mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-05 04:55:06 +01:00
490 lines
18 KiB
Python
490 lines
18 KiB
Python
import operator
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.common import is_bool_dtype
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
from pandas.core.sorting import nargsort
|
|
|
|
from .base import BaseExtensionTests
|
|
|
|
|
|
class BaseMethodsTests(BaseExtensionTests):
|
|
"""Various Series and DataFrame methods."""
|
|
|
|
@pytest.mark.parametrize("dropna", [True, False])
|
|
def test_value_counts(self, all_data, dropna):
|
|
all_data = all_data[:10]
|
|
if dropna:
|
|
other = np.array(all_data[~all_data.isna()])
|
|
else:
|
|
other = all_data
|
|
|
|
result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
|
|
expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
|
|
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_value_counts_with_normalize(self, data):
|
|
# GH 33172
|
|
data = data[:10].unique()
|
|
values = np.array(data[~data.isna()])
|
|
|
|
result = (
|
|
pd.Series(data, dtype=data.dtype).value_counts(normalize=True).sort_index()
|
|
)
|
|
|
|
expected = pd.Series([1 / len(values)] * len(values), index=result.index)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_count(self, data_missing):
|
|
df = pd.DataFrame({"A": data_missing})
|
|
result = df.count(axis="columns")
|
|
expected = pd.Series([0, 1])
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_series_count(self, data_missing):
|
|
# GH#26835
|
|
ser = pd.Series(data_missing)
|
|
result = ser.count()
|
|
expected = 1
|
|
assert result == expected
|
|
|
|
def test_apply_simple_series(self, data):
|
|
result = pd.Series(data).apply(id)
|
|
assert isinstance(result, pd.Series)
|
|
|
|
def test_argsort(self, data_for_sorting):
|
|
result = pd.Series(data_for_sorting).argsort()
|
|
expected = pd.Series(np.array([2, 0, 1], dtype=np.int64))
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_argsort_missing_array(self, data_missing_for_sorting):
|
|
result = data_missing_for_sorting.argsort()
|
|
expected = np.array([2, 0, 1], dtype=np.dtype("int"))
|
|
# we don't care whether it's int32 or int64
|
|
result = result.astype("int64", casting="safe")
|
|
expected = expected.astype("int64", casting="safe")
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_argsort_missing(self, data_missing_for_sorting):
|
|
result = pd.Series(data_missing_for_sorting).argsort()
|
|
expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
|
|
# GH 24382
|
|
|
|
# data_for_sorting -> [B, C, A] with A < B < C
|
|
assert data_for_sorting.argmax() == 1
|
|
assert data_for_sorting.argmin() == 2
|
|
|
|
# with repeated values -> first occurence
|
|
data = data_for_sorting.take([2, 0, 0, 1, 1, 2])
|
|
assert data.argmax() == 3
|
|
assert data.argmin() == 0
|
|
|
|
# with missing values
|
|
# data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
|
|
assert data_missing_for_sorting.argmax() == 0
|
|
assert data_missing_for_sorting.argmin() == 2
|
|
|
|
@pytest.mark.parametrize(
|
|
"method", ["argmax", "argmin"],
|
|
)
|
|
def test_argmin_argmax_empty_array(self, method, data):
|
|
# GH 24382
|
|
err_msg = "attempt to get"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
getattr(data[:0], method)()
|
|
|
|
@pytest.mark.parametrize(
|
|
"method", ["argmax", "argmin"],
|
|
)
|
|
def test_argmin_argmax_all_na(self, method, data, na_value):
|
|
# all missing with skipna=True is the same as emtpy
|
|
err_msg = "attempt to get"
|
|
data_na = type(data)._from_sequence([na_value, na_value], dtype=data.dtype)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
getattr(data_na, method)()
|
|
|
|
@pytest.mark.parametrize(
|
|
"na_position, expected",
|
|
[
|
|
("last", np.array([2, 0, 1], dtype=np.dtype("intp"))),
|
|
("first", np.array([1, 2, 0], dtype=np.dtype("intp"))),
|
|
],
|
|
)
|
|
def test_nargsort(self, data_missing_for_sorting, na_position, expected):
|
|
# GH 25439
|
|
result = nargsort(data_missing_for_sorting, na_position=na_position)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
|
