int | str, list[ErrorMechanism]] ) -> dict[int | str, int]: """Builds a dictionary mapping from column names to the number of error models to apply to that column. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (nw.DataFrame): The DataFrame to create errors in. column_types (dict[int | str, list[ErrorType]]): A dictionary mapping from column names to the list of valid error types to apply to that column. column_mechanisms (dict[int | str, list[ErrorMechanism]]): A dictionary mapping from column names to the list of valid error mechanisms to apply. @@ -189,7 +190,7 @@ def _build_column_number_of_models_dictionary( def create_errors( # noqa: PLR0913 - data: pd.DataFrame, + data: IntoDataFrame, error_rate: float, n_error_models_per_column: int = 1, error_types_to_include: list[ErrorType] | None = None, @@ -197,11 +198,11 @@ def create_errors( # noqa: PLR0913 error_mechanisms_to_include: list[ErrorMechanism] | None = None, error_mechanisms_to_exclude: list[ErrorMechanism] | None = None, seed: int | None = None, -) -> tuple[pd.DataFrame, pd.DataFrame]: +) -> tuple[IntoDataFrame, IntoDataFrame]: """Creates errors in a given DataFrame, at a rate of *approximately* max_error_rate. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and other narwhals-compatible backends. error_rate (float): The maximum error rate to be introduced to each column in the DataFrame. n_error_models_per_column (int, optional): The number of valid error models to apply to each column. Defaults to 1. error_types_to_include (list[ErrorType] | None, optional): A list of the error types to be included when building error models. Defaults to None. @@ -215,39 +216,44 @@ def create_errors( # noqa: PLR0913 seed (int | None, optional): Random seed. Defaults to None. Returns: - tuple[pd.DataFrame, pd.DataFrame]: + tuple[IntoDataFrame, IntoDataFrame]: - The first element is a copy of 'data' with errors. - The second element is the associated error mask. + Both are returned in the same format as the input data. """ + # Wrap native DataFrame to narwhals + data_nw = nw.from_native(data, eager_only=True) + random_generator = seed_randomness_and_get_generator(seed=seed) # Input Checking check_error_rate(error_rate) - check_data_emptiness(data) + check_data_emptiness(data_nw) # Set Up Data - data_copy = data.copy() - error_mask = pd.DataFrame(data=False, index=data.index, columns=data.columns) + data_copy = data_nw.clone() + error_mask = create_empty_boolean_mask(data_nw) # Build Dictionaries col_type = _build_column_type_dictionary( - data=data, random_generator=random_generator, error_types_to_include=error_types_to_include, error_types_to_exclude=error_types_to_exclude + data=data_nw, random_generator=random_generator, error_types_to_include=error_types_to_include, error_types_to_exclude=error_types_to_exclude ) col_mechanisms = _build_column_mechanism_dictionary( - data=data, + data=data_nw, random_generator=random_generator, error_mechanisms_to_include=error_mechanisms_to_include, error_mechanisms_to_exclude=error_mechanisms_to_exclude, ) - col_num_models = _build_column_number_of_models_dictionary(data=data, column_types=col_type, column_mechanisms=col_mechanisms) + col_num_models = _build_column_number_of_models_dictionary(data=data_nw, column_types=col_type, column_mechanisms=col_mechanisms) if n_error_models_per_column > 0: error_rate = error_rate / n_error_models_per_column config_dictionary: dict[str | int, list[ErrorModel]] = { - column: [] for column in data.columns if col_num_models[column] > 0 + column: [] for column in data_nw.columns if col_num_models[column] > 0 } # Filter out those columns with no valid error models - if error_rate * len(data) < 1: # This value is calculated and rounded to 0 in the sample function of the error mechanism subclasses "n_errors" - msg = f"With a per-model error rate of: {error_rate} and {len(data)} rows, 0 errors will be introduced." + n_rows = len(data_nw) + if error_rate * n_rows < 1: # This value is calculated and rounded to 0 in the sample function of the error mechanism subclasses "n_errors" + msg = f"With a per-model error rate of: {error_rate} and {n_rows} rows, 0 errors will be introduced." warnings.warn(msg, stacklevel=2) for column, error_model_list in config_dictionary.items(): @@ -264,6 +270,6 @@ def create_errors( # noqa: PLR0913 msg = f"n_error_models_per_column is: {n_error_models_per_column} and should be a positive integer" raise ValueError(msg) - # Create Errors & Return - dirty_data, error_mask = mid_level.create_errors(data_copy, config) + # Create Errors & Return (mid_level handles native conversion) + dirty_data, error_mask = mid_level.create_errors(nw.to_native(data_copy), config) return dirty_data, error_mask diff --git a/tab_err/api/low_level.py b/tab_err/api/low_level.py index e2f6311..8893189 100644 --- a/tab_err/api/low_level.py +++ b/tab_err/api/low_level.py @@ -2,37 +2,46 @@ from typing import TYPE_CHECKING +import narwhals as nw + from tab_err._utils import check_data_emptiness, check_error_rate, set_column if TYPE_CHECKING: - import pandas as pd + from narwhals.typing import IntoDataFrame from tab_err import ErrorMechanism, ErrorType def create_errors( - data: pd.DataFrame, column: str | int, error_rate: float, error_mechanism: ErrorMechanism, error_type: ErrorType -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Creates errors in a given column of a pandas DataFrame. + data: IntoDataFrame, column: str | int, error_rate: float, error_mechanism: ErrorMechanism, error_type: ErrorType +) -> tuple[IntoDataFrame, IntoDataFrame]: + """Creates errors in a given column of a DataFrame. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and other narwhals-compatible backends. column (str | int): The column to create errors in. error_rate (float): The rate at which errors will be created. error_mechanism (ErrorMechanism): The mechanism, controls the error distribution. error_type (ErrorType): The type of the error that will be distributed. Returns: - tuple[pd.DataFrame, pd.DataFrame]: + tuple[IntoDataFrame, IntoDataFrame]: - The first element is a copy of 'data' with errors. - The second element is the associated error mask. + Both are returned in the same format as the input data. """ + # Wrap native DataFrame to narwhals + data_nw = nw.from_native(data, eager_only=True) + check_error_rate(error_rate) - check_data_emptiness(data) - data_copy = data.copy() + check_data_emptiness(data_nw) + + # Clone the data to avoid modifying the original + data_copy = data_nw.clone() error_mask = error_mechanism.sample(data_copy, column, error_rate, error_mask=None) series = error_type.apply(data_copy, error_mask, column) - set_column(data_copy, column, series) + data_copy = set_column(data_copy, column, series) - return data_copy, error_mask + # Return in the original format + return nw.to_native(data_copy), nw.to_native(error_mask) diff --git a/tab_err/api/mid_level.py b/tab_err/api/mid_level.py index 22eb6d0..7dec6a0 100644 --- a/tab_err/api/mid_level.py +++ b/tab_err/api/mid_level.py @@ -3,11 +3,13 @@ import dataclasses from typing import TYPE_CHECKING, Any -import pandas as pd +import narwhals as nw -from tab_err._utils import check_data_emptiness, check_error_rate, set_column +from tab_err._utils import check_data_emptiness, check_error_rate, create_empty_boolean_mask, set_column if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + from tab_err._error_model import ErrorModel @@ -35,22 +37,26 @@ def from_dict(data: dict[str, Any]) -> MidLevelConfig: return MidLevelConfig(**data) -def create_errors(data: pd.DataFrame, config: MidLevelConfig | dict) -> tuple[pd.DataFrame, pd.DataFrame]: +def create_errors(data: IntoDataFrame, config: MidLevelConfig | dict) -> tuple[IntoDataFrame, IntoDataFrame]: """Creates errors in a given DataFrame, following a user-defined configuration. Args: - data (pd.DataFrame): The pandas DataFrame to create errors in. + data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and other narwhals-compatible backends. config (MidLevelConfig | dict): The configuration for the error generation process. Returns: - tuple[pd.DataFrame, pd.DataFrame]: + tuple[IntoDataFrame, IntoDataFrame]: - The first element is a copy of 'data' with errors. - The second element is the associated error mask. + Both are returned in the same format as the input data. Raises: TypeError: If `config` has incorrect type. """ - check_data_emptiness(data) + # Wrap native DataFrame to narwhals + data_nw = nw.from_native(data, eager_only=True) + + check_data_emptiness(data_nw) if isinstance(config, dict): _config = MidLevelConfig(config) @@ -61,8 +67,8 @@ def create_errors(data: pd.DataFrame, config: MidLevelConfig | dict) -> tuple[pd msg = f"The type of 'config' must be either MidLevelConfig or dict but was {type(config)}." raise TypeError(msg) - data_dirty = data.copy() - error_mask = pd.DataFrame(data=False, index=data.index, columns=data.columns) + data_dirty = data_nw.clone() + error_mask = create_empty_boolean_mask(data_nw) for column in _config.columns: for error_model in _config.columns[column]: @@ -72,10 +78,21 @@ def create_errors(data: pd.DataFrame, config: MidLevelConfig | dict) -> tuple[pd error_type = error_model.error_type error_rate = error_model.error_rate - old_error_mask = error_mask.copy() - error_mask = error_mechanism.sample(data, column, error_rate, error_mask) + old_error_mask = error_mask.clone() + error_mask = error_mechanism.sample(data_nw, column, error_rate, error_mask) + + # Compute new error positions (where mask changed from False to True) + # We need to XOR the old and new masks to find new errors + old_mask_col = old_error_mask[column if isinstance(column, str) else data_nw.columns[column]] + new_mask_col = error_mask[column if isinstance(column, str) else data_nw.columns[column]] + + # Create a mask for just the new errors + new_errors_arr = new_mask_col.to_numpy() & ~old_mask_col.to_numpy() + col_name = column if isinstance(column, str) else data_nw.columns[column] + new_errors_mask = old_error_mask.with_columns(nw.new_series(col_name, new_errors_arr.tolist(), backend=nw.get_native_namespace(old_error_mask))) - series = error_type.apply(data_dirty, old_error_mask != error_mask, column) - set_column(data_dirty, column, series) + series = error_type.apply(data_dirty, new_errors_mask, column) + data_dirty = set_column(data_dirty, column, series) - return data_dirty, error_mask + # Return in the original format + return nw.to_native(data_dirty), nw.to_native(error_mask) diff --git a/tab_err/error_mechanism/_ear.py b/tab_err/error_mechanism/_ear.py index b410f33..5788c6b 100644 --- a/tab_err/error_mechanism/_ear.py +++ b/tab_err/error_mechanism/_ear.py @@ -1,24 +1,23 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING + +import narwhals as nw +import numpy as np from tab_err._utils import check_error_rate, get_column, get_column_str from ._error_mechanism import ErrorMechanism -if TYPE_CHECKING: - import pandas as pd - class EAR(ErrorMechanism): - """`ErrorMechanism` subclass implementing the `Erroneous Completely At Random` error mechanism. + """`ErrorMechanism` subclass implementing the `Erroneous At Random` error mechanism. Description: - Errors are assumed to be completely independent of the data distribution + Errors are assumed to depend on another column's values """ - def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float, error_mask: pd.DataFrame) -> pd.DataFrame: + def _sample(self: EAR, data: nw.DataFrame, column: str | int, error_rate: float, error_mask: nw.DataFrame) -> nw.DataFrame: """Creates an error mask according to the `Erroneous At Random` error mechanism. Description: @@ -28,17 +27,17 @@ def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float, This ensures that occurrence of errors is related to the value of the another `column`. Args: - data (pd.DataFrame): `DataFrame` containins the column to add errors to + data (nw.DataFrame): `DataFrame` containing the column to add errors to column (str | int): The column of `data` to create an error mask for - error_rate (float): Proportion of rows to be affected by errors; in ranse [0,1] - error_mask (pd.DataFrame): A Pandas `DataFrame` with the same index & columns as `data` that will be modified and returned + error_rate (float): Proportion of rows to be affected by errors; in range [0,1] + error_mask (nw.DataFrame): A `DataFrame` with the same index & columns as `data` that will be modified and returned Raises: ValueError: If there are fewer than two columns in `data`, a `ValueError` will be returned ValueError: If there are insufficient entries to add errors to with respect to the error rate, a `ValueError` will be returned Returns: - pd.DataFrame: A Pandas `DataFrame` with `True` values at entries where an error should be introduced, `False` otherwise + nw.DataFrame: A `DataFrame` with `True` values at entries where an error should be introduced, `False` otherwise """ check_error_rate(error_rate) @@ -46,8 +45,9 @@ def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float, msg = "The data into which error at random (EAR) are to be injected requires at least 2 columns." raise ValueError(msg) + col = get_column_str(data, column) + if self.condition_to_column is None: - col = get_column_str(data, column) column_selection = [x for x in data.columns if x != col] condition_to_column = self._random_generator.choice(column_selection) warnings.warn( @@ -60,22 +60,35 @@ def _sample(self: EAR, data: pd.DataFrame, column: str | int, error_rate: float, se_data = get_column(data, column) se_mask = get_column(error_mask, column) - n_errors = int(se_data.size * error_rate) + se_condition = get_column(data, condition_to_column) - se_mask_error_free = se_mask[~se_mask] - data_column_error_free = data.loc[se_mask_error_free.index, :] + n_errors = int(len(se_data) * error_rate) - if len(se_mask_error_free) < n_errors: + # Get arrays + mask_arr = se_mask.to_numpy() + condition_arr = se_condition.to_numpy() + + # Get indices where mask is False (error-free cells) + error_free_indices = np.where(~mask_arr)[0] + + if len(error_free_indices) < n_errors: msg = f"The error rate of {error_rate} requires {n_errors} error-free cells. " - msg += f"However, only {len(se_mask_error_free)} error-free cells are available." + msg += f"However, only {len(error_free_indices)} error-free cells are available." raise ValueError(msg) - # we offset the upper bound of the lower_error_index by a) the existing number of errors in the row, and b) the number of errors to-be generated. - upper_bound = len(se_data) - sum(se_mask) - n_errors # upper bound = length of data - current number of errors - number of errors to be generated + # Get values at error-free indices from the conditioning column and sort them + error_free_condition_values = condition_arr[error_free_indices] + sorted_order = np.argsort(error_free_condition_values) + sorted_error_free_indices = error_free_indices[sorted_order] + + # Calculate upper bound for lower_error_index + upper_bound = len(error_free_indices) - n_errors lower_error_index = self._random_generator.integers(0, upper_bound) if upper_bound > 0 else 0 - error_index_range = range(lower_error_index, lower_error_index + n_errors) - selected_rows = data_column_error_free.sort_values(by=condition_to_column).iloc[error_index_range, :] # Sort by the condition_to_column values + selected_indices = sorted_error_free_indices[lower_error_index : lower_error_index + n_errors] - se_mask.loc[selected_rows.index] = True + # Create new mask array with selected indices set to True + new_mask_arr = mask_arr.copy() + new_mask_arr[selected_indices] = True - return error_mask + # Update the error_mask DataFrame + return error_mask.with_columns(nw.new_series(col, new_mask_arr.tolist(), backend=nw.get_native_namespace(error_mask))) diff --git a/tab_err/error_mechanism/_ecar.py b/tab_err/error_mechanism/_ecar.py index 01d3aca..34d22fb 100644 --- a/tab_err/error_mechanism/_ecar.py +++ b/tab_err/error_mechanism/_ecar.py @@ -1,14 +1,13 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING -from tab_err._utils import check_error_rate, get_column +import narwhals as nw +import numpy as np -from ._error_mechanism import ErrorMechanism +from tab_err._utils import check_error_rate, get_column, get_column_str -if TYPE_CHECKING: - import pandas as pd +from ._error_mechanism import ErrorMechanism class ECAR(ErrorMechanism): @@ -20,43 +19,52 @@ class ECAR(ErrorMechanism): def _sample( self: ECAR, - data: pd.DataFrame, # noqa: ARG002 + data: nw.DataFrame, # noqa: ARG002 column: str | int, error_rate: float, - error_mask: pd.DataFrame, - ) -> pd.DataFrame: + error_mask: nw.DataFrame, + ) -> nw.DataFrame: """Creates an error mask according to the 'Erroneous Completely At Random' error mechanism. Description: - Sells are chosen uniform randomly by a NumPy random number generator + Cells are chosen uniform randomly by a NumPy random number generator Args: - data (pd.DataFrame): DataFrame containing the column to add errors to + data (nw.DataFrame): DataFrame containing the column to add errors to column (str | int): The column of 'data' to create an error mask for error_rate (float): Proportion of rows to be affected by errors; in range [0,1] - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned Raises: ValueError: If there are insufficient entries to add errors to with respect to the error rate, a ValueError will be returned Returns: - pd.DataFrame: A Pandas DataFrame with True values at entries where an error should be introduced, False otherwise + nw.DataFrame: A DataFrame with True values at entries where an error should be introduced, False otherwise """ check_error_rate(error_rate) + col_name = get_column_str(error_mask, column) se_mask = get_column(error_mask, column) - se_mask_error_free = se_mask[~se_mask] + + # Get indices where mask is False (error-free cells) + mask_arr = se_mask.to_numpy() + error_free_indices = np.where(~mask_arr)[0] if self.condition_to_column is not None: warnings.warn("'condition_to_column' is set but will be ignored by ECAR.", stacklevel=1) - n_errors = int(se_mask.size * error_rate) + n_errors = int(len(mask_arr) * error_rate) - if len(se_mask_error_free) < n_errors: + if len(error_free_indices) < n_errors: msg = f"The error rate of {error_rate} requires {n_errors} error-free cells. " - msg += f"However, only {len(se_mask_error_free)} error-free cells are available." + msg += f"However, only {len(error_free_indices)} error-free cells are available." raise ValueError(msg) # Uniform randomly choose error-cells - error_indices = self._random_generator.choice(se_mask_error_free.index, n_errors, replace=False) - se_mask[error_indices] = True - return error_mask + error_indices = self._random_generator.choice(error_free_indices, n_errors, replace=False) + + # Create new mask array with selected indices set to True + new_mask_arr = mask_arr.copy() + new_mask_arr[error_indices] = True + + # Update the error_mask DataFrame + return error_mask.with_columns(nw.new_series(col_name, new_mask_arr.tolist(), backend=nw.get_native_namespace(error_mask))) diff --git a/tab_err/error_mechanism/_enar.py b/tab_err/error_mechanism/_enar.py index c7d63f6..7f7b00a 100644 --- a/tab_err/error_mechanism/_enar.py +++ b/tab_err/error_mechanism/_enar.py @@ -1,14 +1,13 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING -from tab_err._utils import check_error_rate, get_column +import narwhals as nw +import numpy as np -from ._error_mechanism import ErrorMechanism +from tab_err._utils import check_error_rate, get_column, get_column_str -if TYPE_CHECKING: - import pandas as pd +from ._error_mechanism import ErrorMechanism class ENAR(ErrorMechanism): @@ -18,7 +17,7 @@ class ENAR(ErrorMechanism): Errors are assumed to depend on either other variables, the incorrect data itself, or both. """ - def _sample(self: ENAR, data: pd.DataFrame, column: str | int, error_rate: float, error_mask: pd.DataFrame) -> pd.DataFrame: + def _sample(self: ENAR, data: nw.DataFrame, column: str | int, error_rate: float, error_mask: nw.DataFrame) -> nw.DataFrame: """Creates an error mask according to the `Erroneous Not At Random` error mechanism. Description: @@ -29,40 +28,52 @@ def _sample(self: ENAR, data: pd.DataFrame, column: str | int, error_rate: float Args: - data (pd.DataFrame): DataFrame containing the column to add errors to + data (nw.DataFrame): DataFrame containing the column to add errors to column (str | int): The column of `data` to create an error mask for error_rate (float): Proportion of rows to be affected by errors; in range [0,1] - error_mask (pd.DataFrame): A Pandas `DataFrame` with the same index & columns as `data` that will be modified and returned + error_mask (nw.DataFrame): A `DataFrame` with the same index & columns as `data` that will be modified and returned Raises: ValueError: If there are insufficient entries to add errors to with respect to the error rate, `a` ValueError will be returned Returns: - pd.DataFrame: A Pandas DataFrame with `True` values at entries where an error should be introduced, `False` otherwise + nw.DataFrame: A DataFrame with `True` values at entries where an error should be introduced, `False` otherwise """ check_error_rate(error_rate) + col_name = get_column_str(data, column) se_data = get_column(data, column) se_mask = get_column(error_mask, column) if self.condition_to_column is not None: warnings.warn("'condition_to_column' is set but will be ignored by ENAR.", stacklevel=1) - n_errors = int(len(se_data) * error_rate) + n_rows = len(se_data) + n_errors = int(n_rows * error_rate) - # if mid-level or high-level API call ENAR, the error_mask already contains errors. Below we make sure that we only sample rows that do not - # already contain errors. - se_data_error_free = se_data[~se_mask] + # Get arrays + data_arr = se_data.to_numpy() + mask_arr = se_mask.to_numpy() - if len(se_data_error_free) < n_errors: + # Get indices where mask is False (error-free cells) + error_free_indices = np.where(~mask_arr)[0] + + if len(error_free_indices) < n_errors: msg = f"The error rate of {error_rate} requires {n_errors} error-free cells. " - msg += f"However, only {len(se_data_error_free)} error-free cells are available." + msg += f"However, only {len(error_free_indices)} error-free cells are available." raise ValueError(msg) - # TODO(anyone): ensure that the implementation is consistent between the ear and enar implementations of _sample -- upper_bound_variable? - lower_error_index = self._random_generator.integers(0, len(se_data_error_free) - n_errors) if len(se_data_error_free) != n_errors else 0 - error_index_range = range(lower_error_index, lower_error_index + n_errors) - selected_rows = se_data_error_free.sort_values().iloc[error_index_range] # Introduce errors to locations of sorted values + # Get values at error-free indices and sort them + error_free_values = data_arr[error_free_indices] + sorted_order = np.argsort(error_free_values) + sorted_error_free_indices = error_free_indices[sorted_order] + + # Choose a contiguous block of indices from the sorted error-free data + lower_error_index = self._random_generator.integers(0, len(error_free_indices) - n_errors) if len(error_free_indices) != n_errors else 0 + selected_indices = sorted_error_free_indices[lower_error_index : lower_error_index + n_errors] - se_mask.loc[selected_rows.index] = True + # Create new mask array with selected indices set to True + new_mask_arr = mask_arr.copy() + new_mask_arr[selected_indices] = True - return error_mask + # Update the error_mask DataFrame + return error_mask.with_columns(nw.new_series(col_name, new_mask_arr.tolist(), backend=nw.get_native_namespace(error_mask))) diff --git a/tab_err/error_mechanism/_error_mechanism.py b/tab_err/error_mechanism/_error_mechanism.py index d85934f..7427676 100644 --- a/tab_err/error_mechanism/_error_mechanism.py +++ b/tab_err/error_mechanism/_error_mechanism.py @@ -3,9 +3,9 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING -import pandas as pd +import narwhals as nw -from tab_err._utils import seed_randomness_and_get_generator +from tab_err._utils import create_empty_boolean_mask, seed_randomness_and_get_generator if TYPE_CHECKING: import numpy as np @@ -40,12 +40,12 @@ def __init__(self: ErrorMechanism, condition_to_column: int | str | None = None, def sample( self: ErrorMechanism, - data: pd.DataFrame, + data: nw.DataFrame, column: str | int, error_rate: float, - error_mask: pd.DataFrame | None = None, - ) -> pd.DataFrame: - """Returns an error mask for locations to introduce errors in a pandas DataFrame. + error_mask: nw.DataFrame | None = None, + ) -> nw.DataFrame: + """Returns an error mask for locations to introduce errors in a DataFrame. Description: Does error checking for the abstract method '_sample'. @@ -53,24 +53,24 @@ def sample( Calls subclass _sample method. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to + data (nw.DataFrame): DataFrame containing the column to add errors to column (str | int): The column of 'data' to create an error mask for error_rate (float): Percentage of rows to be affected by errors in range [0,1]. - error_mask (pd.DataFrame | None, optional): An existing error mask to add more errors to in the case of the mid-/high-level APIs. Defaults to None. + error_mask (nw.DataFrame | None, optional): An existing error mask to add more errors to in the case of the mid-/high-level APIs. Defaults to None. Raises: ValueError: If error rate is out of the [0,1] interval, a ValueError is thrown - TypeError: If the 'data' argument is not a pandas dataframe or the data is empty, a TypeError is thrown + TypeError: If the 'data' argument is not a DataFrame or the data is empty, a TypeError is thrown ValueError: If required and there are not 2 columns in the 'data' argument, a ValueError is thrown. Returns: - pd.DataFrame: Updated dataframe with the generated error mask + nw.DataFrame: Updated dataframe with the generated error mask """ if error_rate < 0 or error_rate > 1: error_rate_msg = "'error_rate' need to be float: 0 <= error_rate <= 1." raise ValueError(error_rate_msg) - if not isinstance(data, pd.DataFrame) or data.empty: + if not isinstance(data, nw.DataFrame) or data.is_empty(): data_msg = "'data' needs to be a non-empty DataFrame." raise TypeError(data_msg) @@ -84,21 +84,21 @@ def sample( # already inserted errors into, we have error mechanisms sample only from cells that # do not contain errors. if error_mask is None: # initialize empty error_mask - error_mask = pd.DataFrame(data=False, index=data.index, columns=data.columns) + error_mask = create_empty_boolean_mask(data) self._random_generator = seed_randomness_and_get_generator(self._seed) return self._sample(data, column, error_rate, error_mask) @abstractmethod - def _sample(self: ErrorMechanism, data: pd.DataFrame, column: str | int, error_rate: float, error_mask: pd.DataFrame) -> pd.DataFrame: - """Abstract method for the creation of an error mask over a given Pandas DataFrame. + def _sample(self: ErrorMechanism, data: nw.DataFrame, column: str | int, error_rate: float, error_mask: nw.DataFrame) -> nw.DataFrame: + """Abstract method for the creation of an error mask over a given DataFrame. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to + data (nw.DataFrame): DataFrame containing the column to add errors to column (str | int): The column of `data` to create an error mask for error_rate (float): Proportion of rows to be affected by errors; in range [0,1] - error_mask (pd.DataFrame): A Pandas `DataFrame` with the same index & columns as `data` that will be modified and returned + error_mask (nw.DataFrame): A `DataFrame` with the same index & columns as `data` that will be modified and returned Returns: - pd.DataFrame: A Pandas `DataFrame` with `True` values at entries where an error should be introduced, `False` otherwise + nw.DataFrame: A `DataFrame` with `True` values at entries where an error should be introduced, `False` otherwise """ diff --git a/tab_err/error_type/_add_delta.py b/tab_err/error_type/_add_delta.py index e19c73a..7e89596 100644 --- a/tab_err/error_type/_add_delta.py +++ b/tab_err/error_type/_add_delta.py @@ -2,10 +2,10 @@ import warnings -import pandas as pd -from pandas.api.types import is_datetime64_dtype, is_numeric_dtype +import narwhals as nw +import numpy as np -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_datetime_dtype, is_numeric_dtype, select_numeric_or_datetime_columns from ._error_type import ErrorType @@ -14,49 +14,61 @@ class AddDelta(ErrorType): """Adds a delta to values in a column.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) - if not (is_numeric_dtype(series) or is_datetime64_dtype(series)): + if not (is_numeric_dtype(series) or is_datetime_dtype(series)): msg = f"Column {column} with dtype: {series.dtype} does not contain numeric or datetime64 values. Cannot apply AddDelta." raise TypeError(msg) - def _get_valid_columns(self: AddDelta, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: AddDelta, data: nw.DataFrame) -> list[str | int]: """Returns all column names with numeric dtype elements.""" - return data.select_dtypes(include=["number", "datetime64"]).columns.tolist() + return select_numeric_or_datetime_columns(data) - def _apply(self: AddDelta, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: AddDelta, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the AddDelta ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Raises: ValueError: If the add_delta_value is None, a ValueError will be thrown. Returns: - pd.Series: The data column, 'column', after AddDelta errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after AddDelta errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - was_datetime = False # Default was_datetime to false -- changes occur only in the special case of datetime + was_datetime = False + + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() - if is_datetime64_dtype(series): # Convert to int (number of seconds) if datetime - series = series.astype("int64") // 10**9 + if is_datetime_dtype(series): + # Convert datetime to seconds since epoch + data_arr = data_arr.astype("datetime64[ns]").astype("int64") // 10**9 was_datetime = True + # Ensure float for calculations + data_arr = data_arr.astype(np.float64) + if self.config.add_delta_value is None: msg = f"self.config.add_delta_value is none, sampling a random delta value uniformly from the range of column: {column}." warnings.warn(msg, stacklevel=2) - self.config.add_delta_value = ( - self._random_generator.choice(series) - series.mean() - ) / series.std() # Ensures a smaller value than uniform sampling + mean_val = np.nanmean(data_arr) + std_val = np.nanstd(data_arr) + random_choice = self._random_generator.choice(data_arr[~np.isnan(data_arr)]) + self.config.add_delta_value = (random_choice - mean_val) / std_val if std_val != 0 else 0 - series = series.where(~series_mask, series + self.config.add_delta_value) # Avoids in-place modification + # Apply delta where mask is True + data_arr[mask_arr] += self.config.add_delta_value - if was_datetime: # Convert back to datetime if it was initially - series = pd.to_datetime(series, unit="s") + if was_datetime: + # Convert back to datetime (from seconds) + data_arr = (data_arr * 10**9).astype("int64").astype("datetime64[ns]") - return series + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_category_swap.py b/tab_err/error_type/_category_swap.py index 429f956..dab5035 100644 --- a/tab_err/error_type/_category_swap.py +++ b/tab_err/error_type/_category_swap.py @@ -2,9 +2,10 @@ import random -import pandas as pd +import narwhals as nw +import numpy as np -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str from ._error_type import ErrorType @@ -13,11 +14,11 @@ class CategorySwap(ErrorType): """Simulate incorrect labels in a column that contains categorical values.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: """Checks that the data type is Categorical and the number of categories is at least two in the column to be modified. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. + data (nw.DataFrame): DataFrame containing the column to add errors to. column (int | str): The column of 'data' to create an error mask for. Raises: @@ -26,57 +27,82 @@ def _check_type(data: pd.DataFrame, column: int | str) -> None: """ series = get_column(data, column) - if not isinstance(series.dtype, pd.CategoricalDtype): - msg = f"Column {column} does not contain values of the Categorical dtype. Cannot insert Mislables.\n" - msg += "Try casting the column to CategoricalDtype using df[column].astype('category')." + if series.dtype != nw.Categorical: + msg = f"Column {column} does not contain values of the Categorical dtype. Cannot insert Mislabels.\n" + msg += "Try casting the column to Categorical dtype." raise TypeError(msg) - if len(series.cat.categories) <= 1: - msg = f"Column {column} contains {len(series.cat.categories)} categories. Require at least 2 categories to insert mislabels." + # Get unique values to check number of categories + unique_vals = series.unique() + if len(unique_vals) <= 1: + msg = f"Column {column} contains {len(unique_vals)} categories. Require at least 2 categories to insert mislabels." raise ValueError(msg) - def _get_valid_columns(self: CategorySwap, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: CategorySwap, data: nw.DataFrame) -> list[str | int]: """Checks which columns are categorical and returns the indices of those with two or more categories.""" valid_columns = [] for col_name in data.columns: series = get_column(data, col_name) - if isinstance(series.dtype, pd.CategoricalDtype) and len(series.cat.categories) > 1: - valid_columns.append(col_name) + if series.dtype == nw.Categorical: + unique_vals = series.unique() + if len(unique_vals) > 1: + valid_columns.append(col_name) return valid_columns - def _apply(self: CategorySwap, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: CategorySwap, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the CategorySwap ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Raises: ValueError: If the value for parameter 'config.mislabel_weighing' is invalid (not 'uniform' or 'frequency'), a ValueError will be thrown. Returns: - pd.Series: The data column, 'column', after CategorySwap errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after CategorySwap errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) + series_mask = get_column(error_mask, column) + + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + + # Get categories + categories = series.unique().to_numpy() if self.config.mislabel_weighing == "uniform": - def sample_label(old_label: pd.Series) -> pd.Series: - choices = [x for x in series.cat.categories.to_numpy() if x != old_label] + def sample_label(old_label: str) -> str: + choices = [x for x in categories if x != old_label] return random.choice(choices) elif self.config.mislabel_weighing == "frequency": - - def sample_label(old_label: pd.Series) -> pd.Series: - se_sample = series.loc[series != old_label] - return se_sample.sample(1, replace=True).to_numpy()[0] + # Calculate frequency weights + value_counts = {} + for val in data_arr: + if val not in value_counts: + value_counts[val] = 0 + value_counts[val] += 1 + + def sample_label(old_label: str) -> str: + choices = [x for x in categories if x != old_label] + weights = [value_counts.get(x, 1) for x in choices] + total = sum(weights) + weights = [w / total for w in weights] + return random.choices(choices, weights=weights, k=1)[0] else: msg = "Invalid value for parameter 'config.mislabel_weighing'. Allowed values are: 'uniform', 'frequency'." raise ValueError(msg) - series_mask = get_column(error_mask, column) - series.loc[series_mask] = series.loc[series_mask].apply(sample_label) - return series + # Apply mislabeling where mask is True + for i in range(len(data_arr)): + if mask_arr[i]: + data_arr[i] = sample_label(data_arr[i]) + + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_error_type.py b/tab_err/error_type/_error_type.py index 6be42f4..b33facb 100644 --- a/tab_err/error_type/_error_type.py +++ b/tab_err/error_type/_error_type.py @@ -3,13 +3,14 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any +import narwhals as nw + from tab_err._utils import seed_randomness_and_get_generator from ._config import ErrorTypeConfig if TYPE_CHECKING: import numpy as np - import pandas as pd class ErrorType(ABC): @@ -46,16 +47,16 @@ def __init__(self: ErrorType, config: ErrorTypeConfig | dict | None = None, seed self._seed = seed self._random_generator: np.random.Generator - def apply(self: ErrorType, data: pd.DataFrame, error_mask: pd.DataFrame, column: str | int) -> pd.Series: + def apply(self: ErrorType, data: nw.DataFrame, error_mask: nw.DataFrame, column: str | int) -> nw.Series: """Applies an ErrorType to a column of 'data'. Does type and shape checking and creates a random number generator. Args: - data (pd.DataFrame): The Pandas DataFrame containing the column where errors are to be introduced. - error_mask (pd.DataFrame): The Pandas DataFrame containing the error mask for 'column'. + data (nw.DataFrame): The DataFrame containing the column where errors are to be introduced. + error_mask (nw.DataFrame): The DataFrame containing the error mask for 'column'. column (str | int): The index in the 'data' and 'error_mask' DataFrames where errors are to be introduced. Returns: - pd.Series: The data column, 'column', after errors of ErrorType at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after errors of ErrorType at the locations specified by 'error_mask' are introduced. """ self._check_type(data, column) @@ -66,35 +67,35 @@ def apply(self: ErrorType, data: pd.DataFrame, error_mask: pd.DataFrame, column: self._random_generator = seed_randomness_and_get_generator(self._seed) return self._apply(data, error_mask, column) - def get_valid_columns(self: ErrorType, data: pd.DataFrame) -> list[str | int]: + def get_valid_columns(self: ErrorType, data: nw.DataFrame) -> list[str | int]: """Finds the valid columns to which the error type can be applied. Wrapper around _get_valid_columns.""" return self._get_valid_columns(data) @staticmethod @abstractmethod - def _check_type(data: pd.DataFrame, column: str | int) -> None: + def _check_type(data: nw.DataFrame, column: str | int) -> None: """Static abstract method that checks if the given columns are valid for this 'ErrorType'. Args: - data (pd.DataFrame): The Pandas DataFrame containing the column where errors are to be introduced. + data (nw.DataFrame): The DataFrame containing the column where errors are to be introduced. column (str | int): The 'column' of 'data' where errors are to be introduced. """ @abstractmethod - def _get_valid_columns(self: ErrorType, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: ErrorType, data: nw.DataFrame) -> list[str | int]: """Finds the valid columns to which the error type can be applied.""" @abstractmethod - def _apply(self: ErrorType, data: pd.DataFrame, error_mask: pd.DataFrame, column: str | int) -> pd.Series: + def _apply(self: ErrorType, data: nw.DataFrame, error_mask: nw.DataFrame, column: str | int) -> nw.Series: """Abstract method for the application of an ErrorType to the cells in 'data' where 'error_mask' is True. Args: - data (pd.DataFrame): The Pandas DataFrame containing the column where errors are to be introduced. - error_mask (pd.DataFrame): The Pandas DataFrame containing the error mask for 'column'. + data (nw.DataFrame): The DataFrame containing the column where errors are to be introduced. + error_mask (nw.DataFrame): The DataFrame containing the error mask for 'column'. column (str | int): The index in the 'data' and 'error_mask' DataFrames where errors are to be introduced. Returns: - pd.Series: The data column, 'column', after errors of ErrorType at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after errors of ErrorType at the locations specified by 'error_mask' are introduced. """ def to_dict(self: ErrorType) -> dict[str, Any]: diff --git a/tab_err/error_type/_extraneous.py b/tab_err/error_type/_extraneous.py index a542209..54309da 100644 --- a/tab_err/error_type/_extraneous.py +++ b/tab_err/error_type/_extraneous.py @@ -2,14 +2,12 @@ import string import warnings -from typing import TYPE_CHECKING -from tab_err._utils import get_column +import narwhals as nw -from ._error_type import ErrorType +from tab_err._utils import get_column, get_column_str, select_string_columns -if TYPE_CHECKING: - import pandas as pd +from ._error_type import ErrorType class Extraneous(ErrorType): @@ -25,31 +23,36 @@ def _generate_value_template_string(self: Extraneous, min_n: int = 0, max_n: int return prepend + r"{value}" + append @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: # all data types are fine pass - def _get_valid_columns(self: Extraneous, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Extraneous, data: nw.DataFrame) -> list[str | int]: """Returns all column names with string dtype elements. Necessary for high level API.""" - return data.select_dtypes(include=["string", "object"]).columns.to_list() + return select_string_columns(data) - def _apply(self: Extraneous, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Extraneous, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Extraneous ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Raises: ValueError: If extraneous_value_template does not contain the placeholder value, a ValueError will be thrown. Returns: - pd.Series: The data column, 'column', after Extraneous errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Extraneous errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + if self.config.extraneous_value_template is None: msg = "self.config.extraneous_value_template is not set. Choosing a random string augmentation." warnings.warn(msg, stacklevel=2) @@ -60,5 +63,10 @@ def _apply(self: Extraneous, data: pd.DataFrame, error_mask: pd.DataFrame, colum msg += "{value}. Please add it for a valid format." raise ValueError(msg) - series.loc[series_mask] = series.loc[series_mask].apply(lambda x: self.config.extraneous_value_template.format(value=x)) - return series + # Apply extraneous template where mask is True + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + data_arr[i] = self.config.extraneous_value_template.format(value=val) + + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_missing.py b/tab_err/error_type/_missing.py index 87373aa..0e851a8 100644 --- a/tab_err/error_type/_missing.py +++ b/tab_err/error_type/_missing.py @@ -1,9 +1,9 @@ from __future__ import annotations -import pandas as pd -from pandas.api.types import is_string_dtype +import narwhals as nw +import numpy as np -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns from ._error_type import ErrorType @@ -11,39 +11,50 @@ class MissingValue(ErrorType): """Insert missing values into a column. - Missing value handling is not a solved problem in pandas and under active development. - Today, the best heuristic for inserting missing values is to assign None to the value. - Pandas will choose the missing value sentinel based on the column dtype - (https://pandas.pydata.org/docs/user_guide/missing_data.html#inserting-missing-data). + Missing value handling varies across DataFrame libraries. This implementation + inserts None/null values which will be handled appropriately by the underlying library. """ @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: # all dtypes are supported pass - def _get_valid_columns(self: MissingValue, data: pd.DataFrame) -> list[str | int]: - """If the config mising value is None, returns all columns. Otherwise, only the columns with the same type.""" - return data.columns.to_list() if self.config.missing_value is None else data.select_dtypes(include=["object", "string"]).columns.to_list() + def _get_valid_columns(self: MissingValue, data: nw.DataFrame) -> list[str | int]: + """If the config missing value is None, returns all columns. Otherwise, only the columns with string type.""" + return data.columns if self.config.missing_value is None else select_string_columns(data) - def _apply(self: MissingValue, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: MissingValue, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the MissingValue ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Returns: - pd.Series: The data column, 'column', after MissingValue errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after MissingValue errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - if is_string_dtype(series) and self.config.missing_value is None: # Strings are finicky - series[series_mask] = pd.NA - series = series.astype(str) - else: - series[series_mask] = self.config.missing_value + # Get numpy arrays for manipulation + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() - return series + # Set values to missing_value or None where mask is True + if is_string_dtype(series) and self.config.missing_value is None: + # For string columns, convert to object array to allow None + data_arr = data_arr.astype(object) + data_arr[mask_arr] = None + else: + # For other types, use the configured missing value or None + missing_val = self.config.missing_value + if missing_val is None: + # Convert to object to allow None + data_arr = data_arr.astype(object) + data_arr[mask_arr] = missing_val + + # Create new series with the modified data + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_mistype.py b/tab_err/error_type/_mistype.py index bb07136..3a8335f 100644 --- a/tab_err/error_type/_mistype.py +++ b/tab_err/error_type/_mistype.py @@ -1,37 +1,35 @@ from __future__ import annotations -from typing import TYPE_CHECKING +import narwhals as nw +import numpy as np -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - class Mistype(ErrorType): """Insert incorrectly typed values into a column. Note that the dtype of the column is changed by this operation. - String / Object is the dead end of typing - In an effort to keep the code relatively simple, we cast the corrupted column to an Object Dtype. + In an effort to keep the code relatively simple, we cast the corrupted column to an Object dtype. """ @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: # all dtypes are supported pass - def _get_valid_columns(self: Mistype, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Mistype, data: nw.DataFrame) -> list[str | int]: """Returns all column names of columns with dtypes other than object. This is necessary for the high level API.""" - return [col_name for col_name in data.columns.tolist() if data[col_name].dtype != "object"] + return [col_name for col_name in data.columns if data[col_name].dtype != nw.Object] - def _apply(self: Mistype, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Mistype, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Mistype ErrorType to a column of data. Note that the dtype of the column is changed by this operation. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Raises: @@ -39,41 +37,61 @@ def _apply(self: Mistype, data: pd.DataFrame, error_mask: pd.DataFrame, column: TypeError: If no type is supplied by the user in the config, and the series' datatype is 'object', a TypeError will be thrown. Returns: - pd.Series: The data column, 'column', after Mistype errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Mistype errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) + series_mask = get_column(error_mask, column) + supported_dtypes = ["object", "string", "int64", "Int64", "float64", "Float64"] if self.config.mistype_dtype is not None: if self.config.mistype_dtype not in supported_dtypes: - msg = f"Unsupported user-specified dtype {self.config.mistype_dtype}. Supported dtypes as {supported_dtypes}." + msg = f"Unsupported user-specified dtype {self.config.mistype_dtype}. Supported dtypes are {supported_dtypes}." raise TypeError(msg) target_dtype = self.config.mistype_dtype - else: # no user-specified dtype, use heuristict to infer one + else: # no user-specified dtype, use heuristic to infer one current_dtype = series.dtype - if current_dtype == "object": + if current_dtype == nw.Object: msg = "Cannot infer a dtype that is safe to cast to if the original dtype is 'object'." raise TypeError(msg) - if current_dtype == "string": + if current_dtype == nw.String: target_dtype = "object" - elif current_dtype == "int64": + elif current_dtype == nw.Int64: target_dtype = "float64" - elif current_dtype == "Int64": - target_dtype = "Float64" - elif current_dtype == "float64": + elif current_dtype == nw.Float64: target_dtype = "int64" - elif current_dtype == "Float64": - target_dtype = "Int64" - elif current_dtype == "bool": + elif current_dtype == nw.Boolean: + target_dtype = "int64" + elif current_dtype.is_integer(): + target_dtype = "float64" + elif current_dtype.is_numeric(): target_dtype = "int64" else: msg = f"The type: {current_dtype} is unsupported. The type must be one of: {*supported_dtypes, 'bool'}." raise ValueError(msg) - # NOTE(PJ): not sure about this logic, there might be a better way to do this. - - series = series.astype("object") - series_mask = get_column(error_mask, column) - series.loc[series_mask] = series.loc[series_mask].astype(target_dtype) - return series + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + + # Convert to object array to allow mixed types + result_arr = data_arr.astype(object) + + # Apply type conversion where mask is True + for i in range(len(result_arr)): + if mask_arr[i]: + val = result_arr[i] + try: + if target_dtype in ("int64", "Int64"): + result_arr[i] = int(val) if val is not None else val + elif target_dtype in ("float64", "Float64"): + result_arr[i] = float(val) if val is not None else val + elif target_dtype in ("object", "string"): + result_arr[i] = str(val) if val is not None else val + except (ValueError, TypeError): + # Keep original value if conversion fails + pass + + return nw.new_series(col_name, result_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_mojibake.py b/tab_err/error_type/_mojibake.py index a7f12e6..1f5dffa 100644 --- a/tab_err/error_type/_mojibake.py +++ b/tab_err/error_type/_mojibake.py @@ -1,43 +1,39 @@ from __future__ import annotations import random -from typing import TYPE_CHECKING -from pandas.api.types import is_string_dtype +import narwhals as nw -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - class Mojibake(ErrorType): """Inserts mojibake into a column containing strings.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_string_dtype(series): msg = f"Column {column} does not contain values of the string dtype. Cannot insert Mojibake." raise TypeError(msg) - def _get_valid_columns(self: Mojibake, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Mojibake, data: nw.DataFrame) -> list[str | int]: """Returns all column names with string dtype elements.""" - return data.select_dtypes(include=["string", "object"]).columns.to_list() + return select_string_columns(data) - def _apply(self: Mojibake, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Mojibake, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Mojibake ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Returns: - pd.Series: The data column, 'column', after Mojibake errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Mojibake errors at the locations specified by 'error_mask' are introduced. """ # Top 10 most used encodings on the internet # https://w3techs.com/technologies/overview/character_encoding @@ -57,7 +53,10 @@ def _apply(self: Mojibake, data: pd.DataFrame, error_mask: pd.DataFrame, column: "iso-8859-2": top10 - {"iso-8859-2", "windows-1250", "iso-8859-1", "windows-1252"}, } - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) + series_mask = get_column(error_mask, column) + encoding_sender = self.config.encoding_sender encoding_receiver = self.config.encoding_receiver @@ -67,8 +66,15 @@ def _apply(self: Mojibake, data: pd.DataFrame, error_mask: pd.DataFrame, column: encoding_sender = random.choice(list(top10)) encoding_receiver = random.choice(list(encodings[encoding_sender])) - series_mask = get_column(error_mask, column) - series.loc[series_mask] = ( - series.loc[series_mask].apply(lambda x: x.encode(encoding_sender, errors="ignore")).apply(lambda x: x.decode(encoding_receiver, errors="ignore")) - ) - return series + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + + # Apply mojibake where mask is True + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = val.encode(encoding_sender, errors="ignore").decode(encoding_receiver, errors="ignore") + + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_outlier.py b/tab_err/error_type/_outlier.py index 4c2303b..fda49f5 100644 --- a/tab_err/error_type/_outlier.py +++ b/tab_err/error_type/_outlier.py @@ -1,10 +1,9 @@ from __future__ import annotations +import narwhals as nw import numpy as np -import pandas as pd -from pandas.api.types import is_datetime64_dtype, is_integer_dtype, is_numeric_dtype -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_datetime_dtype, is_integer_dtype, is_numeric_dtype, select_numeric_or_datetime_columns from ._error_type import ErrorType @@ -27,40 +26,49 @@ class Outlier(ErrorType): """ @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) - if not (is_numeric_dtype(series) or is_datetime64_dtype(series)): + if not (is_numeric_dtype(series) or is_datetime_dtype(series)): msg = f"Column {column} with dtype: {series.dtype} does not contain numeric or datetime64 values. Cannot apply outliers." raise TypeError(msg) - def _get_valid_columns(self: Outlier, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Outlier, data: nw.DataFrame) -> list[str | int]: """Returns all column names with numeric dtype elements.""" - return data.select_dtypes(include=["number", "datetime64"]).columns.tolist() + return select_numeric_or_datetime_columns(data) - def _apply(self: Outlier, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Outlier, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Outlier ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Returns: - pd.Series: The data column, 'column', after Outlier errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Outlier errors at the locations specified by 'error_mask' are introduced. """ - # Get the column series and mask - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - was_datetime = False # Default to false -- changes to code only occur if the series is datetime + was_datetime = False + original_dtype = series.dtype + + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() - if is_datetime64_dtype(series): # Convert to int if datetime (ns since UNIX epoch) -- We need to add robustness against intmax/floatmax - series = series.astype("int64") + if is_datetime_dtype(series): + # Convert datetime to int64 (nanoseconds since epoch) + data_arr = data_arr.astype("datetime64[ns]").astype("int64") was_datetime = True - mean_value = series.mean() - q1 = series.quantile(0.25) - q3 = series.quantile(0.75) + # Ensure float for calculations + data_arr = data_arr.astype(np.float64) + + mean_value = np.nanmean(data_arr) + q1 = np.nanquantile(data_arr, 0.25) + q3 = np.nanquantile(data_arr, 0.75) iqr = q3 - q1 upper_boundary = q3 + 1.5 * iqr @@ -70,32 +78,40 @@ def _apply(self: Outlier, data: pd.DataFrame, error_mask: pd.DataFrame, column: perturbation_upper = self.config.outlier_coefficient * (upper_boundary - mean_value) perturbation_lower = self.config.outlier_coefficient * (mean_value - lower_boundary) - if is_integer_dtype(series): # round float to int when series is int + is_integer = is_integer_dtype(series) and not was_datetime + if is_integer: perturbation_upper = np.ceil(perturbation_upper) perturbation_lower = np.floor(perturbation_lower) # Get masks for the different outlier types depending on the mean - mask_lower = (series < mean_value) & series_mask - mask_upper = (series > mean_value) & series_mask - mask_equal = (series == mean_value) & series_mask + mask_lower = (data_arr < mean_value) & mask_arr + mask_upper = (data_arr > mean_value) & mask_arr + mask_equal = (data_arr == mean_value) & mask_arr # Apply the constant perturbation to the respective mask - series.loc[mask_lower] -= perturbation_lower - series.loc[mask_upper] += perturbation_upper + data_arr[mask_lower] -= perturbation_lower + data_arr[mask_upper] += perturbation_upper # Handle the mean values with a coin flip - coin_flips = self._random_generator.random(mask_equal.sum()) - series.loc[mask_equal] += np.where(coin_flips > self.config.outlier_coin_flip_threshold, perturbation_upper, -perturbation_lower) + n_equal = np.sum(mask_equal) + if n_equal > 0: + coin_flips = self._random_generator.random(n_equal) + perturbations = np.where(coin_flips > self.config.outlier_coin_flip_threshold, perturbation_upper, -perturbation_lower) + data_arr[mask_equal] += perturbations # Apply Gaussian noise to simulate the increase in measurement error of the outliers noise_std = self.config.outlier_noise_coeff * iqr + n_errors = np.sum(mask_arr) - if is_integer_dtype(series): # round float to int when series is int - series.loc[series_mask] += np.rint(self._random_generator.normal(loc=0, scale=noise_std, size=series_mask.sum())) + if is_integer: + data_arr[mask_arr] += np.rint(self._random_generator.normal(loc=0, scale=noise_std, size=n_errors)) else: - series.loc[series_mask] += self._random_generator.normal(loc=0, scale=noise_std, size=series_mask.sum()) + data_arr[mask_arr] += self._random_generator.normal(loc=0, scale=noise_std, size=n_errors) - if was_datetime: # Handle datetime objects - series = pd.to_datetime(series) + if was_datetime: + # Convert back to datetime + data_arr = data_arr.astype("int64").astype("datetime64[ns]") + elif is_integer: + data_arr = data_arr.astype(np.int64) - return series + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_permutate.py b/tab_err/error_type/_permutate.py index b5a2dc5..a427bd8 100644 --- a/tab_err/error_type/_permutate.py +++ b/tab_err/error_type/_permutate.py @@ -1,17 +1,13 @@ from __future__ import annotations import random -from typing import TYPE_CHECKING -from pandas.api.types import is_string_dtype +import narwhals as nw -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - def _generate_shuffle_pattern(format_len: int) -> list[int]: """Generates a list of integers that indicates the positions of each value in a formatted string.""" @@ -36,16 +32,16 @@ class Permutate(ErrorType): """Permutates the parts of a compound value in a column.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_string_dtype(series): msg = f"Column {column} does not contain values of the string dtype. Cannot Permutate values." raise TypeError(msg) - def _get_valid_columns(self: Permutate, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Permutate, data: nw.DataFrame) -> list[str | int]: """Returns column names with string dtype elements.""" - return data.select_dtypes(include=["string", "object"]).columns.to_list() + return select_string_columns(data) def _random_pattern_function(self: Permutate, old_string: str) -> str: """Generates a random permutation of `old_string` elements split on the `permutation_separator`.""" @@ -63,42 +59,66 @@ def _fixed_pattern_function(self: Permutate, old_string: str, new_pattern: list[ return self.config.permutation_separator.join(new_string_as_part_list) - def _apply(self: Permutate, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the `Permutate` `ErrorType` to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Raises: - ValueError: If the column conatins values not supported by the seperator, a ValueError will be thrown. + ValueError: If the column contains values not supported by the separator, a ValueError will be thrown. ValueError: If a fixed_permutation_pattern is selected and all values are not formatted the same way, a ValueError will be thrown. Returns: - pd.Series: The data column, 'column', after Permutate errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Permutate errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - separator_counts = [x.count(self.config.permutation_separator) for x in series.dropna()] + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + + # Get separator counts for non-null values + separator_counts = [] + for val in data_arr: + if val is not None and isinstance(val, str): + separator_counts.append(val.count(self.config.permutation_separator)) + for i, count in enumerate(separator_counts): if count == 0: - msg = f'Cannot permutate values, because column {column} contains value "{series[i]}" that is not separated by the separator ' + msg = f'Cannot permutate values, because column {column} contains value "{data_arr[i]}" that is not separated by the separator ' msg += f'"{self.config.permutation_separator}". To use another separator, define it in the ErrorTypeConfig.' raise ValueError(msg) if self.config.permutation_pattern is not None: # Permutation of each entry from pattern. _check_column_format_consistency(separator_counts, column) new_pattern = self.config.permutation_pattern - series.loc[series_mask] = series.loc[series_mask].apply(self._fixed_pattern_function, args=(new_pattern,)) + + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = self._fixed_pattern_function(val, new_pattern) elif self.config.permutation_automation_pattern == "fixed": # Fixed permutation -- random once, applied to all. _check_column_format_consistency(separator_counts, column) new_pattern = _generate_shuffle_pattern(separator_counts[0]) - series.loc[series_mask] = series.loc[series_mask].apply(self._fixed_pattern_function, args=(new_pattern,)) + + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = self._fixed_pattern_function(val, new_pattern) else: # Random permutation -- random for each entry. - series.loc[series_mask] = series.loc[series_mask].apply(self._random_pattern_function) + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = self._random_pattern_function(val) - return series + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_replace.py b/tab_err/error_type/_replace.py index d754b40..3e49d41 100644 --- a/tab_err/error_type/_replace.py +++ b/tab_err/error_type/_replace.py @@ -1,52 +1,65 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING -from pandas.api.types import is_string_dtype +import narwhals as nw -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - class Replace(ErrorType): """Replace a part of strings within a column.