You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
393 lines
11 KiB
393 lines
11 KiB
"""
|
|
test cython .agg behavior
|
|
"""
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.common import is_float_dtype
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
DataFrame,
|
|
Index,
|
|
NaT,
|
|
Series,
|
|
Timedelta,
|
|
Timestamp,
|
|
bdate_range,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"op_name",
|
|
[
|
|
"count",
|
|
"sum",
|
|
"std",
|
|
"var",
|
|
"sem",
|
|
"mean",
|
|
pytest.param(
|
|
"median",
|
|
# ignore mean of empty slice
|
|
# and all-NaN
|
|
marks=[pytest.mark.filterwarnings("ignore::RuntimeWarning")],
|
|
),
|
|
"prod",
|
|
"min",
|
|
"max",
|
|
],
|
|
)
|
|
def test_cythonized_aggers(op_name):
|
|
data = {
|
|
"A": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1.0, np.nan, np.nan],
|
|
"B": ["A", "B"] * 6,
|
|
"C": np.random.randn(12),
|
|
}
|
|
df = DataFrame(data)
|
|
df.loc[2:10:2, "C"] = np.nan
|
|
|
|
op = lambda x: getattr(x, op_name)()
|
|
|
|
# single column
|
|
grouped = df.drop(["B"], axis=1).groupby("A")
|
|
exp = {cat: op(group["C"]) for cat, group in grouped}
|
|
exp = DataFrame({"C": exp})
|
|
exp.index.name = "A"
|
|
result = op(grouped)
|
|
tm.assert_frame_equal(result, exp)
|
|
|
|
# multiple columns
|
|
grouped = df.groupby(["A", "B"])
|
|
expd = {}
|
|
for (cat1, cat2), group in grouped:
|
|
expd.setdefault(cat1, {})[cat2] = op(group["C"])
|
|
exp = DataFrame(expd).T.stack(dropna=False)
|
|
exp.index.names = ["A", "B"]
|
|
exp.name = "C"
|
|
|
|
result = op(grouped)["C"]
|
|
if op_name in ["sum", "prod"]:
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
|
|
def test_cython_agg_boolean():
|
|
frame = DataFrame(
|
|
{
|
|
"a": np.random.randint(0, 5, 50),
|
|
"b": np.random.randint(0, 2, 50).astype("bool"),
|
|
}
|
|
)
|
|
result = frame.groupby("a")["b"].mean()
|
|
expected = frame.groupby("a")["b"].agg(np.mean)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_cython_agg_nothing_to_agg():
|
|
frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
|
|
|
|
with pytest.raises(NotImplementedError, match="does not implement"):
|
|
frame.groupby("a")["b"].mean(numeric_only=True)
|
|
|
|
with pytest.raises(TypeError, match="Could not convert (foo|bar)*"):
|
|
frame.groupby("a")["b"].mean()
|
|
|
|
frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = frame[["b"]].groupby(frame["a"]).mean()
|
|
expected = DataFrame([], index=frame["a"].sort_values().drop_duplicates())
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_cython_agg_nothing_to_agg_with_dates():
|
|
frame = DataFrame(
|
|
{
|
|
"a": np.random.randint(0, 5, 50),
|
|
"b": ["foo", "bar"] * 25,
|
|
"dates": pd.date_range("now", periods=50, freq="T"),
|
|
}
|
|
)
|
|
with pytest.raises(NotImplementedError, match="does not implement"):
|
|
frame.groupby("b").dates.mean(numeric_only=True)
|
|
|
|
|
|
def test_cython_agg_frame_columns():
|
|
# #2113
|
|
df = DataFrame({"x": [1, 2, 3], "y": [3, 4, 5]})
|
|
|
|
df.groupby(level=0, axis="columns").mean()
|
|
df.groupby(level=0, axis="columns").mean()
|
|
df.groupby(level=0, axis="columns").mean()
|
|
df.groupby(level=0, axis="columns").mean()
|
|
|
|
|
|
def test_cython_agg_return_dict():
|
|
# GH 16741
|
|
df = DataFrame(
|
|
{
|
|
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
|
|
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
|
|
"C": np.random.randn(8),
|
|
"D": np.random.randn(8),
|
|
}
|
|
)
|
|
|
|
ts = df.groupby("A")["B"].agg(lambda x: x.value_counts().to_dict())
