Sindbad~EG File Manager
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas._libs.algos import (
Infinity,
NegInfinity,
)
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
class TestRank:
s = Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])
df = DataFrame({"A": s, "B": s})
results = {
"average": np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5]),
"min": np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5]),
"max": np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6]),
"first": np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6]),
"dense": np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]),
}
@pytest.fixture(params=["average", "min", "max", "first", "dense"])
def method(self, request):
"""
Fixture for trying all rank methods
"""
return request.param
def test_rank(self, float_frame):
sp_stats = pytest.importorskip("scipy.stats")
float_frame.loc[::2, "A"] = np.nan
float_frame.loc[::3, "B"] = np.nan
float_frame.loc[::4, "C"] = np.nan
float_frame.loc[::5, "D"] = np.nan
ranks0 = float_frame.rank()
ranks1 = float_frame.rank(1)
mask = np.isnan(float_frame.values)
fvals = float_frame.fillna(np.inf).values
exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fvals)
exp0[mask] = np.nan
exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fvals)
exp1[mask] = np.nan
tm.assert_almost_equal(ranks0.values, exp0)
tm.assert_almost_equal(ranks1.values, exp1)
# integers
df = DataFrame(
np.random.default_rng(2).integers(0, 5, size=40).reshape((10, 4))
)
result = df.rank()
exp = df.astype(float).rank()
tm.assert_frame_equal(result, exp)
result = df.rank(1)
exp = df.astype(float).rank(1)
tm.assert_frame_equal(result, exp)
def test_rank2(self):
df = DataFrame([[1, 3, 2], [1, 2, 3]])
expected = DataFrame([[1.0, 3.0, 2.0], [1, 2, 3]]) / 3.0
result = df.rank(1, pct=True)
tm.assert_frame_equal(result, expected)
df = DataFrame([[1, 3, 2], [1, 2, 3]])
expected = df.rank(0) / 2.0
result = df.rank(0, pct=True)
tm.assert_frame_equal(result, expected)
df = DataFrame([["b", "c", "a"], ["a", "c", "b"]])
expected = DataFrame([[2.0, 3.0, 1.0], [1, 3, 2]])
result = df.rank(1, numeric_only=False)
tm.assert_frame_equal(result, expected)
expected = DataFrame([[2.0, 1.5, 1.0], [1, 1.5, 2]])
result = df.rank(0, numeric_only=False)
tm.assert_frame_equal(result, expected)
df = DataFrame([["b", np.nan, "a"], ["a", "c", "b"]])
expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 3.0, 2.0]])
result = df.rank(1, numeric_only=False)
tm.assert_frame_equal(result, expected)
expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 1.0, 2.0]])
result = df.rank(0, numeric_only=False)
tm.assert_frame_equal(result, expected)
# f7u12, this does not work without extensive workaround
data = [
[datetime(2001, 1, 5), np.nan, datetime(2001, 1, 2)],
[datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 1)],
]
df = DataFrame(data)
# check the rank
expected = DataFrame([[2.0, np.nan, 1.0], [2.0, 3.0, 1.0]])
result = df.rank(1, numeric_only=False, ascending=True)
tm.assert_frame_equal(result, expected)
expected = DataFrame([[1.0, np.nan, 2.0], [2.0, 1.0, 3.0]])
result = df.rank(1, numeric_only=False, ascending=False)
tm.assert_frame_equal(result, expected)
df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10, 1e60, 1e80, 1e-30]})
exp = DataFrame({"a": [3.5, 1.0, 3.5, 5.0, 6.0, 7.0, 2.0]})
tm.assert_frame_equal(df.rank(), exp)
def test_rank_does_not_mutate(self):
# GH#18521
# Check rank does not mutate DataFrame
df = DataFrame(
np.random.default_rng(2).standard_normal((10, 3)), dtype="float64"
)
expected = df.copy()
df.rank()
result = df
tm.assert_frame_equal(result, expected)
def test_rank_mixed_frame(self, float_string_frame):
float_string_frame["datetime"] = datetime.now()
float_string_frame["timedelta"] = timedelta(days=1, seconds=1)
float_string_frame.rank(numeric_only=False)
with pytest.raises(TypeError, match="not supported between instances of"):
float_string_frame.rank(axis=1)
def test_rank_na_option(self, float_frame):
sp_stats = pytest.importorskip("scipy.stats")
float_frame.loc[::2, "A"] = np.nan
float_frame.loc[::3, "B"] = np.nan
float_frame.loc[::4, "C"] = np.nan
float_frame.loc[::5, "D"] = np.nan
# bottom
ranks0 = float_frame.rank(na_option="bottom")
ranks1 = float_frame.rank(1, na_option="bottom")
fvals = float_frame.fillna(np.inf).values
exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fvals)
exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fvals)
tm.assert_almost_equal(ranks0.values, exp0)
tm.assert_almost_equal(ranks1.values, exp1)
# top
ranks0 = float_frame.rank(na_option="top")
ranks1 = float_frame.rank(1, na_option="top")
fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
fval1 = float_frame.T
fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
fval1 = fval1.fillna(np.inf).values
exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fval0)
exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fval1)
tm.assert_almost_equal(ranks0.values, exp0)
tm.assert_almost_equal(ranks1.values, exp1)
# descending
# bottom
ranks0 = float_frame.rank(na_option="top", ascending=False)
ranks1 = float_frame.rank(1, na_option="top", ascending=False)
fvals = float_frame.fillna(np.inf).values
exp0 = np.apply_along_axis(sp_stats.rankdata, 0, -fvals)
exp1 = np.apply_along_axis(sp_stats.rankdata, 1, -fvals)
tm.assert_almost_equal(ranks0.values, exp0)
tm.assert_almost_equal(ranks1.values, exp1)
# descending
# top
ranks0 = float_frame.rank(na_option="bottom", ascending=False)
ranks1 = float_frame.rank(1, na_option="bottom", ascending=False)
fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
fval1 = float_frame.T
fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
fval1 = fval1.fillna(np.inf).values
exp0 = np.apply_along_axis(sp_stats.rankdata, 0, -fval0)
exp1 = np.apply_along_axis(sp_stats.rankdata, 1, -fval1)
tm.assert_numpy_array_equal(ranks0.values, exp0)
tm.assert_numpy_array_equal(ranks1.values, exp1)
# bad values throw error
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
with pytest.raises(ValueError, match=msg):
float_frame.rank(na_option="bad", ascending=False)
# invalid type
with pytest.raises(ValueError, match=msg):
float_frame.rank(na_option=True, ascending=False)
def test_rank_axis(self):
# check if using axes' names gives the same result
df = DataFrame([[2, 1], [4, 3]])
tm.assert_frame_equal(df.rank(axis=0), df.rank(axis="index"))
tm.assert_frame_equal(df.rank(axis=1), df.rank(axis="columns"))
@pytest.mark.parametrize("ax", [0, 1])
@pytest.mark.parametrize("m", ["average", "min", "max", "first", "dense"])
def test_rank_methods_frame(self, ax, m):
sp_stats = pytest.importorskip("scipy.stats")
xs = np.random.default_rng(2).integers(0, 21, (100, 26))
xs = (xs - 10.0) / 10.0
cols = [chr(ord("z") - i) for i in range(xs.shape[1])]
for vals in [xs, xs + 1e6, xs * 1e-6]:
df = DataFrame(vals, columns=cols)
result = df.rank(axis=ax, method=m)
sprank = np.apply_along_axis(
sp_stats.rankdata, ax, vals, m if m != "first" else "ordinal"
)
sprank = sprank.astype(np.float64)
expected = DataFrame(sprank, columns=cols).astype("float64")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("dtype", ["O", "f8", "i8"])
def test_rank_descending(self, method, dtype):
if "i" in dtype:
df = self.df.dropna().astype(dtype)
else:
df = self.df.astype(dtype)
res = df.rank(ascending=False)
expected = (df.max() - df).rank()
tm.assert_frame_equal(res, expected)
expected = (df.max() - df).rank(method=method)
if dtype != "O":
res2 = df.rank(method=method, ascending=False, numeric_only=True)
tm.assert_frame_equal(res2, expected)
res3 = df.rank(method=method, ascending=False, numeric_only=False)
tm.assert_frame_equal(res3, expected)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("dtype", [None, object])
def test_rank_2d_tie_methods(self, method, axis, dtype):
df = self.df
def _check2d(df, expected, method="average", axis=0):
exp_df = DataFrame({"A": expected, "B": expected})
if axis == 1:
df = df.T
exp_df = exp_df.T
result = df.rank(method=method, axis=axis)
tm.assert_frame_equal(result, exp_df)
frame = df if dtype is None else df.astype(dtype)
_check2d(frame, self.results[method], method=method, axis=axis)
@pytest.mark.parametrize(
"method,exp",
[
("dense", [[1.0, 1.0, 1.0], [1.0, 0.5, 2.0 / 3], [1.0, 0.5, 1.0 / 3]]),
(
"min",
[
[1.0 / 3, 1.0, 1.0],
[1.0 / 3, 1.0 / 3, 2.0 / 3],
[1.0 / 3, 1.0 / 3, 1.0 / 3],
],
),
(
"max",
[[1.0, 1.0, 1.0], [1.0, 2.0 / 3, 2.0 / 3], [1.0, 2.0 / 3, 1.0 / 3]],
),
(
"average",
[[2.0 / 3, 1.0, 1.0], [2.0 / 3, 0.5, 2.0 / 3], [2.0 / 3, 0.5, 1.0 / 3]],
),
(
"first",
[
[1.0 / 3, 1.0, 1.0],
[2.0 / 3, 1.0 / 3, 2.0 / 3],
[3.0 / 3, 2.0 / 3, 1.0 / 3],
],
),
],
)
def test_rank_pct_true(self, method, exp):
