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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]})
| 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")
| 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,
| ]
|
| expected = df.groupby(level=0, axis=0).fillna(method="ffill")
| 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
|
|
