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import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
date_range,
)
import pandas._testing as tm
@pytest.mark.parametrize("func", ["ffill", "bfill"])
def test_groupby_column_index_name_lost_fill_funcs(func):
# GH: 29764 groupby loses index sometimes
df = DataFrame(
[[1, 1.0, -1.0], [1, np.nan, np.nan], [1, 2.0, -2.0]],
columns=Index(["type", "a", "b"], name="idx"),
)
df_grouped = df.groupby(["type"])[["a", "b"]]
result = getattr(df_grouped, func)().columns
expected = Index(["a", "b"], name="idx")
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("func", ["ffill", "bfill"])
def test_groupby_fill_duplicate_column_names(func):
# GH: 25610 ValueError with duplicate column names
df1 = DataFrame({"field1": [1, 3, 4], "field2": [1, 3, 4]})
df2 = DataFrame({"field1": [1, np.nan, 4]})
df_grouped = pd.concat([df1, df2], axis=1).groupby(by=["field2"])
expected = DataFrame(
[[1, 1.0], [3, np.nan], [4, 4.0]], columns=["field1", "field1"]
)
result = getattr(df_grouped, func)()
tm.assert_frame_equal(result, expected)
def test_ffill_missing_arguments():
# GH 14955
df = DataFrame({"a": [1, 2], "b": [1, 1]})
msg = "DataFrameGroupBy.fillna is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
with pytest.raises(ValueError, match="Must specify a fill"):
df.groupby("b").fillna()
@pytest.mark.parametrize(
"method, expected", [("ffill", [None, "a", "a"]), ("bfill", ["a", "a", None])]
)
def test_fillna_with_string_dtype(method, expected):
# GH 40250
df = DataFrame({"a": pd.array([None, "a", None], dtype="string"), "b": [0, 0, 0]})
grp = df.groupby("b")
msg = "DataFrameGroupBy.fillna is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = grp.fillna(method=method)
expected = DataFrame({"a": pd.array(expected, dtype="string")})
tm.assert_frame_equal(result, expected)
def test_fill_consistency():
# GH9221
# pass thru keyword arguments to the generated wrapper
# are set if the passed kw is None (only)
df = DataFrame(
index=pd.MultiIndex.from_product(
[["value1", "value2"], date_range("2014-01-01", "2014-01-06")]
),
columns=Index(["1", "2"], name="id"),
)
df["1"] = [
np.nan,
1,
np.nan,
np.nan,
11,
np.nan,
np.nan,
2,
np.nan,
np.nan,
22,
np.nan,
]
df["2"] = [
np.nan,
3,
np.nan,
np.nan,
33,
np.nan,
np.nan,
4,
np.nan,
np.nan,
44,
np.nan,
]
msg = "The 'axis' keyword in DataFrame.groupby is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
expected = df.groupby(level=0, axis=0).fillna(method="ffill")
msg = "DataFrame.groupby with axis=1 is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df.T.groupby(level=0, axis=1).fillna(method="ffill").T
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("method", ["ffill", "bfill"])
@pytest.mark.parametrize("dropna", [True, False])
@pytest.mark.parametrize("has_nan_group", [True, False])
def test_ffill_handles_nan_groups(dropna, method, has_nan_group):
# GH 34725
df_without_nan_rows = DataFrame([(1, 0.1), (2, 0.2)])
ridx = [-1, 0, -1, -1, 1, -1]
df = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
group_b = np.nan if has_nan_group else "b"
df["group_col"] = pd.Series(["a"] * 3 + [group_b] * 3)
grouped = df.groupby(by="group_col", dropna=dropna)
result = getattr(grouped, method)(limit=None)
expected_rows = {
("ffill", True, True): [-1, 0, 0, -1, -1, -1],
("ffill", True, False): [-1, 0, 0, -1, 1, 1],
("ffill", False, True): [-1, 0, 0, -1, 1, 1],
("ffill", False, False): [-1, 0, 0, -1, 1, 1],
("bfill", True, True): [0, 0, -1, -1, -1, -1],
("bfill", True, False): [0, 0, -1, 1, 1, -1],
("bfill", False, True): [0, 0, -1, 1, 1, -1],
("bfill", False, False): [0, 0, -1, 1, 1, -1],
}
ridx = expected_rows.get((method, dropna, has_nan_group))
expected = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
# columns are a 'take' on df.columns, which are object dtype
expected.columns = expected.columns.astype(object)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("min_count, value", [(2, np.nan), (-1, 1.0)])
@pytest.mark.parametrize("func", ["first", "last", "max", "min"])
def test_min_count(func, min_count, value):
# GH#37821
df = DataFrame({"a": [1] * 3, "b": [1, np.nan, np.nan], "c": [np.nan] * 3})
result = getattr(df.groupby("a"), func)(min_count=min_count)
expected = DataFrame({"b": [value], "c": [np.nan]}, index=Index([1], name="a"))
tm.assert_frame_equal(result, expected)
def test_indices_with_missing():
# GH 9304
df = DataFrame({"a": [1, 1, np.nan], "b": [2, 3, 4], "c": [5, 6, 7]})
g = df.groupby(["a", "b"])
result = g.indices
expected = {(1.0, 2): np.array([0]), (1.0, 3): np.array([1])}
assert result == expected
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