def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
|
|
ser = pd.Series(data_for_sorting)
|
|
result = ser.sort_values(ascending=ascending, key=sort_by_key)
|
|
expected = ser.iloc[[2, 0, 1]]
|
|
if not ascending:
|
|
expected = expected[::-1]
|
|
|
|
self.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
|
def test_sort_values_missing(
|
|
self, data_missing_for_sorting, ascending, sort_by_key
|
|
):
|
|
ser = pd.Series(data_missing_for_sorting)
|
|
result = ser.sort_values(ascending=ascending, key=sort_by_key)
|
|
if ascending:
|
|
expected = ser.iloc[[2, 0, 1]]
|
|
else:
|
|
expected = ser.iloc[[0, 2, 1]]
|
|
self.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
|
def test_sort_values_frame(self, data_for_sorting, ascending):
|
|
df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
|
|
result = df.sort_values(["A", "B"])
|
|
expected = pd.DataFrame(
|
|
{"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
|
|
)
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("box", [pd.Series, lambda x: x])
|
|
@pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
|
|
def test_unique(self, data, box, method):
|
|
duplicated = box(data._from_sequence([data[0], data[0]]))
|
|
|
|
result = method(duplicated)
|
|
|
|
assert len(result) == 1
|
|
assert isinstance(result, type(data))
|
|
assert result[0] == duplicated[0]
|
|
|
|
@pytest.mark.parametrize("na_sentinel", [-1, -2])
|
|
def test_factorize(self, data_for_grouping, na_sentinel):
|
|
codes, uniques = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
|
|
expected_codes = np.array(
|
|
[0, 0, na_sentinel, na_sentinel, 1, 1, 0, 2], dtype=np.intp
|
|
)
|
|
expected_uniques = data_for_grouping.take([0, 4, 7])
|
|
|
|
tm.assert_numpy_array_equal(codes, expected_codes)
|
|
self.assert_extension_array_equal(uniques, expected_uniques)
|
|
|
|
@pytest.mark.parametrize("na_sentinel", [-1, -2])
|
|
def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
|
|
codes_1, uniques_1 = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
|
|
codes_2, uniques_2 = data_for_grouping.factorize(na_sentinel=na_sentinel)
|
|
|
|
tm.assert_numpy_array_equal(codes_1, codes_2)
|
|
self.assert_extension_array_equal(uniques_1, uniques_2)
|
|
assert len(uniques_1) == len(pd.unique(uniques_1))
|
|
assert uniques_1.dtype == data_for_grouping.dtype
|
|
|
|
def test_factorize_empty(self, data):
|
|
codes, uniques = pd.factorize(data[:0])
|
|
expected_codes = np.array([], dtype=np.intp)
|
|
expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
|
|
|
|
tm.assert_numpy_array_equal(codes, expected_codes)
|
|
self.assert_extension_array_equal(uniques, expected_uniques)
|
|
|
|
def test_fillna_copy_frame(self, data_missing):
|
|
arr = data_missing.take([1, 1])
|
|
df = pd.DataFrame({"A": arr})
|
|
|
|
filled_val = df.iloc[0, 0]
|
|
result = df.fillna(filled_val)
|
|
|
|
assert df.A.values is not result.A.values
|
|
|
|
def test_fillna_copy_series(self, data_missing):
|
|
arr = data_missing.take([1, 1])
|
|
ser = pd.Series(arr)
|
|
|
|
filled_val = ser[0]
|
|
result = ser.fillna(filled_val)
|
|
|
|
assert ser._values is not result._values
|
|
assert ser._values is arr
|
|
|
|
def test_fillna_length_mismatch(self, data_missing):
|
|
msg = "Length of 'value' does not match."
|
|
with pytest.raises(ValueError, match=msg):
|
|
data_missing.fillna(data_missing.take([1]))
|
|
|
|
def test_combine_le(self, data_repeated):
|
|
# GH 20825
|
|
# Test that combine works when doing a <= (le) comparison
|
|
orig_data1, orig_data2 = data_repeated(2)
|
|
s1 = pd.Series(orig_data1)
|
|
s2 = pd.Series(orig_data2)
|
|
result = s1.combine(s2, lambda x1, x2: x1 <= x2)
|
|
expected = pd.Series(
|
|
[a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))]
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
val = s1.iloc[0]
|
|
result = s1.combine(val, lambda x1, x2: x1 <= x2)
|
|
expected = pd.Series([a <= val for a in list(orig_data1)])
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_combine_add(self, data_repeated):
|
|
# GH 20825
|
|
orig_data1, orig_data2 = data_repeated(2)
|
|
s1 = pd.Series(orig_data1)
|
|
