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test_pandas.py
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from __future__ import annotations
import datetime as dt
import random
from typing import (
TYPE_CHECKING,
Any,
Union,
)
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import Grouper
from pandas.api.extensions import ExtensionArray
import pandas.util as pdutil
# TODO: github.com/pandas-dev/pandas/issues/55023
import pytest
from typing_extensions import (
Never,
assert_type,
)
from pandas._libs.missing import NAType
from pandas._libs.tslibs import NaTType
from pandas._typing import Scalar
from tests import (
TYPE_CHECKING_INVALID_USAGE,
check,
pytest_warns_bounded,
)
def test_types_to_datetime() -> None:
df = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
r1: pd.Series = pd.to_datetime(df)
r2: pd.Series = pd.to_datetime(df, unit="s", origin="unix")
r3: pd.Series = pd.to_datetime(
df, unit="ns", dayfirst=True, utc=False, format="%M:%D", exact=False
)
r4: pd.DatetimeIndex = pd.to_datetime(
[1, 2], unit="D", origin=pd.Timestamp("01/01/2000")
)
r5: pd.DatetimeIndex = pd.to_datetime([1, 2], unit="D", origin=3)
r6: pd.DatetimeIndex = pd.to_datetime(["2022-01-03", "2022-02-22"])
r7: pd.DatetimeIndex = pd.to_datetime(pd.Index(["2022-01-03", "2022-02-22"]))
r8: pd.Series = pd.to_datetime(
{"year": [2015, 2016], "month": [2, 3], "day": [4, 5]}
)
def test_types_concat_none() -> None:
"""Test concatenation with None values."""
series = pd.Series([7, -5, 10])
df = pd.DataFrame({"a": [7, -5, 10]})
check(assert_type(pd.concat([None, series]), pd.Series), pd.Series)
check(assert_type(pd.concat([None, df]), pd.DataFrame), pd.DataFrame)
check(
assert_type(pd.concat([None, series, df], axis=1), pd.DataFrame), pd.DataFrame
)
check(assert_type(pd.concat([None, series, df]), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat({"a": None, "b": series}), pd.Series), pd.Series)
check(assert_type(pd.concat({"a": None, "b": df}), pd.DataFrame), pd.DataFrame)
check(
assert_type(pd.concat({"a": None, "b": series, "c": df}, axis=1), pd.DataFrame),
pd.DataFrame,
)
check(
assert_type(pd.concat({"a": None, "b": series, "c": df}), pd.DataFrame),
pd.DataFrame,
)
if TYPE_CHECKING_INVALID_USAGE:
# using assert_type as otherwise the second call would not be type-checked
assert_type(pd.concat({"a": None}), Never)
assert_type(pd.concat([None]), Never)
def test_types_concat() -> None:
s = pd.Series([0, 1, -10])
s2 = pd.Series([7, -5, 10])
check(assert_type(pd.concat([s, s2]), pd.Series), pd.Series)
check(assert_type(pd.concat([s, s2], axis=1), pd.DataFrame), pd.DataFrame)
check(
assert_type(pd.concat([s, s2], keys=["first", "second"], sort=True), pd.Series),
pd.Series,
)
check(
assert_type(
pd.concat([s, s2], keys=["first", "second"], names=["source", "row"]),
pd.Series,
),
pd.Series,
)
# Depends on the axis
check(
assert_type(pd.concat({"a": s, "b": s2}), pd.Series),
pd.Series,
)
check(
assert_type(pd.concat({"a": s, "b": s2}, axis=1), pd.DataFrame),
pd.DataFrame,
)
check(assert_type(pd.concat({1: s, 2: s2}), pd.Series), pd.Series)
check(assert_type(pd.concat({1: s, 2: s2}, axis=1), pd.DataFrame), pd.DataFrame)
check(
assert_type(pd.concat({1: s, None: s2}), pd.Series),
pd.Series,
)
check(
assert_type(
pd.concat({1: s, None: s2}, axis=1),
