import builtins
|
from io import StringIO
|
|
import numpy as np
|
import pytest
|
|
from pandas._libs import lib
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from pandas.errors import UnsupportedFunctionCall
|
|
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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MultiIndex,
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Series,
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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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from pandas.core import nanops
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from pandas.tests.groupby import get_groupby_method_args
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from pandas.util import _test_decorators as td
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|
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@pytest.fixture(
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params=[np.int32, np.int64, np.float32, np.float64, "Int64", "Float64"],
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ids=["np.int32", "np.int64", "np.float32", "np.float64", "Int64", "Float64"],
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)
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def dtypes_for_minmax(request):
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"""
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Fixture of dtypes with min and max values used for testing
|
cummin and cummax
|
"""
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dtype = request.param
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np_type = dtype
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if dtype == "Int64":
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np_type = np.int64
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elif dtype == "Float64":
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np_type = np.float64
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min_val = (
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np.iinfo(np_type).min
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if np.dtype(np_type).kind == "i"
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else np.finfo(np_type).min
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)
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max_val = (
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np.iinfo(np_type).max
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if np.dtype(np_type).kind == "i"
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else np.finfo(np_type).max
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)
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return (dtype, min_val, max_val)
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|
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def test_intercept_builtin_sum():
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s = Series([1.0, 2.0, np.nan, 3.0])
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grouped = s.groupby([0, 1, 2, 2])
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result = grouped.agg(builtins.sum)
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result2 = grouped.apply(builtins.sum)
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expected = grouped.sum()
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tm.assert_series_equal(result, expected)
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tm.assert_series_equal(result2, expected)
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|
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@pytest.mark.parametrize("f", [max, min, sum])
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@pytest.mark.parametrize("keys", ["jim", ["jim", "joe"]]) # Single key # Multi-key
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def test_builtins_apply(keys, f):
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# see gh-8155
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df = DataFrame(np.random.randint(1, 50, (1000, 2)), columns=["jim", "joe"])
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df["jolie"] = np.random.randn(1000)
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gb = df.groupby(keys)
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fname = f.__name__
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result = gb.apply(f)
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ngroups = len(df.drop_duplicates(subset=keys))
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assert_msg = f"invalid frame shape: {result.shape} (expected ({ngroups}, 3))"
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assert result.shape == (ngroups, 3), assert_msg
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npfunc = lambda x: getattr(np, fname)(x, axis=0) # numpy's equivalent function
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expected = gb.apply(npfunc)
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tm.assert_frame_equal(result, expected)
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with tm.assert_produces_warning(None):
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expected2 = gb.apply(lambda x: npfunc(x))
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tm.assert_frame_equal(result, expected2)
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if f != sum:
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expected = gb.agg(fname).reset_index()
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expected.set_index(keys, inplace=True, drop=False)
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tm.assert_frame_equal(result, expected, check_dtype=False)
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tm.assert_series_equal(getattr(result, fname)(axis=0), getattr(df, fname)(axis=0))
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class TestNumericOnly:
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# make sure that we are passing thru kwargs to our agg functions
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@pytest.fixture
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def df(self):
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# GH3668
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# GH5724
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df = DataFrame(
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{
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"group": [1, 1, 2],
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"int": [1, 2, 3],
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"float": [4.0, 5.0, 6.0],
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"string": list("abc"),
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"category_string": Series(list("abc")).astype("category"),
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"category_int": [7, 8, 9],
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"datetime": date_range("20130101", periods=3),
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"datetimetz": date_range("20130101", periods=3, tz="US/Eastern"),
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"timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
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},
