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test_min_max.py
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test_min_max.py
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import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
from pandas.core.api import Int64Index
def test_max_min_non_numeric():
# #2700
aa = DataFrame({"nn": [11, 11, 22, 22], "ii": [1, 2, 3, 4], "ss": 4 * ["mama"]})
result = aa.groupby("nn").max()
assert "ss" in result
result = aa.groupby("nn").max(numeric_only=False)
assert "ss" in result
result = aa.groupby("nn").min()
assert "ss" in result
result = aa.groupby("nn").min(numeric_only=False)
assert "ss" in result
def test_max_min_object_multiple_columns(using_array_manager):
# GH#41111 case where the aggregation is valid for some columns but not
# others; we split object blocks column-wise, consistent with
# DataFrame._reduce
df = DataFrame(
{
"A": [1, 1, 2, 2, 3],
"B": [1, "foo", 2, "bar", False],
"C": ["a", "b", "c", "d", "e"],
}
)
df._consolidate_inplace() # should already be consolidate, but double-check
if not using_array_manager:
assert len(df._mgr.blocks) == 2
gb = df.groupby("A")
with pytest.raises(TypeError, match="not supported between instances"):
gb.max(numeric_only=False)
result = gb[["C"]].max()
# "max" is valid for column "C" but not for "B"
ei = Index([1, 2, 3], name="A")
expected = DataFrame({"C": ["b", "d", "e"]}, index=ei)
tm.assert_frame_equal(result, expected)
with pytest.raises(TypeError, match="not supported between instances"):
gb.max(numeric_only=False)
result = gb[["C"]].min()
# "min" is valid for column "C" but not for "B"
ei = Index([1, 2, 3], name="A")
expected = DataFrame({"C": ["a", "c", "e"]}, index=ei)
tm.assert_frame_equal(result, expected)
def test_min_date_with_nans():
# GH26321
dates = pd.to_datetime(
Series(["2019-05-09", "2019-05-09", "2019-05-09"]), format="%Y-%m-%d"
).dt.date
df = DataFrame({"a": [np.nan, "1", np.nan], "b": [0, 1, 1], "c": dates})
result = df.groupby("b", as_index=False)["c"].min()["c"]
expected = pd.to_datetime(
Series(["2019-05-09", "2019-05-09"], name="c"), format="%Y-%m-%d"
).dt.date
tm.assert_series_equal(result, expected)
result = df.groupby("b")["c"].min()
expected.index.name = "b"
tm.assert_series_equal(result, expected)
def test_max_inat():
# GH#40767 dont interpret iNaT as NaN
ser = Series([1, iNaT])
key = np.array([1, 1], dtype=np.int64)
gb = ser.groupby(key)
result = gb.max(min_count=2)
expected = Series({1: 1}, dtype=np.int64)
tm.assert_series_equal(result, expected, check_exact=True)
result = gb.min(min_count=2)
expected = Series({1: iNaT}, dtype=np.int64)
tm.assert_series_equal(result, expected, check_exact=True)
# not enough entries -> gets masked to NaN
result = gb.min(min_count=3)
expected = Series({1: np.nan})
tm.assert_series_equal(result, expected, check_exact=True)
def test_max_inat_not_all_na():
# GH#40767 dont interpret iNaT as NaN
# make sure we dont round iNaT+1 to iNaT
ser = Series([1, iNaT, 2, iNaT + 1])
gb = ser.groupby([1, 2, 3, 3])
result = gb.min(min_count=2)
# Note: in converting to float64, the iNaT + 1 maps to iNaT, i.e. is lossy
expected = Series({1: np.nan, 2: np.nan, 3: iNaT + 1})
expected.index = expected.index.astype(np.int_)
tm.assert_series_equal(result, expected, check_exact=True)
@pytest.mark.parametrize("func", ["min", "max"])
def test_groupby_aggregate_period_column(func):
# GH 31471
groups = [1, 2]
periods = pd.period_range("2020", periods=2, freq="Y")
df = DataFrame({"a": groups, "b": periods})