pd.DataFrame,
),
pd.DataFrame,
)
df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
df2 = pd.DataFrame(data={"col1": [10, 20], "col2": [30, 40]})
check(assert_type(pd.concat([df, df2]), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], axis=1), pd.DataFrame), pd.DataFrame)
check(
assert_type(
pd.concat([df, df2], keys=["first", "second"], sort=True), pd.DataFrame
),
pd.DataFrame,
)
check(
assert_type(
pd.concat([df, df2], keys=["first", "second"], names=["source", "row"]),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(
pd.concat(
{"a": pd.DataFrame([1, 2, 3]), "b": pd.DataFrame([4, 5, 6])}, axis=1
),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(
pd.concat({"a": pd.Series([1, 2, 3]), "b": pd.Series([4, 5, 6])}, axis=1),
pd.DataFrame,
),
pd.DataFrame,
)
check(assert_type(pd.concat({"a": df, "b": df2}), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat({1: df, 2: df2}), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat({1: df, None: df2}), pd.DataFrame), pd.DataFrame)
check(
assert_type(
pd.concat(map(lambda x: s2, ["some_value", 3]), axis=1), pd.DataFrame
),
pd.DataFrame,
)
adict = {"a": df, 2: df2}
check(assert_type(pd.concat(adict), pd.DataFrame), pd.DataFrame)
data: pd.DataFrame | pd.Series = pd.Series()
check(assert_type(pd.concat([pd.DataFrame(), data]), pd.DataFrame), pd.DataFrame)
def test_concat_args() -> None:
df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
df2 = pd.DataFrame(data={"col1": [10, 20], "col2": [30, 40]}, index=[2, 3])
check(
assert_type(pd.concat([df, df2], keys=["df1", "df2"]), pd.DataFrame),
pd.DataFrame,
)
check(
assert_type(
pd.concat([df, df2], keys=["df1", "df2"], names=["one"]), pd.DataFrame
),
pd.DataFrame,
)
check(
assert_type(
pd.concat([df, df2], keys=["df1", "df2"], names=[pd.Timedelta(1, "D")]),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(
pd.concat(
[df, df2], keys=[("df1", "ff"), (pd.Timestamp(2000, 1, 1), "gg")]
),
pd.DataFrame,
),
pd.DataFrame,
)
df_dict = {"df1": df, "df2": df2}
check(
assert_type(
pd.concat(df_dict.values(), keys=df_dict.keys()),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(pd.concat([df, df2], ignore_index=True), pd.DataFrame), pd.DataFrame
)
check(
assert_type(pd.concat([df, df2], verify_integrity=True), pd.DataFrame),
pd.DataFrame,
)
check(assert_type(pd.concat([df, df2], sort=True), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], copy=True), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], join="inner"), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], join="outer"), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], axis=0), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], axis=1), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], axis="index"), pd.DataFrame), pd.DataFrame)
check(assert_type(pd.concat([df, df2], axis="columns"), pd.DataFrame), pd.DataFrame)
df = pd.DataFrame(np.random.randn(1, 3))
df2 = pd.DataFrame(np.random.randn(1, 4))
levels = [["foo", "baz"], ["one", "two"]]
names = ["first", "second"]
check(
assert_type(
pd.concat(
[df, df2, df, df2],
keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
levels=levels,
names=names,
),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(
pd.concat(
[df, df2, df, df2],
keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
levels=levels,
),
pd.DataFrame,
),
pd.DataFrame,
)
def test_types_json_normalize() -> None:
data1: list[dict[str, Any]] = [
{"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
{"name": {"given": "Mose", "family": "Regner"}},
{"id": 2, "name": "Faye Raker"},
]
df1: pd.DataFrame = pd.json_normalize(data=data1)
df2: pd.DataFrame = pd.json_normalize(data=data1, max_level=0, sep=";")
df3: pd.DataFrame = pd.json_normalize(
data=data1, meta_prefix="id", record_prefix="name", errors="raise"
)
df4: pd.DataFrame = pd.json_normalize(data=data1, record_path=None, meta="id")
data2: dict[str, Any] = {"name": {"given": "Mose", "family": "Regner"}}
df5: pd.DataFrame = pd.json_normalize(data=data2)
def test_isna() -> None:
# https://github.com/pandas-dev/pandas-stubs/issues/264
s1 = pd.Series([1, np.nan, 3.2])
check(assert_type(pd.isna(s1), "pd.Series[bool]"), pd.Series, np.bool_)
s2 = pd.Series([1, 3.2])
check(assert_type(pd.notna(s2), "pd.Series[bool]"), pd.Series, np.bool_)
df1 = pd.DataFrame({"a": [1, 2, 1, 2], "b": [1, 1, 2, np.nan]})
check(assert_type(pd.isna(df1), "pd.DataFrame"), pd.DataFrame)
idx1 = pd.Index([1, 2, np.nan])
check(assert_type(pd.isna(idx1), npt.NDArray[np.bool_]), np.ndarray, np.bool_)
idx2 = pd.Index([1, 2])
check(assert_type(pd.notna(idx2), npt.NDArray[np.bool_]), np.ndarray, np.bool_)
assert check(assert_type(pd.isna(pd.NA), bool), bool)
assert not check(assert_type(pd.notna(pd.NA), bool), bool)
assert check(assert_type(pd.isna(pd.NaT), bool), bool)
assert not check(assert_type(pd.notna(pd.NaT), bool), bool)
assert check(assert_type(pd.isna(None), bool), bool)
assert not check(assert_type(pd.notna(None), bool), bool)
assert not check(assert_type(pd.isna(2.5), bool), bool)
assert check(assert_type(pd.notna(2.5), bool), bool)
# Checks for datetime, timedelta, np.datetime64 and np.timedelta64
py_dt = dt.datetime.now()
assert check(assert_type(pd.notna(py_dt), bool), bool)
assert not check(assert_type(pd.isna(py_dt), bool), bool)
py_td = dt.datetime.now() - py_dt
assert check(assert_type(pd.notna(py_td), bool), bool)
assert not check(assert_type(pd.isna(py_td), bool), bool)
np_dt = np.datetime64(py_dt)
assert check(assert_type(pd.notna(np_dt), bool), bool)
assert not check(assert_type(pd.isna(np_dt), bool), bool)
np_td = np.timedelta64(py_td)
assert check(assert_type(pd.notna(np_td), bool), bool)
assert not check(assert_type(pd.isna(np_td), bool), bool)
np_nat = np.timedelta64("NaT")
assert check(assert_type(pd.isna(np_nat), bool), bool)
assert not check(assert_type(pd.notna(np_nat), bool), bool)
# Check TypeGuard type narrowing functionality
# TODO: Due to limitations in TypeGuard spec, the true annotations are not always viable
# and as a result the type narrowing does not always work as it intuitively should
# There is a proposal being floated for a StrictTypeGuard that will have more rigid narrowing semantics
# In the test cases below, a commented out assertion will be included to document the optimal test result
nullable1: str | None | NAType | NaTType = random.choice(
["value", None, pd.NA, pd.NaT]
)
if pd.notna(nullable1):
check(assert_type(nullable1, str), str)
if not pd.isna(nullable1):