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columns=[
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"group",
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"int",
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"float",
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"string",
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"category_string",
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"category_int",
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"datetime",
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"datetimetz",
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"timedelta",
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],
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)
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return df
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@pytest.mark.parametrize("method", ["mean", "median"])
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def test_averages(self, df, method):
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# mean / median
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expected_columns_numeric = Index(["int", "float", "category_int"])
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gb = df.groupby("group")
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expected = DataFrame(
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{
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"category_int": [7.5, 9],
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"float": [4.5, 6.0],
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"timedelta": [pd.Timedelta("1.5s"), pd.Timedelta("3s")],
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"int": [1.5, 3],
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"datetime": [
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Timestamp("2013-01-01 12:00:00"),
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Timestamp("2013-01-03 00:00:00"),
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],
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"datetimetz": [
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Timestamp("2013-01-01 12:00:00", tz="US/Eastern"),
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Timestamp("2013-01-03 00:00:00", tz="US/Eastern"),
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],
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},
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index=Index([1, 2], name="group"),
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columns=[
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"int",
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"float",
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"category_int",
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],
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)
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result = getattr(gb, method)(numeric_only=True)
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tm.assert_frame_equal(result.reindex_like(expected), expected)
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expected_columns = expected.columns
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self._check(df, method, expected_columns, expected_columns_numeric)
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@pytest.mark.parametrize("method", ["min", "max"])
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def test_extrema(self, df, method):
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# TODO: min, max *should* handle
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# categorical (ordered) dtype
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expected_columns = Index(
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[
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"int",
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"float",
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"string",
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"category_int",
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"datetime",
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"datetimetz",
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"timedelta",
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]
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)
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expected_columns_numeric = expected_columns
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self._check(df, method, expected_columns, expected_columns_numeric)
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@pytest.mark.parametrize("method", ["first", "last"])
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def test_first_last(self, df, method):
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expected_columns = Index(
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[
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"int",
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"float",
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"string",
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"category_string",
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"category_int",
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"datetime",
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"datetimetz",
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"timedelta",
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]
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)
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expected_columns_numeric = expected_columns
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self._check(df, method, expected_columns, expected_columns_numeric)
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@pytest.mark.parametrize("method", ["sum", "cumsum"])
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def test_sum_cumsum(self, df, method):
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expected_columns_numeric = Index(["int", "float", "category_int"])
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expected_columns = Index(
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["int", "float", "string", "category_int", "timedelta"]
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)
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if method == "cumsum":
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# cumsum loses string
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expected_columns = Index(["int", "float", "category_int", "timedelta"])
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self._check(df, method, expected_columns, expected_columns_numeric)
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@pytest.mark.parametrize("method", ["prod", "cumprod"])
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def test_prod_cumprod(self, df, method):
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expected_columns = Index(["int", "float", "category_int"])
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expected_columns_numeric = expected_columns
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self._check(df, method, expected_columns, expected_columns_numeric)
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@pytest.mark.parametrize("method", ["cummin", "cummax"])
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def test_cummin_cummax(self, df, method):
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# like min, max, but don't include strings
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expected_columns = Index(
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["int", "float", "category_int", "datetime", "datetimetz", "timedelta"]
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)
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# GH#15561: numeric_only=False set by default like min/max
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expected_columns_numeric = expected_columns
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self._check(df, method, expected_columns, expected_columns_numeric)
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def _check(self, df, method, expected_columns, expected_columns_numeric):
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gb = df.groupby("group")
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# object dtypes for transformations are not implemented in Cython and
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# have no Python fallback
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exception = NotImplementedError if method.startswith("cum") else TypeError
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if method in ("min", "max", "cummin", "cummax", "cumsum", "cumprod"):
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# The methods default to numeric_only=False and raise TypeError
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msg = "|".join(
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[
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"Categorical is not ordered",
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"function is not implemented for this dtype",
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f"Cannot perform {method} with non-ordered Categorical",
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]
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)
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with pytest.raises(exception, match=msg):
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getattr(gb, method)()
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elif method in ("sum", "mean", "median", "prod"):
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msg = "|".join(
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[
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"category type does not support sum operations",
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"[Cc]ould not convert",
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"can't multiply sequence by non-int of type 'str'",
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]
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)
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with pytest.raises(exception, match=msg):
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getattr(gb, method)()
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else:
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result = getattr(gb, method)()
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tm.assert_index_equal(result.columns, expected_columns_numeric)
|
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if method not in ("first", "last"):
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msg = "|".join(
|
[
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"[Cc]ould not convert",
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"Categorical is not ordered",
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"category type does not support",
|
"can't multiply sequence",
|
"function is not implemented for this dtype",
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f"Cannot perform {method} with non-ordered Categorical",
|
]
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)
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with pytest.raises(exception, match=msg):
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getattr(gb, method)(numeric_only=False)
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else:
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result = getattr(gb, method)(numeric_only=False)
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tm.assert_index_equal(result.columns, expected_columns)
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class TestGroupByNonCythonPaths:
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# GH#5610 non-cython calls should not include the grouper
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# Tests for code not expected to go through cython paths.
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@pytest.fixture
|
def df(self):
|
df = DataFrame(
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[[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
|
columns=["A", "B", "C"],
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)
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return df
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|
@pytest.fixture
|
def gb(self, df):
|
gb = df.groupby("A")
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return gb
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@pytest.fixture
|
def gni(self, df):
|
gni = df.groupby("A", as_index=False)
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return gni
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def test_describe(self, df, gb, gni):
|
# describe
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expected_index = Index([1, 3], name="A")
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expected_col = MultiIndex(
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levels=[["B"], ["count", "mean", "std", "min", "25%", "50%", "75%", "max"]],
|
codes=[[0] * 8, list(range(8))],
|
)
|
expected = DataFrame(
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[
|
[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],
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],
|
index=expected_index,
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columns=expected_col,
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)
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result = gb.describe()
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tm.assert_frame_equal(result, expected)
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expected = expected.reset_index()
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result = gni.describe()
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tm.assert_frame_equal(result, expected)
|
|
|
def test_cython_api2():
|
# this takes the fast apply path
|
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# cumsum (GH5614)
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df = DataFrame([[1, 2, np.nan], [1, np.nan, 9], [3, 4, 9]], columns=["A", "B", "C"])
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expected = DataFrame([[2, np.nan], [np.nan, 9], [4, 9]], columns=["B", "C"])
|
result = df.groupby("A").cumsum()
|
tm.assert_frame_equal(result, expected)
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|
# GH 5755 - cumsum is a transformer and should ignore as_index
|
result = df.groupby("A", as_index=False).cumsum()
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tm.assert_frame_equal(result, expected)
|
|
# GH 13994
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result = df.groupby("A").cumsum(axis=1)
|
expected = df.cumsum(axis=1)
|
tm.assert_frame_equal(result, expected)
|
result = df.groupby("A").cumprod(axis=1)
|
expected = df.cumprod(axis=1)
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_cython_median():
|
arr = np.random.randn(1000)
|
arr[::2] = np.nan
|
df = DataFrame(arr)
|
|
labels = np.random.randint(0, 50, size=1000).astype(float)
|
labels[::17] = np.nan
|
|
result = df.groupby(labels).median()
|
exp = df.groupby(labels).agg(nanops.nanmedian)
|
tm.assert_frame_equal(result, exp)
|
|
df = DataFrame(np.random.randn(1000, 5))
|
rs = df.groupby(labels).agg(np.median)
|
xp = df.groupby(labels).median()
|
tm.assert_frame_equal(rs, xp)
|
|
|
def test_median_empty_bins(observed):
|
df = DataFrame(np.random.randint(0, 44, 500))
|
|
grps = range(0, 55, 5)
|
bins = pd.cut(df[0], grps)
|
|
result = df.groupby(bins, observed=observed).median()
|
expected = df.groupby(bins, observed=observed).agg(lambda x: x.median())
|
tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"dtype", ["int8", "int16", "int32", "int64", "float32", "float64", "uint64"]
|
)
|
@pytest.mark.parametrize(
|
"method,data",
|
[
|
("first", {"df": [{"a": 1, "b": 1}, {"a": 2, "b": 3}]}),
|
("last", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 4}]}),
|
("min", {"df": [{"a": 1, "b": 1}, {"a": 2, "b": 3}]}),
|
("max", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 4}]}),
|
("count", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 2}], "out_type": "int64"}),
|
],
|
)
|
def test_groupby_non_arithmetic_agg_types(dtype, method, data):
|
# GH9311, GH6620
|
df = DataFrame(
|
[{"a": 1, "b": 1}, {"a": 1, "b": 2}, {"a": 2, "b": 3}, {"a": 2, "b": 4}]
|
)
|
|
df["b"] = df.b.astype(dtype)
|
|
if "args" not in data:
|
data["args"] = []
|
|
if "out_type" in data:
|
out_type = data["out_type"]
|
else:
|
out_type = dtype
|
|
exp = data["df"]
|
df_out = DataFrame(exp)
|
|
df_out["b"] = df_out.b.astype(out_type)
|
df_out.set_index("a", inplace=True)
|
|
grpd = df.groupby("a")
|
t = getattr(grpd, method)(*data["args"])
|
tm.assert_frame_equal(t, df_out)
|
|
|
@pytest.mark.parametrize(
|
"i",
|
[
|
(
|
Timestamp("2011-01-15 12:50:28.502376"),
|
Timestamp("2011-01-20 12:50:28.593448"),
|
),
|
(24650000000000001, 24650000000000002),
|
],
|
)
|
def test_groupby_non_arithmetic_agg_int_like_precision(i):
|
# see gh-6620, gh-9311
|
df = DataFrame([{"a": 1, "b": i[0]}, {"a": 1, "b": i[1]}])
|
|
grp_exp = {
|
"first": {"expected": i[0]},
|
"last": {"expected": i[1]},
|
"min": {"expected": i[0]},
|
"max": {"expected": i[1]},
|
"nth": {"expected": i[1], "args": [1]},
|
"count": {"expected": 2},
|
}
|
|
for method, data in grp_exp.items():
|
if "args" not in data:
|
data["args"] = []
|
|
grouped = df.groupby("a")
|
res = getattr(grouped, method)(*data["args"])
|
|
assert res.iloc[0].b == data["expected"]
|
|
|
@pytest.mark.parametrize(
|
"func, values",
|
[
|
("idxmin", {"c_int": [0, 2], "c_float": [1, 3], "c_date": [1, 2]}),
|
("idxmax", {"c_int": [1, 3], "c_float": [0, 2], "c_date": [0, 3]}),
|
],
|
)
|
@pytest.mark.parametrize("numeric_only", [True, False])
|
def test_idxmin_idxmax_returns_int_types(func, values, numeric_only):
|
# GH 25444
|
df = DataFrame(
|
{
|
"name": ["A", "A", "B", "B"],
|
"c_int": [1, 2, 3, 4],
|
"c_float": [4.02, 3.03, 2.04, 1.05],
|
"c_date": ["2019", "2018", "2016", "2017"],
|
}
|
)
|
df["c_date"] = pd.to_datetime(df["c_date"])
|
df["c_date_tz"] = df["c_date"].dt.tz_localize("US/Pacific")
|
df["c_timedelta"] = df["c_date"] - df["c_date"].iloc[0]
|
df["c_period"] = df["c_date"].dt.to_period("W")
|
df["c_Integer"] = df["c_int"].astype("Int64")
|
df["c_Floating"] = df["c_float"].astype("Float64")
|
|
result = getattr(df.groupby("name"), func)(numeric_only=numeric_only)
|
|
expected = DataFrame(values, index=Index(["A", "B"], name="name"))
|
if numeric_only:
|
expected = expected.drop(columns=["c_date"])
|
else:
|
expected["c_date_tz"] = expected["c_date"]
|
expected["c_timedelta"] = expected["c_date"]
|
expected["c_period"] = expected["c_date"]
|
expected["c_Integer"] = expected["c_int"]
|
expected["c_Floating"] = expected["c_float"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_idxmin_idxmax_axis1():
|
df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
|
df["A"] = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4]
|
|
gb = df.groupby("A")
|
|
res = gb.idxmax(axis=1)
|
|
alt = df.iloc[:, 1:].idxmax(axis=1)
|
indexer = res.index.get_level_values(1)
|
|
tm.assert_series_equal(alt[indexer], res.droplevel("A"))
|
|
df["E"] = date_range("2016-01-01", periods=10)
|
gb2 = df.groupby("A")
|
|
msg = "reduction operation 'argmax' not allowed for this dtype"
|
with pytest.raises(TypeError, match=msg):
|
gb2.idxmax(axis=1)
|
|
|
@pytest.mark.parametrize("numeric_only", [True, False, None])
|
def test_axis1_numeric_only(request, groupby_func, numeric_only):
|
if groupby_func in ("idxmax", "idxmin"):
|
pytest.skip("idxmax and idx_min tested in test_idxmin_idxmax_axis1")
|
if groupby_func in ("corrwith", "skew"):
|
msg = "GH#47723 groupby.corrwith and skew do not correctly implement axis=1"
|
request.node.add_marker(pytest.mark.xfail(reason=msg))
|
|
df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
|
df["E"] = "x"
|
groups = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4]
|
gb = df.groupby(groups)
|
method = getattr(gb, groupby_func)
|
args = get_groupby_method_args(groupby_func, df)
|
kwargs = {"axis": 1}
|
if numeric_only is not None:
|
# when numeric_only is None we don't pass any argument
|
kwargs["numeric_only"] = numeric_only
|
|
# Functions without numeric_only and axis args
|
no_args = ("cumprod", "cumsum", "diff", "fillna", "pct_change", "rank", "shift")
|
# Functions with axis args
|
has_axis = (
|
"cumprod",
|
"cumsum",
|
"diff",
|
"pct_change",
|
"rank",
|
"shift",
|
"cummax",
|
"cummin",
|
"idxmin",
|
"idxmax",
|
"fillna",
|
)
|
if numeric_only is not None and groupby_func in no_args:
|
msg = "got an unexpected keyword argument 'numeric_only'"
|
with pytest.raises(TypeError, match=msg):
|
method(*args, **kwargs)
|
elif groupby_func not in has_axis:
|
msg = "got an unexpected keyword argument 'axis'"
|
with pytest.raises(TypeError, match=msg):
|
method(*args, **kwargs)
|
# fillna and shift are successful even on object dtypes
|
elif (numeric_only is None or not numeric_only) and groupby_func not in (
|
"fillna",
|
"shift",
|
):
|
msgs = (
|
# cummax, cummin, rank
|
"not supported between instances of",
|
# cumprod
|
"can't multiply sequence by non-int of type 'float'",
|
# cumsum, diff, pct_change
|
"unsupported operand type",
|
)
|
with pytest.raises(TypeError, match=f"({'|'.join(msgs)})"):
|
method(*args, **kwargs)
|
else:
|
result = method(*args, **kwargs)
|
|
df_expected = df.drop(columns="E").T if numeric_only else df.T
|
expected = getattr(df_expected, groupby_func)(*args).T
|
if groupby_func == "shift" and not numeric_only:
|
# shift with axis=1 leaves the leftmost column as numeric
|
# but transposing for expected gives us object dtype
|
expected = expected.astype(float)
|
|
tm.assert_equal(result, expected)
|
|
|
def test_groupby_cumprod():
|
# GH 4095
|
df = DataFrame({"key": ["b"] * 10, "value": 2})
|
|
actual = df.groupby("key")["value"].cumprod()
|
expected = df.groupby("key", group_keys=False)["value"].apply(lambda x: x.cumprod())
|
expected.name = "value"
|
tm.assert_series_equal(actual, expected)
|
|
df = DataFrame({"key": ["b"] * 100, "value": 2})
|
df["value"] = df["value"].astype(float)
|
actual = df.groupby("key")["value"].cumprod()
|
expected = df.groupby("key", group_keys=False)["value"].apply(lambda x: x.cumprod())
|
expected.name = "value"
|
tm.assert_series_equal(actual, expected)
|
|
|
def test_groupby_cumprod_overflow():
|
# GH#37493 if we overflow we return garbage consistent with numpy
|
df = DataFrame({"key": ["b"] * 4, "value": 100_000})
|
actual = df.groupby("key")["value"].cumprod()
|
expected = Series(
|
[100_000, 10_000_000_000, 1_000_000_000_000_000, 7766279631452241920],
|
name="value",
|
)
|
tm.assert_series_equal(actual, expected)
|
|
numpy_result = df.groupby("key", group_keys=False)["value"].apply(
|
lambda x: x.cumprod()
|
)
|
numpy_result.name = "value"
|
tm.assert_series_equal(actual, numpy_result)
|
|
|
def test_groupby_cumprod_nan_influences_other_columns():
|
# GH#48064
|
df = DataFrame(
|
{
|
"a": 1,
|
"b": [1, np.nan, 2],
|
"c": [1, 2, 3.0],
|
}
|
)
|
result = df.groupby("a").cumprod(numeric_only=True, skipna=False)
|
expected = DataFrame({"b": [1, np.nan, np.nan], "c": [1, 2, 6.0]})
|
tm.assert_frame_equal(result, expected)
|
|
|
def scipy_sem(*args, **kwargs):
|
from scipy.stats import sem
|
|
return sem(*args, ddof=1, **kwargs)
|
|
|
@pytest.mark.parametrize(
|
"op,targop",
|
[
|
("mean", np.mean),
|
("median", np.median),
|
("std", np.std),
|
("var", np.var),
|
("sum", np.sum),
|
("prod", np.prod),
|
("min", np.min),
|
("max", np.max),
|
("first", lambda x: x.iloc[0]),
|
("last", lambda x: x.iloc[-1]),
|
("count", np.size),
|
pytest.param("sem", scipy_sem, marks=td.skip_if_no_scipy),
|
],
|
)
|
def test_ops_general(op, targop):
|
df = DataFrame(np.random.randn(1000))
|
labels = np.random.randint(0, 50, size=1000).astype(float)
|
|
result = getattr(df.groupby(labels), op)()
|
expected = df.groupby(labels).agg(targop)
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_max_nan_bug():
|
raw = """,Date,app,File
|
-04-23,2013-04-23 00:00:00,,log080001.log
|
-05-06,2013-05-06 00:00:00,,log.log
|
-05-07,2013-05-07 00:00:00,OE,xlsx"""
|
|
with tm.assert_produces_warning(UserWarning, match="Could not infer format"):
|
df = pd.read_csv(StringIO(raw), parse_dates=[0])
|
gb = df.groupby("Date")
|
r = gb[["File"]].max()
|
e = gb["File"].max().to_frame()
|
tm.assert_frame_equal(r, e)
|
assert not r["File"].isna().any()
|
|
|
def test_nlargest():
|
a = Series([1, 3, 5, 7, 2, 9, 0, 4, 6, 10])
|
b = Series(list("a" * 5 + "b" * 5))
|
gb = a.groupby(b)
|
r = gb.nlargest(3)
|
e = Series(
|
[7, 5, 3, 10, 9, 6],
|
index=MultiIndex.from_arrays([list("aaabbb"), [3, 2, 1, 9, 5, 8]]),
|
)
|
tm.assert_series_equal(r, e)
|
|
a = Series([1, 1, 3, 2, 0, 3, 3, 2, 1, 0])
|
gb = a.groupby(b)
|
e = Series(
|
[3, 2, 1, 3, 3, 2],
|
index=MultiIndex.from_arrays([list("aaabbb"), [2, 3, 1, 6, 5, 7]]),
|
)
|
tm.assert_series_equal(gb.nlargest(3, keep="last"), e)
|
|
|
def test_nlargest_mi_grouper():
|
# see gh-21411
|
npr = np.random.RandomState(123456789)
|
|
dts = date_range("20180101", periods=10)
|
iterables = [dts, ["one", "two"]]
|
|
idx = MultiIndex.from_product(iterables, names=["first", "second"])
|
s = Series(npr.randn(20), index=idx)
|
|
result = s.groupby("first").nlargest(1)
|
|
exp_idx = MultiIndex.from_tuples(
|
[
|
(dts[0], dts[0], "one"),
|
(dts[1], dts[1], "one"),
|
(dts[2], dts[2], "one"),
|
(dts[3], dts[3], "two"),
|
(dts[4], dts[4], "one"),
|
(dts[5], dts[5], "one"),
|
(dts[6], dts[6], "one"),
|
(dts[7], dts[7], "one"),
|
(dts[8], dts[8], "two"),
|
(dts[9], dts[9], "one"),
|
],
|
names=["first", "first", "second"],
|
)
|
|
exp_values = [
|
2.2129019979039612,
|
1.8417114045748335,
|
0.858963679564603,
|
1.3759151378258088,
|
0.9430284594687134,
|
0.5296914208183142,
|
0.8318045593815487,
|
-0.8476703342910327,
|
0.3804446884133735,
|
-0.8028845810770998,
|
]
|
|
expected = Series(exp_values, index=exp_idx)
|
tm.assert_series_equal(result, expected, check_exact=False, rtol=1e-3)
|
|
|
def test_nsmallest():
|
a = Series([1, 3, 5, 7, 2, 9, 0, 4, 6, 10])
|
b = Series(list("a" * 5 + "b" * 5))
|
gb = a.groupby(b)
|
r = gb.nsmallest(3)
|
e = Series(
|
[1, 2, 3, 0, 4, 6],
|
index=MultiIndex.from_arrays([list("aaabbb"), [0, 4, 1, 6, 7, 8]]),
|
)
|
tm.assert_series_equal(r, e)
|
|
a = Series([1, 1, 3, 2, 0, 3, 3, 2, 1, 0])
|
gb = a.groupby(b)
|
e = Series(
|
[0, 1, 1, 0, 1, 2],
|
index=MultiIndex.from_arrays([list("aaabbb"), [4, 1, 0, 9, 8, 7]]),
|
)
|
tm.assert_series_equal(gb.nsmallest(3, keep="last"), e)
|
|
|
@pytest.mark.parametrize(
|
"data, groups",
|
[([0, 1, 2, 3], [0, 0, 1, 1]), ([0], [0])],
|
)
|
@pytest.mark.parametrize("dtype", [None, *tm.ALL_INT_NUMPY_DTYPES])
|
@pytest.mark.parametrize("method", ["nlargest", "nsmallest"])
|
def test_nlargest_and_smallest_noop(data, groups, dtype, method):
|
# GH 15272, GH 16345, GH 29129
|
# Test nlargest/smallest when it results in a noop,
|
# i.e. input is sorted and group size <= n
|
if dtype is not None:
|
data = np.array(data, dtype=dtype)
|
if method == "nlargest":
|
data = list(reversed(data))
|
ser = Series(data, name="a")
|
result = getattr(ser.groupby(groups), method)(n=2)
|
expidx = np.array(groups, dtype=np.int_) if isinstance(groups, list) else groups
|
expected = Series(data, index=MultiIndex.from_arrays([expidx, ser.index]), name="a")
|
tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize("func", ["cumprod", "cumsum"])
|
def test_numpy_compat(func):
|
# see gh-12811
|
df = DataFrame({"A": [1, 2, 1], "B": [1, 2, 3]})
|
g = df.groupby("A")
|
|
msg = "numpy operations are not valid with groupby"
|
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
getattr(g, func)(1, 2, 3)
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
getattr(g, func)(foo=1)
|
|
|
def test_cummin(dtypes_for_minmax):
|
dtype = dtypes_for_minmax[0]
|
min_val = dtypes_for_minmax[1]
|
|
# GH 15048
|
base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]})
|
expected_mins = [3, 3, 3, 2, 2, 2, 2, 1]
|
|
df = base_df.astype(dtype)
|
|
expected = DataFrame({"B": expected_mins}).astype(dtype)
|
result = df.groupby("A").cummin()
|
tm.assert_frame_equal(result, expected)
|
result = df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
|
tm.assert_frame_equal(result, expected)
|
|
# Test w/ min value for dtype
|
df.loc[[2, 6], "B"] = min_val
|
df.loc[[1, 5], "B"] = min_val + 1
|
expected.loc[[2, 3, 6, 7], "B"] = min_val
|
expected.loc[[1, 5], "B"] = min_val + 1 # should not be rounded to min_val
|
result = df.groupby("A").cummin()
|
tm.assert_frame_equal(result, expected, check_exact=True)
|
expected = (
|
df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
|
)
|
tm.assert_frame_equal(result, expected, check_exact=True)
|
|
# Test nan in some values
|
# Explicit cast to float to avoid implicit cast when setting nan
|
base_df = base_df.astype({"B": "float"})
|
base_df.loc[[0, 2, 4, 6], "B"] = np.nan
|
expected = DataFrame({"B": [np.nan, 4, np.nan, 2, np.nan, 3, np.nan, 1]})
|
result = base_df.groupby("A").cummin()
|
tm.assert_frame_equal(result, expected)
|
expected = (
|
base_df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
|
)
|
tm.assert_frame_equal(result, expected)
|
|
# GH 15561
|
df = DataFrame({"a": [1], "b": pd.to_datetime(["2001"])})
|
expected = Series(pd.to_datetime("2001"), index=[0], name="b")
|
|
result = df.groupby("a")["b"].cummin()
|
tm.assert_series_equal(expected, result)
|
|
# GH 15635
|
df = DataFrame({"a": [1, 2, 1], "b": [1, 2, 2]})
|
result = df.groupby("a").b.cummin()
|
expected = Series([1, 2, 1], name="b")
|
tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize("method", ["cummin", "cummax"])
|
@pytest.mark.parametrize("dtype", ["UInt64", "Int64", "Float64", "float", "boolean"])
|
def test_cummin_max_all_nan_column(method, dtype):
|
base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8})
|
base_df["B"] = base_df["B"].astype(dtype)
|
grouped = base_df.groupby("A")
|
|
expected = DataFrame({"B": [np.nan] * 8}, dtype=dtype)
|
result = getattr(grouped, method)()
|
tm.assert_frame_equal(expected, result)
|
|
result = getattr(grouped["B"], method)().to_frame()
|
tm.assert_frame_equal(expected, result)
|
|
|
def test_cummax(dtypes_for_minmax):
|
dtype = dtypes_for_minmax[0]
|
max_val = dtypes_for_minmax[2]
|
|
# GH 15048
|
base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]})
|
expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3]
|
|
df = base_df.astype(dtype)
|
|
expected = DataFrame({"B": expected_maxs}).astype(dtype)
|
result = df.groupby("A").cummax()
|
tm.assert_frame_equal(result, expected)
|
result = df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
|
tm.assert_frame_equal(result, expected)
|
|
# Test w/ max value for dtype
|
df.loc[[2, 6], "B"] = max_val
|
expected.loc[[2, 3, 6, 7], "B"] = max_val
|
result = df.groupby("A").cummax()
|
tm.assert_frame_equal(result, expected)
|
expected = (
|
df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
|
)
|
tm.assert_frame_equal(result, expected)
|
|
# Test nan in some values
|
# Explicit cast to float to avoid implicit cast when setting nan
|
base_df = base_df.astype({"B": "float"})
|
base_df.loc[[0, 2, 4, 6], "B"] = np.nan
|
expected = DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]})
|
result = base_df.groupby("A").cummax()
|
tm.assert_frame_equal(result, expected)
|
expected = (
|
base_df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
|
)
|
tm.assert_frame_equal(result, expected)
|
|
# GH 15561
|
df = DataFrame({"a": [1], "b": pd.to_datetime(["2001"])})
|
expected = Series(pd.to_datetime("2001"), index=[0], name="b")
|
|
result = df.groupby("a")["b"].cummax()
|
tm.assert_series_equal(expected, result)
|
|
# GH 15635
|
df = DataFrame({"a": [1, 2, 1], "b": [2, 1, 1]})
|
result = df.groupby("a").b.cummax()
|
expected = Series([2, 1, 2], name="b")
|
tm.assert_series_equal(result, expected)
|
|
|
def test_cummax_i8_at_implementation_bound():
|
# the minimum value used to be treated as NPY_NAT+1 instead of NPY_NAT
|
# for int64 dtype GH#46382
|
ser = Series([pd.NaT._value + n for n in range(5)])
|
df = DataFrame({"A": 1, "B": ser, "C": ser.view("M8[ns]")})
|
gb = df.groupby("A")
|
|
res = gb.cummax()
|
exp = df[["B", "C"]]
|
tm.assert_frame_equal(res, exp)
|
|
|
@pytest.mark.parametrize("method", ["cummin", "cummax"])
|
@pytest.mark.parametrize("dtype", ["float", "Int64", "Float64"])
|
@pytest.mark.parametrize(
|
"groups,expected_data",
|
[
|
([1, 1, 1], [1, None, None]),
|
([1, 2, 3], [1, None, 2]),
|
([1, 3, 3], [1, None, None]),
|
],
|
)
|
def test_cummin_max_skipna(method, dtype, groups, expected_data):
|
# GH-34047
|
df = DataFrame({"a": Series([1, None, 2], dtype=dtype)})
|
orig = df.copy()
|
gb = df.groupby(groups)["a"]
|
|
result = getattr(gb, method)(skipna=False)
|
expected = Series(expected_data, dtype=dtype, name="a")
|
|
# check we didn't accidentally alter df
|
tm.assert_frame_equal(df, orig)
|
|
tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize("method", ["cummin", "cummax"])
|
def test_cummin_max_skipna_multiple_cols(method):
|
# Ensure missing value in "a" doesn't cause "b" to be nan-filled
|
df = DataFrame({"a": [np.nan, 2.0, 2.0], "b": [2.0, 2.0, 2.0]})
|
gb = df.groupby([1, 1, 1])[["a", "b"]]
|
|
result = getattr(gb, method)(skipna=False)
|
expected = DataFrame({"a": [np.nan, np.nan, np.nan], "b": [2.0, 2.0, 2.0]})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
@td.skip_if_32bit
|
@pytest.mark.parametrize("method", ["cummin", "cummax"])
|
@pytest.mark.parametrize(
|
"dtype,val", [("UInt64", np.iinfo("uint64").max), ("Int64", 2**53 + 1)]
|
)
|
def test_nullable_int_not_cast_as_float(method, dtype, val):
|
data = [val, pd.NA]
|
df = DataFrame({"grp": [1, 1], "b": data}, dtype=dtype)
|
grouped = df.groupby("grp")
|
|
result = grouped.transform(method)
|
expected = DataFrame({"b": data}, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"in_vals, out_vals",
|
[
|
# Basics: strictly increasing (T), strictly decreasing (F),
|
# abs val increasing (F), non-strictly increasing (T)
|
([1, 2, 5, 3, 2, 0, 4, 5, -6, 1, 1], [True, False, False, True]),
|
# Test with inf vals
|
(
|
[1, 2.1, np.inf, 3, 2, np.inf, -np.inf, 5, 11, 1, -np.inf],
|
[True, False, True, False],
|
),
|
# Test with nan vals; should always be False
|
(
|
[1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
|
[False, False, False, False],
|
),
|
],
|
)
|
def test_is_monotonic_increasing(in_vals, out_vals):
|
# GH 17015
|
source_dict = {
|
"A": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
|
"B": ["a", "a", "a", "b", "b", "b", "c", "c", "c", "d", "d"],
|
"C": in_vals,
|
}
|
df = DataFrame(source_dict)
|
result = df.groupby("B").C.is_monotonic_increasing
|
index = Index(list("abcd"), name="B")
|
expected = Series(index=index, data=out_vals, name="C")
|
tm.assert_series_equal(result, expected)
|
|
# Also check result equal to manually taking x.is_monotonic_increasing.
|
expected = df.groupby(["B"]).C.apply(lambda x: x.is_monotonic_increasing)
|
tm.assert_series_equal(result, expected)
|
|
|
@pytest.mark.parametrize(
|
"in_vals, out_vals",
|
[
|
# Basics: strictly decreasing (T), strictly increasing (F),
|
# abs val decreasing (F), non-strictly increasing (T)
|
([10, 9, 7, 3, 4, 5, -3, 2, 0, 1, 1], [True, False, False, True]),
|
# Test with inf vals
|
(
|
[np.inf, 1, -np.inf, np.inf, 2, -3, -np.inf, 5, -3, -np.inf, -np.inf],
|
[True, True, False, True],
|
),
|
# Test with nan vals; should always be False
|
(
|
[1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
|
[False, False, False, False],
|
),
|
],
|
)
|
def test_is_monotonic_decreasing(in_vals, out_vals):
|
# GH 17015
|
source_dict = {
|
"A": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
|
"B": ["a", "a", "a", "b", "b", "b", "c", "c", "c", "d", "d"],
|
"C": in_vals,
|
}
|
|
df = DataFrame(source_dict)
|
result = df.groupby("B").C.is_monotonic_decreasing
|
index = Index(list("abcd"), name="B")
|
expected = Series(index=index, data=out_vals, name="C")
|
tm.assert_series_equal(result, expected)
|
|
|
# describe
|
# --------------------------------
|
|
|
def test_apply_describe_bug(mframe):
|
grouped = mframe.groupby(level="first")
|
grouped.describe() # it works!
|
|
|
def test_series_describe_multikey():
|
ts = tm.makeTimeSeries()
|
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 = tm.makeTimeSeries()
|
grouped = ts.groupby(lambda x: x.month)
|
result = grouped.apply(lambda x: x.describe())
|
expected = grouped.describe().stack()
|
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_series_index_name(df):
|
grouped = df.loc[:, ["C"]].groupby(df["A"])
|
result = grouped.agg(lambda x: x.mean())
|
assert result.index.name == "A"
|
|
|
def test_frame_describe_multikey(tsframe):
|
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=[[col], 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)
|
|
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)
|
|
|
def test_groupby_mean_no_overflow():
|
# Regression test for (#22487)
|
df = DataFrame(
|
{
|
"user": ["A", "A", "A", "A", "A"],
|
"connections": [4970, 4749, 4719, 4704, 18446744073699999744],
|
}
|
)
|
assert df.groupby("user")["connections"].mean()["A"] == 3689348814740003840
|
|
|
@pytest.mark.parametrize(
|
"values",
|
[
|
{
|
"a": [1, 1, 1, 2, 2, 2, 3, 3, 3],
|
"b": [1, pd.NA, 2, 1, pd.NA, 2, 1, pd.NA, 2],
|
},
|
{"a": [1, 1, 2, 2, 3, 3], "b": [1, 2, 1, 2, 1, 2]},
|
],
|
)
|
@pytest.mark.parametrize("function", ["mean", "median", "var"])
|
def test_apply_to_nullable_integer_returns_float(values, function):
|
# https://github.com/pandas-dev/pandas/issues/32219
|
output = 0.5 if function == "var" else 1.5
|
arr = np.array([output] * 3, dtype=float)
|
idx = Index([1, 2, 3], name="a", dtype="Int64")
|
expected = DataFrame({"b": arr}, index=idx).astype("Float64")
|
|
groups = DataFrame(values, dtype="Int64").groupby("a")
|
|
result = getattr(groups, function)()
|
tm.assert_frame_equal(result, expected)
|
|
result = groups.agg(function)
|
tm.assert_frame_equal(result, expected)
|
|
result = groups.agg([function])
|
expected.columns = MultiIndex.from_tuples([("b", function)])
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_groupby_sum_below_mincount_nullable_integer():
|
# https://github.com/pandas-dev/pandas/issues/32861
|
df = DataFrame({"a": [0, 1, 2], "b": [0, 1, 2], "c": [0, 1, 2]}, dtype="Int64")
|
grouped = df.groupby("a")
|
idx = Index([0, 1, 2], name="a", dtype="Int64")
|
|
result = grouped["b"].sum(min_count=2)
|
expected = Series([pd.NA] * 3, dtype="Int64", index=idx, name="b")
|
tm.assert_series_equal(result, expected)
|
|
result = grouped.sum(min_count=2)
|
expected = DataFrame({"b": [pd.NA] * 3, "c": [pd.NA] * 3}, dtype="Int64", index=idx)
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_mean_on_timedelta():
|
# GH 17382
|
df = DataFrame({"time": pd.to_timedelta(range(10)), "cat": ["A", "B"] * 5})
|
result = df.groupby("cat")["time"].mean()
|
expected = Series(
|
pd.to_timedelta([4, 5]), name="time", index=Index(["A", "B"], name="cat")
|
)
|
tm.assert_series_equal(result, expected)
|
|
|
def test_groupby_sum_timedelta_with_nat():
|
# GH#42659
|
df = DataFrame(
|
{
|
"a": [1, 1, 2, 2],
|
"b": [pd.Timedelta("1d"), pd.Timedelta("2d"), pd.Timedelta("3d"), pd.NaT],
|
}
|
)
|
td3 = pd.Timedelta(days=3)
|
|
gb = df.groupby("a")
|
|
res = gb.sum()
|
expected = DataFrame({"b": [td3, td3]}, index=Index([1, 2], name="a"))
|
tm.assert_frame_equal(res, expected)
|
|
res = gb["b"].sum()
|
tm.assert_series_equal(res, expected["b"])
|
|
res = gb["b"].sum(min_count=2)
|
expected = Series([td3, pd.NaT], dtype="m8[ns]", name="b", index=expected.index)
|
tm.assert_series_equal(res, expected)
|
|
|
@pytest.mark.parametrize(
|
"kernel, has_arg",
|
[
|
("all", False),
|
("any", False),
|
("bfill", False),
|
("corr", True),
|
("corrwith", True),
|
("cov", True),
|
("cummax", True),
|
("cummin", True),
|
("cumprod", True),
|
("cumsum", True),
|
("diff", False),
|
("ffill", False),
|
("fillna", False),
|
("first", True),
|
("idxmax", True),
|
("idxmin", True),
|
("last", True),
|
("max", True),
|
("mean", True),
|
("median", True),
|
("min", True),
|
("nth", False),
|
("nunique", False),
|
("pct_change", False),
|
("prod", True),
|
("quantile", True),
|
("sem", True),
|
("skew", True),
|
("std", True),
|
("sum", True),
|
("var", True),
|
],
|
)
|
@pytest.mark.parametrize("numeric_only", [True, False, lib.no_default])
|
@pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
|
def test_numeric_only(kernel, has_arg, numeric_only, keys):
|
# GH#46072
|
# drops_nuisance: Whether the op drops nuisance columns even when numeric_only=False
|
# has_arg: Whether the op has a numeric_only arg
|
df = DataFrame({"a1": [1, 1], "a2": [2, 2], "a3": [5, 6], "b": 2 * [object]})
|
|
args = get_groupby_method_args(kernel, df)
|
kwargs = {} if numeric_only is lib.no_default else {"numeric_only": numeric_only}
|
|
gb = df.groupby(keys)
|
method = getattr(gb, kernel)
|
if has_arg and numeric_only is True:
|
# Cases where b does not appear in the result
|
result = method(*args, **kwargs)
|
assert "b" not in result.columns
|
elif (
|
# kernels that work on any dtype and have numeric_only arg
|
kernel in ("first", "last")
|
or (
|
# kernels that work on any dtype and don't have numeric_only arg
|
kernel in ("any", "all", "bfill", "ffill", "fillna", "nth", "nunique")
|
and numeric_only is lib.no_default
|
)
|
):
|
result = method(*args, **kwargs)
|
assert "b" in result.columns
|
elif has_arg or kernel in ("idxmax", "idxmin"):
|
assert numeric_only is not True
|
# kernels that are successful on any dtype were above; this will fail
|
|
# object dtypes for transformations are not implemented in Cython and
|
# have no Python fallback
|
exception = NotImplementedError if kernel.startswith("cum") else TypeError
|
|
msg = "|".join(
|
[
|
"not allowed for this dtype",
|
"must be a string or a number",
|
"cannot be performed against 'object' dtypes",
|
"must be a string or a real number",
|
"unsupported operand type",
|
"not supported between instances of",
|
"function is not implemented for this dtype",
|
]
|
)
|
with pytest.raises(exception, match=msg):
|
method(*args, **kwargs)
|
elif not has_arg and numeric_only is not lib.no_default:
|
with pytest.raises(
|
TypeError, match="got an unexpected keyword argument 'numeric_only'"
|
):
|
method(*args, **kwargs)
|
else:
|
assert kernel in ("diff", "pct_change")
|
assert numeric_only is lib.no_default
|
# Doesn't have numeric_only argument and fails on nuisance columns
|
with pytest.raises(TypeError, match=r"unsupported operand type"):
|
method(*args, **kwargs)
|
|
|
@pytest.mark.parametrize("dtype", [bool, int, float, object])
|
def test_deprecate_numeric_only_series(dtype, groupby_func, request):
|
# GH#46560
|
if groupby_func == "corrwith":
|
msg = "corrwith is not implemented on SeriesGroupBy"
|
request.node.add_marker(pytest.mark.xfail(reason=msg))
|
|
grouper = [0, 0, 1]
|
|
ser = Series([1, 0, 0], dtype=dtype)
|
gb = ser.groupby(grouper)
|
method = getattr(gb, groupby_func)
|
|
expected_ser = Series([1, 0, 0])
|
expected_gb = expected_ser.groupby(grouper)
|
expected_method = getattr(expected_gb, groupby_func)
|
|
args = get_groupby_method_args(groupby_func, ser)
|
|
fails_on_numeric_object = (
|
"corr",
|
"cov",
|
"cummax",
|
"cummin",
|
"cumprod",
|
"cumsum",
|
"idxmax",
|
"idxmin",
|
"quantile",
|
)
|
# ops that give an object result on object input
|
obj_result = (
|
"first",
|
"last",
|
"nth",
|
"bfill",
|
"ffill",
|
"shift",
|
"sum",
|
"diff",
|
"pct_change",
|
"var",
|
"mean",
|
"median",
|
"min",
|
"max",
|
"prod",
|
)
|
|
# Test default behavior; kernels that fail may be enabled in the future but kernels
|
# that succeed should not be allowed to fail (without deprecation, at least)
|
if groupby_func in fails_on_numeric_object and dtype is object:
|
if groupby_func in ("idxmax", "idxmin"):
|
msg = "not allowed for this dtype"
|
elif groupby_func == "quantile":
|
msg = "cannot be performed against 'object' dtypes"
|
else:
|
msg = "is not supported for object dtype"
|
with pytest.raises(TypeError, match=msg):
|
method(*args)
|
elif dtype is object:
|
result = method(*args)
|
expected = expected_method(*args)
|
if groupby_func in obj_result:
|
expected = expected.astype(object)
|
tm.assert_series_equal(result, expected)
|
|
has_numeric_only = (
|
"first",
|
"last",
|
"max",
|
"mean",
|
"median",
|
"min",
|
"prod",
|
"quantile",
|
"sem",
|
"skew",
|
"std",
|
"sum",
|
"var",
|
"cummax",
|
"cummin",
|
"cumprod",
|
"cumsum",
|
)
|
if groupby_func not in has_numeric_only:
|
msg = "got an unexpected keyword argument 'numeric_only'"
|
with pytest.raises(TypeError, match=msg):
|
method(*args, numeric_only=True)
|
elif dtype is object:
|
msg = "|".join(
|
[
|
"SeriesGroupBy.sem called with numeric_only=True and dtype object",
|
"Series.skew does not allow numeric_only=True with non-numeric",
|
"cum(sum|prod|min|max) is not supported for object dtype",
|
r"Cannot use numeric_only=True with SeriesGroupBy\..* and non-numeric",
|
]
|
)
|
with pytest.raises(TypeError, match=msg):
|
method(*args, numeric_only=True)
|
else:
|
result = method(*args, numeric_only=True)
|
expected = method(*args, numeric_only=False)
|
tm.assert_series_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([])
|
tm.assert_frame_equal(result, expected)
|
|
|
def test_corrwith_with_1_axis():
|
# GH 47723
|
df = DataFrame({"a": [1, 1, 2], "b": [3, 7, 4]})
|
result = df.groupby("a").corrwith(df, axis=1)
|
index = Index(
|
data=[(1, 0), (1, 1), (1, 2), (2, 2), (2, 0), (2, 1)],
|
name=("a", None),
|
)
|
expected = Series([np.nan] * 6, index=index)
|
tm.assert_series_equal(result, expected)
|
|
|
def test_multiindex_group_all_columns_when_empty(groupby_func):
|
# GH 32464
|
df = DataFrame({"a": [], "b": [], "c": []}).set_index(["a", "b", "c"])
|
gb = df.groupby(["a", "b", "c"], group_keys=False)
|
method = getattr(gb, groupby_func)
|
args = get_groupby_method_args(groupby_func, df)
|
|
result = method(*args).index
|
expected = df.index
|
tm.assert_index_equal(result, expected)
|
|
|
def test_duplicate_columns(request, groupby_func, as_index):
|
# GH#50806
|
if groupby_func == "corrwith":
|
msg = "GH#50845 - corrwith fails when there are duplicate columns"
|
request.node.add_marker(pytest.mark.xfail(reason=msg))
|
df = DataFrame([[1, 3, 6], [1, 4, 7], [2, 5, 8]], columns=list("abb"))
|
args = get_groupby_method_args(groupby_func, df)
|
gb = df.groupby("a", as_index=as_index)
|
result = getattr(gb, groupby_func)(*args)
|
|
expected_df = df.set_axis(["a", "b", "c"], axis=1)
|
expected_args = get_groupby_method_args(groupby_func, expected_df)
|
expected_gb = expected_df.groupby("a", as_index=as_index)
|
expected = getattr(expected_gb, groupby_func)(*expected_args)
|
if groupby_func not in ("size", "ngroup", "cumcount"):
|
expected = expected.rename(columns={"c": "b"})
|
tm.assert_equal(result, expected)
|
