from __future__ import annotations import operator from typing import ( TYPE_CHECKING, Literal, cast, ) import numpy as np from pandas._libs.missing import is_matching_na from pandas._libs.sparse import SparseIndex import pandas._libs.testing as _testing from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions from pandas.core.dtypes.common import ( is_bool, is_integer_dtype, is_number, is_numeric_dtype, needs_i8_conversion, ) from pandas.core.dtypes.dtypes import ( CategoricalDtype, DatetimeTZDtype, ExtensionDtype, NumpyEADtype, ) from pandas.core.dtypes.missing import array_equivalent import pandas as pd from pandas import ( Categorical, DataFrame, DatetimeIndex, Index, IntervalDtype, IntervalIndex, MultiIndex, PeriodIndex, RangeIndex, Series, TimedeltaIndex, ) from pandas.core.algorithms import take_nd from pandas.core.arrays import ( DatetimeArray, ExtensionArray, IntervalArray, PeriodArray, TimedeltaArray, ) from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin from pandas.core.arrays.string_ import StringDtype from pandas.core.indexes.api import safe_sort_index from pandas.io.formats.printing import pprint_thing if TYPE_CHECKING: from pandas._typing import DtypeObj def assert_almost_equal( left, right, check_dtype: bool | Literal["equiv"] = "equiv", rtol: float = 1.0e-5, atol: float = 1.0e-8, **kwargs, ) -> None: """ Check that the left and right objects are approximately equal. By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision. Parameters ---------- left : object right : object check_dtype : bool or {'equiv'}, default 'equiv' Check dtype if both a and b are the same type. If 'equiv' is passed in, then `RangeIndex` and `Index` with int64 dtype are also considered equivalent when doing type checking. rtol : float, default 1e-5 Relative tolerance. atol : float, default 1e-8 Absolute tolerance. """ if isinstance(left, Index): assert_index_equal( left, right, check_exact=False, exact=check_dtype, rtol=rtol, atol=atol, **kwargs, ) elif isinstance(left, Series): assert_series_equal( left, right, check_exact=False, check_dtype=check_dtype, rtol=rtol, atol=atol, **kwargs, ) elif isinstance(left, DataFrame): assert_frame_equal( left, right, check_exact=False, check_dtype=check_dtype, rtol=rtol, atol=atol, **kwargs, ) else: # Other sequences. if check_dtype: if is_number(left) and is_number(right): # Do not compare numeric classes, like np.float64 and float. pass elif is_bool(left) and is_bool(right): # Do not compare bool classes, like np.bool_ and bool. pass else: if isinstance(left, np.ndarray) or isinstance(right, np.ndarray): obj = "numpy array" else: obj = "Input" assert_class_equal(left, right, obj=obj) # if we have "equiv", this becomes True _testing.assert_almost_equal( left, right, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, **kwargs ) def _check_isinstance(left, right, cls): """ Helper method for our assert_* methods that ensures that the two objects being compared have the right type before proceeding with the comparison. Parameters ---------- left : The first object being compared. right : The second object being compared. cls : The class type to check against. Raises ------ AssertionError : Either `left` or `right` is not an instance of `cls`. """ cls_name = cls.__name__ if not isinstance(left, cls): raise AssertionError( f"{cls_name} Expected type {cls}, found {type(left)} instead" ) if not isinstance(right, cls): raise AssertionError( f"{cls_name} Expected type {cls}, found {type(right)} instead" ) def assert_dict_equal(left, right, compare_keys: bool = True) -> None: _check_isinstance(left, right, dict) _testing.assert_dict_equal(left, right, compare_keys=compare_keys) def assert_index_equal( left: Index, right: Index, exact: bool | str = "equiv", check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_order: bool = True, rtol: float = 1.0e-5, atol: float = 1.0e-8, obj: str = "Index", ) -> None: """ Check that left and right Index are equal. Parameters ---------- left : Index right : Index exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Index with an int64 dtype as well. check_names : bool, default True Whether to check the names attribute. check_exact : bool, default True Whether to compare number exactly. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_order : bool, default True Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. obj : str, default 'Index' Specify object name being compared, internally used to show appropriate assertion message. Examples -------- >>> from pandas import testing as tm >>> a = pd.Index([1, 2, 3]) >>> b = pd.Index([1, 2, 3]) >>> tm.assert_index_equal(a, b) """ __tracebackhide__ = True def _check_types(left, right, obj: str = "Index") -> None: if not exact: return assert_class_equal(left, right, exact=exact, obj=obj) assert_attr_equal("inferred_type", left, right, obj=obj) # Skip exact dtype checking when `check_categorical` is False if isinstance(left.dtype, CategoricalDtype) and isinstance( right.dtype, CategoricalDtype ): if check_categorical: assert_attr_equal("dtype", left, right, obj=obj) assert_index_equal(left.categories, right.categories, exact=exact) return assert_attr_equal("dtype", left, right, obj=obj) def _get_ilevel_values(index, level): # accept level number only unique = index.levels[level] level_codes = index.codes[level] filled = take_nd(unique._values, level_codes, fill_value=unique._na_value) return unique._shallow_copy(filled, name=index.names[level]) # instance validation _check_isinstance(left, right, Index) # class / dtype comparison _check_types(left, right, obj=obj) # level comparison if left.nlevels != right.nlevels: msg1 = f"{obj} levels are different" msg2 = f"{left.nlevels}, {left}" msg3 = f"{right.nlevels}, {right}" raise_assert_detail(obj, msg1, msg2, msg3) # length comparison if len(left) != len(right): msg1 = f"{obj} length are different" msg2 = f"{len(left)}, {left}" msg3 = f"{len(right)}, {right}" raise_assert_detail(obj, msg1, msg2, msg3) # If order doesn't matter then sort the index entries if not check_order: left = safe_sort_index(left) right = safe_sort_index(right) # MultiIndex special comparison for little-friendly error messages if isinstance(left, MultiIndex): right = cast(MultiIndex, right) for level in range(left.nlevels): # cannot use get_level_values here because it can change dtype llevel = _get_ilevel_values(left, level) rlevel = _get_ilevel_values(right, level) lobj = f"MultiIndex level [{level}]" assert_index_equal( llevel, rlevel, exact=exact, check_names=check_names, check_exact=check_exact, check_categorical=check_categorical, rtol=rtol, atol=atol, obj=lobj, ) # get_level_values may change dtype _check_types(left.levels[level], right.levels[level], obj=obj) # skip exact index checking when `check_categorical` is False elif check_exact and check_categorical: if not left.equals(right): mismatch = left._values != right._values if not isinstance(mismatch, np.ndarray): mismatch = cast("ExtensionArray", mismatch).fillna(True) diff = np.sum(mismatch.astype(int)) * 100.0 / len(left) msg = f"{obj} values are different ({np.round(diff, 5)} %)" raise_assert_detail(obj, msg, left, right) else: # if we have "equiv", this becomes True exact_bool = bool(exact) _testing.assert_almost_equal( left.values, right.values, rtol=rtol, atol=atol, check_dtype=exact_bool, obj=obj, lobj=left, robj=right, ) # metadata comparison if check_names: assert_attr_equal("names", left, right, obj=obj) if isinstance(left, PeriodIndex) or isinstance(right, PeriodIndex): assert_attr_equal("dtype", left, right, obj=obj) if isinstance(left, IntervalIndex) or isinstance(right, IntervalIndex): assert_interval_array_equal(left._values, right._values) if check_categorical: if isinstance(left.dtype, CategoricalDtype) or isinstance( right.dtype, CategoricalDtype ): assert_categorical_equal(left._values, right._values, obj=f"{obj} category") def assert_class_equal( left, right, exact: bool | str = True, obj: str = "Input" ) -> None: """ Checks classes are equal. """ __tracebackhide__ = True def repr_class(x): if isinstance(x, Index): # return Index as it is to include values in the error message return x return type(x).