1162 lines
32 KiB
Python
1162 lines
32 KiB
Python
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from __future__ import annotations
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import collections
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from datetime import datetime
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from decimal import Decimal
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import operator
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import os
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import re
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import string
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from sys import byteorder
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from typing import (
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TYPE_CHECKING,
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Callable,
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ContextManager,
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Counter,
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Iterable,
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)
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import warnings
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import numpy as np
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from pandas._config.localization import (
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can_set_locale,
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get_locales,
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set_locale,
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)
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from pandas._typing import Dtype
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from pandas.compat import pa_version_under1p01
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from pandas.core.dtypes.common import (
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is_float_dtype,
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is_integer_dtype,
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is_sequence,
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is_unsigned_integer_dtype,
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pandas_dtype,
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)
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import pandas as pd
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from pandas import (
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Categorical,
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CategoricalIndex,
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DataFrame,
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DatetimeIndex,
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Index,
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IntervalIndex,
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MultiIndex,
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RangeIndex,
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Series,
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bdate_range,
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)
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from pandas._testing._io import (
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close,
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network,
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round_trip_localpath,
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round_trip_pathlib,
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round_trip_pickle,
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write_to_compressed,
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)
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from pandas._testing._random import (
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randbool,
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rands,
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rands_array,
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)
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from pandas._testing._warnings import (
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assert_produces_warning,
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maybe_produces_warning,
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)
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from pandas._testing.asserters import (
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assert_almost_equal,
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assert_attr_equal,
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assert_categorical_equal,
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assert_class_equal,
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assert_contains_all,
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assert_copy,
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assert_datetime_array_equal,
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assert_dict_equal,
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assert_equal,
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assert_extension_array_equal,
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assert_frame_equal,
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assert_index_equal,
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assert_indexing_slices_equivalent,
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assert_interval_array_equal,
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assert_is_sorted,
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assert_is_valid_plot_return_object,
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assert_metadata_equivalent,
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assert_numpy_array_equal,
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assert_period_array_equal,
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assert_series_equal,
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assert_sp_array_equal,
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assert_timedelta_array_equal,
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raise_assert_detail,
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)
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from pandas._testing.compat import (
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get_dtype,
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get_obj,
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)
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from pandas._testing.contexts import (
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RNGContext,
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decompress_file,
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ensure_clean,
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ensure_clean_dir,
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ensure_safe_environment_variables,
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set_timezone,
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use_numexpr,
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with_csv_dialect,
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)
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from pandas.core.api import (
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Float64Index,
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Int64Index,
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NumericIndex,
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UInt64Index,
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)
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from pandas.core.arrays import (
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BaseMaskedArray,
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ExtensionArray,
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PandasArray,
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)
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from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
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from pandas.core.construction import extract_array
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if TYPE_CHECKING:
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from pandas import (
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PeriodIndex,
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TimedeltaIndex,
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)
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_N = 30
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_K = 4
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UNSIGNED_INT_NUMPY_DTYPES: list[Dtype] = ["uint8", "uint16", "uint32", "uint64"]
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UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
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SIGNED_INT_NUMPY_DTYPES: list[Dtype] = [int, "int8", "int16", "int32", "int64"]
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SIGNED_INT_EA_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
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ALL_INT_NUMPY_DTYPES = UNSIGNED_INT_NUMPY_DTYPES + SIGNED_INT_NUMPY_DTYPES
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ALL_INT_EA_DTYPES = UNSIGNED_INT_EA_DTYPES + SIGNED_INT_EA_DTYPES
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FLOAT_NUMPY_DTYPES: list[Dtype] = [float, "float32", "float64"]
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FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
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COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
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STRING_DTYPES: list[Dtype] = [str, "str", "U"]
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DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
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TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
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BOOL_DTYPES: list[Dtype] = [bool, "bool"]
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BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
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OBJECT_DTYPES: list[Dtype] = [object, "object"]
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ALL_REAL_NUMPY_DTYPES = FLOAT_NUMPY_DTYPES + ALL_INT_NUMPY_DTYPES
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ALL_NUMPY_DTYPES = (
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ALL_REAL_NUMPY_DTYPES
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+ COMPLEX_DTYPES
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+ STRING_DTYPES
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+ DATETIME64_DTYPES
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+ TIMEDELTA64_DTYPES
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+ BOOL_DTYPES
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+ OBJECT_DTYPES
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+ BYTES_DTYPES
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)
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NARROW_NP_DTYPES = [
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np.float16,
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np.float32,
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np.int8,
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np.int16,
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np.int32,
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np.uint8,
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np.uint16,
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np.uint32,
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]
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ENDIAN = {"little": "<", "big": ">"}[byteorder]
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NULL_OBJECTS = [None, np.nan, pd.NaT, float("nan"), pd.NA, Decimal("NaN")]
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NP_NAT_OBJECTS = [
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cls("NaT", unit)
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for cls in [np.datetime64, np.timedelta64]
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for unit in [
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"Y",
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"M",
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"W",
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"D",
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"h",
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"m",
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"s",
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"ms",
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"us",
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"ns",
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"ps",
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"fs",
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"as",
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]
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]
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if not pa_version_under1p01:
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import pyarrow as pa
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UNSIGNED_INT_PYARROW_DTYPES = [pa.uint8(), pa.uint16(), pa.uint32(), pa.uint64()]
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SIGNED_INT_PYARROW_DTYPES = [pa.int8(), pa.int16(), pa.int32(), pa.int64()]
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ALL_INT_PYARROW_DTYPES = UNSIGNED_INT_PYARROW_DTYPES + SIGNED_INT_PYARROW_DTYPES
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FLOAT_PYARROW_DTYPES = [pa.float32(), pa.float64()]
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STRING_PYARROW_DTYPES = [pa.string(), pa.utf8()]
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TIME_PYARROW_DTYPES = [
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pa.time32("s"),
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pa.time32("ms"),
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pa.time64("us"),
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pa.time64("ns"),
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]
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DATE_PYARROW_DTYPES = [pa.date32(), pa.date64()]
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DATETIME_PYARROW_DTYPES = [
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pa.timestamp(unit=unit, tz=tz)
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for unit in ["s", "ms", "us", "ns"]
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for tz in [None, "UTC", "US/Pacific", "US/Eastern"]
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]
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TIMEDELTA_PYARROW_DTYPES = [pa.duration(unit) for unit in ["s", "ms", "us", "ns"]]
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BOOL_PYARROW_DTYPES = [pa.bool_()]
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# TODO: Add container like pyarrow types:
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# https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
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ALL_PYARROW_DTYPES = (
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ALL_INT_PYARROW_DTYPES
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+ FLOAT_PYARROW_DTYPES
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+ TIME_PYARROW_DTYPES
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+ DATE_PYARROW_DTYPES
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+ DATETIME_PYARROW_DTYPES
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+ TIMEDELTA_PYARROW_DTYPES
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+ BOOL_PYARROW_DTYPES
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)
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EMPTY_STRING_PATTERN = re.compile("^$")
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# set testing_mode
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_testing_mode_warnings = (DeprecationWarning, ResourceWarning)
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def set_testing_mode() -> None:
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# set the testing mode filters
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testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None")
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if "deprecate" in testing_mode:
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for category in _testing_mode_warnings:
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warnings.simplefilter("always", category)
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def reset_testing_mode() -> None:
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# reset the testing mode filters
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testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None")
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if "deprecate" in testing_mode:
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for category in _testing_mode_warnings:
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warnings.simplefilter("ignore", category)
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set_testing_mode()
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def reset_display_options() -> None:
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"""
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Reset the display options for printing and representing objects.
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"""
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pd.reset_option("^display.", silent=True)
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# -----------------------------------------------------------------------------
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# Comparators
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def equalContents(arr1, arr2) -> bool:
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"""
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Checks if the set of unique elements of arr1 and arr2 are equivalent.
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"""
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return frozenset(arr1) == frozenset(arr2)
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def box_expected(expected, box_cls, transpose=True):
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"""
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Helper function to wrap the expected output of a test in a given box_class.
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Parameters
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----------
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expected : np.ndarray, Index, Series
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box_cls : {Index, Series, DataFrame}
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Returns
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-------
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subclass of box_cls
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"""
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if box_cls is pd.array:
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if isinstance(expected, RangeIndex):
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# pd.array would return an IntegerArray
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expected = PandasArray(np.asarray(expected._values))
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else:
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expected = pd.array(expected)
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elif box_cls is Index:
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expected = Index._with_infer(expected)
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elif box_cls is Series:
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expected = Series(expected)
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elif box_cls is DataFrame:
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expected = Series(expected).to_frame()
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if transpose:
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# for vector operations, we need a DataFrame to be a single-row,
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# not a single-column, in order to operate against non-DataFrame
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# vectors of the same length. But convert to two rows to avoid
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# single-row special cases in datetime arithmetic
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expected = expected.T
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expected = pd.concat([expected] * 2, ignore_index=True)
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elif box_cls is np.ndarray or box_cls is np.array:
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expected = np.array(expected)
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elif box_cls is to_array:
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expected = to_array(expected)
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else:
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raise NotImplementedError(box_cls)
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return expected
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def to_array(obj):
|
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|
|
"""
|
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|
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Similar to pd.array, but does not cast numpy dtypes to nullable dtypes.
|
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"""
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# temporary implementation until we get pd.array in place
|
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dtype = getattr(obj, "dtype", None)
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|
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if dtype is None:
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return np.asarray(obj)
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|
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return extract_array(obj, extract_numpy=True)
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|
|
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|
|
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|
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# -----------------------------------------------------------------------------
|
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# Others
|
||
|
|
|
||
|
|
|
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def getCols(k) -> str:
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|
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return string.ascii_uppercase[:k]
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|
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|
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|
|
|
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|
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# make index
|
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|
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def makeStringIndex(k=10, name=None) -> Index:
|
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return Index(rands_array(nchars=10, size=k), name=name)
|
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|
|
|
||
|
|
|
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|
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def makeCategoricalIndex(k=10, n=3, name=None, **kwargs) -> CategoricalIndex:
|
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|
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"""make a length k index or n categories"""
|
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|
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x = rands_array(nchars=4, size=n, replace=False)
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return CategoricalIndex(
|
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|
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Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs
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|
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)
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|
|
|
||
|
|
|
||
|
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def makeIntervalIndex(k=10, name=None, **kwargs) -> IntervalIndex:
|
||
|
|
"""make a length k IntervalIndex"""
|
||
|
|
x = np.linspace(0, 100, num=(k + 1))
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|
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return IntervalIndex.from_breaks(x, name=name, **kwargs)
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|
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|
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|
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|
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def makeBoolIndex(k=10, name=None) -> Index:
|
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|
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if k == 1:
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|
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return Index([True], name=name)
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elif k == 2:
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return Index([False, True], name=name)
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return Index([False, True] + [False] * (k - 2), name=name)
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|
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|
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|
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def makeNumericIndex(k=10, name=None, *, dtype) -> NumericIndex:
|
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|
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dtype = pandas_dtype(dtype)
|
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|
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assert isinstance(dtype, np.dtype)
|
||
|
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|
||
|
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if is_integer_dtype(dtype):
|
||
|
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values = np.arange(k, dtype=dtype)
|
||
|
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if is_unsigned_integer_dtype(dtype):
|
||
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values += 2 ** (dtype.itemsize * 8 - 1)
|
||
|
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elif is_float_dtype(dtype):
|
||
|
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values = np.random.random_sample(k) - np.random.random_sample(1)
|
||
|
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values.sort()
|
||
|
|
values = values * (10 ** np.random.randint(0, 9))
|
||
|
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else:
|
||
|
|
raise NotImplementedError(f"wrong dtype {dtype}")
|
||
|
|
|
||
|
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return NumericIndex(values, dtype=dtype, name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def makeIntIndex(k=10, name=None) -> Int64Index:
|
||
|
|
base_idx = makeNumericIndex(k, name=name, dtype="int64")
|
||
|
|
return Int64Index(base_idx)
|
||
|
|
|
||
|
|
|
||
|
|
def makeUIntIndex(k=10, name=None) -> UInt64Index:
|
||
|
|
base_idx = makeNumericIndex(k, name=name, dtype="uint64")
|
||
|
|
return UInt64Index(base_idx)
|
||
|
|
|
||
|
|
|
||
|
|
def makeRangeIndex(k=10, name=None, **kwargs) -> RangeIndex:
|
||
|
|
return RangeIndex(0, k, 1, name=name, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def makeFloatIndex(k=10, name=None) -> Float64Index:
|
||
|
|
base_idx = makeNumericIndex(k, name=name, dtype="float64")
|
||
|
|
return Float64Index(base_idx)
|
||
|
|
|
||
|
|
|
||
|
|
def makeDateIndex(k: int = 10, freq="B", name=None, **kwargs) -> DatetimeIndex:
|
||
|
|
dt = datetime(2000, 1, 1)
|
||
|
|
dr = bdate_range(dt, periods=k, freq=freq, name=name)
|
||
|
|
return DatetimeIndex(dr, name=name, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def makeTimedeltaIndex(k: int = 10, freq="D", name=None, **kwargs) -> TimedeltaIndex:
|
||
|
|
return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def makePeriodIndex(k: int = 10, name=None, **kwargs) -> PeriodIndex:
|
||
|
|
dt = datetime(2000, 1, 1)
|
||
|
|
return pd.period_range(start=dt, periods=k, freq="B", name=name, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def makeMultiIndex(k=10, names=None, **kwargs):
|
||
|
|
N = (k // 2) + 1
|
||
|
|
rng = range(N)
|
||
|
|
mi = MultiIndex.from_product([("foo", "bar"), rng], names=names, **kwargs)
|
||
|
|
assert len(mi) >= k # GH#38795
|
||
|
|
return mi[:k]
|
||
|
|
|
||
|
|
|
||
|
|
def index_subclass_makers_generator():
|
||
|
|
make_index_funcs = [
|
||
|
|
makeDateIndex,
|
||
|
|
makePeriodIndex,
|
||
|
|
makeTimedeltaIndex,
|
||
|
|
makeRangeIndex,
|
||
|
|
makeIntervalIndex,
|
||
|
|
makeCategoricalIndex,
|
||
|
|
makeMultiIndex,
|
||
|
|
]
|
||
|
|
yield from make_index_funcs
|
||
|
|
|
||
|
|
|
||
|
|
def all_timeseries_index_generator(k: int = 10) -> Iterable[Index]:
|
||
|
|
"""
|
||
|
|
Generator which can be iterated over to get instances of all the classes
|
||
|
|
which represent time-series.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
k: length of each of the index instances
|
||
|
|
"""
|
||
|
|
make_index_funcs: list[Callable[..., Index]] = [
|
||
|
|
makeDateIndex,
|
||
|
|
makePeriodIndex,
|
||
|
|
makeTimedeltaIndex,
|
||
|
|
]
|
||
|
|
for make_index_func in make_index_funcs:
|
||
|
|
yield make_index_func(k=k)
|
||
|
|
|
||
|
|
|
||
|
|
# make series
|
||
|
|
def make_rand_series(name=None, dtype=np.float64) -> Series:
|
||
|
|
index = makeStringIndex(_N)
|
||
|
|
data = np.random.randn(_N)
|
||
|
|
with np.errstate(invalid="ignore"):
|
||
|
|
data = data.astype(dtype, copy=False)
|
||
|
|
return Series(data, index=index, name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def makeFloatSeries(name=None) -> Series:
|
||
|
|
return make_rand_series(name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def makeStringSeries(name=None) -> Series:
|
||
|
|
return make_rand_series(name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def makeObjectSeries(name=None) -> Series:
|
||
|
|
data = makeStringIndex(_N)
|
||
|
|
data = Index(data, dtype=object)
|
||
|
|
index = makeStringIndex(_N)
|
||
|
|
return Series(data, index=index, name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def getSeriesData() -> dict[str, Series]:
|
||
|
|
index = makeStringIndex(_N)
|
||
|
|
return {c: Series(np.random.randn(_N), index=index) for c in getCols(_K)}
|
||
|
|
|
||
|
|
|
||
|
|
def makeTimeSeries(nper=None, freq="B", name=None) -> Series:
|
||
|
|
if nper is None:
|
||
|
|
nper = _N
|
||
|
|
return Series(
|
||
|
|
np.random.randn(nper), index=makeDateIndex(nper, freq=freq), name=name
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def makePeriodSeries(nper=None, name=None) -> Series:
|
||
|
|
if nper is None:
|
||
|
|
nper = _N
|
||
|
|
return Series(np.random.randn(nper), index=makePeriodIndex(nper), name=name)
|
||
|
|
|
||
|
|
|
||
|
|
def getTimeSeriesData(nper=None, freq="B") -> dict[str, Series]:
|
||
|
|
return {c: makeTimeSeries(nper, freq) for c in getCols(_K)}
|
||
|
|
|
||
|
|
|
||
|
|
def getPeriodData(nper=None) -> dict[str, Series]:
|
||
|
|
return {c: makePeriodSeries(nper) for c in getCols(_K)}
|
||
|
|
|
||
|
|
|
||
|
|
# make frame
|
||
|
|
def makeTimeDataFrame(nper=None, freq="B") -> DataFrame:
|
||
|
|
data = getTimeSeriesData(nper, freq)
|
||
|
|
return DataFrame(data)
|
||
|
|
|
||
|
|
|
||
|
|
def makeDataFrame() -> DataFrame:
|
||
|
|
data = getSeriesData()
|
||
|
|
return DataFrame(data)
|
||
|
|
|
||
|
|
|
||
|
|
def getMixedTypeDict():
|
||
|
|
index = Index(["a", "b", "c", "d", "e"])
|
||
|
|
|
||
|
|
data = {
|
||
|
|
"A": [0.0, 1.0, 2.0, 3.0, 4.0],
|
||
|
|
"B": [0.0, 1.0, 0.0, 1.0, 0.0],
|
||
|
|
"C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
|
||
|
|
"D": bdate_range("1/1/2009", periods=5),
|
||
|
|
}
|
||
|
|
|
||
|
|
return index, data
|
||
|
|
|
||
|
|
|
||
|
|
def makeMixedDataFrame() -> DataFrame:
|
||
|
|
return DataFrame(getMixedTypeDict()[1])
|
||
|
|
|
||
|
|
|
||
|
|
def makePeriodFrame(nper=None) -> DataFrame:
|
||
|
|
data = getPeriodData(nper)
|
||
|
|
return DataFrame(data)
|
||
|
|
|
||
|
|
|
||
|
|
def makeCustomIndex(
|
||
|
|
nentries,
|
||
|
|
nlevels,
|
||
|
|
prefix="#",
|
||
|
|
names: bool | str | list[str] | None = False,
|
||
|
|
ndupe_l=None,
|
||
|
|
idx_type=None,
|
||
|
|
) -> Index:
|
||
|
|
"""
|
||
|
|
Create an index/multindex with given dimensions, levels, names, etc'
|
||
|
|
|
||
|
|
nentries - number of entries in index
|
||
|
|
nlevels - number of levels (> 1 produces multindex)
|
||
|
|
prefix - a string prefix for labels
|
||
|
|
names - (Optional), bool or list of strings. if True will use default
|
||
|
|
names, if false will use no names, if a list is given, the name of
|
||
|
|
each level in the index will be taken from the list.
|
||
|
|
ndupe_l - (Optional), list of ints, the number of rows for which the
|
||
|
|
label will repeated at the corresponding level, you can specify just
|
||
|
|
the first few, the rest will use the default ndupe_l of 1.
|
||
|
|
len(ndupe_l) <= nlevels.
|
||
|
|
idx_type - "i"/"f"/"s"/"dt"/"p"/"td".
|
||
|
|
If idx_type is not None, `idx_nlevels` must be 1.
|
||
|
|
"i"/"f" creates an integer/float index,
|
||
|
|
"s" creates a string
|
||
|
|
"dt" create a datetime index.
|
||
|
|
"td" create a datetime index.
|
||
|
|
|
||
|
|
if unspecified, string labels will be generated.
|
||
|
|
"""
|
||
|
|
if ndupe_l is None:
|
||
|
|
ndupe_l = [1] * nlevels
|
||
|
|
assert is_sequence(ndupe_l) and len(ndupe_l) <= nlevels
|
||
|
|
assert names is None or names is False or names is True or len(names) is nlevels
|
||
|
|
assert idx_type is None or (
|
||
|
|
idx_type in ("i", "f", "s", "u", "dt", "p", "td") and nlevels == 1
|
||
|
|
)
|
||
|
|
|
||
|
|
if names is True:
|
||
|
|
# build default names
|
||
|
|
names = [prefix + str(i) for i in range(nlevels)]
|
||
|
|
if names is False:
|
||
|
|
# pass None to index constructor for no name
|
||
|
|
names = None
|
||
|
|
|
||
|
|
# make singleton case uniform
|
||
|
|
if isinstance(names, str) and nlevels == 1:
|
||
|
|
names = [names]
|
||
|
|
|
||
|
|
# specific 1D index type requested?
|
||
|
|
idx_func_dict: dict[str, Callable[..., Index]] = {
|
||
|
|
"i": makeIntIndex,
|
||
|
|
"f": makeFloatIndex,
|
||
|
|
"s": makeStringIndex,
|
||
|
|
"dt": makeDateIndex,
|
||
|
|
"td": makeTimedeltaIndex,
|
||
|
|
"p": makePeriodIndex,
|
||
|
|
}
|
||
|
|
idx_func = idx_func_dict.get(idx_type)
|
||
|
|
if idx_func:
|
||
|
|
idx = idx_func(nentries)
|
||
|
|
# but we need to fill in the name
|
||
|
|
if names:
|
||
|
|
idx.name = names[0]
|
||
|
|
return idx
|
||
|
|
elif idx_type is not None:
|
||
|
|
raise ValueError(
|
||
|
|
f"{repr(idx_type)} is not a legal value for `idx_type`, "
|
||
|
|
"use 'i'/'f'/'s'/'dt'/'p'/'td'."