|
|
expected = Series(
|
|
[{"two": 1, "one": 1, "three": 1}, {"two": 2, "one": 2, "three": 1}],
|
|
index=Index(["bar", "foo"], name="A"),
|
|
name="B",
|
|
)
|
|
tm.assert_series_equal(ts, expected)
|
|
|
|
|
|
def test_cython_fail_agg():
|
|
dr = bdate_range("1/1/2000", periods=50)
|
|
ts = Series(["A", "B", "C", "D", "E"] * 10, index=dr)
|
|
|
|
grouped = ts.groupby(lambda x: x.month)
|
|
summed = grouped.sum()
|
|
expected = grouped.agg(np.sum)
|
|
tm.assert_series_equal(summed, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"op, targop",
|
|
[
|
|
("mean", np.mean),
|
|
("median", np.median),
|
|
("var", np.var),
|
|
("add", np.sum),
|
|
("prod", np.prod),
|
|
("min", np.min),
|
|
("max", np.max),
|
|
("first", lambda x: x.iloc[0]),
|
|
("last", lambda x: x.iloc[-1]),
|
|
],
|
|
)
|
|
def test__cython_agg_general(op, targop):
|
|
df = DataFrame(np.random.randn(1000))
|
|
labels = np.random.randint(0, 50, size=1000).astype(float)
|
|
|
|
result = df.groupby(labels)._cython_agg_general(op, alt=None, numeric_only=True)
|
|
expected = df.groupby(labels).agg(targop)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"op, targop",
|
|
[
|
|
("mean", np.mean),
|
|
("median", lambda x: np.median(x) if len(x) > 0 else np.nan),
|
|
("var", lambda x: np.var(x, ddof=1)),
|
|
("min", np.min),
|
|
("max", np.max),
|
|
],
|
|
)
|
|
def test_cython_agg_empty_buckets(op, targop, observed):
|
|
df = DataFrame([11, 12, 13])
|
|
grps = range(0, 55, 5)
|
|
|
|
# calling _cython_agg_general directly, instead of via the user API
|
|
# which sets different values for min_count, so do that here.
|
|
g = df.groupby(pd.cut(df[0], grps), observed=observed)
|
|
result = g._cython_agg_general(op, alt=None, numeric_only=True)
|
|
|
|
g = df.groupby(pd.cut(df[0], grps), observed=observed)
|
|
expected = g.agg(lambda x: targop(x))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_cython_agg_empty_buckets_nanops(observed):
|
|
# GH-18869 can't call nanops on empty groups, so hardcode expected
|
|
# for these
|
|
df = DataFrame([11, 12, 13], columns=["a"])
|
|
grps = range(0, 25, 5)
|
|
# add / sum
|
|
result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
|
|
"add", alt=None, numeric_only=True
|
|
)
|
|
intervals = pd.interval_range(0, 20, freq=5)
|
|
expected = DataFrame(
|
|
{"a": [0, 0, 36, 0]},
|
|
index=pd.CategoricalIndex(intervals, name="a", ordered=True),
|
|
)
|
|
if observed:
|
|
expected = expected[expected.a != 0]
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# prod
|
|
result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
|
|
"prod", alt=None, numeric_only=True
|
|
)
|
|
expected = DataFrame(
|
|
{"a": [1, 1, 1716, 1]},
|
|
index=pd.CategoricalIndex(intervals, name="a", ordered=True),
|
|
)
|
|
if observed:
|
|
expected = expected[expected.a != 1]
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("op", ["first", "last", "max", "min"])
|
|
@pytest.mark.parametrize(
|
|
"data", [Timestamp("2016-10-14 21:00:44.557"), Timedelta("17088 days 21:00:44.557")]
|
|
)
|
|
def test_cython_with_timestamp_and_nat(op, data):
|
|
# https://github.com/pandas-dev/pandas/issues/19526
|
|
df = DataFrame({"a": [0, 1], "b": [data, NaT]})
|
|
index = Index([0, 1], name="a")
|
|
|
|
# We will group by a and test the cython aggregations
|
|
expected = DataFrame({"b": [data, NaT]}, index=index)
|
|
|
|
result = df.groupby("a").aggregate(op)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"agg",
|
|
[
|
|
"min",
|
|
"max",
|
|
"count",
|
|
"sum",
|
|
"prod",
|
|
"var",
|
|
"mean",
|
|
"median",
|
|
"ohlc",
|
|
"cumprod",
|
|
"cumsum",
|
|
"shift",
|
|
"any",
|
|
"all",
|
|
"quantile",
|
|
"first",
|
|
"last",
|
|
"rank",
|
|
"cummin",
|
|
"cummax",
|
|
],
|
|
)
|
|
def test_read_only_buffer_source_agg(agg):
|
|
# https://github.com/pandas-dev/pandas/issues/36014
|
|
df = DataFrame(
|
|
{
|
|
"sepal_length": [5.1, 4.9, 4.7, 4.6, 5.0],
|
|
"species": ["setosa", "setosa", "setosa", "setosa", "setosa"],
|
|
}
|
|
)
|
|
df._mgr.arrays[0].flags.writeable = False
|
|
|
|
result = df.groupby(["species"]).agg({"sepal_length": agg})
|
|
expected = df.copy().groupby(["species"]).agg({"sepal_length": agg})
|
|
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"op_name",
|
|
[
|
|
"count",
|
|
"sum",
|
|
"std",
|
|
"var",
|
|
"sem",
|
|
"mean",
|
|
"median",
|
|
"prod",
|
|
"min",
|
|
"max",
|
|
],
|
|
)
|
|
def test_cython_agg_nullable_int(op_name):
|
|
# ensure that the cython-based aggregations don't fail for nullable dtype
|
|
# (eg https://github.com/pandas-dev/pandas/issues/37415)
|
|
df = DataFrame(
|
|
{
|
|
"A": ["A", "B"] * 5,
|
|
"B": pd.array([1, 2, 3, 4, 5, 6, 7, 8, 9, pd.NA], dtype="Int64"),
|
|
}
|
|
)
|
|
result = getattr(df.groupby("A")["B"], op_name)()
|
|
df2 = df.assign(B=df["B"].astype("float64"))
|
|
expected = getattr(df2.groupby("A")["B"], op_name)()
|
|
|
|
if op_name != "count":
|
|
# the result is not yet consistently using Int64/Float64 dtype,
|
|
# so for now just checking the values by casting to float
|
|
result = result.astype("float64")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("with_na", [True, False])
|
|
@pytest.mark.parametrize(
|
|
"op_name, action",
|
|
[
|
|
# ("count", "always_int"),
|
|
("sum", "large_int"),
|
|
# ("std", "always_float"),
|
|
("var", "always_float"),
|
|
# ("sem", "always_float"),
|
|
("mean", "always_float"),
|
|
("median", "always_float"),
|
|
("prod", "large_int"),
|
|
("min", "preserve"),
|
|
("max", "preserve"),
|
|
("first", "preserve"),
|
|
("last", "preserve"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"data",
|
|
[
|
|
pd.array([1, 2, 3, 4], dtype="Int64"),
|
|
pd.array([1, 2, 3, 4], dtype="Int8"),
|
|
pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float32"),
|
|
pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64"),
|
|
pd.array([True, True, False, False], dtype="boolean"),
|
|
],
|
|
)
|
|
def test_cython_agg_EA_known_dtypes(data, op_name, action, with_na):
|
|
if with_na:
|
|
data[3] = pd.NA
|
|
|
|
df = DataFrame({"key": ["a", "a", "b", "b"], "col": data})
|
|
grouped = df.groupby("key")
|
|
|
|
if action == "always_int":
|
|
# always Int64
|
|
expected_dtype = pd.Int64Dtype()
|
|
elif action == "large_int":
|
|
# for any int/bool use Int64, for float preserve dtype
|
|
if is_float_dtype(data.dtype):
|
|
expected_dtype = data.dtype
|
|
else:
|
|
expected_dtype = pd.Int64Dtype()
|
|
elif action == "always_float":
|
|
# for any int/bool use Float64, for float preserve dtype
|
|
if is_float_dtype(data.dtype):
|
|
expected_dtype = data.dtype
|
|
else:
|
|
expected_dtype = pd.Float64Dtype()
|
|
elif action == "preserve":
|
|
expected_dtype = data.dtype
|
|
|
|
result = getattr(grouped, op_name)()
|
|
assert result["col"].dtype == expected_dtype
|
|
|
|
result = grouped.aggregate(op_name)
|
|
assert result["col"].dtype == expected_dtype
|
|
|
|
result = getattr(grouped["col"], op_name)()
|
|
assert result.dtype == expected_dtype
|
|
|
|
result = grouped["col"].aggregate(op_name)
|
|
assert result.dtype == expected_dtype
|
|
|