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_string_dtype(series): msg = f"Column {column} does not contain values of the string dtype. Cannot Permutate values." raise TypeError(msg) - def _get_valid_columns(self: Replace, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Replace, data: nw.DataFrame) -> list[str | int]: """Returns column names with string dtype elements.""" - return data.select_dtypes(include=["string", "object"]).columns.to_list() + return select_string_columns(data) - def _apply(self: Replace, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Replace, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Replace ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Returns: - pd.Series: The data column, 'column', after Replace errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Replace errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + if self.config.replace_what is None: msg = "The 'replace_what' parameter is not configured, defaulting to a random character from the given series. Replacements are not guaranteed." warnings.warn(msg, stacklevel=2) - random_row = self._random_generator.choice(series.index) - self.config.replace_what = self._random_generator.choice(list(series[random_row])) + # Get valid values (non-None strings) + valid_values = [v for v in data_arr if v is not None and isinstance(v, str) and len(v) > 0] + if valid_values: + random_row = self._random_generator.choice(len(valid_values)) + random_val = valid_values[random_row] + self.config.replace_what = self._random_generator.choice(list(random_val)) + else: + self.config.replace_what = "" + + # Apply replace where mask is True + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = val.replace(self.config.replace_what, self.config.replace_with) - series.loc[series_mask] = series.loc[series_mask].apply(lambda x: x.replace(self.config.replace_what, self.config.replace_with)) - return series + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/tab_err/error_type/_typo.py b/tab_err/error_type/_typo.py index 1f690db..2753037 100644 --- a/tab_err/error_type/_typo.py +++ b/tab_err/error_type/_typo.py @@ -1,17 +1,13 @@ from __future__ import annotations import random -from typing import TYPE_CHECKING -from pandas.api.types import is_string_dtype +import narwhals as nw -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - class Typo(ErrorType): """Inserts realistic typos into a column containing strings. @@ -26,37 +22,45 @@ class Typo(ErrorType): """ @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_string_dtype(series): msg = f"Column {column} does not contain values of the string dtype. Cannot apply Typos." raise TypeError(msg) - def _get_valid_columns(self: Typo, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: Typo, data: nw.DataFrame) -> list[str | int]: """Returns column names with string dtype elements.""" - return data.select_dtypes(include=["string", "object"]).columns.to_list() + return select_string_columns(data) - def _apply(self: Typo, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: Typo, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Typo ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. typo_error_period: specifies how frequent typo corruptions are - see class description for details. Returns: - pd.Series: The data column, 'column', after Typo errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Typo errors at the locations specified by 'error_mask' are introduced. """ - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - def butterfn(x: str) -> str: - return typo(x, self.config.typo_error_period, self.config.typo_keyboard_layout) + # Get numpy arrays + data_arr = series.to_numpy().copy() + mask_arr = series_mask.to_numpy() + + # Apply typo function to masked elements + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = typo(val, self.config.typo_error_period, self.config.typo_keyboard_layout) - series.loc[series_mask] = series.loc[series_mask].apply(butterfn) - return series + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) def typo(input_text: str, typo_error_period: int = 10, layout: str = "ansi-qwerty") -> str: diff --git a/tab_err/error_type/_wrong_unit.py b/tab_err/error_type/_wrong_unit.py index 8a609e3..c33f74e 100644 --- a/tab_err/error_type/_wrong_unit.py +++ b/tab_err/error_type/_wrong_unit.py @@ -1,51 +1,57 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING -from pandas.api.types import is_numeric_dtype +import narwhals as nw +import numpy as np -from tab_err._utils import get_column +from tab_err._utils import get_column, get_column_str, is_numeric_dtype, select_numeric_columns from ._error_type import ErrorType -if TYPE_CHECKING: - import pandas as pd - class WrongUnit(ErrorType): """Simulate a column containing values that are scaled because they are not stored in the same unit.""" @staticmethod - def _check_type(data: pd.DataFrame, column: int | str) -> None: + def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_numeric_dtype(series): msg = f"Column {column} with dtype: {series.dtype} does not contain scalars. Cannot apply a wrong unit." raise TypeError(msg) - def _get_valid_columns(self: WrongUnit, data: pd.DataFrame) -> list[str | int]: + def _get_valid_columns(self: WrongUnit, data: nw.DataFrame) -> list[str | int]: """Returns all column names with numeric dtype elements.""" - return data.select_dtypes(include=["number"]).columns.tolist() + return select_numeric_columns(data) - def _apply(self: WrongUnit, data: pd.DataFrame, error_mask: pd.DataFrame, column: int | str) -> pd.Series: + def _apply(self: WrongUnit, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the WrongUnit ErrorType to a column of data. Args: - data (pd.DataFrame): DataFrame containing the column to add errors to. - error_mask (pd.DataFrame): A Pandas DataFrame with the same index & columns as 'data' that will be modified and returned. + data (nw.DataFrame): DataFrame containing the column to add errors to. + error_mask (nw.DataFrame): A DataFrame with the same index & columns as 'data' that will be modified and returned. column (int | str): The column of 'data' to create an error mask for. Returns: - pd.Series: The data column, 'column', after Replace errors at the locations specified by 'error_mask' are introduced. + nw.Series: The data column, 'column', after Replace errors at the locations specified by 'error_mask' are introduced. """ if self.config.wrong_unit_scaling is None: msg = "No scaling function was supplied for WrongUnit, defaulting to multiplication by 10.0." warnings.warn(msg, stacklevel=2) self.config.wrong_unit_scaling = lambda x: 10.0 * x - series = get_column(data, column).copy() + col_name = get_column_str(data, column) + series = get_column(data, column) series_mask = get_column(error_mask, column) - series.loc[series_mask] = series.loc[series_mask].apply(self.config.wrong_unit_scaling) - return series + # Get numpy arrays + data_arr = series.to_numpy().copy().astype(np.float64) + mask_arr = series_mask.to_numpy() + + # Apply scaling function where mask is True + for i in range(len(data_arr)): + if mask_arr[i]: + data_arr[i] = self.config.wrong_unit_scaling(data_arr[i]) + + return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) diff --git a/uv.lock b/uv.lock index 82ce954..354bdde 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,12 @@ version = 1 -revision = 3 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719350e0ea3038a8dcf92761fc19cf26f4069c72 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Sun, 18 Jan 2026 20:54:17 +0100 Subject: [PATCH 02/13] test: add backend parity tests for pandas/polars Adds comprehensive tests to verify pandas and polars backends produce identical results with the same seed. This ensures the narwhals abstraction layer maintains consistent behavior across both backends. Adds pyarrow as a dev dependency to enable polars.to_pandas() conversion, simplifying DataFrame comparison in tests using pd.testing.assert_frame_equal. Ignores user warnings in pytest outputs, cleaning up clutter in the CLI. --- pyproject.toml | 6 + tests/api/test_backend_parity.py | 430 +++++++++++++++++++++++++++++++ tests/api/test_high_level_api.py | 117 ++++++--- tests/api/test_low_level_api.py | 25 +- tests/conftest.py | 91 ++++++- uv.lock | 119 +++++++++ 6 files changed, 730 insertions(+), 58 deletions(-) create mode 100644 tests/api/test_backend_parity.py diff --git a/pyproject.toml b/pyproject.toml index 695ac4d..95bff58 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,7 @@ dev = [ "numpy>=1.24.0", "pandas>=2.0.0", "polars>=1.0.0", + "pyarrow>=14.0.0", # Required for polars.to_pandas() "pytest>=8.3.5,<9.0.0", "ruff>=0.9.8", "pre-commit>=4.0.1,<5.0.0", @@ -100,3 +101,8 @@ convention = "google" [tool.ty.environment] python-version = "3.10" + +[tool.pytest.ini_options] +filterwarnings = [ + "ignore::UserWarning" +] diff --git a/tests/api/test_backend_parity.py b/tests/api/test_backend_parity.py new file mode 100644 index 0000000..36b8549 --- /dev/null +++ b/tests/api/test_backend_parity.py @@ -0,0 +1,430 @@ +"""Tests to verify pandas and polars produce identical outputs with the same seed.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import narwhals as nw +import numpy as np +import pandas as pd +import polars as pl +import pytest + +from tab_err import ErrorModel, error_mechanism, error_type +from tab_err.api import MidLevelConfig, low_level, mid_level +from tab_err.api.high_level import create_errors as high_level_create_errors +from tests.conftest import assert_dataframes_equal + +if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + + +def _mask_sum(df: IntoDataFrame) -> int: + """Sum all boolean values in a mask DataFrame.""" + df_nw = nw.from_native(df, eager_only=True) + total = 0 + for col in df_nw.columns: + total += int(df_nw[col].to_numpy().sum()) + return total + + +def _column_all(df: IntoDataFrame, column: str) -> bool: + """Check if all values in a column are True.""" + df_nw = nw.from_native(df, eager_only=True) + return bool(df_nw[column].to_numpy().all()) + + +class TestLowLevelBackendParity: + """Tests for low-level API backend parity.""" + + @pytest.mark.parametrize("error_rate", [0.0, 0.1, 0.5, 1.0]) + @pytest.mark.parametrize("seed", [42, 123, 999]) + def test_ecar_add_delta_parity(self, error_rate: float, seed: int) -> None: + """Test ECAR + AddDelta produces identical results for pandas and polars.""" + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, "A", error_rate, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, "A", error_rate, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + @pytest.mark.parametrize( + ("mechanism_cls", "error_type_cls", "column"), + [ + (error_mechanism.ECAR, error_type.AddDelta, "A"), # numeric column + (error_mechanism.ECAR, error_type.Outlier, "A"), # numeric column + (error_mechanism.ECAR, error_type.Typo, "C"), # string column + (error_mechanism.ECAR, error_type.MissingValue, "A"), # any column + (error_mechanism.ECAR, error_type.Replace, "C"), # string column + (error_mechanism.ENAR, error_type.AddDelta, "A"), # numeric column + ], + ) + def test_error_type_mechanism_combinations( + self, + mechanism_cls: type, + error_type_cls: type, + column: str, + ) -> None: + """Test various error type and mechanism combinations produce identical results.""" + seed = 42 + error_rate = 0.3 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, column, error_rate, mechanism_cls(seed=seed), error_type_cls(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, column, error_rate, mechanism_cls(seed=seed), error_type_cls(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_category_swap_parity(self) -> None: + """Test CategorySwap produces identical results for pandas and polars with Categorical dtype.""" + seed = 42 + error_rate = 0.3 + rng = np.random.default_rng(42) + choices = rng.choice(["X", "Y", "Z"], 10).tolist() + + # Create pandas DataFrame with categorical dtype + pd_df = pd.DataFrame( + { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": pd.Categorical(choices), + } + ) + + # Create polars DataFrame with categorical dtype + pl_df = pl.DataFrame( + { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": pl.Series(choices).cast(pl.Categorical), + } + ) + + _pd_result, pd_mask = low_level.create_errors(pd_df, "C", error_rate, error_mechanism.ECAR(seed=seed), error_type.CategorySwap(seed=seed)) + _pl_result, pl_mask = low_level.create_errors(pl_df, "C", error_rate, error_mechanism.ECAR(seed=seed), error_type.CategorySwap(seed=seed)) + + assert_dataframes_equal(pd_mask, pl_mask) + + def test_ear_mechanism_parity(self) -> None: + """Test EAR conditioned on another column produces identical results.""" + seed = 42 + error_rate = 0.3 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors( + pd_df, "A", error_rate, error_mechanism.EAR(condition_to_column="B", seed=seed), error_type.AddDelta(seed=seed) + ) + pl_result, pl_mask = low_level.create_errors( + pl_df, "A", error_rate, error_mechanism.EAR(condition_to_column="B", seed=seed), error_type.AddDelta(seed=seed) + ) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + +class TestMidLevelBackendParity: + """Tests for mid-level API backend parity.""" + + def test_basic_config_parity(self) -> None: + """Test single error model per column produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + config = MidLevelConfig( + columns={ + "A": [ErrorModel(error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed), error_rate=0.3)], + } + ) + + pd_result, pd_mask = mid_level.create_errors(pd_df, config) + pl_result, pl_mask = mid_level.create_errors(pl_df, config) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_multiple_models_per_column_parity(self) -> None: + """Test multiple error models on same column produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + config = MidLevelConfig( + columns={ + "A": [ + ErrorModel(error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed), error_rate=0.2), + ErrorModel(error_mechanism.ECAR(seed=seed + 1), error_type.Outlier(seed=seed + 1), error_rate=0.2), + ], + } + ) + + pd_result, pd_mask = mid_level.create_errors(pd_df, config) + pl_result, pl_mask = mid_level.create_errors(pl_df, config) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + +class TestHighLevelBackendParity: + """Tests for high-level API backend parity.""" + + @pytest.mark.parametrize("error_rate", [0.1, 0.5]) + @pytest.mark.parametrize("n_models", [1, 2]) + def test_automatic_error_generation_parity(self, error_rate: float, n_models: int) -> None: + """Test automatic error generation produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 100).tolist(), + "B": rng.random(100).tolist(), + "C": rng.choice(["X", "Y", "Z"], 100).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = high_level_create_errors(pd_df, error_rate, n_error_models_per_column=n_models, seed=seed) + pl_result, pl_mask = high_level_create_errors(pl_df, error_rate, n_error_models_per_column=n_models, seed=seed) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_with_error_type_filters_parity(self) -> None: + """Test include/exclude error type filters produce identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 100).tolist(), + "B": rng.random(100).tolist(), + "C": rng.choice(["X", "Y", "Z"], 100).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + # Test with include filter + pd_result, pd_mask = high_level_create_errors( + pd_df, + error_rate=0.3, + error_types_to_include=[error_type.AddDelta(seed=seed), error_type.MissingValue(seed=seed)], + seed=seed, + ) + pl_result, pl_mask = high_level_create_errors( + pl_df, + error_rate=0.3, + error_types_to_include=[error_type.AddDelta(seed=seed), error_type.MissingValue(seed=seed)], + seed=seed, + ) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_with_mechanism_filters_parity(self) -> None: + """Test include/exclude mechanism filters produce identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 100).tolist(), + "B": rng.random(100).tolist(), + "C": rng.choice(["X", "Y", "Z"], 100).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + # Test with mechanism exclude filter + pd_result, pd_mask = high_level_create_errors( + pd_df, + error_rate=0.3, + error_mechanisms_to_exclude=[error_mechanism.EAR(condition_to_column=None)], + seed=seed, + ) + pl_result, pl_mask = high_level_create_errors( + pl_df, + error_rate=0.3, + error_mechanisms_to_exclude=[error_mechanism.EAR(condition_to_column=None)], + seed=seed, + ) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_datetime_column_parity(self) -> None: + """Test datetime column handling produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": pd.date_range(start="2025-03-04", periods=10, freq="2h").tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df = pd.DataFrame(raw) + pl_df = pl.DataFrame(raw) + + pd_result, pd_mask = high_level_create_errors(pd_df, error_rate=0.3, seed=seed) + pl_result, pl_mask = high_level_create_errors(pl_df, error_rate=0.3, seed=seed) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + +class TestReturnTypePreservation: + """Tests verifying input type is preserved in output.""" + + def test_pandas_input_returns_pandas(self) -> None: + """Verify pandas input returns pandas output.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df = pd.DataFrame(raw) + + # Low-level API + result, mask = low_level.create_errors(pd_df, "A", 0.3, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + assert isinstance(result, pd.DataFrame) + assert isinstance(mask, pd.DataFrame) + + # Mid-level API + config = MidLevelConfig(columns={"A": [ErrorModel(error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed), error_rate=0.3)]}) + result, mask = mid_level.create_errors(pd_df, config) + assert isinstance(result, pd.DataFrame) + assert isinstance(mask, pd.DataFrame) + + # High-level API + result, mask = high_level_create_errors(pd_df, error_rate=0.3, seed=seed) + assert isinstance(result, pd.DataFrame) + assert isinstance(mask, pd.DataFrame) + + def test_polars_input_returns_polars(self) -> None: + """Verify polars input returns polars output.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pl_df = pl.DataFrame(raw) + + # Low-level API + result, mask = low_level.create_errors(pl_df, "A", 0.3, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + assert isinstance(result, pl.DataFrame) + assert isinstance(mask, pl.DataFrame) + + # Mid-level API + config = MidLevelConfig(columns={"A": [ErrorModel(error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed), error_rate=0.3)]}) + result, mask = mid_level.create_errors(pl_df, config) + assert isinstance(result, pl.DataFrame) + assert isinstance(mask, pl.DataFrame) + + # High-level API + result, mask = high_level_create_errors(pl_df, error_rate=0.3, seed=seed) + assert isinstance(result, pl.DataFrame) + assert isinstance(mask, pl.DataFrame) + + +class TestEdgeCases: + """Tests for edge cases in backend parity.""" + + def test_zero_error_rate_parity(self) -> None: + """Test 0% error rate produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, "A", 0.0, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, "A", 0.0, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + # Verify no errors were introduced + assert _mask_sum(pd_mask) == 0 + assert _mask_sum(pl_mask) == 0 + + def test_full_error_rate_parity(self) -> None: + """Test 100% error rate produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, "A", 1.0, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, "A", 1.0, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + # Verify all rows in column A have errors + assert _column_all(pd_mask, "A") + assert _column_all(pl_mask, "A") + + def test_large_dataset_parity(self) -> None: + """Test 10,000 rows produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10000).tolist(), + "B": rng.random(10000).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10000).tolist(), + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, "A", 0.3, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, "A", 0.3, error_mechanism.ECAR(seed=seed), error_type.AddDelta(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) + + def test_empty_string_handling_parity(self) -> None: + """Test empty strings in string columns produces identical results.""" + seed = 42 + rng = np.random.default_rng(42) + raw = { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": ["", "X", "", "Y", "Z", "", "X", "Y", "", "Z"], + } + pd_df, pl_df = pd.DataFrame(raw), pl.DataFrame(raw) + + pd_result, pd_mask = low_level.create_errors(pd_df, "C", 0.3, error_mechanism.ECAR(seed=seed), error_type.Typo(seed=seed)) + pl_result, pl_mask = low_level.create_errors(pl_df, "C", 0.3, error_mechanism.ECAR(seed=seed), error_type.Typo(seed=seed)) + + assert_dataframes_equal(pd_result, pl_result) + assert_dataframes_equal(pd_mask, pl_mask) diff --git a/tests/api/test_high_level_api.py b/tests/api/test_high_level_api.py index ad062a7..61d1f7e 100644 --- a/tests/api/test_high_level_api.py +++ b/tests/api/test_high_level_api.py @@ -1,41 +1,74 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import narwhals as nw import numpy as np -import pandas as pd import pytest from tab_err.api.high_level import create_errors +from tests.conftest import assert_dataframes_equal + +if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + + +def _get_shape(df: IntoDataFrame) -> tuple[int, int]: + """Get shape of a DataFrame using narwhals.""" + return nw.from_native(df, eager_only=True).shape + + +def _get_numpy_mean(df: IntoDataFrame) -> float: + """Get numpy array mean from a DataFrame using narwhals.""" + df_nw = nw.from_native(df, eager_only=True) + all_values = [] + for col in df_nw.columns: + all_values.extend(df_nw[col].to_numpy().tolist()) + return float(np.mean(all_values)) + + +def _check_all_bool_dtypes(df: IntoDataFrame) -> bool: + """Check if all columns in a DataFrame have boolean dtype.""" + df_nw = nw.from_native(df, eager_only=True) + return all(df_nw[col].dtype == nw.Boolean for col in df_nw.columns) + + +def _check_same_type(df1: IntoDataFrame, df2: IntoDataFrame) -> bool: + """Check if two DataFrames are of the same type.""" + return type(df1) is type(df2) class TestHighLevelAPI: """Tests the high-level API.""" - def test_create_errors_basic(self, test_data: dict[str, pd.DataFrame]) -> None: + def test_create_errors_basic(self, test_data: dict[str, IntoDataFrame]) -> None: """Test that create_errors returns two DataFrames with expected properties.""" seed = 42 error_rate = 0.5 modified_data_4rows_5columns, data_4rows_5columns_error_mask = create_errors(test_data["data_4rows_5columns"], error_rate, seed=seed) modified_data_10rows_3columns, data_10rows_3columns_error_mask = create_errors(test_data["data_10rows_3columns"], error_rate, seed=seed) - # Check that they are still dataframes - assert isinstance(modified_data_4rows_5columns, pd.DataFrame) - assert isinstance(data_4rows_5columns_error_mask, pd.DataFrame) - assert isinstance(modified_data_10rows_3columns, pd.DataFrame) - assert isinstance(data_10rows_3columns_error_mask, pd.DataFrame) + # Check that the output type matches the input type + assert _check_same_type(modified_data_4rows_5columns, test_data["data_4rows_5columns"]) + assert _check_same_type(data_4rows_5columns_error_mask, test_data["data_4rows_5columns"]) + assert _check_same_type(modified_data_10rows_3columns, test_data["data_10rows_3columns"]) + assert _check_same_type(data_10rows_3columns_error_mask, test_data["data_10rows_3columns"]) # Check Shapes - assert modified_data_4rows_5columns.shape == test_data["data_4rows_5columns"].shape - assert data_4rows_5columns_error_mask.shape == test_data["data_4rows_5columns"].shape - assert modified_data_10rows_3columns.shape == test_data["data_10rows_3columns"].shape - assert data_10rows_3columns_error_mask.shape == test_data["data_10rows_3columns"].shape + assert _get_shape(modified_data_4rows_5columns) == _get_shape(test_data["data_4rows_5columns"]) + assert _get_shape(data_4rows_5columns_error_mask) == _get_shape(test_data["data_4rows_5columns"]) + assert _get_shape(modified_data_10rows_3columns) == _get_shape(test_data["data_10rows_3columns"]) + assert _get_shape(data_10rows_3columns_error_mask) == _get_shape(test_data["data_10rows_3columns"]) # Assert the error masks contain only boolean values - assert data_4rows_5columns_error_mask.dtypes.apply(lambda dt: np.issubdtype(dt, np.bool_)).all() - assert data_10rows_3columns_error_mask.dtypes.apply(lambda dt: np.issubdtype(dt, np.bool_)).all() + assert _check_all_bool_dtypes(data_4rows_5columns_error_mask) + assert _check_all_bool_dtypes(data_10rows_3columns_error_mask) # Assert that the error masks have the correct proportion of True to False - assert pytest.approx(error_rate) == data_4rows_5columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate) == data_10rows_3columns_error_mask.to_numpy().mean() + assert pytest.approx(error_rate) == _get_numpy_mean(data_4rows_5columns_error_mask) + assert pytest.approx(error_rate) == _get_numpy_mean(data_10rows_3columns_error_mask) - def test_create_errors_seed(self, test_data: dict[str, pd.DataFrame]) -> None: + def test_create_errors_seed(self, test_data: dict[str, IntoDataFrame]) -> None: """Test that create_errors returns the same dataframe when a seed is used.""" seed = 42 error_rate = 0.5 @@ -44,10 +77,10 @@ def test_create_errors_seed(self, test_data: dict[str, pd.DataFrame]) -> None: modified_data_2, error_mask_2 = create_errors(test_data["data_10rows_3columns"], error_rate=error_rate, seed=seed) # Ensure same seed yields same dataframes - pd.testing.assert_frame_equal(modified_data_1, modified_data_2) - pd.testing.assert_frame_equal(error_mask_1, error_mask_2) + assert_dataframes_equal(modified_data_1, modified_data_2) + assert_dataframes_equal(error_mask_1, error_mask_2) - def test_create_errors_error_rates(self, test_data: dict[str, pd.DataFrame]) -> None: + def test_create_errors_error_rates(self, test_data: dict[str, IntoDataFrame]) -> None: """Test that create_errors returns two DataFrames with expected properties.""" seed = 42 for i in range(11): @@ -57,11 +90,11 @@ def test_create_errors_error_rates(self, test_data: dict[str, pd.DataFrame]) -> _, data_10rows_3columns_with_datetime_error_mask = create_errors(test_data["data_10rows_3columns_with_datetime"], error_rate, seed=seed) # Assert that the error masks have the correct proportion of True to False - assert pytest.approx(error_rate) == data_100rows_3columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate) == data_10rows_3columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate) == data_10rows_3columns_with_datetime_error_mask.to_numpy().mean() + assert pytest.approx(error_rate) == _get_numpy_mean(data_100rows_3columns_error_mask) + assert pytest.approx(error_rate) == _get_numpy_mean(data_10rows_3columns_error_mask) + assert pytest.approx(error_rate) == _get_numpy_mean(data_10rows_3columns_with_datetime_error_mask) - def test_create_errors_more_models(self, test_data: dict[str, pd.DataFrame]) -> None: + def test_create_errors_more_models(self, test_data: dict[str, IntoDataFrame]) -> None: """Test that when more error models are introduced, the create_errors method has expected DataFrame return.""" error_rate = 1.0 seed = 42 @@ -77,28 +110,28 @@ def test_create_errors_more_models(self, test_data: dict[str, pd.DataFrame]) -> test_data["data_100rows_3columns"], error_rate, n_error_models_per_column=n_error_models, seed=seed ) - # Check that they are still dataframes - assert isinstance(modified_data_4rows_5columns, pd.DataFrame) - assert isinstance(data_4rows_5columns_error_mask, pd.DataFrame) - assert isinstance(modified_data_10rows_3columns, pd.DataFrame) - assert isinstance(data_10rows_3columns_error_mask, pd.DataFrame) - assert isinstance(modified_data_100rows_3columns, pd.DataFrame) - assert isinstance(data_100rows_3columns_error_mask, pd.DataFrame) + # Check that the output type matches the input type + assert _check_same_type(modified_data_4rows_5columns, test_data["data_4rows_5columns"]) + assert _check_same_type(data_4rows_5columns_error_mask, test_data["data_4rows_5columns"]) + assert _check_same_type(modified_data_10rows_3columns, test_data["data_10rows_3columns"]) + assert _check_same_type(data_10rows_3columns_error_mask, test_data["data_10rows_3columns"]) + assert _check_same_type(modified_data_100rows_3columns, test_data["data_100rows_3columns"]) + assert _check_same_type(data_100rows_3columns_error_mask, test_data["data_100rows_3columns"]) # Check Shapes - assert modified_data_4rows_5columns.shape == test_data["data_4rows_5columns"].shape - assert data_4rows_5columns_error_mask.shape == test_data["data_4rows_5columns"].shape - assert modified_data_10rows_3columns.shape == test_data["data_10rows_3columns"].shape - assert data_10rows_3columns_error_mask.shape == test_data["data_10rows_3columns"].shape - assert modified_data_100rows_3columns.shape == test_data["data_100rows_3columns"].shape - assert data_100rows_3columns_error_mask.shape == test_data["data_100rows_3columns"].shape + assert _get_shape(modified_data_4rows_5columns) == _get_shape(test_data["data_4rows_5columns"]) + assert _get_shape(data_4rows_5columns_error_mask) == _get_shape(test_data["data_4rows_5columns"]) + assert _get_shape(modified_data_10rows_3columns) == _get_shape(test_data["data_10rows_3columns"]) + assert _get_shape(data_10rows_3columns_error_mask) == _get_shape(test_data["data_10rows_3columns"]) + assert _get_shape(modified_data_100rows_3columns) == _get_shape(test_data["data_100rows_3columns"]) + assert _get_shape(data_100rows_3columns_error_mask) == _get_shape(test_data["data_100rows_3columns"]) # Assert the error masks contain only boolean values - assert data_4rows_5columns_error_mask.dtypes.apply(lambda dt: np.issubdtype(dt, np.bool_)).all() - assert data_10rows_3columns_error_mask.dtypes.apply(lambda dt: np.issubdtype(dt, np.bool_)).all() - assert data_100rows_3columns_error_mask.dtypes.apply(lambda dt: np.issubdtype(dt, np.bool_)).all() + assert _check_all_bool_dtypes(data_4rows_5columns_error_mask) + assert _check_all_bool_dtypes(data_10rows_3columns_error_mask) + assert _check_all_bool_dtypes(data_100rows_3columns_error_mask) # Assert that the error masks have the correct proportion of True to False - assert pytest.approx(error_rate) == data_4rows_5columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate) == data_10rows_3columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate) == data_100rows_3columns_error_mask.to_numpy().mean() + assert pytest.approx(error_rate) == _get_numpy_mean(data_4rows_5columns_error_mask) + assert pytest.approx(error_rate) == _get_numpy_mean(data_10rows_3columns_error_mask) + assert pytest.approx(error_rate) == _get_numpy_mean(data_100rows_3columns_error_mask) diff --git a/tests/api/test_low_level_api.py b/tests/api/test_low_level_api.py index f4bc3d0..f2c70a8 100644 --- a/tests/api/test_low_level_api.py +++ b/tests/api/test_low_level_api.py @@ -1,14 +1,31 @@ -import pandas as pd +from __future__ import annotations + +from typing import TYPE_CHECKING + +import narwhals as nw +import numpy as np import pytest from tab_err import error_mechanism, error_type from tab_err.api.low_level import create_errors +if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + + +def _get_numpy_mean(df: IntoDataFrame) -> float: + """Get numpy array mean from a DataFrame using narwhals.""" + df_nw = nw.from_native(df, eager_only=True) + all_values = [] + for col in df_nw.columns: + all_values.extend(df_nw[col].to_numpy().tolist()) + return float(np.mean(all_values)) + class TestLowLevelAPI: """Tests the low-level API.""" - def test_create_errors_error_rates(self, test_data: dict[str, pd.DataFrame]) -> None: + def test_create_errors_error_rates(self, test_data: dict[str, IntoDataFrame]) -> None: """Test that create_errors returns two DataFrames with expected properties.""" for i in range(11): error_rate = 0.1 * float(i) @@ -20,5 +37,5 @@ def test_create_errors_error_rates(self, test_data: dict[str, pd.DataFrame]) -> ) # Assert that the error masks have the correct proportion of True to False - Note only one column is errored - assert pytest.approx(error_rate / 3.0) == data_100rows_3columns_error_mask.to_numpy().mean() - assert pytest.approx(error_rate / 3.0) == data_10rows_3columns_error_mask.to_numpy().mean() + assert pytest.approx(error_rate / 3.0) == _get_numpy_mean(data_100rows_3columns_error_mask) + assert pytest.approx(error_rate / 3.0) == _get_numpy_mean(data_10rows_3columns_error_mask) diff --git a/tests/conftest.py b/tests/conftest.py index f48207f..6f56b3a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,19 +1,86 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import narwhals as nw import numpy as np import pandas as pd +import polars as pl import pytest +if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + from numpy.random import Generator -@pytest.fixture -def test_data() -> dict[str, pd.DataFrame]: - """Fixture to provide test data before each test runs.""" - rng = np.random.default_rng(42) + +def _create_raw_test_data(rng: Generator) -> dict[str, dict]: + """Create raw data dicts that can be converted to any backend. + + Args: + rng: NumPy random generator for reproducible data generation. + + Returns: + Dictionary mapping dataset names to column data dictionaries. + """ return { - "data_10rows_3columns": pd.DataFrame({"A": rng.integers(0, 100, 10), "B": rng.random(10), "C": rng.choice(["X", "Y", "Z"], 10)}), - "data_4rows_5columns": pd.DataFrame( - {"A": rng.integers(0, 100, 4), "B": rng.random(4), "C": rng.choice(["X", "Y", "Z"], 4), "D": rng.integers(0, 100, 4), "E": rng.random(4)} - ), - "data_100rows_3columns": pd.DataFrame({"A": rng.integers(0, 100, 100), "B": rng.random(100), "C": rng.choice(["X", "Y", "Z"], 100)}), - "data_10rows_3columns_with_datetime": pd.DataFrame( - {"A": rng.integers(0, 100, 10), "B": pd.date_range(start="2025-03-04", periods=10, freq="2h"), "C": rng.choice(["X", "Y", "Z"], 10)} - ), + "data_10rows_3columns": { + "A": rng.integers(0, 100, 10).tolist(), + "B": rng.random(10).tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + }, + "data_4rows_5columns": { + "A": rng.integers(0, 100, 4).tolist(), + "B": rng.random(4).tolist(), + "C": rng.choice(["X", "Y", "Z"], 4).tolist(), + "D": rng.integers(0, 100, 4).tolist(), + "E": rng.random(4).tolist(), + }, + "data_100rows_3columns": { + "A": rng.integers(0, 100, 100).tolist(), + "B": rng.random(100).tolist(), + "C": rng.choice(["X", "Y", "Z"], 100).tolist(), + }, + "data_10rows_3columns_with_datetime": { + "A": rng.integers(0, 100, 10).tolist(), + "B": pd.date_range(start="2025-03-04", periods=10, freq="2h").tolist(), + "C": rng.choice(["X", "Y", "Z"], 10).tolist(), + }, } + + +def assert_dataframes_equal( + df1: IntoDataFrame, + df2: IntoDataFrame, + rtol: float = 1e-7, + atol: float = 1e-10, +) -> None: + """Compare DataFrames by converting to pandas and using pd.testing.assert_frame_equal. + + Args: + df1: First DataFrame (any narwhals-compatible DataFrame). + df2: Second DataFrame (any narwhals-compatible DataFrame). + rtol: Relative tolerance for numeric comparisons. + atol: Absolute tolerance for numeric comparisons. + + Raises: + AssertionError: If DataFrames are not equal. + """ + # Use narwhals to wrap and then get native back for conversion + df1_native = nw.from_native(df1, eager_only=True).to_pandas() + df2_native = nw.from_native(df2, eager_only=True).to_pandas() + + pd.testing.assert_frame_equal(df1_native, df2_native, check_dtype=False, rtol=rtol, atol=atol) + + +@pytest.fixture(params=["pandas", "polars"]) +def test_data(request: pytest.FixtureRequest) -> dict[str, IntoDataFrame]: + """Fixture to provide test data before each test runs. + + Parametrized to run tests with both pandas and polars backends. + """ + rng = np.random.default_rng(42) + raw_data = _create_raw_test_data(rng) + + if request.param == "pandas": + return {k: pd.DataFrame(v) for k, v in raw_data.items()} + 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@@ dev = [ { name = "polars", version = "1.37.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, { name = "pre-commit", version = "4.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" }, { name = "pre-commit", version = "4.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, + { name = "pyarrow", version = "21.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" }, + { name = "pyarrow", version = "23.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, { name = "pydata-sphinx-theme" }, { name = "pytest" }, { name = "ruff" }, @@ -3362,6 +3480,7 @@ dev = [ { name = "pandas", specifier = ">=2.0.0" }, { name = "polars", specifier = ">=1.0.0" }, { name = "pre-commit", specifier = ">=4.0.1,<5.0.0" }, + { name = "pyarrow", specifier = ">=14.0.0" }, { name = "pydata-sphinx-theme", specifier = ">=0.16.1,<0.17.0" }, { name = "pytest", specifier = ">=8.3.5,<9.0.0" }, { name = "ruff", specifier = ">=0.9.8" }, From c72653506227867e3a7508a145e09e72fd1c9676 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Mon, 19 Jan 2026 19:25:04 +0100 Subject: [PATCH 03/13] test: add test for dtype preservation After moving to narwhals, it seems urgent that we're able to keep track of dtype preservation after applying error scenarios to a table. The added test does this. I also fixed an pylance issue in test_typo while at it. --- tests/api/test_dtype_preservation.py | 85 ++++++++++++++++++++++++++++ tests/error_type/test_typo.py | 5 +- 2 files changed, 89 insertions(+), 1 deletion(-) create mode 100644 tests/api/test_dtype_preservation.py diff --git a/tests/api/test_dtype_preservation.py b/tests/api/test_dtype_preservation.py new file mode 100644 index 0000000..9398520 --- /dev/null +++ b/tests/api/test_dtype_preservation.py @@ -0,0 +1,85 @@ +"""Tests to verify dtype preservation after error application.