s2 = pd.Series(orig_data2)
|
|
result = s1.combine(s2, lambda x1, x2: x1 + x2)
|
|
with np.errstate(over="ignore"):
|
|
expected = pd.Series(
|
|
orig_data1._from_sequence(
|
|
[a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
|
|
)
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
val = s1.iloc[0]
|
|
result = s1.combine(val, lambda x1, x2: x1 + x2)
|
|
expected = pd.Series(
|
|
orig_data1._from_sequence([a + val for a in list(orig_data1)])
|
|
)
|
|
self.assert_series_equal(result, expected)
|
|
|
|
def test_combine_first(self, data):
|
|
# https://github.com/pandas-dev/pandas/issues/24147
|
|
a = pd.Series(data[:3])
|
|
b = pd.Series(data[2:5], index=[2, 3, 4])
|
|
result = a.combine_first(b)
|
|
expected = pd.Series(data[:5])
|
|
self.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("frame", [True, False])
|
|
@pytest.mark.parametrize(
|
|
"periods, indices",
|
|
[(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
|
|
)
|
|
def test_container_shift(self, data, frame, periods, indices):
|
|
# https://github.com/pandas-dev/pandas/issues/22386
|
|
subset = data[:5]
|
|
data = pd.Series(subset, name="A")
|
|
expected = pd.Series(subset.take(indices, allow_fill=True), name="A")
|
|
|
|
if frame:
|
|
result = data.to_frame(name="A").assign(B=1).shift(periods)
|
|
expected = pd.concat(
|
|
[expected, pd.Series([1] * 5, name="B").shift(periods)], axis=1
|
|
)
|
|
compare = self.assert_frame_equal
|
|
else:
|
|
result = data.shift(periods)
|
|
compare = self.assert_series_equal
|
|
|
|
compare(result, expected)
|
|
|
|
def test_shift_0_periods(self, data):
|
|
# GH#33856 shifting with periods=0 should return a copy, not same obj
|
|
result = data.shift(0)
|
|
assert data[0] != data[1] # otherwise below is invalid
|
|
data[0] = data[1]
|
|
assert result[0] != result[1] # i.e. not the same object/view
|
|
|
|
@pytest.mark.parametrize("periods", [1, -2])
|
|
def test_diff(self, data, periods):
|
|
data = data[:5]
|
|
if is_bool_dtype(data.dtype):
|
|
op = operator.xor
|
|
else:
|
|
op = operator.sub
|
|
try:
|
|
# does this array implement ops?
|
|
op(data, data)
|
|
except Exception:
|
|
pytest.skip(f"{type(data)} does not support diff")
|
|
s = pd.Series(data)
|
|
result = s.diff(periods)
|
|
expected = pd.Series(op(data, data.shift(periods)))
|
|
self.assert_series_equal(result, expected)
|
|
|
|
df = pd.DataFrame({"A": data, "B": [1.0] * 5})
|
|
result = df.diff(periods)
|
|
if periods == 1:
|
|
b = [np.nan, 0, 0, 0, 0]
|
|
else:
|
|
b = [0, 0, 0, np.nan, np.nan]
|
|
expected = pd.DataFrame({"A": expected, "B": b})
|
|
self.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"periods, indices",
|
|
[[-4, [-1, -1]], [-1, [1, -1]], [0, [0, 1]], [1, [-1, 0]], [4, [-1, -1]]],
|
|
)
|
|
def test_shift_non_empty_array(self, data, periods, indices):
|
|
# https://github.com/pandas-dev/pandas/issues/23911
|
|
subset = data[:2]
|
|
result = subset.shift(periods)
|
|
expected = subset.take(indices, allow_fill=True)
|
|
self.assert_extension_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("periods", [-4, -1, 0, 1, 4])
|
|
def test_shift_empty_array(self, data, periods):
|
|
# https://github.com/pandas-dev/pandas/issues/23911
|
|
empty = data[:0]
|
|
result = empty.shift(periods)
|
|
expected = empty
|
|
self.assert_extension_array_equal(result, expected)
|
|
|
|
def test_shift_zero_copies(self, data):
|
|
result = data.shift(0)
|
|
assert result is not data
|
|
|
|
result = data[:0].shift(2)
|
|
assert result is not data
|
|
|
|
def test_shift_fill_value(self, data):
|
|
arr = data[:4]
|
|
fill_value = data[0]
|
|
result = arr.shift(1, fill_value=fill_value)
|
|
expected = data.take([0, 0, 1, 2])
|
|
self.assert_extension_array_equal(result, expected)
|
|
|
|
result = arr.shift(-2, fill_value=fill_value)
|
|
expected = data.take([2, 3, 0, 0])
|
|
self.assert_extension_array_equal(result, expected)
|
|
|
|
def test_not_hashable(self, data):
|
|
# We are in general mutable, so not hashable
|
|
with pytest.raises(TypeError, match="unhashable type"):
|
|
hash(data)
|
|
|
|
def test_hash_pandas_object_works(self, data, as_frame):