# check(assert_type(nullable1, str), str) # TODO: Desired result (see comments above)
check(assert_type(nullable1, Union[str, NaTType, NAType, None]), str)
if pd.isna(nullable1):
assert_type(nullable1, Union[NaTType, NAType, None])
if not pd.notna(nullable1):
# assert_type(nullable1, Union[NaTType, NAType, None]) # TODO: Desired result (see comments above)
assert_type(nullable1, Union[str, NaTType, NAType, None])
nullable2: int | None = random.choice([2, None])
if pd.notna(nullable2):
check(assert_type(nullable2, int), int)
if not pd.isna(nullable2):
# check(assert_type(nullable2, int), int) # TODO: Desired result (see comments above)
check(assert_type(nullable2, Union[int, None]), int)
if pd.isna(nullable2):
# check(assert_type(nullable2, None), type(None)) # TODO: Desired result (see comments above)
check(assert_type(nullable2, Union[NaTType, NAType, None]), type(None))
if not pd.notna(nullable2):
# check(assert_type(nullable2, None), type(None)) # TODO: Desired result (see comments above)
# TODO: MyPy and Pyright produce conflicting results:
# assert_type(nullable2, Union[int, None]) # MyPy result
# assert_type(
# nullable2, Union[int, NaTType, NAType, None]
# ) # Pyright result
pass
nullable3: bool | None | NAType = random.choice([True, None, pd.NA])
if pd.notna(nullable3):
check(assert_type(nullable3, bool), bool)
if not pd.isna(nullable3):
# check(assert_type(nullable3, bool), bool) # TODO: Desired result (see comments above)
check(assert_type(nullable3, Union[bool, NAType, None]), bool)
if pd.isna(nullable3):
# assert_type(nullable3, Union[NAType, None]) # TODO: Desired result (see comments above)
assert_type(nullable3, Union[NaTType, NAType, None])
if not pd.notna(nullable3):
# assert_type(nullable3, Union[NAType, None]) # TODO: Desired result (see comments above)
# TODO: MyPy and Pyright produce conflicting results:
# assert_type(nullable3, Union[bool, NAType, None]) # Mypy result
# assert_type(
# nullable3, Union[bool, NaTType, NAType, None]
# ) # Pyright result
pass
# GH 55
def test_read_xml() -> None:
if TYPE_CHECKING: # Skip running pytest
check(
assert_type(
pd.read_xml(
"path/to/file", xpath=".//row", stylesheet="path/to/stylesheet"
),
pd.DataFrame,
),
pd.DataFrame,
)
def test_unique() -> None:
# Taken from the docs
check(
assert_type(
pd.unique(pd.Series([2, 1, 3, 3])), Union[np.ndarray, ExtensionArray]
),
np.ndarray,
)
check(
assert_type(
pd.unique(pd.Series([2] + [1] * 5)), Union[np.ndarray, ExtensionArray]
),
np.ndarray,
)
check(
assert_type(
pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")])),
Union[np.ndarray, ExtensionArray],
),
np.ndarray,
)
check(
assert_type(
pd.unique(
pd.Series(
[
pd.Timestamp("20160101", tz="US/Eastern"),
pd.Timestamp("20160101", tz="US/Eastern"),
]
)
),
Union[np.ndarray, ExtensionArray],
),
pd.arrays.DatetimeArray,
)
check(
assert_type(
pd.unique(
pd.Index(
[
pd.Timestamp("20160101", tz="US/Eastern"),
pd.Timestamp("20160101", tz="US/Eastern"),
]
)
),
np.ndarray,
),
pd.DatetimeIndex,
)
with pytest_warns_bounded(
FutureWarning,
"unique with argument that is not not a Series, Index, ExtensionArray, "
"or np.ndarray is deprecated",
lower="2.0.99",
):
check(assert_type(pd.unique(list("baabc")), np.ndarray), np.ndarray)
check(
assert_type(
pd.unique(pd.Series(pd.Categorical(list("baabc")))),
Union[np.ndarray, ExtensionArray],
),
pd.Categorical,