# see gh-15630.
df = DataFrame([[2012, 66, 3], [2012, 65, 2], [2012, 65, 1]])
result = df.rank(method=method, pct=True)
expected = DataFrame(exp)
tm.assert_frame_equal(result, expected)
@pytest.mark.single_cpu
def test_pct_max_many_rows(self):
# GH 18271
df = DataFrame(
{"A": np.arange(2**24 + 1), "B": np.arange(2**24 + 1, 0, -1)}
)
result = df.rank(pct=True).max()
assert (result == 1).all()
@pytest.mark.parametrize(
"contents,dtype",
[
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-50,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float64",
),
(
[
-np.inf,
-50,
-1,
-1e-20,
-1e-25,
-1e-45,
0,
1e-40,
1e-20,
1e-10,
2,
40,
np.inf,
],
"float32",
),
([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max], "uint8"),
(
[
np.iinfo(np.int64).min,
-100,
0,
1,
9999,
100000,
1e10,
np.iinfo(np.int64).max,
],
"int64",
),
([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"),
(
[datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 5)],
"datetime64",
),
],
)
def test_rank_inf_and_nan(self, contents, dtype, frame_or_series):
dtype_na_map = {
"float64": np.nan,
"float32": np.nan,
"object": None,
"datetime64": np.datetime64("nat"),
}
# Insert nans at random positions if underlying dtype has missing
# value. Then adjust the expected order by adding nans accordingly
# This is for testing whether rank calculation is affected
# when values are interwined with nan values.
values = np.array(contents, dtype=dtype)
exp_order = np.array(range(len(values)), dtype="float64") + 1.0
if dtype in dtype_na_map:
na_value = dtype_na_map[dtype]
nan_indices = np.random.default_rng(2).choice(range(len(values)), 5)
values = np.insert(values, nan_indices, na_value)
exp_order = np.insert(exp_order, nan_indices, np.nan)
# Shuffle the testing array and expected results in the same way
random_order = np.random.default_rng(2).permutation(len(values))
obj = frame_or_series(values[random_order])
expected = frame_or_series(exp_order[random_order], dtype="float64")
result = obj.rank()
tm.assert_equal(result, expected)
def test_df_series_inf_nan_consistency(self):
# GH#32593
index = [5, 4, 3, 2, 1, 6, 7, 8, 9, 10]
col1 = [5, 4, 3, 5, 8, 5, 2, 1, 6, 6]
col2 = [5, 4, np.nan, 5, 8, 5, np.inf, np.nan, 6, -np.inf]
df = DataFrame(
data={
"col1": col1,
"col2": col2,
},
index=index,
dtype="f8",
)
df_result = df.rank()
series_result = df.copy()
series_result["col1"] = df["col1"].rank()
series_result["col2"] = df["col2"].rank()
tm.assert_frame_equal(df_result, series_result)
def test_rank_both_inf(self):
# GH#32593
df = DataFrame({"a": [-np.inf, 0, np.inf]})
expected = DataFrame({"a": [1.0, 2.0, 3.0]})
result = df.rank()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"na_option,ascending,expected",
[
("top", True, [3.0, 1.0, 2.0]),
("top", False, [2.0, 1.0, 3.0]),
("bottom", True, [2.0, 3.0, 1.0]),
("bottom", False, [1.0, 3.0, 2.0]),
],
)
def test_rank_inf_nans_na_option(
self, frame_or_series, method, na_option, ascending, expected
):
obj = frame_or_series([np.inf, np.nan, -np.inf])
result = obj.rank(method=method, na_option=na_option, ascending=ascending)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"na_option,ascending,expected",
[
("bottom", True, [1.0, 2.0, 4.0, 3.0]),
("bottom", False, [1.0, 2.0, 4.0, 3.0]),
("top", True, [2.0, 3.0, 1.0, 4.0]),
("top", False, [2.0, 3.0, 1.0, 4.0]),
],
)
def test_rank_object_first(
self, frame_or_series, na_option, ascending, expected, using_infer_string
):
obj = frame_or_series(["foo", "foo", None, "foo"])
result = obj.rank(method="first", na_option=na_option, ascending=ascending)
expected = frame_or_series(expected)
if using_infer_string and isinstance(obj, Series):
expected = expected.astype("uint64")
tm.assert_equal(result, expected)
@pytest.mark.parametrize(
"data,expected",
[
(
{"a": [1, 2, "a"], "b": [4, 5, 6]},
DataFrame({"b": [1.0, 2.0, 3.0]}, columns=Index(["b"], dtype=object)),
),
({"a": [1, 2, "a"]}, DataFrame(index=range(3), columns=[])),
],
)
def test_rank_mixed_axis_zero(self, data, expected):
df = DataFrame(data, columns=Index(list(data.keys()), dtype=object))
with pytest.raises(TypeError, match="'<' not supported between instances of"):
df.rank()
result = df.rank(numeric_only=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"dtype, exp_dtype",
[("string[pyarrow]", "Int64"), ("string[pyarrow_numpy]", "float64")],
)
def test_rank_string_dtype(self, dtype, exp_dtype):
# GH#55362
pytest.importorskip("pyarrow")
obj = Series(["foo", "foo", None, "foo"], dtype=dtype)
result = obj.rank(method="first")
expected = Series([1, 2, None, 3], dtype=exp_dtype)
tm.assert_series_equal(result, expected)
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists