text: """
  test all other .agg behavior
  """
  
  import datetime as dt
  from functools import partial
  
  import numpy as np
  import pytest
  
  import pandas as pd
  from pandas import (
      DataFrame,
      Index,
      MultiIndex,
      PeriodIndex,
      Series,
      date_range,
      period_range,
  )
  import pandas._testing as tm
  from pandas.core.base import SpecificationError
  
  from pandas.io.formats.printing import pprint_thing
  
  
  def test_agg_api():
      # GH 6337
      # https://stackoverflow.com/questions/21706030/pandas-groupby-agg-function-column-dtype-error
      # different api for agg when passed custom function with mixed frame
  
      df = DataFrame(
          {
              "data1": np.random.randn(5),
              "data2": np.random.randn(5),
              "key1": ["a", "a", "b", "b", "a"],
              "key2": ["one", "two", "one", "two", "one"],
          }
      )
      grouped = df.groupby("key1")
  
      def peak_to_peak(arr):
          return arr.max() - arr.min()
  
      expected = grouped.agg([peak_to_peak])
      expected.columns = ["data1", "data2"]
      result = grouped.agg(peak_to_peak)
      tm.assert_frame_equal(result, expected)
  
  
  def test_agg_datetimes_mixed():
      data = [[1, "2012-01-01", 1.0], [2, "2012-01-02", 2.0], [3, None, 3.0]]
  
      df1 = DataFrame(
          {
              "key": [x[0] for x in data],
              "date": [x[1] for x in data],
              "value": [x[2] for x in data],
          }
      )
  
      data = [
          [
              row[0],
              (dt.datetime.strptime(row[1], "%Y-%m-%d").date() if row[1] else None),
              row[2],
          ]
          for row in data
      ]
  
      df2 = DataFrame(
          {
              "key": [x[0] for x in data],
              "date": [x[1] for x in data],
              "value": [x[2] for x in data],
          }
      )
  
      df1["weights"] = df1["value"] / df1["value"].sum()
      gb1 = df1.groupby("date").aggregate(np.sum)
  
      df2["weights"] = df1["value"] / df1["value"].sum()
      gb2 = df2.groupby("date").aggregate(np.sum)
  
      assert len(gb1) == len(gb2)
  
  
  def test_agg_period_index():
      prng = period_range("2012-1-1", freq="M", periods=3)
      df = DataFrame(np.random.randn(3, 2), index=prng)
      rs = df.groupby(level=0).sum()
      assert isinstance(rs.index, PeriodIndex)
  
      # GH 3579
      index = period_range(start="1999-01", periods=5, freq="M")
      s1 = Series(np.random.rand(len(index)), index=index)
      s2 = Series(np.random.rand(len(index)), index=index)
      df = DataFrame.from_dict({"s1": s1, "s2": s2})
      grouped = df.groupby(df.index.month)
      list(grouped)
  
  
  def test_agg_dict_parameter_cast_result_dtypes():
      # GH 12821
  
      df = DataFrame(
          {
              "class": ["A", "A", "B", "B", "C", "C", "D", "D"],
              "time": date_range("1/1/2011", periods=8, freq="H"),
          }
      )
      df.loc[[0, 1, 2, 5], "time"] = None
  
      # test for `first` function
      exp = df.loc[[0, 3, 4, 6]].set_index("class")
      grouped = df.groupby("class")
      tm.assert_frame_equal(grouped.first(), exp)
      tm.assert_frame_equal(grouped.agg("first"), exp)
      tm.assert_frame_equal(grouped.agg({"time": "first"}), exp)
      tm.assert_series_equal(grouped.time.first(), exp["time"])
      tm.assert_series_equal(grouped.time.agg("first"), exp["time"])
  
      # test for `last` function
      exp = df.loc[[0, 3, 4, 7]].set_index("class")
      grouped = df.groupby("class")
      tm.assert_frame_equal(grouped.last(), exp)
      tm.assert_frame_equal(grouped.agg("last"), exp)
      tm.assert_frame_equal(grouped.agg({"time": "last"}), exp)
      tm.assert_series_equal(grouped.time.last(), exp["time"])
      tm.assert_series_equal(grouped.time.agg("last"), exp["time"])
  
      # count
      exp = Series([2, 2, 2, 2], index=Index(list("ABCD"), name="class"), name="time")
      tm.assert_series_equal(grouped.time.agg(len), exp)
      tm.assert_series_equal(grouped.time.size(), exp)
  
      exp = Series([0, 1, 1, 2], index=Index(list("ABCD"), name="class"), name="time")
      tm.assert_series_equal(grouped.time.count(), exp)
  
  
  def test_agg_cast_results_dtypes():
      # similar to GH12821
      # xref #11444
      u = [dt.datetime(2015, x + 1, 1) for x in range(12)]
      v = list("aaabbbbbbccd")
      df = DataFrame({"X": v, "Y": u})
  
      result = df.groupby("X")["Y"].agg(len)
      expected = df.groupby("X")["Y"].count()
      tm.assert_series_equal(result, expected)
  
  
  def test_aggregate_float64_no_int64():
      # see gh-11199
      df = DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 2, 2, 4, 5], "c": [1, 2, 3, 4, 5]})
  
      expected = DataFrame({"a": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
      expected.index.name = "b"
  
      result = df.groupby("b")[["a"]].mean()
      tm.assert_frame_equal(result, expected)
  
      expected = DataFrame({"a": [1, 2.5, 4, 5], "c": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
      expected.index.name = "b"
  
      result = df.groupby("b")[["a", "c"]].mean()
      tm.assert_frame_equal(result, expected)
  
  
  def test_aggregate_api_consistency():
      # GH 9052
      # make sure that the aggregates via dict
      # are consistent
      df = DataFrame(
          {
              "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
              "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
              "C": np.random.randn(8) + 1.0,
              "D": np.arange(8),
          }
      )
  
      grouped = df.groupby(["A", "B"])
      c_mean = grouped["C"].mean()
      c_sum = grouped["C"].sum()
      d_mean = grouped["D"].mean()
      d_sum = grouped["D"].sum()
  
      result = grouped["D"].agg(["sum", "mean"])
      expected = pd.concat([d_sum, d_mean], axis=1)
      expected.columns = ["sum", "mean"]
      tm.assert_frame_equal(result, expected, check_like=True)
  
      result = grouped.agg([np.sum, np.mean])
      expected = pd.concat([c_sum, c_mean, d_sum, d_mean], axis=1)
      expected.columns = MultiIndex.from_product([["C", "D"], ["sum", "mean"]])
      tm.assert_frame_equal(result, expected, check_like=True)
  
      result = grouped[["D", "C"]].agg([np.sum, np.mean])
      expected = pd.concat([d_sum, d_mean, c_sum, c_mean], axis=1)
      expected.columns = MultiIndex.from_product([["D", "C"], ["sum", "mean"]])
      tm.assert_frame_equal(result, expected, check_like=True)
  
      result = grouped.agg({"C": "mean", "D": "sum"})
      expected = pd.concat([d_sum, c_mean], axis=1)
      tm.assert_frame_equal(result, expected, check_like=True)
  
      result = grouped.agg({"C": ["mean", "sum"], "D": ["mean", "sum"]})
      expected = pd.concat([c_mean, c_sum, d_mean, d_sum], axis=1)
      expected.columns = MultiIndex.from_product([["C", "D"], ["mean", "sum"]])
  
      msg = r"Column\(s\) \['r', 'r2'\] do not exist"
      with pytest.raises(SpecificationError, match=msg):
          grouped[["D", "C"]].agg({"r": np.sum, "r2": np.mean})
  
  
  def test_agg_dict_renaming_deprecation():
      # 15931
      df = DataFrame({"A": [1, 1, 1, 2, 2], "B": range(5), "C": range(5)})
  
      msg = r"nested renamer is not supported"
      with pytest.raises(SpecificationError, match=msg):
          df.groupby("A").agg(
              {"B": {"foo": ["sum", "max"]}, "C": {"bar": ["count", "min"]}}
          )
  
      msg = r"Column\(s\) \['ma'\] do not exist"
      with pytest.raises(SpecificationError, match=msg):
          df.groupby("A")[["B", "C"]].agg({"ma": "max"})
  
      msg = r"nested renamer is not supported"
      with pytest.raises(SpecificationError, match=msg):
          df.groupby("A").B.agg({"foo": "count"})
  
  
  def test_agg_compat():
      # GH 12334
      df = DataFrame(
          {
              "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
              "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
              "C": np.random.randn(8) + 1.0,
              "D": np.arange(8),
          }
      )
  
      g = df.groupby(["A", "B"])
  
      msg = r"nested renamer is not supported"
      with pytest.raises(SpecificationError, match=msg):
          g["D"].agg({"C": ["sum", "std"]})
  
      with pytest.raises(SpecificationError, match=msg):
          g["D"].agg({"C": "sum", "D": "std"})
  
  
  def test_agg_nested_dicts():
      # API change for disallowing these types of nested dicts
      df = DataFrame(
          {
              "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
              "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
              "C": np.random.randn(8) + 1.0,
              "D": np.arange(8),
          }
      )
  
      g = df.groupby(["A", "B"])
  
      msg = r"nested renamer is not supported"
      with pytest.raises(SpecificationError, match=msg):
          g.aggregate({"r1": {"C": ["mean", "sum"]}, "r2": {"D": ["mean", "sum"]}})
  
      with pytest.raises(SpecificationError, match=msg):
          g.agg({"C": {"ra": ["mean", "std"]}, "D": {"rb": ["mean", "std"]}})
  
      # same name as the original column
      # GH9052
      with pytest.raises(SpecificationError, match=msg):
          g["D"].agg({"result1": np.sum, "result2": np.mean})
  
      with pytest.raises(SpecificationError, match=msg):
          g["D"].agg({"D": np.sum, "result2": np.mean})
  
  
  def test_agg_item_by_item_raise_typeerror():
      df = DataFrame(np.random.randint(10, size=(20, 10)))
  
      def raiseException(df):
          pprint_thing("----------------------------------------")
          pprint_thing(df.to_string())
          raise TypeError("test")
  
      with pytest.raises(TypeError, match="test"):
          df.groupby(0).agg(raiseException)
  
  
  def test_series_agg_multikey():
      ts = tm.makeTimeSeries()
      grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
  
      result = grouped.agg(np.sum)
      expected = grouped.sum()
      tm.assert_series_equal(result, expected)
  
  
  def test_series_agg_multi_pure_python():
      data = DataFrame(
          {
              "A": [
                  "foo",
                  "foo",
                  "foo",
                  "foo",
                  "bar",
                  "bar",
                  "bar",
                  "bar",
                  "foo",
                  "foo",
                  "foo",
              ],
              "B": [
                  "one",
                  "one",
                  "one",
                  "two",
                  "one",
                  "one",
                  "one",
                  "two",
                  "two",
                  "two",
                  "one",
              ],
              "C": [
                  "dull",
                  "dull",
                  "shiny",
                  "dull",
                  "dull",
                  "shiny",
                  "shiny",
                  "dull",
                  "shiny",
                  "shiny",
                  "shiny",
              ],
              "D": np.random.randn(11),
              "E": np.random.randn(11),
              "F": np.random.randn(11),
          }
      )
  
      def bad(x):
          assert len(x.values.base) > 0
          return "foo"
  
      result = data.groupby(["A", "B"]).agg(bad)
      expected = data.groupby(["A", "B"]).agg(lambda x: "foo")
      tm.assert_frame_equal(result, expected)
  
  
  def test_agg_consistency():
      # agg with ([]) and () not consistent
      # GH 6715
      def P1(a):
          return np.percentile(a.dropna(), q=1)
  
      df = DataFrame(
          {
              "col1": [1, 2, 3, 4],
              "col2": [10, 25, 26, 31],
              "date": [
                  dt.date(2013, 2, 10),
                  dt.date(2013, 2, 10),
                  dt.date(2013, 2, 11),
                  dt.date(2013, 2, 11),
              ],
          }
      )
  
      g = df.groupby("date")
  
      expected = g.agg([P1])
      expected.columns = expected.columns.levels[0]
  
      result = g.agg(P1)
      tm.assert_frame_equal(result, expected)
  
  
  def test_agg_callables():
      # GH 7929
      df = DataFrame({"foo": [1, 2], "bar": [3, 4]}).astype(np.int64)
  
      class fn_class:
          def __call__(self, x):
              return sum(x)
  
      equiv_callables = [
          sum,
          np.sum,
          lambda x: sum(x),
          lambda x: x.sum(),
          partial(sum),
          fn_class(),
      ]
  
      expected = df.groupby("foo").agg(sum)
      for ecall in equiv_callables:
          result = df.groupby("foo").agg(ecall)
          tm.assert_frame_equal(result, expected)
  
  
  def test_agg_over_numpy_arrays():
      # GH 3788
      df = DataFrame(
          [
              [1, np.array([10, 20, 30])],
              [1, np.array([40, 50, 60])],
              [2, np.array([20, 30, 40])],
          ],
          columns=["category", "arraydata"],
      )
      result = df.groupby("category").agg(sum)
  
      expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]]
      expected_index = Index([1, 2], name="category")
      expected_column = ["arraydata"]
      expected = DataFrame(expected_data, index=expected_index, columns=expected_column)
  
      tm.assert_frame_equal(result, expected)
  
  
  def test_agg_tzaware_non_datetime_result():
      # discussed in GH#29589, fixed in GH#29641, operating on tzaware values
      #  with function that is not dtype-preserving
      dti = pd.date_range("2012-01-01", periods=4, tz="UTC")
      df = DataFrame({"a": [0, 0, 1, 1], "b": dti})
      gb = df.groupby("a")
  
      # Case that _does_ preserve the dtype
      result = gb["b"].agg(lambda x: x.iloc[0])
      expected = Series(dti[::2], name="b")
      expected.index.name = "a"
      tm.assert_series_equal(result, expected)
  
      # Cases that do _not_ preserve the dtype
      result = gb["b"].agg(lambda x: x.iloc[0].year)
      expected = Series([2012, 2012], name="b")
      expected.index.name = "a"
      tm.assert_series_equal(result, expected)
  
      result = gb["b"].agg(lambda x: x.iloc[-1] - x.iloc[0])
      expected = Series([pd.Timedelta(days=1), pd.Timedelta(days=1)], name="b")
      expected.index.name = "a"
      tm.assert_series_equal(result, expected)
  
  
  def test_agg_timezone_round_trip():
      # GH 15426
      ts = pd.Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
      df = DataFrame({"a": 1, "b": [ts + dt.timedelta(minutes=nn) for nn in range(10)]})
  
      result1 = df.groupby("a")["b"].agg(np.min).iloc[0]
      result2 = df.groupby("a")["b"].agg(lambda x: np.min(x)).iloc[0]
      result3 = df.groupby("a")["b"].min().iloc[0]
  
      assert result1 == ts
      assert result2 == ts
      assert result3 == ts
  
      dates = [
          pd.Timestamp(f"2016-01-0{i:d} 12:00:00", tz="US/Pacific") for i in range(1, 5)
      ]
      df = DataFrame({"A": ["a", "b"] * 2, "B": dates})
      grouped = df.groupby("A")
  
      ts = df["B"].iloc[0]
      assert ts == grouped.nth(0)["B"].iloc[0]
      assert ts == grouped.head(1)["B"].iloc[0]
      assert ts == grouped.first()["B"].iloc[0]
  
      # GH#27110 applying iloc should return a DataFrame
      assert ts == grouped.apply(lambda x: x.iloc[0]).iloc[0, 1]
  
      ts = df["B"].iloc[2]
      assert ts == grouped.last()["B"].iloc[0]
  
      # GH#27110 applying iloc should return a DataFrame
      assert ts == grouped.apply(lambda x: x.iloc[-1]).iloc[0, 1]
  
  
  def test_sum_uint64_overflow():
      # see gh-14758
      # Convert to uint64 and don't overflow
      df = DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object)
      df = df + 9223372036854775807
  
      index = Index(
          [9223372036854775808, 9223372036854775810, 9223372036854775812], dtype=np.uint64
      )
      expected = DataFrame(
          {1: [9223372036854775809, 9223372036854775811, 9223372036854775813]},
          index=index,
      )
  
      expected.index.name = 0
      result = df.groupby(0).sum()
      tm.assert_frame_equal(result, expected)
  
  
  @pytest.mark.parametrize(
      "structure, expected",
      [
          (tuple, DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}})),
          (list, DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}})),
          (
              lambda x: tuple(x),
              DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}}),
          ),
          (
              lambda x: list(x),
              DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}}),
          ),
      ],
  )
  def test_agg_structs_dataframe(structure, expected):
      df = DataFrame(
          {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
      )
  
      result = df.groupby(["A", "B"]).aggregate(structure)
      expected.index.names = ["A", "B"]
      tm.assert_frame_equal(result, expected)
  
  
  @pytest.mark.parametrize(
      "structure, expected",
      [
          (tuple, Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
          (list, Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
          (lambda x: tuple(x), Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
          (lambda x: list(x), Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
      ],
  )
  def test_agg_structs_series(structure, expected):
      # Issue #18079
      df = DataFrame(
          {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
      )
  
      result = df.groupby("A")["C"].aggregate(structure)
      expected.index.name = "A"
      tm.assert_series_equal(result, expected)
  
  
  def test_agg_category_nansum(observed):
      categories = ["a", "b", "c"]
      df = DataFrame(
          {"A": pd.Categorical(["a", "a", "b"], categories=categories), "B": [1, 2, 3]}
      )
      result = df.groupby("A", observed=observed).B.agg(np.nansum)
      expected = Series(
          [3, 3, 0],
          index=pd.CategoricalIndex(["a", "b", "c"], categories=categories, name="A"),
          name="B",
      )
      if observed:
          expected = expected[expected != 0]
      tm.assert_series_equal(result, expected)
  
  
  def test_agg_list_like_func():
      # GH 18473
      df = DataFrame({"A": [str(x) for x in range(3)], "B": [str(x) for x in range(3)]})
      grouped = df.groupby("A", as_index=False, sort=False)
      result = grouped.agg({"B": lambda x: list(x)})
      expected = DataFrame(
          {"A": [str(x) for x in range(3)], "B": [[str(x)] for x in range(3)]}
      )
      tm.assert_frame_equal(result, expected)
  
  
  def test_agg_lambda_with_timezone():
      # GH 23683
      df = DataFrame(
          {
              "tag": [1, 1],
              "date": [
                  pd.Timestamp("2018-01-01", tz="UTC"),
                  pd.Timestamp("2018-01-02", tz="UTC"),
              ],
          }
      )
      result = df.groupby("tag").agg({"date": lambda e: e.head(1)})
      expected = DataFrame(
          [pd.Timestamp("2018-01-01", tz="UTC")],
          index=Index([1], name="tag"),
          columns=["date"],
      )
      tm.assert_frame_equal(result, expected)
  
  
  @pytest.mark.parametrize(
      "err_cls",
      [
          NotImplementedError,
          RuntimeError,
          KeyError,
          IndexError,
          OSError,
          ValueError,
          ArithmeticError,
          AttributeError,
      ],
  )
  def test_groupby_agg_err_catching(err_cls):
      # make sure we suppress anything other than TypeError or AssertionError
      #  in _python_agg_general
  
      # Use a non-standard EA to make sure we don't go down ndarray paths
      from pandas.tests.extension.decimal.array import DecimalArray, make_data, to_decimal
  
      data = make_data()[:5]
      df = DataFrame(
          {"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
      )
  
      expected = Series(to_decimal([data[0], data[3]]))
  
      def weird_func(x):
          # weird function that raise something other than TypeError or IndexError
          #  in _python_agg_general
          if len(x) == 0:
              raise err_cls
          return x.iloc[0]
  
      result = df["decimals"].groupby(df["id1"]).agg(weird_func)
      tm.assert_series_equal(result, expected, check_names=False)
  