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import narwhals as nw +import numpy as np +import pandas as pd +import polars as pl +import pytest + +if TYPE_CHECKING: + from narwhals.typing import IntoDataFrame + +from tab_err import error_mechanism, error_type +from tab_err.api import low_level + + +def _nw_dtype(df: IntoDataFrame, column: str) -> object: + return nw.from_native(df, eager_only=True)[column].dtype + + +def _assert_dtype_preserved(original: IntoDataFrame, mutated: IntoDataFrame, column: str) -> None: + assert _nw_dtype(original, column) == _nw_dtype(mutated, column) + + +@pytest.mark.parametrize("backend", ["pandas", "polars"]) +def test_categorical_dtype_preserved(backend: str) -> None: + """Ensure categorical dtype is preserved after error injection.""" + raw = {"cat": ["a", "b", "a", "c"], "num": [1, 2, 3, 4]} + if backend == "pandas": + df = pd.DataFrame(raw) + df["cat"] = pd.Categorical(df["cat"]) + else: + df = pl.DataFrame(raw).with_columns(pl.col("cat").cast(pl.Categorical)) + + mutated, _mask = low_level.create_errors( + df, + "cat", + error_rate=0.5, + error_mechanism=error_mechanism.ECAR(seed=0), + error_type=error_type.CategorySwap(seed=0), + ) + + _assert_dtype_preserved(df, mutated, "cat") + + +@pytest.mark.parametrize("backend", ["pandas", "polars"]) +def test_datetime_dtype_preserved(backend: str) -> None: + """Ensure datetime dtype is preserved after error injection.""" + ts = np.array(["2024-01-01", "2024-01-02", "2024-01-03"], dtype="datetime64[ns]") + raw = {"ts": ts, "num": [1, 2, 3]} + df = pd.DataFrame(raw) if backend == "pandas" else pl.DataFrame(raw) + + mutated, _mask = low_level.create_errors( + df, + "ts", + error_rate=0.5, + error_mechanism=error_mechanism.ECAR(seed=0), + error_type=error_type.AddDelta(config={"add_delta_value": 3600}, seed=0), + ) + + _assert_dtype_preserved(df, mutated, "ts") + + +@pytest.mark.parametrize("backend", ["pandas", "polars"]) +def test_nullable_int_dtype_preserved(backend: str) -> None: + """Ensure nullable integer dtype is preserved after error injection.""" + raw = {"x": [1, 2, 3, 4]} + df = ( + pd.DataFrame({"x": pd.Series(raw["x"], dtype="Int64")}) + if backend == "pandas" + else pl.DataFrame({"x": pl.Series(raw["x"], dtype=pl.Int64)}) + ) + + mutated, _mask = low_level.create_errors( + df, + "x", + error_rate=0.5, + error_mechanism=error_mechanism.ECAR(seed=0), + error_type=error_type.AddDelta(config={"add_delta_value": 1}, seed=0), + ) + + _assert_dtype_preserved(df, mutated, "x") diff --git a/tests/error_type/test_typo.py b/tests/error_type/test_typo.py index 1032e79..575e1fa 100644 --- a/tests/error_type/test_typo.py +++ b/tests/error_type/test_typo.py @@ -1,3 +1,5 @@ +from typing import cast + import pandas as pd from tab_err import error_mechanism, error_type @@ -12,4 +14,5 @@ def test_typo() -> None: } ) modified_df, _ = create_errors(test_data, "A", 1, error_mechanism.ECAR(), error_type.Typo()) - assert modified_df.iloc[0, 0] != "" + modified_df_pd = cast("pd.DataFrame", modified_df) + assert modified_df_pd.iloc[0, 0] != "" From 1e10af2f9db44d2bcdcf458ac52ebae77ba77d61 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Mon, 19 Jan 2026 19:26:29 +0100 Subject: [PATCH 04/13] feat: preserver dtypes across pandas and polars Adopting narwhals, it's important that dtypes are preserved across backends - a value that's NaN in pandas should be NaN in polars, datetimes should remain datetimes, etc. . This is achieved by - using narwhals dtypes support as much as possible - explicitly casting dtypes after transforming values when necessary --- tab_err/_utils.py | 48 +++++++++++++++++++++++++--- tab_err/api/high_level.py | 13 ++++---- tab_err/error_type/_add_delta.py | 16 ++++++---- tab_err/error_type/_category_swap.py | 14 ++++---- tab_err/error_type/_error_type.py | 3 +- tab_err/error_type/_extraneous.py | 10 +++--- tab_err/error_type/_missing.py | 14 ++++---- tab_err/error_type/_mistype.py | 5 +-- tab_err/error_type/_mojibake.py | 10 +++--- tab_err/error_type/_outlier.py | 15 +++++---- tab_err/error_type/_permutate.py | 18 ++++++----- tab_err/error_type/_replace.py | 17 ++++++---- tab_err/error_type/_typo.py | 9 +++--- tab_err/error_type/_wrong_unit.py | 10 +++--- 14 files changed, 123 insertions(+), 79 deletions(-) diff --git a/tab_err/_utils.py b/tab_err/_utils.py index 19d3277..5061139 100644 --- a/tab_err/_utils.py +++ b/tab_err/_utils.py @@ -1,6 +1,15 @@ from __future__ import annotations +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from collections.abc import Sequence + + from narwhals.typing import IntoDType + import random +import warnings +from typing import Any import narwhals as nw import numpy as np @@ -60,7 +69,7 @@ def check_data_emptiness(data: nw.DataFrame) -> None: def is_string_dtype(series: nw.Series) -> bool: """Check if a series has a string dtype.""" - return series.dtype == nw.String or series.dtype == nw.Object + return series.dtype in {nw.String, nw.Object} def is_numeric_dtype(series: nw.Series) -> bool: @@ -78,22 +87,22 @@ def is_datetime_dtype(series: nw.Series) -> bool: return series.dtype == nw.Datetime -def select_string_columns(data: nw.DataFrame) -> list[str]: +def select_string_columns(data: nw.DataFrame) -> list[str | int]: """Select columns with string dtype.""" return [col for col in data.columns if is_string_dtype(data[col])] -def select_numeric_columns(data: nw.DataFrame) -> list[str]: +def select_numeric_columns(data: nw.DataFrame) -> list[str | int]: """Select columns with numeric dtype.""" return [col for col in data.columns if is_numeric_dtype(data[col])] -def select_datetime_columns(data: nw.DataFrame) -> list[str]: +def select_datetime_columns(data: nw.DataFrame) -> list[str | int]: """Select columns with datetime dtype.""" return [col for col in data.columns if is_datetime_dtype(data[col])] -def select_numeric_or_datetime_columns(data: nw.DataFrame) -> list[str]: +def select_numeric_or_datetime_columns(data: nw.DataFrame) -> list[str | int]: """Select columns with numeric or datetime dtype.""" return [col for col in data.columns if is_numeric_dtype(data[col]) or is_datetime_dtype(data[col])] @@ -106,3 +115,32 @@ def create_empty_boolean_mask(data: nw.DataFrame) -> nw.DataFrame: dict.fromkeys(data.columns, mask_values), backend=nw.get_native_namespace(data), ) + + +def cast_series_like(series: nw.Series, like: nw.Series, column: int | str) -> nw.Series: + """Cast series to the dtype of 'like' when possible, otherwise keep original.""" + if series.dtype == like.dtype: + return series + dtype: IntoDType = like.dtype + + try: + return series.cast(dtype) + except Exception as exc: # noqa: BLE001 + msg = f"Failed to cast column {column} to dtype {like.dtype}: {exc}. Keeping inferred dtype." + warnings.warn(msg, stacklevel=2) + return series + + +def _values_to_list(values: Sequence[Any] | np.ndarray) -> list[Any]: + """Normalize values into a list for nw.new_series.""" + if isinstance(values, np.ndarray): + return values.tolist() + return list(values) + + +def new_series_like(data: nw.DataFrame, column: int | str, values: Sequence[Any] | np.ndarray) -> nw.Series: + """Create a new series for 'column' and cast it back to the original dtype.""" + col_name = get_column_str(data, column) + original = get_column(data, column) + series = nw.new_series(col_name, _values_to_list(values), backend=nw.get_native_namespace(data)) + return cast_series_like(series, original, column) diff --git a/tab_err/api/high_level.py b/tab_err/api/high_level.py index 936d6a7..b83d219 100644 --- a/tab_err/api/high_level.py +++ b/tab_err/api/high_level.py @@ -7,7 +7,7 @@ from tab_err import ErrorMechanism, ErrorType, error_mechanism, error_type from tab_err._error_model import ErrorModel -from tab_err._utils import check_data_emptiness, check_error_rate, create_empty_boolean_mask, seed_randomness_and_get_generator +from tab_err._utils import check_data_emptiness, check_error_rate, seed_randomness_and_get_generator from tab_err.api import MidLevelConfig, mid_level if TYPE_CHECKING: @@ -124,7 +124,7 @@ def _build_column_mechanism_dictionary( msg = "Possible conflict in error mechanisms to apply. Set at least on of: error_mechanisms_to_exclude or error_mechanisms_to_include to None." raise ValueError(msg) - columns_mechanisms = {} + columns_mechanisms: dict[int | str, list[ErrorMechanism]] = {} if error_mechanisms_to_include is not None and error_mechanisms_to_exclude is None: # Include specified if not all(issubclass(type(cls), ErrorMechanism) for cls in error_mechanisms_to_include): # Check input @@ -177,7 +177,7 @@ def _build_column_number_of_models_dictionary( Returns: dict[int | str, int]: A dictionary mapping from column names to the number of error models to apply to that column. """ - column_num_models = {} + column_num_models: dict[int | str, int] = {} for column in data.columns: column_num_models[column] = len(column_types[column]) * len(column_mechanisms[column]) @@ -202,7 +202,7 @@ def create_errors( # noqa: PLR0913 """Creates errors in a given DataFrame, at a rate of *approximately* max_error_rate. Args: - data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and other narwhals-compatible backends. + data (IntoDataFrame): The DataFrame to create errors in. Supports pandas, Polars, and (experimental) other narwhals-compatible backends. error_rate (float): The maximum error rate to be introduced to each column in the DataFrame. n_error_models_per_column (int, optional): The number of valid error models to apply to each column. Defaults to 1. error_types_to_include (list[ErrorType] | None, optional): A list of the error types to be included when building error models. Defaults to None. @@ -231,7 +231,6 @@ def create_errors( # noqa: PLR0913 # Set Up Data data_copy = data_nw.clone() - error_mask = create_empty_boolean_mask(data_nw) # Build Dictionaries col_type = _build_column_type_dictionary( @@ -271,5 +270,5 @@ def create_errors( # noqa: PLR0913 raise ValueError(msg) # Create Errors & Return (mid_level handles native conversion) - dirty_data, error_mask = mid_level.create_errors(nw.to_native(data_copy), config) - return dirty_data, error_mask + dirty_data_native, error_mask_native = mid_level.create_errors(nw.to_native(data_copy), config) + return dirty_data_native, error_mask_native diff --git a/tab_err/error_type/_add_delta.py b/tab_err/error_type/_add_delta.py index 7e89596..b065a55 100644 --- a/tab_err/error_type/_add_delta.py +++ b/tab_err/error_type/_add_delta.py @@ -1,11 +1,13 @@ from __future__ import annotations import warnings +from typing import TYPE_CHECKING -import narwhals as nw +if TYPE_CHECKING: + import narwhals as nw import numpy as np -from tab_err._utils import get_column, get_column_str, is_datetime_dtype, is_numeric_dtype, select_numeric_or_datetime_columns +from tab_err._utils import get_column, is_datetime_dtype, is_numeric_dtype, new_series_like, select_numeric_or_datetime_columns from ._error_type import ErrorType @@ -39,7 +41,6 @@ def _apply(self: AddDelta, data: nw.DataFrame, error_mask: nw.DataFrame, column: Returns: nw.Series: The data column, 'column', after AddDelta errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) was_datetime = False @@ -62,13 +63,14 @@ def _apply(self: AddDelta, data: nw.DataFrame, error_mask: nw.DataFrame, column: mean_val = np.nanmean(data_arr) std_val = np.nanstd(data_arr) random_choice = self._random_generator.choice(data_arr[~np.isnan(data_arr)]) - self.config.add_delta_value = (random_choice - mean_val) / std_val if std_val != 0 else 0 + delta_value = (random_choice - mean_val) / std_val if std_val != 0 else 0 + else: + delta_value = self.config.add_delta_value - # Apply delta where mask is True - data_arr[mask_arr] += self.config.add_delta_value + data_arr[mask_arr] += delta_value if was_datetime: # Convert back to datetime (from seconds) data_arr = (data_arr * 10**9).astype("int64").astype("datetime64[ns]") - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_category_swap.py b/tab_err/error_type/_category_swap.py index dab5035..7c8e9f3 100644 --- a/tab_err/error_type/_category_swap.py +++ b/tab_err/error_type/_category_swap.py @@ -3,9 +3,8 @@ import random import narwhals as nw -import numpy as np -from tab_err._utils import get_column, get_column_str +from tab_err._utils import get_column, new_series_like from ._error_type import ErrorType @@ -40,7 +39,7 @@ def _check_type(data: nw.DataFrame, column: int | str) -> None: def _get_valid_columns(self: CategorySwap, data: nw.DataFrame) -> list[str | int]: """Checks which columns are categorical and returns the indices of those with two or more categories.""" - valid_columns = [] + valid_columns: list[str | int] = [] for col_name in data.columns: series = get_column(data, col_name) @@ -65,7 +64,6 @@ def _apply(self: CategorySwap, data: nw.DataFrame, error_mask: nw.DataFrame, col Returns: nw.Series: The data column, 'column', after CategorySwap errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -84,7 +82,7 @@ def sample_label(old_label: str) -> str: elif self.config.mislabel_weighing == "frequency": # Calculate frequency weights - value_counts = {} + value_counts: dict[str, int] = {} for val in data_arr: if val not in value_counts: value_counts[val] = 0 @@ -92,8 +90,8 @@ def sample_label(old_label: str) -> str: def sample_label(old_label: str) -> str: choices = [x for x in categories if x != old_label] - weights = [value_counts.get(x, 1) for x in choices] - total = sum(weights) + weights: list[float] = [float(value_counts.get(x, 1)) for x in choices] + total = float(sum(weights)) weights = [w / total for w in weights] return random.choices(choices, weights=weights, k=1)[0] else: @@ -105,4 +103,4 @@ def sample_label(old_label: str) -> str: if mask_arr[i]: data_arr[i] = sample_label(data_arr[i]) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_error_type.py b/tab_err/error_type/_error_type.py index b33facb..ce81d0e 100644 --- a/tab_err/error_type/_error_type.py +++ b/tab_err/error_type/_error_type.py @@ -3,13 +3,12 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any -import narwhals as nw - from tab_err._utils import seed_randomness_and_get_generator from ._config import ErrorTypeConfig if TYPE_CHECKING: + import narwhals as nw import numpy as np diff --git a/tab_err/error_type/_extraneous.py b/tab_err/error_type/_extraneous.py index 54309da..1e62990 100644 --- a/tab_err/error_type/_extraneous.py +++ b/tab_err/error_type/_extraneous.py @@ -2,13 +2,14 @@ import string import warnings +from typing import TYPE_CHECKING -import narwhals as nw - -from tab_err._utils import get_column, get_column_str, select_string_columns +from tab_err._utils import get_column, new_series_like, select_string_columns from ._error_type import ErrorType +if TYPE_CHECKING: + import narwhals as nw class Extraneous(ErrorType): """Adds Extraneous strings around the values in a column.""" @@ -45,7 +46,6 @@ def _apply(self: Extraneous, data: nw.DataFrame, error_mask: nw.DataFrame, colum Returns: nw.Series: The data column, 'column', after Extraneous errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -69,4 +69,4 @@ def _apply(self: Extraneous, data: nw.DataFrame, error_mask: nw.DataFrame, colum val = data_arr[i] data_arr[i] = self.config.extraneous_value_template.format(value=val) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_missing.py b/tab_err/error_type/_missing.py index 0e851a8..2856f3f 100644 --- a/tab_err/error_type/_missing.py +++ b/tab_err/error_type/_missing.py @@ -1,12 +1,13 @@ from __future__ import annotations -import narwhals as nw -import numpy as np +from typing import TYPE_CHECKING, Union, cast -from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns +from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns from ._error_type import ErrorType +if TYPE_CHECKING: + import narwhals as nw class MissingValue(ErrorType): """Insert missing values into a column. @@ -22,7 +23,9 @@ def _check_type(data: nw.DataFrame, column: int | str) -> None: def _get_valid_columns(self: MissingValue, data: nw.DataFrame) -> list[str | int]: """If the config missing value is None, returns all columns. Otherwise, only the columns with string type.""" - return data.columns if self.config.missing_value is None else select_string_columns(data) + if self.config.missing_value is None: + return cast("list[Union[str, int]]", list(data.columns)) + return select_string_columns(data) def _apply(self: MissingValue, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the MissingValue ErrorType to a column of data. @@ -35,7 +38,6 @@ def _apply(self: MissingValue, data: nw.DataFrame, error_mask: nw.DataFrame, col Returns: nw.Series: The data column, 'column', after MissingValue errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -57,4 +59,4 @@ def _apply(self: MissingValue, data: nw.DataFrame, error_mask: nw.DataFrame, col data_arr[mask_arr] = missing_val # Create new series with the modified data - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_mistype.py b/tab_err/error_type/_mistype.py index 3a8335f..60790dc 100644 --- a/tab_err/error_type/_mistype.py +++ b/tab_err/error_type/_mistype.py @@ -1,7 +1,6 @@ from __future__ import annotations import narwhals as nw -import numpy as np from tab_err._utils import get_column, get_column_str @@ -60,9 +59,7 @@ def _apply(self: Mistype, data: nw.DataFrame, error_mask: nw.DataFrame, column: target_dtype = "object" elif current_dtype == nw.Int64: target_dtype = "float64" - elif current_dtype == nw.Float64: - target_dtype = "int64" - elif current_dtype == nw.Boolean: + elif current_dtype in {nw.Float64, nw.Boolean}: target_dtype = "int64" elif current_dtype.is_integer(): target_dtype = "float64" diff --git a/tab_err/error_type/_mojibake.py b/tab_err/error_type/_mojibake.py index 1f5dffa..912987e 100644 --- a/tab_err/error_type/_mojibake.py +++ b/tab_err/error_type/_mojibake.py @@ -1,13 +1,14 @@ from __future__ import annotations import random +from typing import TYPE_CHECKING -import narwhals as nw - -from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns +from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns from ._error_type import ErrorType +if TYPE_CHECKING: + import narwhals as nw class Mojibake(ErrorType): """Inserts mojibake into a column containing strings.""" @@ -53,7 +54,6 @@ def _apply(self: Mojibake, data: nw.DataFrame, error_mask: nw.DataFrame, column: "iso-8859-2": top10 - {"iso-8859-2", "windows-1250", "iso-8859-1", "windows-1252"}, } - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -77,4 +77,4 @@ def _apply(self: Mojibake, data: nw.DataFrame, error_mask: nw.DataFrame, column: if val is not None and isinstance(val, str): data_arr[i] = val.encode(encoding_sender, errors="ignore").decode(encoding_receiver, errors="ignore") - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_outlier.py b/tab_err/error_type/_outlier.py index fda49f5..daf8faa 100644 --- a/tab_err/error_type/_outlier.py +++ b/tab_err/error_type/_outlier.py @@ -1,12 +1,15 @@ from __future__ import annotations -import narwhals as nw +from typing import TYPE_CHECKING + import numpy as np -from tab_err._utils import get_column, get_column_str, is_datetime_dtype, is_integer_dtype, is_numeric_dtype, select_numeric_or_datetime_columns +from tab_err._utils import get_column, is_datetime_dtype, is_integer_dtype, is_numeric_dtype, new_series_like, select_numeric_or_datetime_columns from ._error_type import ErrorType +if TYPE_CHECKING: + import narwhals as nw class Outlier(ErrorType): """Inserts outliers into a column by pushing data points outside the interquartile range (IQR) boundaries. @@ -48,11 +51,9 @@ def _apply(self: Outlier, data: nw.DataFrame, error_mask: nw.DataFrame, column: Returns: nw.Series: The data column, 'column', after Outlier errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) was_datetime = False - original_dtype = series.dtype # Get numpy arrays data_arr = series.to_numpy().copy() @@ -93,7 +94,7 @@ def _apply(self: Outlier, data: nw.DataFrame, error_mask: nw.DataFrame, column: data_arr[mask_upper] += perturbation_upper # Handle the mean values with a coin flip - n_equal = np.sum(mask_equal) + n_equal: int = int(np.sum(mask_equal)) if n_equal > 0: coin_flips = self._random_generator.random(n_equal) perturbations = np.where(coin_flips > self.config.outlier_coin_flip_threshold, perturbation_upper, -perturbation_lower) @@ -101,7 +102,7 @@ def _apply(self: Outlier, data: nw.DataFrame, error_mask: nw.DataFrame, column: # Apply Gaussian noise to simulate the increase in measurement error of the outliers noise_std = self.config.outlier_noise_coeff * iqr - n_errors = np.sum(mask_arr) + n_errors: int = int(np.sum(mask_arr)) if is_integer: data_arr[mask_arr] += np.rint(self._random_generator.normal(loc=0, scale=noise_std, size=n_errors)) @@ -114,4 +115,4 @@ def _apply(self: Outlier, data: nw.DataFrame, error_mask: nw.DataFrame, column: elif is_integer: data_arr = data_arr.astype(np.int64) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_permutate.py b/tab_err/error_type/_permutate.py index a427bd8..4b4d916 100644 --- a/tab_err/error_type/_permutate.py +++ b/tab_err/error_type/_permutate.py @@ -1,10 +1,12 @@ from __future__ import annotations import random +from typing import TYPE_CHECKING -import narwhals as nw +if TYPE_CHECKING: + import narwhals as nw -from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns +from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns from ._error_type import ErrorType @@ -74,7 +76,6 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column Returns: nw.Series: The data column, 'column', after Permutate errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -83,10 +84,11 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column mask_arr = series_mask.to_numpy() # Get separator counts for non-null values - separator_counts = [] - for val in data_arr: - if val is not None and isinstance(val, str): - separator_counts.append(val.count(self.config.permutation_separator)) + separator_counts = [ + val.count(self.config.permutation_separator) + for val in data_arr + if val is not None and isinstance(val, str) + ] for i, count in enumerate(separator_counts): if count == 0: @@ -121,4 +123,4 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column if val is not None and isinstance(val, str): data_arr[i] = self._random_pattern_function(val) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_replace.py