result = getattr(df.groupby("a")["b"], func)()
idx = Int64Index([1, 2], name="a")
expected = Series(periods, index=idx, name="b")
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("func", ["min", "max"])
def test_groupby_aggregate_period_frame(func):
# GH 31471
groups = [1, 2]
periods = pd.period_range("2020", periods=2, freq="Y")
df = DataFrame({"a": groups, "b": periods})
result = getattr(df.groupby("a"), func)()
idx = Int64Index([1, 2], name="a")
expected = DataFrame({"b": periods}, index=idx)
tm.assert_frame_equal(result, expected)
def test_aggregate_numeric_object_dtype():
# https://github.com/pandas-dev/pandas/issues/39329
# simplified case: multiple object columns where one is all-NaN
# -> gets split as the all-NaN is inferred as float
df = DataFrame(
{"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": [np.nan] * 4},
).astype(object)
result = df.groupby("key").min()
expected = DataFrame(
{"key": ["A", "B"], "col1": ["a", "c"], "col2": [np.nan, np.nan]}
).set_index("key")
tm.assert_frame_equal(result, expected)
# same but with numbers
df = DataFrame(
{"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": range(4)},
).astype(object)
result = df.groupby("key").min()
expected = DataFrame(
{"key": ["A", "B"], "col1": ["a", "c"], "col2": [0, 2]}
).set_index("key")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("func", ["min", "max"])
def test_aggregate_categorical_lost_index(func: str):
# GH: 28641 groupby drops index, when grouping over categorical column with min/max
ds = Series(["b"], dtype="category").cat.as_ordered()
df = DataFrame({"A": [1997], "B": ds})
result = df.groupby("A").agg({"B": func})
expected = DataFrame({"B": ["b"]}, index=Index([1997], name="A"))
# ordered categorical dtype should be preserved
expected["B"] = expected["B"].astype(ds.dtype)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Float64", "Float32", "boolean"])
def test_groupby_min_max_nullable(dtype):
if dtype == "Int64":
# GH#41743 avoid precision loss
ts = 1618556707013635762
elif dtype == "boolean":
ts = 0
else:
ts = 4.0
df = DataFrame({"id": [2, 2], "ts": [ts, ts + 1]})
df["ts"] = df["ts"].astype(dtype)
gb = df.groupby("id")
result = gb.min()
expected = df.iloc[:1].set_index("id")
tm.assert_frame_equal(result, expected)
res_max = gb.max()
expected_max = df.iloc[1:].set_index("id")
tm.assert_frame_equal(res_max, expected_max)
result2 = gb.min(min_count=3)
expected2 = DataFrame({"ts": [pd.NA]}, index=expected.index, dtype=dtype)
tm.assert_frame_equal(result2, expected2)
res_max2 = gb.max(min_count=3)
tm.assert_frame_equal(res_max2, expected2)
# Case with NA values
df2 = DataFrame({"id": [2, 2, 2], "ts": [ts, pd.NA, ts + 1]})
df2["ts"] = df2["ts"].astype(dtype)
gb2 = df2.groupby("id")
result3 = gb2.min()
tm.assert_frame_equal(result3, expected)
res_max3 = gb2.max()
tm.assert_frame_equal(res_max3, expected_max)
result4 = gb2.min(min_count=100)
tm.assert_frame_equal(result4, expected2)
res_max4 = gb2.max(min_count=100)
tm.assert_frame_equal(res_max4, expected2)
def test_min_max_nullable_uint64_empty_group():
# don't raise NotImplementedError from libgroupby
cat = pd.Categorical([0] * 10, categories=[0, 1])
df = DataFrame({"A": cat, "B": pd.array(np.arange(10, dtype=np.uint64))})
gb = df.groupby("A")
res = gb.min()
idx = pd.CategoricalIndex([0, 1], dtype=cat.dtype, name="A")
expected = DataFrame({"B": pd.array([0, pd.NA], dtype="UInt64")}, index=idx)
tm.assert_frame_equal(res, expected)
res = gb.max()
expected.iloc[0, 0] = 9
tm.assert_frame_equal(res, expected)