)
check(
assert_type(
pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc")))),
Union[np.ndarray, ExtensionArray],
),
pd.Categorical,
)
check(
assert_type(
pd.unique(
pd.Series(
pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
)
),
Union[np.ndarray, ExtensionArray],
),
pd.Categorical,
)
with pytest_warns_bounded(
FutureWarning,
"unique with argument that is not not a Series, Index, ExtensionArray, "
"or np.ndarray is deprecated",
lower="2.0.99",
):
check(
assert_type(
pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")]), np.ndarray
),
np.ndarray,
)
check(
assert_type(pd.unique(pd.Index(["a", "b", "c", "a"])), np.ndarray),
np.ndarray,
)
check(
assert_type(pd.unique(pd.RangeIndex(0, 10)), np.ndarray),
np.ndarray,
)
check(
assert_type(pd.unique(pd.Categorical(["a", "b", "c", "a"])), pd.Categorical),
pd.Categorical,
)
check(
assert_type(
pd.unique(pd.period_range("2001Q1", periods=10, freq="D")),
pd.PeriodIndex,
),
pd.PeriodIndex,
)
check(
assert_type(
pd.unique(pd.timedelta_range(start="1 day", periods=4)),
np.ndarray,
),
np.ndarray,
)
# GH 200
def test_crosstab() -> None:
df = pd.DataFrame({"a": [1, 2, 1, 2], "b": [1, 1, 2, 2]})
check(
assert_type(
pd.crosstab(
index=df["a"],
columns=df["b"],
margins=True,
dropna=False,
normalize="columns",
),
pd.DataFrame,
),
pd.DataFrame,
)
def test_arrow_dtype() -> None:
pytest.importorskip("pyarrow")
import pyarrow as pa
check(
assert_type(
pd.ArrowDtype(pa.timestamp("s", tz="America/New_York")), pd.ArrowDtype
),
pd.ArrowDtype,
)
def test_hashing():
a = np.array([1, 2, 3])
check(assert_type(pdutil.hash_array(a), npt.NDArray[np.uint64]), np.ndarray)
check(
assert_type(
pdutil.hash_array(a, encoding="latin1", hash_key="1", categorize=True),
npt.NDArray[np.uint64],
),
np.ndarray,
)
b = pd.Series(a)
c = pd.DataFrame({"a": a, "b": a})
d = pd.Index(b)
check(assert_type(pdutil.hash_pandas_object(b), pd.Series), pd.Series)
check(assert_type(pdutil.hash_pandas_object(c), pd.Series), pd.Series)
check(assert_type(pdutil.hash_pandas_object(d), pd.Series), pd.Series)
check(
assert_type(
pdutil.hash_pandas_object(
d, index=True, encoding="latin1", hash_key="apple", categorize=True
),
pd.Series,
),
pd.Series,
)
def test_eval():
df = pd.DataFrame({"animal": ["dog", "pig"], "age": [10, 20]})
check(
assert_type(
pd.eval("double_age = df.age * 2", target=df),
Union[npt.NDArray, Scalar, pd.DataFrame, pd.Series, None],
),
pd.DataFrame,
)
def test_to_numeric_scalar() -> None:
check(assert_type(pd.to_numeric(1), float), int)
check(assert_type(pd.to_numeric("1.2"), float), float)
check(assert_type(pd.to_numeric("blerg", errors="coerce"), float), float)
check(assert_type(pd.to_numeric(1, downcast="signed"), float), int)
check(assert_type(pd.to_numeric(1, downcast="unsigned"), float), int)
check(assert_type(pd.to_numeric(1, downcast="float"), float), int)
check(assert_type(pd.to_numeric(1, downcast="integer"), float), int)
def test_to_numeric_array_like() -> None:
check(
assert_type(
pd.to_numeric([1, 2, 3]),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(
pd.to_numeric([1.0, 2.0, 3.0]),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(
pd.to_numeric([1.0, 2.0, "3.0"]),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(