b/tab_err/error_type/_replace.py index 3e49d41..dd45a3a 100644 --- a/tab_err/error_type/_replace.py +++ b/tab_err/error_type/_replace.py @@ -1,10 +1,12 @@ from __future__ import annotations import warnings +from typing import TYPE_CHECKING -import narwhals as nw +if TYPE_CHECKING: + import narwhals as nw -from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns +from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns from ._error_type import ErrorType @@ -35,7 +37,6 @@ def _apply(self: Replace, data: nw.DataFrame, error_mask: nw.DataFrame, column: Returns: nw.Series: The data column, 'column', after Replace errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -51,15 +52,17 @@ def _apply(self: Replace, data: nw.DataFrame, error_mask: nw.DataFrame, column: if valid_values: random_row = self._random_generator.choice(len(valid_values)) random_val = valid_values[random_row] - self.config.replace_what = self._random_generator.choice(list(random_val)) + replace_what = self._random_generator.choice(list(random_val)) else: - self.config.replace_what = "" + replace_what = "" + else: + replace_what = self.config.replace_what # Apply replace where mask is True for i in range(len(data_arr)): if mask_arr[i]: val = data_arr[i] if val is not None and isinstance(val, str): - data_arr[i] = val.replace(self.config.replace_what, self.config.replace_with) + data_arr[i] = val.replace(replace_what, self.config.replace_with) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) diff --git a/tab_err/error_type/_typo.py b/tab_err/error_type/_typo.py index 2753037..8f516c8 100644 --- a/tab_err/error_type/_typo.py +++ b/tab_err/error_type/_typo.py @@ -1,10 +1,12 @@ from __future__ import annotations import random +from typing import TYPE_CHECKING -import narwhals as nw +if TYPE_CHECKING: + import narwhals as nw -from tab_err._utils import get_column, get_column_str, is_string_dtype, select_string_columns +from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns from ._error_type import ErrorType @@ -45,7 +47,6 @@ def _apply(self: Typo, data: nw.DataFrame, error_mask: nw.DataFrame, column: int Returns: nw.Series: The data column, 'column', after Typo errors at the locations specified by 'error_mask' are introduced. """ - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -60,7 +61,7 @@ def _apply(self: Typo, data: nw.DataFrame, error_mask: nw.DataFrame, column: int if val is not None and isinstance(val, str): data_arr[i] = typo(val, self.config.typo_error_period, self.config.typo_keyboard_layout) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) def typo(input_text: str, typo_error_period: int = 10, layout: str = "ansi-qwerty") -> str: diff --git a/tab_err/error_type/_wrong_unit.py b/tab_err/error_type/_wrong_unit.py index c33f74e..b247f95 100644 --- a/tab_err/error_type/_wrong_unit.py +++ b/tab_err/error_type/_wrong_unit.py @@ -1,11 +1,14 @@ from __future__ import annotations import warnings +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + import narwhals as nw -import narwhals as nw import numpy as np -from tab_err._utils import get_column, get_column_str, is_numeric_dtype, select_numeric_columns +from tab_err._utils import get_column, is_numeric_dtype, new_series_like, select_numeric_columns from ._error_type import ErrorType @@ -41,7 +44,6 @@ def _apply(self: WrongUnit, data: nw.DataFrame, error_mask: nw.DataFrame, column warnings.warn(msg, stacklevel=2) self.config.wrong_unit_scaling = lambda x: 10.0 * x - col_name = get_column_str(data, column) series = get_column(data, column) series_mask = get_column(error_mask, column) @@ -54,4 +56,4 @@ def _apply(self: WrongUnit, data: nw.DataFrame, error_mask: nw.DataFrame, column if mask_arr[i]: data_arr[i] = self.config.wrong_unit_scaling(data_arr[i]) - return nw.new_series(col_name, data_arr.tolist(), backend=nw.get_native_namespace(data)) + return new_series_like(data, column, data_arr) From 5250c6b521d0fe409a1f0c6b1ad3491143767f3b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sebastian=20J=C3=A4ger?= Date: Mon, 19 Jan 2026 13:28:04 +0000 Subject: [PATCH 05/13] chore: Remove py39, add py313 and 314 (#26) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit I addition to #24, this one adds full support for python 3.13 and python 3.14 and drops support for 3.9 (because it is EOL). Pandas is now supports up to python 3.14, which was the blocker for supporting these version in `tab-err`. See: - https://pyreadiness.org/3.9/ - https://pyreadiness.org/3.13 - https://pyreadiness.org/3.14 --------- Signed-off-by: Sebastian Jäger --- pyproject.toml | 2 +- uv.lock | 1175 +++++------------------------------------------- 2 files changed, 123 insertions(+), 1054 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 95bff58..17fc9e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ classifiers = [ "Operating System :: Microsoft :: Windows", ] -requires-python = ">=3.9,<3.15" +requires-python = ">=3.10,<3.15" dependencies = [ "narwhals>=1.30.0", ] diff --git a/uv.lock b/uv.lock index e68964c..62c434e 100644 --- a/uv.lock +++ b/uv.lock @@ -1,12 +1,11 @@ version = 1 revision = 2 -requires-python = ">=3.9, <3.15" +requires-python = ">=3.10, <3.15" resolution-markers = [ "python_full_version >= '3.14'", "python_full_version >= '3.12' and python_full_version < '3.14'", "python_full_version == '3.11.*'", - "python_full_version == '3.10.*'", - "python_full_version < '3.10'", + "python_full_version < '3.11'", ] [[package]] @@ -21,28 +20,10 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z" }, ] -[[package]] -name = "alabaster" -version = "0.7.16" -source = { registry = "https://pypi.org/simple" } -resolution-markers = [ - 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-[[package]] -name = "zipp" -version = "3.23.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547, upload-time = "2025-06-08T17:06:39.4Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, -] From f7cf86e98fc45645e4e6440ae6b399d9b322fd59 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Mon, 19 Jan 2026 21:24:12 +0100 Subject: [PATCH 06/13] chore: update README polars support --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8a9240f..83c52de 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ The library's building blocks are `ErrorMechanism`s, `ErrorType`s, and `ErrorMod - An `ErrorType` describes _how_ the value is wrong: a typo, an outlier, a category swap, and so on. Read the documentation for a [full list of supported error types](https://tab-err.readthedocs.io/latest/api/tab_err/error_type/index.html). - An `ErrorModel` is a set of mechanisms and types to perturb existing data with realistic errors. It is shareable as metadata. -`tab_err` is supported by a `pandas` backend. +`tab_err` supports pandas and Polars backends, and has experimental support for cuDF, Modin, and PyArrow. ## Examples From 661774d3b9f8be939286d55b3175c77a4f914c79 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Mon, 19 Jan 2026 22:29:00 +0100 Subject: [PATCH 07/13] chore: add numpy as runtime dependency --- pyproject.toml | 2 +- uv.lock | 10 ++++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 17fc9e0..4e2f1ce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ classifiers = [ requires-python = ">=3.10,<3.15" dependencies = [ "narwhals>=1.30.0", + "numpy>=1.24.0", ] [project.urls] @@ -40,7 +41,6 @@ ci = [ "tomli>=2.2.1", ] dev = [ - "numpy>=1.24.0", "pandas>=2.0.0", "polars>=1.0.0", "pyarrow>=14.0.0", # Required for polars.to_pandas() diff --git a/uv.lock b/uv.lock index 62c434e..da64b5e 100644 --- a/uv.lock +++ b/uv.lock @@ -2539,6 +2539,8 @@ version = "0.2.1" source = { editable = "." } dependencies = [ { name = "narwhals" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, ] [package.dev-dependencies] @@ -2550,8 +2552,6 @@ dev = [ { name = "mypy" }, { name = "myst-parser", version = "4.0.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, { name = "myst-parser", version = "5.0.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, - { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, - { name = "numpy", version = "2.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "pandas" }, { name = "polars" }, { name = "pre-commit" }, @@ -2566,7 +2566,10 @@ dev = [ ] [package.metadata] -requires-dist = [{ name = "narwhals", specifier = ">=1.30.0" }] +requires-dist = [ + { name = "narwhals", specifier = ">=1.30.0" }, + { name = "numpy", specifier = ">=1.24.0" }, +] [package.metadata.requires-dev] ci = [{ name = "tomli", specifier = ">=2.2.1" }] @@ -2574,7 +2577,6 @@ dev = [ { name = "jupyterlab", specifier = ">=4.3.2,<5.0.0" }, { name = "mypy", specifier = ">=1.14.1,<2.0.0" }, { name = "myst-parser", marker = "python_full_version >= '3.10'", specifier = ">=4.0.0" }, - { name = "numpy", specifier = ">=1.24.0" }, { name = "pandas", specifier = ">=2.0.0" }, { name = "polars", specifier = ">=1.0.0" }, { name = "pre-commit", specifier = ">=4.0.1,<5.0.0" }, From 0eb207254031ea561498aa5152a51cba584ac16d Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Fri, 23 Jan 2026 13:07:41 +0100 Subject: [PATCH 08/13] chore: reduce complexity of mistype --- tab_err/error_type/_missing.py | 4 ++-- tab_err/error_type/_mistype.py | 43 ++++++++++++++++++++-------------- 2 files changed, 27 insertions(+), 20 deletions(-) diff --git a/tab_err/error_type/_missing.py b/tab_err/error_type/_missing.py index 2856f3f..f69d4de 100644 --- a/tab_err/error_type/_missing.py +++ b/tab_err/error_type/_missing.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Union, cast +from typing import TYPE_CHECKING, cast from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns @@ -24,7 +24,7 @@ def _check_type(data: nw.DataFrame, column: int | str) -> None: def _get_valid_columns(self: MissingValue, data: nw.DataFrame) -> list[str | int]: """If the config missing value is None, returns all columns. Otherwise, only the columns with string type.""" if self.config.missing_value is None: - return cast("list[Union[str, int]]", list(data.columns)) + return cast("list[str | int]", list(data.columns)) return select_string_columns(data) def _apply(self: MissingValue, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: diff --git a/tab_err/error_type/_mistype.py b/tab_err/error_type/_mistype.py index 60790dc..f243f4b 100644 --- a/tab_err/error_type/_mistype.py +++ b/tab_err/error_type/_mistype.py @@ -1,7 +1,12 @@ from __future__ import annotations +from typing import TYPE_CHECKING + import narwhals as nw +if TYPE_CHECKING: + import numpy as np + from tab_err._utils import get_column, get_column_str from ._error_type import ErrorType @@ -23,6 +28,25 @@ def _get_valid_columns(self: Mistype, data: nw.DataFrame) -> list[str | int]: """Returns all column names of columns with dtypes other than object. This is necessary for the high level API.""" return [col_name for col_name in data.columns if data[col_name].dtype != nw.Object] + def _convert_dtypes(self, data_arr: np.ndarray, mask_arr: np.ndarray, target_dtype: str) -> np.ndarray: + # Convert to object array to allow mixed types + result_arr = data_arr.astype(object) + + # Apply type conversion where mask is True + for i in range(len(result_arr)): + if mask_arr[i]: + val = result_arr[i] + if target_dtype in ("int64", "Int64"): + result_arr[i] = int(val) if val is not None else val + elif target_dtype in ("float64", "Float64"): + result_arr[i] = float(val) if val is not None else val + elif target_dtype in ("object", "string"): + result_arr[i] = str(val) if val is not None else val + else: + msg = f"Unsupported dtype to cast into: {target_dtype}" + raise ValueError(msg) + return result_arr + def _apply(self: Mistype, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the Mistype ErrorType to a column of data. Note that the dtype of the column is changed by this operation. @@ -73,22 +97,5 @@ def _apply(self: Mistype, data: nw.DataFrame, error_mask: nw.DataFrame, column: data_arr = series.to_numpy().copy() mask_arr = series_mask.to_numpy() - # Convert to object array to allow mixed types - result_arr = data_arr.astype(object) - - # Apply type conversion where mask is True - for i in range(len(result_arr)): - if mask_arr[i]: - val = result_arr[i] - try: - if target_dtype in ("int64", "Int64"): - result_arr[i] = int(val) if val is not None else val - elif target_dtype in ("float64", "Float64"): - result_arr[i] = float(val) if val is not None else val - elif target_dtype in ("object", "string"): - result_arr[i] = str(val) if val is not None else val - except (ValueError, TypeError): - # Keep original value if conversion fails - pass - + result_arr = self._convert_dtypes(data_arr, mask_arr, target_dtype) return nw.new_series(col_name, result_arr.tolist(), backend=nw.get_native_namespace(data)) From be1f9ef21be07e55e203f2810395a7ce5d7fbb05 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Fri, 23 Jan 2026 13:21:36 +0100 Subject: [PATCH 09/13] chore: split permutate's apply into functions --- tab_err/error_type/_permutate.py | 42 ++++++++++++++++++-------------- 1 file changed, 24 insertions(+), 18 deletions(-) diff --git a/tab_err/error_type/_permutate.py b/tab_err/error_type/_permutate.py index 4b4d916..4a22364 100644 --- a/tab_err/error_type/_permutate.py +++ b/tab_err/error_type/_permutate.py @@ -5,6 +5,7 @@ if TYPE_CHECKING: import narwhals as nw + import numpy as np from tab_err._utils import get_column, is_string_dtype, new_series_like, select_string_columns @@ -61,6 +62,25 @@ def _fixed_pattern_function(self: Permutate, old_string: str, new_pattern: list[ return self.config.permutation_separator.join(new_string_as_part_list) + def _pattern_permutation(self, permutation_pattern: list[int], data_arr: np.ndarray, mask_arr: np.ndarray) -> np.ndarray: + """Permutates values of a series data_arr as specified in permutation_pattern.""" + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = self._fixed_pattern_function(val, permutation_pattern) + return data_arr + + def _random_permutation(self, data_arr: np.ndarray, mask_arr: np.ndarray) -> np.ndarray: + """Permutates values of a series data_arr using a new, random pattern for each value.""" + for i in range(len(data_arr)): + if mask_arr[i]: + val = data_arr[i] + if val is not None and isinstance(val, str): + data_arr[i] = self._random_pattern_function(val) + + return data_arr + def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column: int | str) -> nw.Series: """Applies the `Permutate` `ErrorType` to a column of data. @@ -98,29 +118,15 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column if self.config.permutation_pattern is not None: # Permutation of each entry from pattern. _check_column_format_consistency(separator_counts, column) - new_pattern = self.config.permutation_pattern - - for i in range(len(data_arr)): - if mask_arr[i]: - val = data_arr[i] - if val is not None and isinstance(val, str): - data_arr[i] = self._fixed_pattern_function(val, new_pattern) + data_arr = self._pattern_permutation(self.config.permutation_pattern, data_arr, mask_arr) elif self.config.permutation_automation_pattern == "fixed": # Fixed permutation -- random once, applied to all. _check_column_format_consistency(separator_counts, column) - new_pattern = _generate_shuffle_pattern(separator_counts[0]) + rnd_permutatin_pattern = _generate_shuffle_pattern(separator_counts[0]) - for i in range(len(data_arr)): - if mask_arr[i]: - val = data_arr[i] - if val is not None and isinstance(val, str): - data_arr[i] = self._fixed_pattern_function(val, new_pattern) + data_arr = self._pattern_permutation(rnd_permutatin_pattern, data_arr, mask_arr) else: # Random permutation -- random for each entry. - for i in range(len(data_arr)): - if mask_arr[i]: - val = data_arr[i] - if val is not None and isinstance(val, str): - data_arr[i] = self._random_pattern_function(val) + data_arr = self._random_permutation(data_arr, mask_arr) return new_series_like(data, column, data_arr) From 64d0068d42921b245f4a3aa7bede2f28c248e5ac Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Fri, 23 Jan 2026 14:52:21 +0100 Subject: [PATCH 10/13] chore: run ruff format --- tab_err/error_type/_extraneous.py | 1 + tab_err/error_type/_missing.py | 1 + tab_err/error_type/_mojibake.py | 1 + tab_err/error_type/_outlier.py | 1 + tab_err/error_type/_permutate.py | 6 +----- tests/api/test_dtype_preservation.py | 6 +----- 6 files changed, 6 insertions(+), 10 deletions(-) diff --git a/tab_err/error_type/_extraneous.py b/tab_err/error_type/_extraneous.py index 1e62990..af24179 100644 --- a/tab_err/error_type/_extraneous.py +++ b/tab_err/error_type/_extraneous.py @@ -11,6 +11,7 @@ if TYPE_CHECKING: import narwhals as nw + class Extraneous(ErrorType): """Adds Extraneous strings around the values in a column.""" diff --git a/tab_err/error_type/_missing.py b/tab_err/error_type/_missing.py index f69d4de..ae1add3 100644 --- a/tab_err/error_type/_missing.py +++ b/tab_err/error_type/_missing.py @@ -9,6 +9,7 @@ if TYPE_CHECKING: import narwhals as nw + class MissingValue(ErrorType): """Insert missing values into a column. diff --git a/tab_err/error_type/_mojibake.py b/tab_err/error_type/_mojibake.py index 912987e..407f126 100644 --- a/tab_err/error_type/_mojibake.py +++ b/tab_err/error_type/_mojibake.py @@ -10,6 +10,7 @@ if TYPE_CHECKING: import narwhals as nw + class Mojibake(ErrorType): """Inserts mojibake into a column containing strings.""" diff --git a/tab_err/error_type/_outlier.py b/tab_err/error_type/_outlier.py index daf8faa..cc2b4d3 100644 --- a/tab_err/error_type/_outlier.py +++ b/tab_err/error_type/_outlier.py @@ -11,6 +11,7 @@ if TYPE_CHECKING: import narwhals as nw + class Outlier(ErrorType): """Inserts outliers into a column by pushing data points outside the interquartile range (IQR) boundaries. diff --git a/tab_err/error_type/_permutate.py b/tab_err/error_type/_permutate.py index 4a22364..8ca500d 100644 --- a/tab_err/error_type/_permutate.py +++ b/tab_err/error_type/_permutate.py @@ -104,11 +104,7 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column mask_arr = series_mask.to_numpy() # Get separator counts for non-null values - separator_counts = [ - val.count(self.config.permutation_separator) - for val in data_arr - if val is not None and isinstance(val, str) - ] + separator_counts = [val.count(self.config.permutation_separator) for val in data_arr if val is not None and isinstance(val, str)] for i, count in enumerate(separator_counts): if count == 0: diff --git a/tests/api/test_dtype_preservation.py b/tests/api/test_dtype_preservation.py index 9398520..21bb795 100644 --- a/tests/api/test_dtype_preservation.py +++ b/tests/api/test_dtype_preservation.py @@ -68,11 +68,7 @@ def test_datetime_dtype_preserved(backend: str) -> None: def test_nullable_int_dtype_preserved(backend: str) -> None: """Ensure nullable integer dtype is preserved after error injection.""" raw = {"x": [1, 2, 3, 4]} - df = ( - pd.DataFrame({"x": pd.Series(raw["x"], dtype="Int64")}) - if backend == "pandas" - else pl.DataFrame({"x": pl.Series(raw["x"], dtype=pl.Int64)}) - ) + df = pd.DataFrame({"x": pd.Series(raw["x"], dtype="Int64")}) if backend == "pandas" else pl.DataFrame({"x": pl.Series(raw["x"], dtype=pl.Int64)}) mutated, _mask = low_level.create_errors( df, From 8bebb3badbfc8e09ab586f7d88040107b4f2d7e2 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Wed, 4 Feb 2026 15:57:39 +0100 Subject: [PATCH 11/13] chore: re-run error types notebook --- examples/Error_Types.ipynb | 321 +++++++++++++++++++------------------ 1 file changed, 165 insertions(+), 156 deletions(-) diff --git a/examples/Error_Types.ipynb b/examples/Error_Types.ipynb index ebb87c9..c84671b 100644 --- a/examples/Error_Types.ipynb +++ b/examples/Error_Types.ipynb @@ -172,10 +172,10 @@ " 3.0\n", " Alice\n", " 1984\n", - " 3.0\n", - " False\n", + " 3.1\n", " False\n", " False\n", + " True\n", " \n", " \n", " 2\n", @@ -184,10 +184,10 @@ " 3.0\n", " Alice\n", " Pride and Prejudice\n", - " 3.1\n", + " 3.0\n", + " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 3\n", @@ -242,8 +242,8 @@ " error_mask \n", " rating typist book_title rating \n", "0 1.0 False False False \n", - "1 3.0 False False False \n", - "2 3.1 False False True \n", + "1 3.1 False False True \n", + "2 3.0 False False False \n", "3 4.0 False False False \n", "4 2.0 False False False \n", "5 1.0 False False False " @@ -322,10 +322,10 @@ " 1.0\n", " Alice\n", " To Kill a Mockingbird\n", - " 1.0\n", - " False\n", + " 1.1\n", " False\n", " False\n", + " True\n", " \n", " \n", " 1\n", @@ -358,10 +358,10 @@ " 4.0\n", " Bob\n", " The Great Gatsby\n", - " 4.1\n", + " 4.0\n", + " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 4\n", @@ -403,10 +403,10 @@ "\n", " error_mask \n", " rating typist book_title rating \n", - "0 1.0 False False False \n", + "0 1.1 False False True \n", "1 3.0 False False False \n", "2 3.0 False False False \n", - "3 4.1 False False True \n", + "3 4.0 False False False \n", "4 2.0 False False False \n", "5 1.0 False False False " ] @@ -524,10 +524,10 @@ " 4.0\n", " Bob\n", " The Great Gatsby\n", - " NaN\n", + " 4.0\n", + " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 4\n", @@ -536,10 +536,10 @@ " 2.0\n", " Bob\n", " Moby-Dick\n", - " 2.0\n", - " False\n", + " NaN\n", " False\n", " False\n", + " True\n", " \n", " \n", " 5\n", @@ -572,8 +572,8 @@ "0 1.0 False False False \n", "1 3.0 False False False \n", "2 3.0 False False False \n", - "3 NaN False False True \n", - "4 2.0 False False False \n", + "3 4.0 False False False \n", + "4 NaN False False True \n", "5 1.0 False False False " ] }, @@ -825,11 +825,11 @@ " 1\n", " Alice\n", " ¿Cómo estás?