|
||
|
|
)
|
||
|
|
|
||
|
|
if len(ndupe_l) < nlevels:
|
||
|
|
ndupe_l.extend([1] * (nlevels - len(ndupe_l)))
|
||
|
|
assert len(ndupe_l) == nlevels
|
||
|
|
|
||
|
|
assert all(x > 0 for x in ndupe_l)
|
||
|
|
|
||
|
|
list_of_lists = []
|
||
|
|
for i in range(nlevels):
|
||
|
|
|
||
|
|
def keyfunc(x):
|
||
|
|
import re
|
||
|
|
|
||
|
|
numeric_tuple = re.sub(r"[^\d_]_?", "", x).split("_")
|
||
|
|
return [int(num) for num in numeric_tuple]
|
||
|
|
|
||
|
|
# build a list of lists to create the index from
|
||
|
|
div_factor = nentries // ndupe_l[i] + 1
|
||
|
|
|
||
|
|
# Deprecated since version 3.9: collections.Counter now supports []. See PEP 585
|
||
|
|
# and Generic Alias Type.
|
||
|
|
cnt: Counter[str] = collections.Counter()
|
||
|
|
for j in range(div_factor):
|
||
|
|
label = f"{prefix}_l{i}_g{j}"
|
||
|
|
cnt[label] = ndupe_l[i]
|
||
|
|
# cute Counter trick
|
||
|
|
result = sorted(cnt.elements(), key=keyfunc)[:nentries]
|
||
|
|
list_of_lists.append(result)
|
||
|
|
|
||
|
|
tuples = list(zip(*list_of_lists))
|
||
|
|
|
||
|
|
# convert tuples to index
|
||
|
|
if nentries == 1:
|
||
|
|
# we have a single level of tuples, i.e. a regular Index
|
||
|
|
name = None if names is None else names[0]
|
||
|
|
index = Index(tuples[0], name=name)
|
||
|
|
elif nlevels == 1:
|
||
|
|
name = None if names is None else names[0]
|
||
|
|
index = Index((x[0] for x in tuples), name=name)
|
||
|
|
else:
|
||
|
|
index = MultiIndex.from_tuples(tuples, names=names)
|
||
|
|
return index
|
||
|
|
|
||
|
|
|
||
|
|
def makeCustomDataframe(
|
||
|
|
nrows,
|
||
|
|
ncols,
|
||
|
|
c_idx_names=True,
|
||
|
|
r_idx_names=True,
|
||
|
|
c_idx_nlevels=1,
|
||
|
|
r_idx_nlevels=1,
|
||
|
|
data_gen_f=None,
|
||
|
|
c_ndupe_l=None,
|
||
|
|
r_ndupe_l=None,
|
||
|
|
dtype=None,
|
||
|
|
c_idx_type=None,
|
||
|
|
r_idx_type=None,
|
||
|
|
) -> DataFrame:
|
||
|
|
"""
|
||
|
|
Create a DataFrame using supplied parameters.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
nrows, ncols - number of data rows/cols
|
||
|
|
c_idx_names, idx_names - False/True/list of strings, yields No names ,
|
||
|
|
default names or uses the provided names for the levels of the
|
||
|
|
corresponding index. You can provide a single string when
|
||
|
|
c_idx_nlevels ==1.
|
||
|
|
c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex
|
||
|
|
r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex
|
||
|
|
data_gen_f - a function f(row,col) which return the data value
|
||
|
|
at that position, the default generator used yields values of the form
|
||
|
|
"RxCy" based on position.
|
||
|
|
c_ndupe_l, r_ndupe_l - list of integers, determines the number
|
||
|
|
of duplicates for each label at a given level of the corresponding
|
||
|
|
index. The default `None` value produces a multiplicity of 1 across
|
||
|
|
all levels, i.e. a unique index. Will accept a partial list of length
|
||
|
|
N < idx_nlevels, for just the first N levels. If ndupe doesn't divide
|
||
|
|
nrows/ncol, the last label might have lower multiplicity.
|
||
|
|
dtype - passed to the DataFrame constructor as is, in case you wish to
|
||
|
|
have more control in conjunction with a custom `data_gen_f`
|
||
|
|
r_idx_type, c_idx_type - "i"/"f"/"s"/"dt"/"td".
|
||
|
|
If idx_type is not None, `idx_nlevels` must be 1.
|
||
|
|
"i"/"f" creates an integer/float index,
|
||
|
|
"s" creates a string index
|
||
|
|
"dt" create a datetime index.
|
||
|
|
"td" create a timedelta index.
|
||
|
|
|
||
|
|
if unspecified, string labels will be generated.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
# 5 row, 3 columns, default names on both, single index on both axis
|
||
|
|
>> makeCustomDataframe(5,3)
|
||
|
|
|
||
|
|
# make the data a random int between 1 and 100
|
||
|
|
>> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100))
|
||
|
|
|
||
|
|
# 2-level multiindex on rows with each label duplicated
|
||
|
|
# twice on first level, default names on both axis, single
|
||
|
|
# index on both axis
|
||
|
|
>> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2])
|
||
|
|
|
||
|
|
# DatetimeIndex on row, index with unicode labels on columns
|
||
|
|
# no names on either axis
|
||
|
|
>> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False,
|
||
|
|
r_idx_type="dt",c_idx_type="u")
|
||
|
|
|
||
|
|
# 4-level multindex on rows with names provided, 2-level multindex
|
||
|
|
# on columns with default labels and default names.
|
||
|
|
>> a=makeCustomDataframe(5,3,r_idx_nlevels=4,
|
||
|
|
r_idx_names=["FEE","FIH","FOH","FUM"],
|
||
|
|
c_idx_nlevels=2)
|
||
|
|
|
||
|
|
>> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
|
||
|
|
"""
|
||
|
|
assert c_idx_nlevels > 0
|
||
|
|
assert r_idx_nlevels > 0
|
||
|
|
assert r_idx_type is None or (
|
||
|
|
r_idx_type in ("i", "f", "s", "dt", "p", "td") and r_idx_nlevels == 1
|
||
|
|
)
|
||
|
|
assert c_idx_type is None or (
|
||
|
|
c_idx_type in ("i", "f", "s", "dt", "p", "td") and c_idx_nlevels == 1
|
||
|
|
)
|
||
|
|
|
||
|
|
columns = makeCustomIndex(
|
||
|
|
ncols,
|
||
|
|
nlevels=c_idx_nlevels,
|
||
|
|
prefix="C",
|
||
|
|
names=c_idx_names,
|
||
|
|
ndupe_l=c_ndupe_l,
|
||
|
|
idx_type=c_idx_type,
|
||
|
|
)
|
||
|
|
index = makeCustomIndex(
|
||
|
|
nrows,
|
||
|
|
nlevels=r_idx_nlevels,
|
||
|
|
prefix="R",
|
||
|
|
names=r_idx_names,
|
||
|
|
ndupe_l=r_ndupe_l,
|
||
|
|
idx_type=r_idx_type,
|
||
|
|
)
|
||
|
|
|
||
|
|
# by default, generate data based on location
|
||
|
|
if data_gen_f is None:
|
||
|
|
data_gen_f = lambda r, c: f"R{r}C{c}"
|
||
|
|
|
||
|
|
data = [[data_gen_f(r, c) for c in range(ncols)] for r in range(nrows)]
|
||
|
|
|
||
|
|
return DataFrame(data, index, columns, dtype=dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def _create_missing_idx(nrows, ncols, density, random_state=None):
|
||
|
|
if random_state is None:
|
||
|
|
random_state = np.random
|
||
|
|
else:
|
||
|
|
random_state = np.random.RandomState(random_state)
|
||
|
|
|
||
|
|
# below is cribbed from scipy.sparse
|
||
|
|
size = round((1 - density) * nrows * ncols)
|
||
|
|
# generate a few more to ensure unique values
|
||
|
|
min_rows = 5
|
||
|
|
fac = 1.02
|
||
|
|
extra_size = min(size + min_rows, fac * size)
|
||
|
|
|
||
|
|
def _gen_unique_rand(rng, _extra_size):
|
||
|
|
ind = rng.rand(int(_extra_size))
|
||
|
|
return np.unique(np.floor(ind * nrows * ncols))[:size]
|
||
|
|
|
||
|
|
ind = _gen_unique_rand(random_state, extra_size)
|
||
|
|
while ind.size < size:
|
||
|
|
extra_size *= 1.05
|
||
|
|
ind = _gen_unique_rand(random_state, extra_size)
|
||
|
|
|
||
|
|
j = np.floor(ind * 1.0 / nrows).astype(int)
|
||
|
|
i = (ind - j * nrows).astype(int)
|
||
|
|
return i.tolist(), j.tolist()
|
||
|
|
|
||
|
|
|
||
|
|
def makeMissingDataframe(density=0.9, random_state=None) -> DataFrame:
|
||
|
|
df = makeDataFrame()
|
||
|
|
i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state)
|
||
|
|
df.values[i, j] = np.nan
|
||
|
|
return df
|
||
|
|
|
||
|
|
|
||
|
|
class SubclassedSeries(Series):
|
||
|
|
_metadata = ["testattr", "name"]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _constructor(self):
|
||
|
|
# For testing, those properties return a generic callable, and not
|
||
|
|
# the actual class. In this case that is equivalent, but it is to
|
||
|
|
# ensure we don't rely on the property returning a class
|
||
|
|
# See https://github.com/pandas-dev/pandas/pull/46018 and
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/32638 and linked issues
|
||
|
|
return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _constructor_expanddim(self):
|
||
|
|
return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
class SubclassedDataFrame(DataFrame):
|
||
|
|
_metadata = ["testattr"]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _constructor(self):
|
||
|
|
return lambda *args, **kwargs: SubclassedDataFrame(*args, **kwargs)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _constructor_sliced(self):
|
||
|
|
return lambda *args, **kwargs: SubclassedSeries(*args, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
class SubclassedCategorical(Categorical):
|
||
|
|
@property
|
||
|
|
def _constructor(self):
|
||
|
|
return SubclassedCategorical
|
||
|
|
|
||
|
|
|
||
|
|
def _make_skipna_wrapper(alternative, skipna_alternative=None):
|
||
|
|
"""
|
||
|
|
Create a function for calling on an array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
alternative : function
|
||
|
|
The function to be called on the array with no NaNs.
|
||
|
|
Only used when 'skipna_alternative' is None.
|
||
|
|
skipna_alternative : function
|
||
|
|
The function to be called on the original array
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
function
|
||
|
|
"""
|
||
|
|
if skipna_alternative:
|
||
|
|
|
||
|
|
def skipna_wrapper(x):
|
||
|
|
return skipna_alternative(x.values)
|
||
|
|
|
||
|
|
else:
|
||
|
|
|
||
|
|
def skipna_wrapper(x):
|
||
|
|
nona = x.dropna()
|
||
|
|
if len(nona) == 0:
|
||
|
|
return np.nan
|
||
|
|
return alternative(nona)
|
||
|
|
|
||
|
|
return skipna_wrapper
|
||
|
|
|
||
|
|
|
||
|
|
def convert_rows_list_to_csv_str(rows_list: list[str]) -> str:
|
||
|
|
"""
|
||
|
|
Convert list of CSV rows to single CSV-formatted string for current OS.
|
||
|
|
|
||
|
|
This method is used for creating expected value of to_csv() method.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
rows_list : List[str]
|
||
|
|
Each element represents the row of csv.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
str
|
||
|
|
Expected output of to_csv() in current OS.
|
||
|
|
"""
|
||
|
|
sep = os.linesep
|
||
|
|
return sep.join(rows_list) + sep
|
||
|
|
|
||
|
|
|
||
|
|
def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
|
||
|
|
"""
|
||
|
|
Helper function to mark pytest.raises that have an external error message.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
expected_exception : Exception
|
||
|
|
Expected error to raise.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Callable
|
||
|
|
Regular `pytest.raises` function with `match` equal to `None`.
|
||
|
|
"""
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
return pytest.raises(expected_exception, match=None) # noqa: PDF010
|
||
|
|
|
||
|
|
|
||
|
|
cython_table = pd.core.common._cython_table.items()
|
||
|
|
|
||
|
|
|
||
|
|
def get_cython_table_params(ndframe, func_names_and_expected):
|
||
|
|
"""
|
||
|
|
Combine frame, functions from com._cython_table
|
||
|
|
keys and expected result.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ndframe : DataFrame or Series
|
||
|
|
func_names_and_expected : Sequence of two items
|
||
|
|
The first item is a name of a NDFrame method ('sum', 'prod') etc.
|
||
|
|
The second item is the expected return value.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list
|
||
|
|
List of three items (DataFrame, function, expected result)
|
||
|
|
"""
|
||
|
|
results = []
|
||
|
|
for func_name, expected in func_names_and_expected:
|
||
|
|
results.append((ndframe, func_name, expected))
|
||
|
|
results += [
|
||
|
|
(ndframe, func, expected)
|
||
|
|
for func, name in cython_table
|
||
|
|
if name == func_name
|
||
|
|
]
|
||
|
|
return results
|
||
|
|
|
||
|
|
|
||
|
|
def get_op_from_name(op_name: str) -> Callable:
|
||
|
|
"""
|
||
|
|
The operator function for a given op name.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
op_name : str
|
||
|
|
The op name, in form of "add" or "__add__".
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
function
|
||
|
|
A function performing the operation.
|
||
|
|
"""
|
||
|
|
short_opname = op_name.strip("_")
|
||
|
|
try:
|
||
|
|
op = getattr(operator, short_opname)
|
||
|
|
except AttributeError:
|
||
|
|
# Assume it is the reverse operator
|
||
|
|
rop = getattr(operator, short_opname[1:])
|
||
|
|
op = lambda x, y: rop(y, x)
|
||
|
|
|
||
|
|
return op
|
||
|
|
|
||
|
|
|
||
|
|
# -----------------------------------------------------------------------------
|
||
|
|
# Indexing test helpers
|
||
|
|
|
||
|
|
|
||
|
|
def getitem(x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def setitem(x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def loc(x):
|
||
|
|
return x.loc
|
||
|
|
|
||
|
|
|
||
|
|
def iloc(x):
|
||
|
|
return x.iloc
|
||
|
|
|
||
|
|
|
||
|
|
def at(x):
|
||
|
|
return x.at
|
||
|
|
|
||
|
|
|
||
|
|
def iat(x):
|
||
|
|
return x.iat
|
||
|
|
|
||
|
|
|
||
|
|
# -----------------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
def shares_memory(left, right) -> bool:
|
||
|
|
"""
|
||
|
|
Pandas-compat for np.shares_memory.
|
||
|
|
"""
|
||
|
|
if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
|
||
|
|
return np.shares_memory(left, right)
|
||
|
|
elif isinstance(left, np.ndarray):
|
||
|
|
# Call with reversed args to get to unpacking logic below.
|
||
|
|
return shares_memory(right, left)
|
||
|
|
|
||
|
|
if isinstance(left, RangeIndex):
|
||
|
|
return False
|
||
|
|
if isinstance(left, MultiIndex):
|
||
|
|
return shares_memory(left._codes, right)
|
||
|
|
if isinstance(left, (Index, Series)):
|
||
|
|
return shares_memory(left._values, right)
|
||
|
|
|
||
|
|
if isinstance(left, NDArrayBackedExtensionArray):
|
||
|
|
return shares_memory(left._ndarray, right)
|
||
|
|
if isinstance(left, pd.core.arrays.SparseArray):
|
||
|
|
return shares_memory(left.sp_values, right)
|
||
|
|
if isinstance(left, pd.core.arrays.IntervalArray):
|
||
|
|
return shares_memory(left._left, right) or shares_memory(left._right, right)
|
||
|
|
|
||
|
|
if isinstance(left, ExtensionArray) and left.dtype == "string[pyarrow]":
|
||
|
|
# https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
|
||
|
|
if isinstance(right, ExtensionArray) and right.dtype == "string[pyarrow]":
|
||
|
|
# error: "ExtensionArray" has no attribute "_data"
|
||
|
|
left_pa_data = left._data # type: ignore[attr-defined]
|
||
|
|
# error: "ExtensionArray" has no attribute "_data"
|
||
|
|
right_pa_data = right._data # type: ignore[attr-defined]
|
||
|
|
left_buf1 = left_pa_data.chunk(0).buffers()[1]
|
||
|
|
right_buf1 = right_pa_data.chunk(0).buffers()[1]
|
||
|
|
return left_buf1 == right_buf1
|
||
|
|
|
||
|
|
if isinstance(left, BaseMaskedArray) and isinstance(right, BaseMaskedArray):
|
||
|
|
# By convention, we'll say these share memory if they share *either*
|
||
|
|
# the _data or the _mask
|
||
|
|
return np.shares_memory(left._data, right._data) or np.shares_memory(
|
||
|
|
left._mask, right._mask
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(left, DataFrame) and len(left._mgr.arrays) == 1:
|
||
|
|
arr = left._mgr.arrays[0]
|
||
|
|
return shares_memory(arr, right)
|
||
|
|
|
||
|
|
raise NotImplementedError(type(left), type(right))
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"ALL_INT_EA_DTYPES",
|
||
|
|
"ALL_INT_NUMPY_DTYPES",
|
||
|
|
"ALL_NUMPY_DTYPES",
|
||
|
|
"ALL_REAL_NUMPY_DTYPES",
|
||
|
|
"all_timeseries_index_generator",
|
||
|
|
"assert_almost_equal",
|
||
|
|
"assert_attr_equal",
|
||
|
|
"assert_categorical_equal",
|
||
|
|
"assert_class_equal",
|
||
|
|
"assert_contains_all",
|
||
|
|
"assert_copy",
|
||
|
|
"assert_datetime_array_equal",
|
||
|
|
"assert_dict_equal",
|
||
|
|
"assert_equal",
|
||
|
|
"assert_extension_array_equal",
|
||
|
|
"assert_frame_equal",
|
||
|
|
"assert_index_equal",
|
||
|
|
"assert_indexing_slices_equivalent",
|
||
|
|
"assert_interval_array_equal",
|
||
|
|
"assert_is_sorted",
|
||
|
|
"assert_is_valid_plot_return_object",
|
||
|
|
"assert_metadata_equivalent",
|
||
|
|
"assert_numpy_array_equal",
|
||
|
|
"assert_period_array_equal",
|
||
|
|
"assert_produces_warning",
|
||
|
|
"assert_series_equal",
|
||
|
|
"assert_sp_array_equal",
|
||
|
|
"assert_timedelta_array_equal",
|
||
|
|
"at",
|
||
|
|
"BOOL_DTYPES",
|
||
|
|
"box_expected",
|
||
|
|
"BYTES_DTYPES",
|
||
|
|
"can_set_locale",
|
||
|
|
"close",
|
||
|
|
"COMPLEX_DTYPES",
|
||
|
|
"convert_rows_list_to_csv_str",
|
||
|
|
"DATETIME64_DTYPES",
|
||
|
|
"decompress_file",
|
||
|
|
"EMPTY_STRING_PATTERN",
|
||
|
|
"ENDIAN",
|
||
|
|
"ensure_clean",
|
||
|
|
"ensure_clean_dir",
|
||
|
|
"ensure_safe_environment_variables",
|
||
|
|
"equalContents",
|
||
|
|
"external_error_raised",
|
||
|
|
"FLOAT_EA_DTYPES",
|
||
|
|
"FLOAT_NUMPY_DTYPES",
|
||
|
|
"getCols",
|
||
|
|
"get_cython_table_params",
|
||
|
|
"get_dtype",
|
||
|
|
"getitem",
|
||
|
|
"get_locales",
|
||
|
|
"getMixedTypeDict",
|
||
|
|
"get_obj",
|
||
|
|
"get_op_from_name",
|
||
|
|
"getPeriodData",
|
||
|
|
"getSeriesData",
|
||
|
|
"getTimeSeriesData",
|
||
|
|
"iat",
|
||
|
|
"iloc",
|
||
|
|
"index_subclass_makers_generator",
|
||
|
|
"loc",
|
||
|
|
"makeBoolIndex",
|
||
|
|
"makeCategoricalIndex",
|
||
|
|
"makeCustomDataframe",
|
||
|
|
"makeCustomIndex",
|
||
|
|
"makeDataFrame",
|
||
|
|
"makeDateIndex",
|
||
|
|
"makeFloatIndex",
|
||
|
|
"makeFloatSeries",
|
||
|
|
"makeIntervalIndex",
|
||
|
|
"makeIntIndex",
|
||
|
|
"makeMissingDataframe",
|
||
|
|
"makeMixedDataFrame",
|
||
|
|
"makeMultiIndex",
|
||
|
|
"makeNumericIndex",
|
||
|
|
"makeObjectSeries",
|
||
|
|
"makePeriodFrame",
|
||
|
|
"makePeriodIndex",
|
||
|
|
"makePeriodSeries",
|
||
|
|
"make_rand_series",
|
||
|
|
"makeRangeIndex",
|
||
|
|
"makeStringIndex",
|
||
|
|
"makeStringSeries",
|
||
|
|
"makeTimeDataFrame",
|
||
|
|
"makeTimedeltaIndex",
|
||
|
|
"makeTimeSeries",
|
||
|
|
"makeUIntIndex",
|
||
|
|
"maybe_produces_warning",
|
||
|
|
"NARROW_NP_DTYPES",
|
||
|
|
"network",
|
||
|
|
"NP_NAT_OBJECTS",
|
||
|
|
"NULL_OBJECTS",
|
||
|
|
"OBJECT_DTYPES",
|
||
|
|
"raise_assert_detail",
|
||
|
|
"randbool",
|
||
|
|
"rands",
|
||
|
|
"reset_display_options",
|
||
|
|
"reset_testing_mode",
|
||
|
|
"RNGContext",
|
||
|
|
"round_trip_localpath",
|
||
|
|
"round_trip_pathlib",
|
||
|
|
"round_trip_pickle",
|
||
|
|
"setitem",
|
||
|
|
"set_locale",
|
||
|
|
"set_testing_mode",
|
||
|
|
"set_timezone",
|
||
|
|
"shares_memory",
|
||
|
|
"SIGNED_INT_EA_DTYPES",
|
||
|
|
"SIGNED_INT_NUMPY_DTYPES",
|
||
|
|
"STRING_DTYPES",
|
||
|
|
"SubclassedCategorical",
|
||
|
|
"SubclassedDataFrame",
|
||
|
|
"SubclassedSeries",
|
||
|
|
"TIMEDELTA64_DTYPES",
|
||
|
|
"to_array",
|
||
|
|
"UNSIGNED_INT_EA_DTYPES",
|
||
|
|
"UNSIGNED_INT_NUMPY_DTYPES",
|
||
|
|
"use_numexpr",
|
||
|
|
"with_csv_dialect",
|
||
|
|
"write_to_compressed",
|
||
|
|
]
|