__name__ def is_class_equiv(idx: Index) -> bool: """Classes that are a RangeIndex (sub-)instance or exactly an `Index` . This only checks class equivalence. There is a separate check that the dtype is int64. """ return type(idx) is Index or isinstance(idx, RangeIndex) if type(left) == type(right): return if exact == "equiv": if is_class_equiv(left) and is_class_equiv(right): return msg = f"{obj} classes are different" raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None: """ Check attributes are equal. Both objects must have attribute. Parameters ---------- attr : str Attribute name being compared. left : object right : object obj : str, default 'Attributes' Specify object name being compared, internally used to show appropriate assertion message """ __tracebackhide__ = True left_attr = getattr(left, attr) right_attr = getattr(right, attr) if left_attr is right_attr or is_matching_na(left_attr, right_attr): # e.g. both np.nan, both NaT, both pd.NA, ... return None try: result = left_attr == right_attr except TypeError: # datetimetz on rhs may raise TypeError result = False if (left_attr is pd.NA) ^ (right_attr is pd.NA): result = False elif not isinstance(result, bool): result = result.all() if not result: msg = f'Attribute "{attr}" are different' raise_assert_detail(obj, msg, left_attr, right_attr) return None def assert_is_valid_plot_return_object(objs) -> None: import matplotlib.pyplot as plt if isinstance(objs, (Series, np.ndarray)): for el in objs.ravel(): msg = ( "one of 'objs' is not a matplotlib Axes instance, " f"type encountered {repr(type(el).__name__)}" ) assert isinstance(el, (plt.Axes, dict)), msg else: msg = ( "objs is neither an ndarray of Artist instances nor a single " "ArtistArtist instance, tuple, or dict, 'objs' is a " f"{repr(type(objs).__name__)}" ) assert isinstance(objs, (plt.Artist, tuple, dict)), msg def assert_is_sorted(seq) -> None: """Assert that the sequence is sorted.""" if isinstance(seq, (Index, Series)): seq = seq.values # sorting does not change precisions assert_numpy_array_equal(seq, np.sort(np.array(seq))) def assert_categorical_equal( left, right, check_dtype: bool = True, check_category_order: bool = True, obj: str = "Categorical", ) -> None: """ Test that Categoricals are equivalent. Parameters ---------- left : Categorical right : Categorical check_dtype : bool, default True Check that integer dtype of the codes are the same. check_category_order : bool, default True Whether the order of the categories should be compared, which implies identical integer codes. If False, only the resulting values are compared. The ordered attribute is checked regardless. obj : str, default 'Categorical' Specify object name being compared, internally used to show appropriate assertion message. """ _check_isinstance(left, right, Categorical) exact: bool | str if isinstance(left.categories, RangeIndex) or isinstance( right.categories, RangeIndex ): exact = "equiv" else: # We still want to require exact matches for Index exact = True if check_category_order: assert_index_equal( left.categories, right.categories, obj=f"{obj}.categories", exact=exact ) assert_numpy_array_equal( left.codes, right.codes, check_dtype=check_dtype, obj=f"{obj}.codes" ) else: try: lc = left.categories.sort_values() rc = right.categories.sort_values() except TypeError: # e.g. '<' not supported between instances of 'int' and 'str' lc, rc = left.categories, right.categories assert_index_equal(lc, rc, obj=f"{obj}.categories", exact=exact) assert_index_equal( left.categories.take(left.codes), right.categories.take(right.codes), obj=f"{obj}.values", exact=exact, ) assert_attr_equal("ordered", left, right, obj=obj) def assert_interval_array_equal( left, right, exact: bool | Literal["equiv"] = "equiv", obj: str = "IntervalArray" ) -> None: """ Test that two IntervalArrays are equivalent. Parameters ---------- left, right : IntervalArray The IntervalArrays to compare. exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Index with an int64 dtype as well. obj : str, default 'IntervalArray' Specify object name being compared, internally used to show appropriate assertion message """ _check_isinstance(left, right, IntervalArray) kwargs = {} if left._left.dtype.kind in "mM": # We have a DatetimeArray or TimedeltaArray kwargs["check_freq"] = False assert_equal(left._left, right._left, obj=f"{obj}.left", **kwargs) assert_equal(left._right, right._right, obj=f"{obj}.left", **kwargs) assert_attr_equal("closed", left, right, obj=obj) def assert_period_array_equal(left, right, obj: str = "PeriodArray") -> None: _check_isinstance(left, right, PeriodArray) assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray") assert_attr_equal("dtype", left, right, obj=obj) def assert_datetime_array_equal( left, right, obj: str = "DatetimeArray", check_freq: bool = True ) -> None: __tracebackhide__ = True _check_isinstance(left, right, DatetimeArray) assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray") if check_freq: assert_attr_equal("freq", left, right, obj=obj) assert_attr_equal("tz", left, right, obj=obj) def assert_timedelta_array_equal( left, right, obj: str = "TimedeltaArray", check_freq: bool = True ) -> None: __tracebackhide__ = True _check_isinstance(left, right, TimedeltaArray) assert_numpy_array_equal(left._ndarray, right._ndarray, obj=f"{obj}._ndarray") if check_freq: assert_attr_equal("freq", left, right, obj=obj) def raise_assert_detail( obj, message, left, right, diff=None, first_diff=None, index_values=None ): __tracebackhide__ = True msg = f"""{obj} are different {message}""" if isinstance(index_values, np.ndarray): msg += f"\n[index]: {pprint_thing(index_values)}" if isinstance(left, np.ndarray): left = pprint_thing(left) elif isinstance(left, (CategoricalDtype, NumpyEADtype, StringDtype)): left = repr(left) if isinstance(right, np.ndarray): right = pprint_thing(right) elif isinstance(right, (CategoricalDtype, NumpyEADtype, StringDtype)): right = repr(right) msg += f""" [left]: {left} [right]: {right}""" if diff is not None: msg += f"\n[diff]: {diff}" if first_diff is not None: msg += f"\n{first_diff}" raise AssertionError(msg) def assert_numpy_array_equal( left, right, strict_nan: bool = False, check_dtype: bool | Literal["equiv"] = True, err_msg=None, check_same=None, obj: str = "numpy array", index_values=None, ) -> None: """ Check that 'np.ndarray' is equivalent. Parameters ---------- left, right : numpy.ndarray or iterable The two arrays to be compared. strict_nan : bool, default False If True, consider NaN and None to be different. check_dtype : bool, default True Check dtype if both a and b are np.ndarray. err_msg : str, default None If provided, used as assertion message. check_same : None|'copy'|'same', default None Ensure left and right refer/do not refer to the same memory area. obj : str, default 'numpy array' Specify object name being compared, internally used to show appropriate assertion message. index_values : numpy.ndarray, default None optional index (shared by both left and right), used in output. """ __tracebackhide__ = True # instance validation # Show a detailed error message when classes are different assert_class_equal(left, right, obj=obj) # both classes must be an np.ndarray _check_isinstance(left, right, np.ndarray) def _get_base(obj): return obj.base if getattr(obj, "base", None) is not None else obj left_base = _get_base(left) right_base = _get_base(right) if check_same == "same": if left_base is not right_base: raise AssertionError(f"{repr(left_base)} is not {repr(right_base)}") elif check_same == "copy": if left_base is right_base: raise AssertionError(f"{repr(left_base)} is {repr(right_base)}") def _raise(left, right, err_msg): if err_msg is None: if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shapes are different", left.shape, right.shape ) diff = 0 for left_arr, right_arr in zip(left, right): # count up differences if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan): diff += 1 diff = diff * 100.0 / left.size msg = f"{obj} values are different ({np.round(diff, 5)} %)" raise_assert_detail(obj, msg, left, right, index_values=index_values) raise AssertionError(err_msg) # compare shape and values if not array_equivalent(left, right, strict_nan=strict_nan): _raise(left, right, err_msg) if check_dtype: if isinstance(left, np.ndarray) and isinstance(right, np.ndarray): assert_attr_equal("dtype", left, right, obj=obj) def assert_extension_array_equal( left, right, check_dtype: bool | Literal["equiv"] = True, index_values=None, check_exact: bool = False, rtol: float = 1.0e-5, atol: float = 1.0e-8, obj: str = "ExtensionArray", ) -> None: """ Check that left and right ExtensionArrays are equal. Parameters ---------- left, right : ExtensionArray The two arrays to compare. check_dtype : bool, default True Whether to check if the ExtensionArray dtypes are identical. index_values : numpy.ndarray, default None Optional index (shared by both left and right), used in output. check_exact : bool, default False Whether to compare number exactly. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. obj : str, default 'ExtensionArray' Specify object name being compared, internally used to show appropriate assertion message. .. versionadded:: 2.0.0 Notes ----- Missing values are checked separately from valid values. A mask of missing values is computed for each and checked to match. The remaining all-valid values are cast to object dtype and checked. Examples -------- >>> from pandas import testing as tm >>> a = pd.Series([1, 2, 3, 4]) >>> b, c = a.array, a.array >>> tm.assert_extension_array_equal(b, c) """ assert isinstance(left, ExtensionArray), "left is not an ExtensionArray" assert isinstance(right, ExtensionArray), "right is not an ExtensionArray" if check_dtype: assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}") if ( isinstance(left, DatetimeLikeArrayMixin) and isinstance(right, DatetimeLikeArrayMixin) and type(right) == type(left) ): # GH 52449 if not check_dtype and left.dtype.kind in "mM": if not isinstance(left.dtype, np.dtype): l_unit = cast(DatetimeTZDtype, left.dtype).unit else: l_unit = np.datetime_data(left.dtype)[0] if not isinstance(right.dtype, np.dtype): r_unit = cast(DatetimeTZDtype, left.dtype).unit else: r_unit = np.datetime_data(right.dtype)[0] if ( l_unit != r_unit and compare_mismatched_resolutions( left._ndarray, right._ndarray, operator.eq ).all() ): return # Avoid slow object-dtype comparisons # np.asarray for case where we have a np.MaskedArray assert_numpy_array_equal( np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values, obj=obj, ) return left_na = np.asarray(left.isna()) right_na = np.asarray(right.isna()) assert_numpy_array_equal( left_na, right_na, obj=f"{obj} NA mask", index_values=index_values ) left_valid = left[~left_na].to_numpy(dtype=object) right_valid = right[~right_na].to_numpy(dtype=object) if check_exact: assert_numpy_array_equal( left_valid, right_valid, obj=obj, index_values=index_values ) else: _testing.assert_almost_equal( left_valid, right_valid, check_dtype=bool(check_dtype), rtol=rtol, atol=atol, obj=obj, index_values=index_values, ) # This could be refactored to use the NDFrame.equals method def assert_series_equal( left, right, check_dtype: bool | Literal["equiv"] = True, check_index_type: bool | Literal["equiv"] = "equiv", check_series_type: bool = True, check_names: bool = True, check_exact: bool = False, check_datetimelike_compat: bool = False, check_categorical: bool = True, check_category_order: bool = True, check_freq: bool = True, check_flags: bool = True, rtol: float = 1.0e-5, atol: float = 1.0e-8, obj: str = "Series", *, check_index: bool = True, check_like: bool = False, ) -> None: """ Check that left and right Series are equal. Parameters ---------- left : Series right : Series check_dtype : bool, default True Whether to check the Series dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_series_type : bool, default True Whether to check the Series class is identical. check_names : bool, default True Whether to check the Series and Index names attribute. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_category_order : bool, default True Whether to compare category order of internal Categoricals. check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. check_flags : bool, default True Whether to check the `flags` attribute. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. obj : str, default 'Series' Specify object name being compared, internally used to show appropriate assertion message. check_index : bool, default True Whether to check index equivalence. If False, then compare only values. .. versionadded:: 1.3.0 check_like : bool, default False If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data. .. versionadded:: 1.5.0 Examples -------- >>> from pandas import testing as tm >>> a = pd.Series([1, 2, 3, 4]) >>> b = pd.Series([1, 2, 3, 4]) >>> tm.assert_series_equal(a, b) """ __tracebackhide__ = True if not check_index and check_like: raise ValueError("check_like must be False if check_index is False") # instance validation _check_isinstance(left, right, Series) if check_series_type: assert_class_equal(left, right, obj=obj) # length comparison if len(left) != len(right): msg1 = f"{len(left)}, {left.index}" msg2 = f"{len(right)}, {right.index}" raise_assert_detail(obj, "Series length are different", msg1, msg2) if check_flags: assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}" if check_index: # GH #38183 assert_index_equal( left.index, right.index, exact=check_index_type, check_names=check_names, check_exact=check_exact, check_categorical=check_categorical, check_order=not check_like, rtol=rtol, atol=atol, obj=f"{obj}.index", ) if check_like: left = left.reindex_like(right) if check_freq and isinstance(left.index, (DatetimeIndex, TimedeltaIndex)): lidx = left.index ridx = right.index assert lidx.freq == ridx.freq, (lidx.freq, ridx.freq) if check_dtype: # We want to skip exact dtype checking when `check_categorical` # is False. We'll still raise if only one is a `Categorical`, # regardless of `check_categorical` if ( isinstance(left.dtype, CategoricalDtype) and isinstance(right.dtype, CategoricalDtype) and not check_categorical ): pass else: assert_attr_equal("dtype", left, right, obj=f"Attributes of {obj}") if check_exact and is_numeric_dtype(left.dtype) and is_numeric_dtype(right.dtype): left_values = left._values right_values = right._values # Only check exact if dtype is numeric if isinstance(left_values, ExtensionArray) and isinstance( right_values, ExtensionArray ): assert_extension_array_equal( left_values, right_values, check_dtype=check_dtype, index_values=np.asarray(left.index), obj=str(obj), ) else: assert_numpy_array_equal( left_values, right_values, check_dtype=check_dtype, obj=str(obj), index_values=np.asarray(left.index), ) elif check_datetimelike_compat and ( needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype) ): # we want to check only if we have compat dtypes # e.g. integer and M|m are NOT compat, but we can simply check # the values in that case # datetimelike may have different objects (e.g. datetime.datetime # vs Timestamp) but will compare equal if not Index(left._values).equals(Index(right._values)): msg = ( f"[datetimelike_compat=True] {left._values} " f"is not equal to {right._values}." ) raise AssertionError(msg) elif isinstance(left.dtype, IntervalDtype) and isinstance( right.dtype, IntervalDtype ): assert_interval_array_equal(left.array, right.array) elif isinstance(left.dtype, CategoricalDtype) or isinstance( right.dtype, CategoricalDtype ): _testing.assert_almost_equal( left._values, right._values, rtol=rtol, atol=atol, check_dtype=bool(check_dtype), obj=str(obj), index_values=np.asarray(left.index), ) elif isinstance(left.dtype, ExtensionDtype) and isinstance( right.dtype, ExtensionDtype ): assert_extension_array_equal( left._values, right._values, rtol=rtol, atol=atol, check_dtype=check_dtype, index_values=np.asarray(left.index), obj=str(obj), ) elif is_extension_array_dtype_and_needs_i8_conversion( left.dtype, right.dtype ) or is_extension_array_dtype_and_needs_i8_conversion(right.dtype, left.dtype): assert_extension_array_equal( left._values, right._values, check_dtype=check_dtype, index_values=np.asarray(left.index), obj=str(obj), ) elif needs_i8_conversion(left.dtype) and needs_i8_conversion(right.dtype): # DatetimeArray or TimedeltaArray assert_extension_array_equal( left._values, right._values, check_dtype=check_dtype, index_values=np.asarray(left.index), obj=str(obj), ) else: _testing.assert_almost_equal( left._values, right._values, rtol=rtol, atol=atol, check_dtype=bool(check_dtype), obj=str(obj), index_values=np.asarray(left.index), ) # metadata comparison if check_names: assert_attr_equal("name", left, right, obj=obj) if check_categorical: if isinstance(left.dtype, CategoricalDtype) or isinstance( right.dtype, CategoricalDtype ): assert_categorical_equal( left._values, right._values, obj=f"{obj} category", check_category_order=check_category_order, ) # This could be refactored to use the NDFrame.equals method def assert_frame_equal( left, right, check_dtype: bool | Literal["equiv"] = True, check_index_type: bool | Literal["equiv"] = "equiv", check_column_type: bool | Literal["equiv"] = "equiv", check_frame_type: bool = True, check_names: bool = True, by_blocks: bool = False, check_exact: bool = False, check_datetimelike_compat: bool = False, check_categorical: bool = True, check_like: bool = False, check_freq: bool = True, check_flags: bool = True, rtol: float = 1.0e-5, atol: float = 1.0e-8, obj: str = "DataFrame", ) -> None: """ Check that left and right DataFrame are equal. This function is intended to compare two DataFrames and output any differences. It is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed. Parameters ---------- left : DataFrame First DataFrame to compare. right : DataFrame Second DataFrame to compare. check_dtype : bool, default True Whether to check the DataFrame dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_column_type : bool or {'equiv'}, default 'equiv' Whether to check the columns class, dtype and inferred_type are identical. Is passed as the ``exact`` argument of :func:`assert_index_equal`. check_frame_type : bool, default True Whether to check the DataFrame class is identical. check_names : bool, default True Whether to check that the `names` attribute for both the `index` and `column` attributes of the DataFrame is identical. by_blocks : bool, default False Specify how to compare internal data. If False, compare by columns. If True, compare by blocks. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_like : bool, default False If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data. check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. check_flags : bool, default True Whether to check the `flags` attribute. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. obj : str, default 'DataFrame' Specify object name being compared, internally used to show appropriate assertion message. See Also -------- assert_series_equal : Equivalent method for asserting Series equality. DataFrame.equals : Check DataFrame equality. Examples -------- This example shows comparing two DataFrames that are equal but with columns of differing dtypes. >>> from pandas.testing import assert_frame_equal >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]}) df1 equals itself. >>> assert_frame_equal(df1, df1) df1 differs from df2 as column 'b' is of a different type. >>> assert_frame_equal(df1, df2) Traceback (most recent call last): ... AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different Attribute "dtype" are different [left]: int64 [right]: float64 Ignore differing dtypes in columns with check_dtype. >>> assert_frame_equal(df1, df2, check_dtype=False) """ __tracebackhide__ = True # instance validation _check_isinstance(left, right, DataFrame) if check_frame_type: assert isinstance(left, type(right)) # assert_class_equal(left, right, obj=obj) # shape comparison if left.shape != right.shape: raise_assert_detail( obj, f"{obj} shape mismatch", f"{repr(left.shape)}", f"{repr(right.shape)}" ) if check_flags: assert left.flags == right.flags, f"{repr(left.flags)} != {repr(right.flags)}" # index comparison assert_index_equal( left.index, right.index, exact=check_index_type, check_names=check_names, check_exact=check_exact, check_categorical=check_categorical, check_order=not check_like, rtol=rtol, atol=atol, obj=f"{obj}.index", ) # column comparison assert_index_equal( left.columns, right.columns, exact=check_column_type, check_names=check_names, check_exact=check_exact, check_categorical=check_categorical, check_order=not check_like, rtol=rtol, atol=atol, obj=f"{obj}.columns", ) if check_like: left = left.reindex_like(right) # compare by blocks if by_blocks: rblocks = right._to_dict_of_blocks(copy=False) lblocks = left._to_dict_of_blocks(copy=False) for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))): assert dtype in lblocks assert dtype in rblocks assert_frame_equal( lblocks[dtype], rblocks[dtype], check_dtype=check_dtype, obj=obj ) # compare by columns else: for i, col in enumerate(left.columns): # We have already checked that columns match, so we can do # fast location-based lookups lcol = left._ixs(i, axis=1) rcol = right._ixs(i, axis=1) # GH #38183 # use check_index=False, because we do not want to run # assert_index_equal for each