\n", - " 11/10 12 a.m.\n", + " 12 a.m.\n", + " False\n", " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 1\n", @@ -840,11 +840,11 @@ " 1\n", " Alice\n", " Привет, как дела?\n", - " 11/10 3 p.m.\n", + " 3 p.m.\n", + " False\n", " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 2\n", @@ -855,11 +855,11 @@ " 2\n", " Bob\n", " 今日はどうですか\n", - " 3 p.m.\n", - " False\n", + " 11/10 3 p.m.\n", " False\n", " False\n", " False\n", + " True\n", " \n", " \n", " 3\n", @@ -870,11 +870,11 @@ " 2\n", " Bob\n", " Ça va bien, merci.\n", - " 4 a.m.\n", - " False\n", + " 11/10 4 a.m.\n", " False\n", " False\n", " False\n", + " True\n", " \n", " \n", " 4\n", @@ -920,14 +920,14 @@ "4 3 Clara ¡Nos vemos mañana! 1 p.m. 3 Clara \n", "5 4 David Ich hätte Hunger. 1 p.m. 4 David \n", "\n", - " error_mask \n", - " content timestamp user_id user content timestamp \n", - "0 ¿Cómo estás? 11/10 12 a.m. False False False True \n", - "1 Привет, как дела? 11/10 3 p.m. False False False True \n", - "2 今日はどうですか 3 p.m. False False False False \n", - "3 Ça va bien, merci. 4 a.m. False False False False \n", - "4 ¡Nos vemos mañana! 1 p.m. False False False False \n", - "5 Ich hätte Hunger. 11/10 1 p.m. False False False True " + " error_mask \n", + " content timestamp user_id user content timestamp \n", + "0 ¿Cómo estás? 12 a.m. False False False False \n", + "1 Привет, как дела? 3 p.m. False False False False \n", + "2 今日はどうですか 11/10 3 p.m. False False False True \n", + "3 Ça va bien, merci. 11/10 4 a.m. False False False True \n", + "4 ¡Nos vemos mañana! 1 p.m. False False False False \n", + "5 Ich hätte Hunger. 11/10 1 p.m. False False False True " ] }, "execution_count": 8, @@ -1008,11 +1008,11 @@ " Alice\n", " ¿Cómo estás?\n", " 12 a.m.\n", - " 1\n", + " 1.0\n", " Alice\n", " ¿Cómo estás?\n", " 12 a.m.\n", - " False\n", + " True\n", " False\n", " False\n", " False\n", @@ -1023,7 +1023,7 @@ " Alice\n", " Привет, как дела?\n", " 3 p.m.\n", - " 1\n", + " 1.0\n", " Alice\n", " Привет, как дела?\n", " 3 p.m.\n", @@ -1053,7 +1053,7 @@ " Bob\n", " Ça va bien, merci.\n", " 4 a.m.\n", - " 2\n", + " 2.0\n", " Bob\n", " Ça va bien, merci.\n", " 4 a.m.\n", @@ -1072,7 +1072,7 @@ " Clara\n", " ¡Nos vemos mañana!\n", " 1 p.m.\n", - " True\n", + " False\n", " False\n", " False\n", " False\n", @@ -1099,20 +1099,20 @@ "text/plain": [ " original perturbed \\\n", " user_id user content timestamp user_id user \n", - "0 1 Alice ¿Cómo estás? 12 a.m. 1 Alice \n", - "1 1 Alice Привет, как дела? 3 p.m. 1 Alice \n", + "0 1 Alice ¿Cómo estás? 12 a.m. 1.0 Alice \n", + "1 1 Alice Привет, как дела? 3 p.m. 1.0 Alice \n", "2 2 Bob 今日はどうですか 3 p.m. 2.0 Bob \n", - "3 2 Bob Ça va bien, merci. 4 a.m. 2 Bob \n", + "3 2 Bob Ça va bien, merci. 4 a.m. 2.0 Bob \n", "4 3 Clara ¡Nos vemos mañana! 1 p.m. 3.0 Clara \n", "5 4 David Ich hätte Hunger. 1 p.m. 4.0 David \n", "\n", " error_mask \n", " content timestamp user_id user content timestamp \n", - "0 ¿Cómo estás? 12 a.m. False False False False \n", + "0 ¿Cómo estás? 12 a.m. True False False False \n", "1 Привет, как дела? 3 p.m. False False False False \n", "2 今日はどうですか 3 p.m. True False False False \n", "3 Ça va bien, merci. 4 a.m. False False False False \n", - "4 ¡Nos vemos mañana! 1 p.m. True False False False \n", + "4 ¡Nos vemos mañana! 1 p.m. False False False False \n", "5 Ich hätte Hunger. 1 p.m. True False False False " ] }, @@ -1134,9 +1134,9 @@ "id": "50e77720-d85c-4154-8fed-9698c22d2057", "metadata": {}, "source": [ - "## [Mojibake](https://en.wikipedia.org/wiki/Mojibake)\n", + "## Mojibake\n", "\n", - "Mojibake occurs if the sender and receiver do not use the same text encoding." + "[Mojibake](https://en.wikipedia.org/wiki/Mojibake) occurs if the sender and receiver do not use the same text encoding." ] }, { @@ -1210,7 +1210,7 @@ " 3 p.m.\n", " 1\n", " Alice\n", - " Ďđčâĺň, ęŕę äĺëŕ?\n", + " ŹąŹâŹÚŹÓŹÖŹä, ŹÜŹŃŹÜ ŹŐŹÖŹÝŹŃ?\n", " 3 p.m.\n", " False\n", " False\n", @@ -1225,11 +1225,11 @@ " 3 p.m.\n", " 2\n", " Bob\n", - " 今日はどうですか\n", + " ĐŃěíŞĎŞÉŞŚŞÇŞšŞŤ\n", " 3 p.m.\n", " False\n", " False\n", - " False\n", + " True\n", " False\n", " \n", " \n", @@ -1240,11 +1240,11 @@ " 4 a.m.\n", " 2\n", " Bob\n", - " a va bien, merci.\n", + " Ça va bien, merci.\n", " 4 a.m.\n", " False\n", " False\n", - " True\n", + " False\n", " False\n", " \n", " \n", @@ -1255,11 +1255,11 @@ " 1 p.m.\n", " 3\n", " Clara\n", - " ¡Nos vemos mañana!\n", + " ˘ŽNos vemos maana!\n", " 1 p.m.\n", " False\n", " False\n", - " False\n", + " True\n", " False\n", " \n", " \n", @@ -1270,11 +1270,11 @@ " 1 p.m.\n", " 4\n", " David\n", - " Ich htte Hunger.\n", + " Ich hätte Hunger.\n", " 1 p.m.\n", " False\n", " False\n", - " True\n", + " False\n", " False\n", " \n", " \n", @@ -1291,14 +1291,23 @@ "4 3 Clara ¡Nos vemos mañana! 1 p.m. 3 Clara \n", "5 4 David Ich hätte Hunger. 1 p.m. 4 David \n", "\n", - " error_mask \n", - " content timestamp user_id user content timestamp \n", - "0 ¿Cómo estás? 12 a.m. False False False False \n", - "1 Ďđčâĺň, ęŕę äĺëŕ? 3 p.m. False False True False \n", - "2 今日はどうですか 3 p.m. False False False False \n", - "3 a va bien, merci. 4 a.m. False False True False \n", - "4 ¡Nos vemos mañana! 1 p.m. False False False False \n", - "5 Ich htte Hunger. 1 p.m. False False True False " + " error_mask \\\n", + " content timestamp user_id user content \n", + "0 ¿Cómo estás? 12 a.m. False False False \n", + "1 ŹąŹâŹÚŹÓŹÖŹä, ŹÜŹŃŹÜ ŹŐŹÖŹÝŹŃ? 3 p.m. False False True \n", + "2 ĐŃěíŞĎŞÉŞŚŞÇŞšŞŤ 3 p.m. False False True \n", + "3 Ça va bien, merci. 4 a.m. False False False \n", + "4 ˘ŽNos vemos maana! 1 p.m. False False True \n", + "5 Ich hätte Hunger. 1 p.m. False False False \n", + "\n", + " \n", + " timestamp \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "5 False " ] }, "execution_count": 10, @@ -1376,7 +1385,7 @@ " 1.0\n", " Alice\n", " To Kill a Mockingbird\n", - " -2.778344\n", + " -2.705336\n", " False\n", " False\n", " True\n", @@ -1388,7 +1397,7 @@ " 3.0\n", " Alice\n", " 1984\n", - " 6.235905\n", + " 6.406944\n", " False\n", " False\n", " True\n", @@ -1400,10 +1409,10 @@ " 3.0\n", " Alice\n", " Pride and Prejudice\n", - " 3.000000\n", - " False\n", + " 6.181281\n", " False\n", " False\n", + " True\n", " \n", " \n", " 3\n", @@ -1424,10 +1433,10 @@ " 2.0\n", " Bob\n", " Moby-Dick\n", - " -1.759203\n", + " 2.000000\n", + " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 5\n", @@ -1457,11 +1466,11 @@ "\n", " error_mask \n", " rating typist book_title rating \n", - "0 -2.778344 False False True \n", - "1 6.235905 False False True \n", - "2 3.000000 False False False \n", + "0 -2.705336 False False True \n", + "1 6.406944 False False True \n", + "2 6.181281 False False True \n", "3 4.000000 False False False \n", - "4 -1.759203 False False True \n", + "4 2.000000 False False False \n", "5 1.000000 False False False " ] }, @@ -1530,14 +1539,14 @@ " \n", " 0\n", " service-A-2024-02-01\n", - " service-A-2024-02-01\n", - " False\n", + " 01-A-02-service-2024\n", + " True\n", " \n", " \n", " 1\n", " service-A-2024-02-02\n", - " service-A-2024-02-02\n", - " False\n", + " 02-2024-service-02-A\n", + " True\n", " \n", " \n", " 2\n", @@ -1548,14 +1557,14 @@ " \n", " 3\n", " service-A-2024-02-01\n", - " 01-A-2024-service-02\n", + " A-service-01-2024-02\n", " True\n", " \n", " \n", " 4\n", " service-B-2024-02-02\n", - " B-service-2024-02-02\n", - " True\n", + " service-B-2024-02-02\n", + " False\n", " \n", " \n", " 5\n", @@ -1566,19 +1575,19 @@ " \n", " 6\n", " service-C-2024-02-01\n", - " 01-02-service-C-2024\n", - " True\n", + " service-C-2024-02-01\n", + " False\n", " \n", " \n", " 7\n", " service-C-2024-02-02\n", - " C-service-02-2024-02\n", + " service-C-02-2024-02\n", " True\n", " \n", " \n", " 8\n", " service-C-2024-02-03\n", - " 03-C-service-02-2024\n", + " C-service-02-03-2024\n", " True\n", " \n", " \n", @@ -1588,15 +1597,15 @@ "text/plain": [ " original perturbed error_mask\n", " service service service\n", - "0 service-A-2024-02-01 service-A-2024-02-01 False\n", - "1 service-A-2024-02-02 service-A-2024-02-02 False\n", + "0 service-A-2024-02-01 01-A-02-service-2024 True\n", + "1 service-A-2024-02-02 02-2024-service-02-A True\n", "2 service-A-2024-02-03 service-A-2024-02-03 False\n", - "3 service-A-2024-02-01 01-A-2024-service-02 True\n", - "4 service-B-2024-02-02 B-service-2024-02-02 True\n", + "3 service-A-2024-02-01 A-service-01-2024-02 True\n", + "4 service-B-2024-02-02 service-B-2024-02-02 False\n", "5 service-B-2024-02-03 service-B-2024-02-03 False\n", - "6 service-C-2024-02-01 01-02-service-C-2024 True\n", - "7 service-C-2024-02-02 C-service-02-2024-02 True\n", - "8 service-C-2024-02-03 03-C-service-02-2024 True" + "6 service-C-2024-02-01 service-C-2024-02-01 False\n", + "7 service-C-2024-02-02 service-C-02-2024-02 True\n", + "8 service-C-2024-02-03 C-service-02-03-2024 True" ] }, "execution_count": 12, @@ -1662,26 +1671,26 @@ " \n", " 0\n", " service-A-2024-02-01\n", - " service-01-A-02-2024\n", - " True\n", + " service-A-2024-02-01\n", + " False\n", " \n", " \n", " 1\n", " service-A-2024-02-02\n", - " service-A-2024-02-02\n", - " False\n", + " service-02-02-A-2024\n", + " True\n", " \n", " \n", " 2\n", " service-A-2024-02-03\n", - " service-03-A-02-2024\n", + " service-02-03-A-2024\n", " True\n", " \n", " \n", " 3\n", " service-A-2024-02-01\n", - " service-01-A-02-2024\n", - " True\n", + " service-A-2024-02-01\n", + " False\n", " \n", " \n", " 4\n", @@ -1692,26 +1701,26 @@ " \n", " 5\n", " service-B-2024-02-03\n", - " service-B-2024-02-03\n", - " False\n", + " service-02-03-B-2024\n", + " True\n", " \n", " \n", " 6\n", " service-C-2024-02-01\n", - " service-01-C-02-2024\n", - " True\n", + " service-C-2024-02-01\n", + " False\n", " \n", " \n", " 7\n", " service-C-2024-02-02\n", - " service-02-C-02-2024\n", + " service-02-02-C-2024\n", " True\n", " \n", " \n", " 8\n", " service-C-2024-02-03\n", - " service-C-2024-02-03\n", - " False\n", + " service-02-03-C-2024\n", + " True\n", " \n", " \n", "\n", @@ -1720,15 +1729,15 @@ "text/plain": [ " original perturbed error_mask\n", " service service service\n", - "0 service-A-2024-02-01 service-01-A-02-2024 True\n", - "1 service-A-2024-02-02 service-A-2024-02-02 False\n", - "2 service-A-2024-02-03 service-03-A-02-2024 True\n", - "3 service-A-2024-02-01 service-01-A-02-2024 True\n", + "0 service-A-2024-02-01 service-A-2024-02-01 False\n", + "1 service-A-2024-02-02 service-02-02-A-2024 True\n", + "2 service-A-2024-02-03 service-02-03-A-2024 True\n", + "3 service-A-2024-02-01 service-A-2024-02-01 False\n", "4 service-B-2024-02-02 service-B-2024-02-02 False\n", - "5 service-B-2024-02-03 service-B-2024-02-03 False\n", - "6 service-C-2024-02-01 service-01-C-02-2024 True\n", - "7 service-C-2024-02-02 service-02-C-02-2024 True\n", - "8 service-C-2024-02-03 service-C-2024-02-03 False" + "5 service-B-2024-02-03 service-02-03-B-2024 True\n", + "6 service-C-2024-02-01 service-C-2024-02-01 False\n", + "7 service-C-2024-02-02 service-02-02-C-2024 True\n", + "8 service-C-2024-02-03 service-02-03-C-2024 True" ] }, "execution_count": 13, @@ -1796,32 +1805,32 @@ " \n", " 0\n", " service-A-2024-02-01\n", - " service_A_2024_02_01\n", - " True\n", + " service-A-2024-02-01\n", + " False\n", " \n", " \n", " 1\n", " service-A-2024-02-02\n", - " service-A-2024-02-02\n", - " False\n", + " service_A_2024_02_02\n", + " True\n", " \n", " \n", " 2\n", " service-A-2024-02-03\n", - " service_A_2024_02_03\n", - " True\n", + " service-A-2024-02-03\n", + " False\n", " \n", " \n", " 3\n", " service-A-2024-02-01\n", - " service-A-2024-02-01\n", - " False\n", + " service_A_2024_02_01\n", + " True\n", " \n", " \n", " 4\n", " service-B-2024-02-02\n", - " service_B_2024_02_02\n", - " True\n", + " service-B-2024-02-02\n", + " False\n", " \n", " \n", " 5\n", @@ -1832,20 +1841,20 @@ " \n", " 6\n", " service-C-2024-02-01\n", - " service_C_2024_02_01\n", - " True\n", + " service-C-2024-02-01\n", + " False\n", " \n", " \n", " 7\n", " service-C-2024-02-02\n", - " service-C-2024-02-02\n", - " False\n", + " service_C_2024_02_02\n", + " True\n", " \n", " \n", " 8\n", " service-C-2024-02-03\n", - " service-C-2024-02-03\n", - " False\n", + " service_C_2024_02_03\n", + " True\n", " \n", " \n", "\n", @@ -1854,15 +1863,15 @@ "text/plain": [ " original perturbed error_mask\n", " service service service\n", - "0 service-A-2024-02-01 service_A_2024_02_01 True\n", - "1 service-A-2024-02-02 service-A-2024-02-02 False\n", - "2 service-A-2024-02-03 service_A_2024_02_03 True\n", - "3 service-A-2024-02-01 service-A-2024-02-01 False\n", - "4 service-B-2024-02-02 service_B_2024_02_02 True\n", + "0 service-A-2024-02-01 service-A-2024-02-01 False\n", + "1 service-A-2024-02-02 service_A_2024_02_02 True\n", + "2 service-A-2024-02-03 service-A-2024-02-03 False\n", + "3 service-A-2024-02-01 service_A_2024_02_01 True\n", + "4 service-B-2024-02-02 service-B-2024-02-02 False\n", "5 service-B-2024-02-03 service_B_2024_02_03 True\n", - "6 service-C-2024-02-01 service_C_2024_02_01 True\n", - "7 service-C-2024-02-02 service-C-2024-02-02 False\n", - "8 service-C-2024-02-03 service-C-2024-02-03 False" + "6 service-C-2024-02-01 service-C-2024-02-01 False\n", + "7 service-C-2024-02-02 service_C_2024_02_02 True\n", + "8 service-C-2024-02-03 service_C_2024_02_03 True" ] }, "execution_count": 14, @@ -1939,7 +1948,7 @@ " To Kill a Mockingbird\n", " 1.0\n", " Alice\n", - " To Kill a Mockingburd\n", + " Fo Kill a Mockingbird\n", " 1.0\n", " False\n", " True\n", @@ -1963,7 +1972,7 @@ " Pride and Prejudice\n", " 3.0\n", " Alice\n", - " Pride abd Prejudice\n", + " Lride and Prejudice\n", " 3.0\n", " False\n", " True\n", @@ -1975,10 +1984,10 @@ " The Great Gatsby\n", " 4.0\n", " Bob\n", - " The Great Gatsby\n", + " Rhe Great Gatsby\n", " 4.0\n", " False\n", - " False\n", + " True\n", " False\n", " \n", " \n", @@ -1999,10 +2008,10 @@ " The Catcher in the Rye\n", " 1.0\n", " Bob\n", - " The Catcher 9n the Rye\n", + " The Catcher in the Rye\n", " 1.0\n", " False\n", - " True\n", + " False\n", " False\n", " \n", " \n", @@ -2012,21 +2021,21 @@ "text/plain": [ " original perturbed \\\n", " typist book_title rating typist book_title \n", - "0 Alice To Kill a Mockingbird 1.0 Alice To Kill a Mockingburd \n", + "0 Alice To Kill a Mockingbird 1.0 Alice Fo Kill a Mockingbird \n", "1 Alice 1984 3.0 Alice 1984 \n", - "2 Alice Pride and Prejudice 3.0 Alice Pride abd Prejudice \n", - "3 Bob The Great Gatsby 4.0 Bob The Great Gatsby \n", + "2 Alice Pride and Prejudice 3.0 Alice Lride and Prejudice \n", + "3 Bob The Great Gatsby 4.0 Bob Rhe Great Gatsby \n", "4 Bob Moby-Dick 2.0 Bob Moby-Dick \n", - "5 Bob The Catcher in the Rye 1.0 Bob The Catcher 9n the Rye \n", + "5 Bob The Catcher in the Rye 1.0 Bob The Catcher in the Rye \n", "\n", " error_mask \n", " rating typist book_title rating \n", "0 1.0 False True False \n", "1 3.0 False False False \n", "2 3.0 False True False \n", - "3 4.0 False False False \n", + "3 4.0 False True False \n", "4 2.0 False False False \n", - "5 1.0 False True False " + "5 1.0 False False False " ] }, "execution_count": 15, @@ -2128,10 +2137,10 @@ " 3.0\n", " Alice\n", " Pride and Prejudice\n", - " 30.0\n", + " 3.0\n", + " False\n", " False\n", " False\n", - " True\n", " \n", " \n", " 3\n", @@ -2140,10 +2149,10 @@ " 4.0\n", " Bob\n", " The Great Gatsby\n", - " 4.0\n", - " False\n", + " 40.0\n", " False\n", " False\n", + " True\n", " \n", " \n", " 4\n", @@ -2187,8 +2196,8 @@ " rating typist book_title rating \n", "0 10.0 False False True \n", "1 3.0 False False False \n", - "2 30.0 False False True \n", - "3 4.0 False False False \n", + "2 3.0 False False False \n", + "3 40.0 False False True \n", "4 20.0 False False True \n", "5 1.0 False False False " ] @@ -2223,7 +2232,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.19" + "version": "3.13.5" } }, "nbformat": 4, From b60ff04d1bb8a55263d2f20866cf6fde3b00b903 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Wed, 4 Feb 2026 16:13:46 +0100 Subject: [PATCH 12/13] chore: remove misleading comment --- tab_err/api/mid_level.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tab_err/api/mid_level.py b/tab_err/api/mid_level.py index 7dec6a0..5f8a125 100644 --- a/tab_err/api/mid_level.py +++ b/tab_err/api/mid_level.py @@ -82,7 +82,6 @@ def create_errors(data: IntoDataFrame, config: MidLevelConfig | dict) -> tuple[I error_mask = error_mechanism.sample(data_nw, column, error_rate, error_mask) # Compute new error positions (where mask changed from False to True) - # We need to XOR the old and new masks to find new errors old_mask_col = old_error_mask[column if isinstance(column, str) else data_nw.columns[column]] new_mask_col = error_mask[column if isinstance(column, str) else data_nw.columns[column]] From 3270772b943273033e6ded684757b35e52981ef4 Mon Sep 17 00:00:00 2001 From: Philipp Jung Date: Sun, 8 Feb 2026 10:42:19 +0100 Subject: [PATCH 13/13] chore: add comment for heuristic, fix typos --- tab_err/error_type/_mistype.py | 5 ++++- tab_err/error_type/_permutate.py | 4 ++-- tab_err/error_type/_replace.py | 2 +- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/tab_err/error_type/_mistype.py b/tab_err/error_type/_mistype.py index f243f4b..6d8c104 100644 --- a/tab_err/error_type/_mistype.py +++ b/tab_err/error_type/_mistype.py @@ -74,7 +74,10 @@ def _apply(self: Mistype, data: nw.DataFrame, error_mask: nw.DataFrame, column: raise TypeError(msg) target_dtype = self.config.mistype_dtype - else: # no user-specified dtype, use heuristic to infer one + # no user-specified dtype, use heuristic to infer one -- that's the hard-coded logic below, with pairs of + # current_dtype and target_dtype that can be cast without raising errors. Not very principled approach, but + # works as a fallback. + else: current_dtype = series.dtype if current_dtype == nw.Object: msg = "Cannot infer a dtype that is safe to cast to if the original dtype is 'object'." diff --git a/tab_err/error_type/_permutate.py b/tab_err/error_type/_permutate.py index 8ca500d..896728f 100644 --- a/tab_err/error_type/_permutate.py +++ b/tab_err/error_type/_permutate.py @@ -118,9 +118,9 @@ def _apply(self: Permutate, data: nw.DataFrame, error_mask: nw.DataFrame, column elif self.config.permutation_automation_pattern == "fixed": # Fixed permutation -- random once, applied to all. _check_column_format_consistency(separator_counts, column) - rnd_permutatin_pattern = _generate_shuffle_pattern(separator_counts[0]) + rnd_permutation_pattern = _generate_shuffle_pattern(separator_counts[0]) - data_arr = self._pattern_permutation(rnd_permutatin_pattern, data_arr, mask_arr) + data_arr = self._pattern_permutation(rnd_permutation_pattern, data_arr, mask_arr) else: # Random permutation -- random for each entry. data_arr = self._random_permutation(data_arr, mask_arr) diff --git a/tab_err/error_type/_replace.py b/tab_err/error_type/_replace.py index dd45a3a..d788543 100644 --- a/tab_err/error_type/_replace.py +++ b/tab_err/error_type/_replace.py @@ -19,7 +19,7 @@ def _check_type(data: nw.DataFrame, column: int | str) -> None: series = get_column(data, column) if not is_string_dtype(series): - msg = f"Column {column} does not contain values of the string dtype. Cannot Permutate values." + msg = f"Column {column} does not contain values of the string dtype. Cannot replace values." raise TypeError(msg) def _get_valid_columns(self: Replace, data: nw.DataFrame) -> list[str | int]: