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| import numpy as np
| import pytest
|
| import pandas as pd
| from pandas import (
| DataFrame,
| Index,
| MultiIndex,
| Series,
| Timestamp,
| date_range,
| )
| import pandas._testing as tm
|
|
| def test_apply_describe_bug(multiindex_dataframe_random_data):
| grouped = multiindex_dataframe_random_data.groupby(level="first")
| grouped.describe() # it works!
|
|
| def test_series_describe_multikey():
| ts = Series(
| np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
| )
| grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
| result = grouped.describe()
| tm.assert_series_equal(result["mean"], grouped.mean(), check_names=False)
| tm.assert_series_equal(result["std"], grouped.std(), check_names=False)
| tm.assert_series_equal(result["min"], grouped.min(), check_names=False)
|
|
| def test_series_describe_single():
| ts = Series(
| np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
| )
| grouped = ts.groupby(lambda x: x.month)
| result = grouped.apply(lambda x: x.describe())
| expected = grouped.describe().stack(future_stack=True)
| tm.assert_series_equal(result, expected)
|
|
| @pytest.mark.parametrize("keys", ["key1", ["key1", "key2"]])
| def test_series_describe_as_index(as_index, keys):
| # GH#49256
| df = DataFrame(
| {
| "key1": ["one", "two", "two", "three", "two"],
| "key2": ["one", "two", "two", "three", "two"],
| "foo2": [1, 2, 4, 4, 6],
| }
| )
| gb = df.groupby(keys, as_index=as_index)["foo2"]
| result = gb.describe()
| expected = DataFrame(
| {
| "key1": ["one", "three", "two"],
| "count": [1.0, 1.0, 3.0],
| "mean": [1.0, 4.0, 4.0],
| "std": [np.nan, np.nan, 2.0],
| "min": [1.0, 4.0, 2.0],
| "25%": [1.0, 4.0, 3.0],
| "50%": [1.0, 4.0, 4.0],
| "75%": [1.0, 4.0, 5.0],
| "max": [1.0, 4.0, 6.0],
| }
| )
| if len(keys) == 2:
| expected.insert(1, "key2", expected["key1"])
| if as_index:
| expected = expected.set_index(keys)
| tm.assert_frame_equal(result, expected)
|
|
| def test_frame_describe_multikey(tsframe, using_infer_string):
| grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
| result = grouped.describe()
| desc_groups = []
| for col in tsframe:
| group = grouped[col].describe()
| # GH 17464 - Remove duplicate MultiIndex levels
| group_col = MultiIndex(
| levels=[Index([col], dtype=tsframe.columns.dtype), group.columns],
| codes=[[0] * len(group.columns), range(len(group.columns))],
| )
| group = DataFrame(group.values, columns=group_col, index=group.index)
| desc_groups.append(group)
| expected = pd.concat(desc_groups, axis=1)
| tm.assert_frame_equal(result, expected)
|
| # remainder of the tests fails with string dtype but is testing deprecated behaviour
| if using_infer_string:
| return
|
| msg = "DataFrame.groupby with axis=1 is deprecated"
| with tm.assert_produces_warning(FutureWarning, match=msg):
| groupedT = tsframe.groupby({"A": 0, "B": 0, "C": 1, "D": 1}, axis=1)
| result = groupedT.describe()
| expected = tsframe.describe().T
| # reverting the change from https://github.com/pandas-dev/pandas/pull/35441/
| expected.index = MultiIndex(
| levels=[[0, 1], expected.index],
| codes=[[0, 0, 1, 1], range(len(expected.index))],
| )
| tm.assert_frame_equal(result, expected)
|
|
| def test_frame_describe_tupleindex():
| # GH 14848 - regression from 0.19.0 to 0.19.1
| df1 = DataFrame(
| {
| "x": [1, 2, 3, 4, 5] * 3,
| "y": [10, 20, 30, 40, 50] * 3,
| "z": [100, 200, 300, 400, 500] * 3,
| }
| )
| df1["k"] = [(0, 0, 1), (0, 1, 0), (1, 0, 0)] * 5
| df2 = df1.rename(columns={"k": "key"})
| msg = "Names should be list-like for a MultiIndex"
| with pytest.raises(ValueError, match=msg):
| df1.groupby("k").describe()
| with pytest.raises(ValueError, match=msg):
| df2.groupby("key").describe()
|
|
| def test_frame_describe_unstacked_format():
| # GH 4792
| prices = {
| Timestamp("2011-01-06 10:59:05", tz=None): 24990,
| Timestamp("2011-01-06 12:43:33", tz=None): 25499,
| Timestamp("2011-01-06 12:54:09", tz=None): 25499,
| }
| volumes = {
| Timestamp("2011-01-06 10:59:05", tz=None): 1500000000,
| Timestamp("2011-01-06 12:43:33", tz=None): 5000000000,
| Timestamp("2011-01-06 12:54:09", tz=None): 100000000,
| }
| df = DataFrame({"PRICE": prices, "VOLUME": volumes})
| result = df.groupby("PRICE").VOLUME.describe()
| data = [
| df[df.PRICE == 24990].VOLUME.describe().values.tolist(),
| df[df.PRICE == 25499].VOLUME.describe().values.tolist(),
| ]
| expected = DataFrame(
| data,
| index=Index([24990, 25499], name="PRICE"),
| columns=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
| )
| tm.assert_frame_equal(result, expected)
|
|
| @pytest.mark.filterwarnings(
| "ignore:"
| "indexing past lexsort depth may impact performance:"
| "pandas.errors.PerformanceWarning"
| )
| @pytest.mark.parametrize("as_index", [True, False])
| @pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
| def test_describe_with_duplicate_output_column_names(as_index, keys):
| # GH 35314
| df = DataFrame(
| {
| "a1": [99, 99, 99, 88, 88, 88],
| "a2": [99, 99, 99, 88, 88, 88],
| "b": [1, 2, 3, 4, 5, 6],
| "c": [10, 20, 30, 40, 50, 60],
| },
| columns=["a1", "a2", "b", "b"],
| copy=False,
| )
| if keys == ["a1"]:
| df = df.drop(columns="a2")
|
| expected = (
| DataFrame.from_records(
| [
| ("b", "count", 3.0, 3.0),
| ("b", "mean", 5.0, 2.0),
| ("b", "std", 1.0, 1.0),
| ("b", "min", 4.0, 1.0),
| ("b", "25%", 4.5, 1.5),
| ("b", "50%", 5.0, 2.0),
| ("b", "75%", 5.5, 2.5),
| ("b", "max", 6.0, 3.0),
| ("b", "count", 3.0, 3.0),
| ("b", "mean", 5.0, 2.0),
| ("b", "std", 1.0, 1.0),
| ("b", "min", 4.0, 1.0),
| ("b", "25%", 4.5, 1.5),
| ("b", "50%", 5.0, 2.0),
| ("b", "75%", 5.5, 2.5),
| ("b", "max", 6.0, 3.0),
| ],
| )
| .set_index([0, 1])
| .T
| )
| expected.columns.names = [None, None]
| if len(keys) == 2:
| expected.index = MultiIndex(
| levels=[[88, 99], [88, 99]], codes=[[0, 1], [0, 1]], names=["a1", "a2"]
| )
| else:
| expected.index = Index([88, 99], name="a1")
|
| if not as_index:
| expected = expected.reset_index()
|
| result = df.groupby(keys, as_index=as_index).describe()
|
| tm.assert_frame_equal(result, expected)
|
|
| def test_describe_duplicate_columns():
| # GH#50806
| df = DataFrame([[0, 1, 2, 3]])
| df.columns = [0, 1, 2, 0]
| gb = df.groupby(df[1])
| result = gb.describe(percentiles=[])
|
| columns = ["count", "mean", "std", "min", "50%", "max"]
| frames = [
| DataFrame([[1.0, val, np.nan, val, val, val]], index=[1], columns=columns)
| for val in (0.0, 2.0, 3.0)
| ]
| expected = pd.concat(frames, axis=1)
| expected.columns = MultiIndex(
| levels=[[0, 2], columns],
| codes=[6 * [0] + 6 * [1] + 6 * [0], 3 * list(range(6))],
| )
| expected.index.names = [1]
| tm.assert_frame_equal(result, expected)
|
|
| class TestGroupByNonCythonPaths:
| # GH#5610 non-cython calls should not include the grouper
| # Tests for code not expected to go through cython paths.
|
| @pytest.fixture
| def df(self):
| df = DataFrame(
| [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
| columns=["A", "B", "C"],
| )
| return df
|
| @pytest.fixture
| def gb(self, df):
| gb = df.groupby("A")
| return gb
|
| @pytest.fixture
| def gni(self, df):
| gni = df.groupby("A", as_index=False)
| return gni
|
| def test_describe(self, df, gb, gni):
| # describe
| expected_index = Index([1, 3], name="A")
| expected_col = MultiIndex(
| levels=[["B"], ["count", "mean", "std", "min", "25%", "50%", "75%", "max"]],
| codes=[[0] * 8, list(range(8))],
| )
| expected = DataFrame(
| [
| [1.0, 2.0, np.nan, 2.0, 2.0, 2.0, 2.0, 2.0],
| [0.0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
| ],
| index=expected_index,
| columns=expected_col,
| )
| result = gb.describe()
| tm.assert_frame_equal(result, expected)
|
| expected = expected.reset_index()
| result = gni.describe()
| tm.assert_frame_equal(result, expected)
|
|
| @pytest.mark.parametrize("dtype", [int, float, object])
| @pytest.mark.parametrize(
| "kwargs",
| [
| {"percentiles": [0.10, 0.20, 0.30], "include": "all", "exclude": None},
| {"percentiles": [0.10, 0.20, 0.30], "include": None, "exclude": ["int"]},
| {"percentiles": [0.10, 0.20, 0.30], "include": ["int"], "exclude": None},
| ],
| )
| def test_groupby_empty_dataset(dtype, kwargs):
| # GH#41575
| df = DataFrame([[1, 2, 3]], columns=["A", "B", "C"], dtype=dtype)
| df["B"] = df["B"].astype(int)
| df["C"] = df["C"].astype(float)
|
| result = df.iloc[:0].groupby("A").describe(**kwargs)
| expected = df.groupby("A").describe(**kwargs).reset_index(drop=True).iloc[:0]
| tm.assert_frame_equal(result, expected)
|
| result = df.iloc[:0].groupby("A").B.describe(**kwargs)
| expected = df.groupby("A").B.describe(**kwargs).reset_index(drop=True).iloc[:0]
| expected.index = Index([], dtype=df.columns.dtype)
| tm.assert_frame_equal(result, expected)
|
|