column, # as we already checked it for the whole dataframe before. assert_series_equal( lcol, rcol, check_dtype=check_dtype, check_index_type=check_index_type, check_exact=check_exact, check_names=check_names, check_datetimelike_compat=check_datetimelike_compat, check_categorical=check_categorical, check_freq=check_freq, obj=f'{obj}.iloc[:, {i}] (column name="{col}")', rtol=rtol, atol=atol, check_index=False, check_flags=False, ) def assert_equal(left, right, **kwargs) -> None: """ Wrapper for tm.assert_*_equal to dispatch to the appropriate test function. Parameters ---------- left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray The two items to be compared. **kwargs All keyword arguments are passed through to the underlying assert method. """ __tracebackhide__ = True if isinstance(left, Index): assert_index_equal(left, right, **kwargs) if isinstance(left, (DatetimeIndex, TimedeltaIndex)): assert left.freq == right.freq, (left.freq, right.freq) elif isinstance(left, Series): assert_series_equal(left, right, **kwargs) elif isinstance(left, DataFrame): assert_frame_equal(left, right, **kwargs) elif isinstance(left, IntervalArray): assert_interval_array_equal(left, right, **kwargs) elif isinstance(left, PeriodArray): assert_period_array_equal(left, right, **kwargs) elif isinstance(left, DatetimeArray): assert_datetime_array_equal(left, right, **kwargs) elif isinstance(left, TimedeltaArray): assert_timedelta_array_equal(left, right, **kwargs) elif isinstance(left, ExtensionArray): assert_extension_array_equal(left, right, **kwargs) elif isinstance(left, np.ndarray): assert_numpy_array_equal(left, right, **kwargs) elif isinstance(left, str): assert kwargs == {} assert left == right else: assert kwargs == {} assert_almost_equal(left, right) def assert_sp_array_equal(left, right) -> None: """ Check that the left and right SparseArray are equal. Parameters ---------- left : SparseArray right : SparseArray """ _check_isinstance(left, right, pd.arrays.SparseArray) assert_numpy_array_equal(left.sp_values, right.sp_values) # SparseIndex comparison assert isinstance(left.sp_index, SparseIndex) assert isinstance(right.sp_index, SparseIndex) left_index = left.sp_index right_index = right.sp_index if not left_index.equals(right_index): raise_assert_detail( "SparseArray.index", "index are not equal", left_index, right_index ) else: # Just ensure a pass assert_attr_equal("fill_value", left, right) assert_attr_equal("dtype", left, right) assert_numpy_array_equal(left.to_dense(), right.to_dense()) def assert_contains_all(iterable, dic) -> None: for k in iterable: assert k in dic, f"Did not contain item: {repr(k)}" def assert_copy(iter1, iter2, **eql_kwargs) -> None: """ iter1, iter2: iterables that produce elements comparable with assert_almost_equal Checks that the elements are equal, but not the same object. (Does not check that items in sequences are also not the same object) """ for elem1, elem2 in zip(iter1, iter2): assert_almost_equal(elem1, elem2, **eql_kwargs) msg = ( f"Expected object {repr(type(elem1))} and object {repr(type(elem2))} to be " "different objects, but they were the same object." ) assert elem1 is not elem2, msg def is_extension_array_dtype_and_needs_i8_conversion( left_dtype: DtypeObj, right_dtype: DtypeObj ) -> bool: """ Checks that we have the combination of an ExtensionArraydtype and a dtype that should be converted to int64 Returns ------- bool Related to issue #37609 """ return isinstance(left_dtype, ExtensionDtype) and needs_i8_conversion(right_dtype) def assert_indexing_slices_equivalent(ser: Series, l_slc: slice, i_slc: slice) -> None: """ Check that ser.iloc[i_slc] matches ser.loc[l_slc] and, if applicable, ser[l_slc]. """ expected = ser.iloc[i_slc] assert_series_equal(ser.loc[l_slc], expected) if not is_integer_dtype(ser.index): # For integer indices, .loc and plain getitem are position-based. assert_series_equal(ser[l_slc], expected) def assert_metadata_equivalent( left: DataFrame | Series, right: DataFrame | Series | None = None ) -> None: """ Check that ._metadata attributes are equivalent. """ for attr in left._metadata: val = getattr(left, attr, None) if right is None: assert val is None else: assert val == getattr(right, attr, None)