pd.to_numeric(np.array([1.0, 2.0, "3.0"], dtype=object)),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(
pd.to_numeric([1.0, 2.0, "blerg"], errors="coerce"),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(
pd.to_numeric((1.0, 2.0, 3.0)),
npt.NDArray,
),
np.ndarray,
)
check(
assert_type(pd.to_numeric([1, 2, 3], downcast="unsigned"), npt.NDArray),
np.ndarray,
)
def test_to_numeric_array_series() -> None:
check(
assert_type(
pd.to_numeric(pd.Series([1, 2, 3])),
pd.Series,
),
pd.Series,
)
check(
assert_type(
pd.to_numeric(pd.Series([1, 2, "blerg"]), errors="coerce"),
pd.Series,
),
pd.Series,
)
check(
assert_type(pd.to_numeric(pd.Series([1, 2, 3]), downcast="signed"), pd.Series),
pd.Series,
)
check(
assert_type(
pd.to_numeric(pd.Series([1, 2, 3]), downcast="unsigned"), pd.Series
),
pd.Series,
)
check(
assert_type(pd.to_numeric(pd.Series([1, 2, 3]), downcast="integer"), pd.Series),
pd.Series,
)
check(
assert_type(pd.to_numeric(pd.Series([1, 2, 3]), downcast="float"), pd.Series),
pd.Series,
)
def test_wide_to_long():
df = pd.DataFrame(
{
"A1970": {0: "a", 1: "b", 2: "c"},
"A1980": {0: "d", 1: "e", 2: "f"},
"B1970": {0: 2.5, 1: 1.2, 2: 0.7},
"B1980": {0: 3.2, 1: 1.3, 2: 0.1},
"X": dict(zip(range(3), np.random.randn(3))),
}
)
df["id"] = df.index
df["id2"] = df.index + 1
check(
assert_type(pd.wide_to_long(df, ["A", "B"], i="id", j="year"), pd.DataFrame),
pd.DataFrame,
)
check(
assert_type(
pd.wide_to_long(df, ["A", "B"], i=["id", "id2"], j="year"), pd.DataFrame
),
pd.DataFrame,
)
def test_melt():
df = pd.DataFrame(
{
"A": {0: "a", 1: "b", 2: "c"},
"B": {0: 1, 1: 3, 2: 5},
"C": {0: 2, 1: 4, 2: 6},
"D": {0: 3, 1: 6, 2: 9},
"E": {0: 3, 1: 6, 2: 9},
}
)
check(
assert_type(
pd.melt(df, id_vars=["A"], value_vars=["B"], ignore_index=False),
pd.DataFrame,
),
pd.DataFrame,
)
check(
assert_type(
pd.melt(df, id_vars=["A"], value_vars=["B"], value_name=("F",)),
pd.DataFrame,
),
pd.DataFrame,
)
df.columns = pd.MultiIndex.from_arrays([list("ABCDE"), list("FGHIJ")])
check(
assert_type(
pd.melt(
df, id_vars=["A"], value_vars=["B"], ignore_index=False, col_level=0
),
pd.DataFrame,
),
pd.DataFrame,
)
def test_lreshape() -> None:
data = pd.DataFrame(
{
"hr1": [514, 573],
"hr2": [545, 526],
"team": ["Red Sox", "Yankees"],
"year1": [2007, 2007],
"year2": [2008, 2008],
}
)
check(
assert_type(
pd.lreshape(
data, {"year": ["year1", "year2"], "hr": ["hr1", "hr2"]}, dropna=True
),
pd.DataFrame,
),
pd.DataFrame,
)
data2 = pd.DataFrame(
{
"hr1": [514, 573],
("hr2",): [545, 526],
"team": ["Red Sox", "Yankees"],
("year1",): [2007, 2007],
"year2": [2008, 2008],
}
)
from collections.abc import Hashable
groups: dict[Hashable, list[Hashable]] = {
("year",): [("year1",), "year2"],
("hr",): ["hr1", ("hr2",)],
}
check(
assert_type(
pd.lreshape(data2, groups=groups),
pd.DataFrame,
),
pd.DataFrame,
)
def test_factorize() -> None:
with pytest_warns_bounded(
FutureWarning,
"factorize with argument that is not not a Series, Index, ExtensionArray, "
"or np.ndarray is deprecated",
lower="2.0.99",
):
codes, uniques = pd.factorize(["b", "b", "a", "c", "b"])
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(uniques, np.ndarray), np.ndarray)
codes, uniques = pd.factorize(np.recarray((1,), dtype=[("x", int)]))
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(uniques, np.ndarray), np.ndarray)
codes, cat_uniques = pd.factorize(pd.Categorical(["b", "b", "a", "c", "b"]))
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(cat_uniques, pd.Categorical), pd.Categorical)
codes, idx_uniques = pd.factorize(pd.Index(["b", "b", "a", "c", "b"]))
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(idx_uniques, pd.Index), pd.Index)
codes, idx_uniques = pd.factorize(pd.Series(["b", "b", "a", "c", "b"]))
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(idx_uniques, pd.Index), pd.Index)
with pytest_warns_bounded(
FutureWarning,
"factorize with argument that is not not a Series, Index, ExtensionArray, "
"or np.ndarray is deprecated",
lower="2.0.99",
):
codes, uniques = pd.factorize("bbacb")
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(uniques, np.ndarray), np.ndarray)
codes, uniques = pd.factorize(
["b", "b", "a", "c", "b"], use_na_sentinel=True, size_hint=10
)
check(assert_type(codes, np.ndarray), np.ndarray)
check(assert_type(uniques, np.ndarray), np.ndarray)
def test_index_unqiue() -> None:
ci = pd.CategoricalIndex(["a", "b", "a", "c"])
dti = pd.DatetimeIndex([pd.Timestamp(2000, 1, 1)])
i = pd.Index(["a", "b", "c", "a"])
pi = pd.period_range("2000Q1", periods=2, freq="Q")
ri = pd.RangeIndex(0, 10)
tdi = pd.timedelta_range("1 day", "10 days", periods=10)
mi = pd.MultiIndex.from_product([["a", "b"], ["apple", "banana"]])
interval_i = pd.interval_range(1, 10, periods=10)
check(assert_type(pd.unique(ci), pd.CategoricalIndex), pd.CategoricalIndex)
check(assert_type(pd.unique(dti), np.ndarray), np.ndarray)
check(assert_type(pd.unique(i), np.ndarray), np.ndarray)
check(assert_type(pd.unique(pi), pd.PeriodIndex), pd.PeriodIndex)
check(assert_type(pd.unique(ri), np.ndarray), np.ndarray)
check(assert_type(pd.unique(tdi), np.ndarray), np.ndarray)
check(assert_type(pd.unique(mi), np.ndarray), np.ndarray)
check(
assert_type(pd.unique(interval_i), "pd.IntervalIndex[pd.Interval[int]]"),
pd.IntervalIndex,
)
def test_cut() -> None:
intval_idx = pd.interval_range(0, 10, 4)
a = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], 4, precision=1, duplicates="drop")
b = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], 4, labels=False, duplicates="raise")
c = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], 4, labels=["1", "2", "3", "4"])
check(assert_type(a, pd.Categorical), pd.Categorical)
check(assert_type(b, npt.NDArray[np.intp]), np.ndarray)
check(assert_type(c, pd.Categorical), pd.Categorical)
d0, d1 = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], 4, retbins=True)
e0, e1 = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], 4, labels=False, retbins=True)
f0, f1 = pd.cut(
[1, 2, 3, 4, 5, 6, 7, 8], 4, labels=["1", "2", "3", "4"], retbins=True
)
check(assert_type(d0, pd.Categorical), pd.Categorical)
check(assert_type(d1, npt.NDArray), np.ndarray)
check(assert_type(e0, npt.NDArray[np.intp]), np.ndarray)
check(assert_type(e1, npt.NDArray), np.ndarray)
check(assert_type(f0, pd.Categorical), pd.Categorical)
check(assert_type(f1, npt.NDArray), np.ndarray)
g = pd.cut(pd.Series([1, 2, 3, 4, 5, 6, 7, 8]), 4, precision=1, duplicates="drop")
h = pd.cut(pd.Series([1, 2, 3, 4, 5, 6, 7, 8]), 4, labels=False, duplicates="raise")
i = pd.cut(pd.Series([1, 2, 3, 4, 5, 6, 7, 8]), 4, labels=["1", "2", "3", "4"])
