90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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)
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import pandas._testing as tm
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@pytest.mark.parametrize("func", ["ffill", "bfill"])
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def test_groupby_column_index_name_lost_fill_funcs(func):
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# GH: 29764 groupby loses index sometimes
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df = DataFrame(
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[[1, 1.0, -1.0], [1, np.nan, np.nan], [1, 2.0, -2.0]],
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columns=Index(["type", "a", "b"], name="idx"),
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)
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df_grouped = df.groupby(["type"])[["a", "b"]]
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result = getattr(df_grouped, func)().columns
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expected = Index(["a", "b"], name="idx")
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize("func", ["ffill", "bfill"])
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def test_groupby_fill_duplicate_column_names(func):
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# GH: 25610 ValueError with duplicate column names
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df1 = DataFrame({"field1": [1, 3, 4], "field2": [1, 3, 4]})
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df2 = DataFrame({"field1": [1, np.nan, 4]})
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df_grouped = pd.concat([df1, df2], axis=1).groupby(by=["field2"])
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expected = DataFrame(
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[[1, 1.0], [3, np.nan], [4, 4.0]], columns=["field1", "field1"]
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)
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result = getattr(df_grouped, func)()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("method", ["ffill", "bfill"])
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@pytest.mark.parametrize("has_nan_group", [True, False])
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def test_ffill_handles_nan_groups(dropna, method, has_nan_group):
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# GH 34725
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df_without_nan_rows = DataFrame([(1, 0.1), (2, 0.2)])
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ridx = [-1, 0, -1, -1, 1, -1]
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df = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
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group_b = np.nan if has_nan_group else "b"
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df["group_col"] = pd.Series(["a"] * 3 + [group_b] * 3)
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grouped = df.groupby(by="group_col", dropna=dropna)
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result = getattr(grouped, method)(limit=None)
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expected_rows = {
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("ffill", True, True): [-1, 0, 0, -1, -1, -1],
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("ffill", True, False): [-1, 0, 0, -1, 1, 1],
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("ffill", False, True): [-1, 0, 0, -1, 1, 1],
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("ffill", False, False): [-1, 0, 0, -1, 1, 1],
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("bfill", True, True): [0, 0, -1, -1, -1, -1],
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("bfill", True, False): [0, 0, -1, 1, 1, -1],
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("bfill", False, True): [0, 0, -1, 1, 1, -1],
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("bfill", False, False): [0, 0, -1, 1, 1, -1],
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}
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ridx = expected_rows.get((method, dropna, has_nan_group))
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expected = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
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# columns are a 'take' on df.columns, which are object dtype
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expected.columns = expected.columns.astype(object)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("min_count, value", [(2, np.nan), (-1, 1.0)])
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@pytest.mark.parametrize("func", ["first", "last", "max", "min"])
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def test_min_count(func, min_count, value):
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# GH#37821
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df = DataFrame({"a": [1] * 3, "b": [1, np.nan, np.nan], "c": [np.nan] * 3})
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result = getattr(df.groupby("a"), func)(min_count=min_count)
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expected = DataFrame({"b": [value], "c": [np.nan]}, index=Index([1], name="a"))
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tm.assert_frame_equal(result, expected)
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def test_indices_with_missing():
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# GH 9304
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df = DataFrame({"a": [1, 1, np.nan], "b": [2, 3, 4], "c": [5, 6, 7]})
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g = df.groupby(["a", "b"])
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result = g.indices
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expected = {(1.0, 2): np.array([0]), (1.0, 3): np.array([1])}
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assert result == expected
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