|
|
# https://github.com/pandas-dev/pandas/issues/23066
|
|
data = pd.Series(data)
|
|
if as_frame:
|
|
data = data.to_frame()
|
|
a = pd.util.hash_pandas_object(data)
|
|
b = pd.util.hash_pandas_object(data)
|
|
self.assert_equal(a, b)
|
|
|
|
def test_searchsorted(self, data_for_sorting, as_series):
|
|
b, c, a = data_for_sorting
|
|
arr = type(data_for_sorting)._from_sequence([a, b, c])
|
|
|
|
if as_series:
|
|
arr = pd.Series(arr)
|
|
assert arr.searchsorted(a) == 0
|
|
assert arr.searchsorted(a, side="right") == 1
|
|
|
|
assert arr.searchsorted(b) == 1
|
|
assert arr.searchsorted(b, side="right") == 2
|
|
|
|
assert arr.searchsorted(c) == 2
|
|
assert arr.searchsorted(c, side="right") == 3
|
|
|
|
result = arr.searchsorted(arr.take([0, 2]))
|
|
expected = np.array([0, 2], dtype=np.intp)
|
|
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
# sorter
|
|
sorter = np.array([1, 2, 0])
|
|
assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
|
|
|
|
def test_where_series(self, data, na_value, as_frame):
|
|
assert data[0] != data[1]
|
|
cls = type(data)
|
|
a, b = data[:2]
|
|
|
|
ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
|
|
cond = np.array([True, True, False, False])
|
|
|
|
if as_frame:
|
|
ser = ser.to_frame(name="a")
|
|
cond = cond.reshape(-1, 1)
|
|
|
|
result = ser.where(cond)
|
|
expected = pd.Series(
|
|
cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
|
|
)
|
|
|
|
if as_frame:
|
|
expected = expected.to_frame(name="a")
|
|
self.assert_equal(result, expected)
|
|
|
|
# array other
|
|
cond = np.array([True, False, True, True])
|
|
other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
|
|
if as_frame:
|
|
other = pd.DataFrame({"a": other})
|
|
cond = pd.DataFrame({"a": cond})
|
|
result = ser.where(cond, other)
|
|
expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
|
|
if as_frame:
|
|
expected = expected.to_frame(name="a")
|
|
self.assert_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
|
|
def test_repeat(self, data, repeats, as_series, use_numpy):
|
|
arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
|
|
if as_series:
|
|
arr = pd.Series(arr)
|
|
|
|
result = np.repeat(arr, repeats) if use_numpy else arr.repeat(repeats)
|
|
|
|
repeats = [repeats] * 3 if isinstance(repeats, int) else repeats
|
|
expected = [x for x, n in zip(arr, repeats) for _ in range(n)]
|
|
expected = type(data)._from_sequence(expected, dtype=data.dtype)
|
|
if as_series:
|
|
expected = pd.Series(expected, index=arr.index.repeat(repeats))
|
|
|
|
self.assert_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"repeats, kwargs, error, msg",
|
|
[
|
|
(2, dict(axis=1), ValueError, "'axis"),
|
|
(-1, dict(), ValueError, "negative"),
|
|
([1, 2], dict(), ValueError, "shape"),
|
|
(2, dict(foo="bar"), TypeError, "'foo'"),
|
|
],
|
|
)
|
|
def test_repeat_raises(self, data, repeats, kwargs, error, msg, use_numpy):
|
|
with pytest.raises(error, match=msg):
|
|
if use_numpy:
|
|
np.repeat(data, repeats, **kwargs)
|
|
else:
|
|
data.repeat(repeats, **kwargs)
|
|
|
|
@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
|
|
def test_equals(self, data, na_value, as_series, box):
|
|
data2 = type(data)._from_sequence([data[0]] * len(data), dtype=data.dtype)
|
|
data_na = type(data)._from_sequence([na_value] * len(data), dtype=data.dtype)
|
|
|
|
data = tm.box_expected(data, box, transpose=False)
|
|
data2 = tm.box_expected(data2, box, transpose=False)
|
|
data_na = tm.box_expected(data_na, box, transpose=False)
|
|
|
|
# we are asserting with `is True/False` explicitly, to test that the
|
|
# result is an actual Python bool, and not something "truthy"
|
|
|
|
assert data.equals(data) is True
|
|
assert data.equals(data.copy()) is True
|
|
|
|
# unequal other data
|
|
assert data.equals(data2) is False
|
|
assert data.equals(data_na) is False
|
|
|
|
# different length
|
|
assert data[:2].equals(data[:3]) is False
|
|
|
|
# emtpy are equal
|
|
assert data[:0].equals(data[:0]) is True
|
|
|
|
# other types
|
|
assert data.equals(None) is False
|
|
assert data[[0]].equals(data[0]) is False
|