check(assert_type(g, pd.Series), pd.Series)
check(assert_type(h, pd.Series), pd.Series)
check(assert_type(i, pd.Series), pd.Series)
j0, j1 = pd.cut(
pd.Series([1, 2, 3, 4, 5, 6, 7, 8]),
4,
precision=1,
duplicates="drop",
retbins=True,
)
k0, k1 = pd.cut(
pd.Series([1, 2, 3, 4, 5, 6, 7, 8]),
4,
labels=False,
duplicates="raise",
retbins=True,
)
l0, l1 = pd.cut(
pd.Series([1, 2, 3, 4, 5, 6, 7, 8]),
4,
labels=["1", "2", "3", "4"],
retbins=True,
)
m0, m1 = pd.cut(
pd.Series([1, 2, 3, 4, 5, 6, 7, 8]),
intval_idx,
retbins=True,
)
check(assert_type(j0, pd.Series), pd.Series)
check(assert_type(j1, npt.NDArray), np.ndarray)
check(assert_type(k0, pd.Series), pd.Series)
check(assert_type(k1, npt.NDArray), np.ndarray)
check(assert_type(l0, pd.Series), pd.Series)
check(assert_type(l1, npt.NDArray), np.ndarray)
check(assert_type(m0, pd.Series), pd.Series)
check(assert_type(m1, pd.IntervalIndex), pd.IntervalIndex)
n0, n1 = pd.cut([1, 2, 3, 4, 5, 6, 7, 8], intval_idx, retbins=True)
check(assert_type(n0, pd.Categorical), pd.Categorical)
check(assert_type(n1, "pd.IntervalIndex[pd.Interval[int]]"), pd.IntervalIndex)
s1 = pd.Series(data=pd.date_range("1/1/2020", periods=300))
check(
assert_type(
pd.cut(s1, bins=[np.datetime64("2020-01-03"), np.datetime64("2020-09-01")]),
"pd.Series[pd.CategoricalDtype]",
),
pd.Series,
)
check(
assert_type(
pd.cut(s1, bins=10),
"pd.Series[pd.CategoricalDtype]",
),
pd.Series,
pd.Interval,
)
s0r, s1r = pd.cut(s1, bins=10, retbins=True)
check(assert_type(s0r, pd.Series), pd.Series, pd.Interval)
check(assert_type(s1r, pd.DatetimeIndex), pd.DatetimeIndex, pd.Timestamp)
s0rlf, s1rlf = pd.cut(s1, bins=10, labels=False, retbins=True)
check(assert_type(s0rlf, pd.Series), pd.Series, np.integer)
check(assert_type(s1rlf, pd.DatetimeIndex), pd.DatetimeIndex, pd.Timestamp)
s0rls, s1rls = pd.cut(s1, bins=4, labels=["1", "2", "3", "4"], retbins=True)
check(assert_type(s0rls, pd.Series), pd.Series, str)
check(assert_type(s1rls, pd.DatetimeIndex), pd.DatetimeIndex, pd.Timestamp)
def test_qcut() -> None:
val_list = [random.random() for _ in range(20)]
val_arr = np.array(val_list)
val_series = pd.Series(val_list)
val_idx = pd.Index(val_list)
check(
assert_type(
pd.qcut(val_list, 4, precision=2, duplicates="raise"), pd.Categorical
),
pd.Categorical,
)
check(
assert_type(
pd.qcut(val_arr, 4, precision=2, duplicates="drop"), pd.Categorical
),
pd.Categorical,
)
check(
assert_type(
pd.qcut(val_idx, 4, precision=2, duplicates="drop"), pd.Categorical
),
pd.Categorical,
)
check(
assert_type(pd.qcut(val_series, 4, precision=2, duplicates="raise"), pd.Series),
pd.Series,
)
a0, a1 = pd.qcut(val_list, 4, retbins=True)
b0, b1 = pd.qcut(val_arr, 4, retbins=True)
c0, c1 = pd.qcut(val_idx, 4, retbins=True)
d0, d1 = pd.qcut(val_series, 4, retbins=True)
check(assert_type(a0, pd.Categorical), pd.Categorical)
check(assert_type(b0, pd.Categorical), pd.Categorical)
check(assert_type(c0, pd.Categorical), pd.Categorical)
check(assert_type(d0, pd.Series), pd.Series)
check(assert_type(a1, npt.NDArray[np.float_]), np.ndarray)
check(assert_type(b1, npt.NDArray[np.float_]), np.ndarray)
check(assert_type(c1, npt.NDArray[np.float_]), np.ndarray)
check(assert_type(d1, npt.NDArray[np.float_]), np.ndarray)
e0, e1 = pd.qcut(val_list, [0.25, 0.5, 0.75], retbins=True)