675 lines
20 KiB
Python
675 lines
20 KiB
Python
"""
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test all other .agg behavior
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"""
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import datetime as dt
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from functools import partial
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import numpy as np
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import pytest
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from pandas.errors import SpecificationError
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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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MultiIndex,
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PeriodIndex,
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Series,
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date_range,
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period_range,
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)
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import pandas._testing as tm
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from pandas.io.formats.printing import pprint_thing
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def test_agg_api():
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# GH 6337
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# https://stackoverflow.com/questions/21706030/pandas-groupby-agg-function-column-dtype-error
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# different api for agg when passed custom function with mixed frame
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df = DataFrame(
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{
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"data1": np.random.randn(5),
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"data2": np.random.randn(5),
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"key1": ["a", "a", "b", "b", "a"],
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"key2": ["one", "two", "one", "two", "one"],
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}
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)
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grouped = df.groupby("key1")
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def peak_to_peak(arr):
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return arr.max() - arr.min()
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with tm.assert_produces_warning(
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FutureWarning,
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match=r"\['key2'\] did not aggregate successfully",
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):
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expected = grouped.agg([peak_to_peak])
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expected.columns = ["data1", "data2"]
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with tm.assert_produces_warning(
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FutureWarning,
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match=r"\['key2'\] did not aggregate successfully",
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):
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result = grouped.agg(peak_to_peak)
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tm.assert_frame_equal(result, expected)
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def test_agg_datetimes_mixed():
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data = [[1, "2012-01-01", 1.0], [2, "2012-01-02", 2.0], [3, None, 3.0]]
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df1 = DataFrame(
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{
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"key": [x[0] for x in data],
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"date": [x[1] for x in data],
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"value": [x[2] for x in data],
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}
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)
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data = [
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[
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row[0],
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(dt.datetime.strptime(row[1], "%Y-%m-%d").date() if row[1] else None),
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row[2],
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]
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for row in data
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]
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df2 = DataFrame(
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{
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"key": [x[0] for x in data],
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"date": [x[1] for x in data],
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"value": [x[2] for x in data],
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}
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)
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df1["weights"] = df1["value"] / df1["value"].sum()
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gb1 = df1.groupby("date").aggregate(np.sum)
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df2["weights"] = df1["value"] / df1["value"].sum()
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gb2 = df2.groupby("date").aggregate(np.sum)
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assert len(gb1) == len(gb2)
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def test_agg_period_index():
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prng = period_range("2012-1-1", freq="M", periods=3)
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df = DataFrame(np.random.randn(3, 2), index=prng)
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rs = df.groupby(level=0).sum()
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assert isinstance(rs.index, PeriodIndex)
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# GH 3579
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index = period_range(start="1999-01", periods=5, freq="M")
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s1 = Series(np.random.rand(len(index)), index=index)
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s2 = Series(np.random.rand(len(index)), index=index)
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df = DataFrame.from_dict({"s1": s1, "s2": s2})
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grouped = df.groupby(df.index.month)
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list(grouped)
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def test_agg_dict_parameter_cast_result_dtypes():
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# GH 12821
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df = DataFrame(
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{
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"class": ["A", "A", "B", "B", "C", "C", "D", "D"],
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"time": date_range("1/1/2011", periods=8, freq="H"),
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}
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)
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df.loc[[0, 1, 2, 5], "time"] = None
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# test for `first` function
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exp = df.loc[[0, 3, 4, 6]].set_index("class")
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grouped = df.groupby("class")
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tm.assert_frame_equal(grouped.first(), exp)
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tm.assert_frame_equal(grouped.agg("first"), exp)
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tm.assert_frame_equal(grouped.agg({"time": "first"}), exp)
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tm.assert_series_equal(grouped.time.first(), exp["time"])
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tm.assert_series_equal(grouped.time.agg("first"), exp["time"])
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# test for `last` function
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exp = df.loc[[0, 3, 4, 7]].set_index("class")
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grouped = df.groupby("class")
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tm.assert_frame_equal(grouped.last(), exp)
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tm.assert_frame_equal(grouped.agg("last"), exp)
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tm.assert_frame_equal(grouped.agg({"time": "last"}), exp)
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tm.assert_series_equal(grouped.time.last(), exp["time"])
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tm.assert_series_equal(grouped.time.agg("last"), exp["time"])
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# count
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exp = Series([2, 2, 2, 2], index=Index(list("ABCD"), name="class"), name="time")
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tm.assert_series_equal(grouped.time.agg(len), exp)
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tm.assert_series_equal(grouped.time.size(), exp)
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exp = Series([0, 1, 1, 2], index=Index(list("ABCD"), name="class"), name="time")
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tm.assert_series_equal(grouped.time.count(), exp)
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def test_agg_cast_results_dtypes():
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# similar to GH12821
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# xref #11444
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u = [dt.datetime(2015, x + 1, 1) for x in range(12)]
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v = list("aaabbbbbbccd")
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df = DataFrame({"X": v, "Y": u})
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result = df.groupby("X")["Y"].agg(len)
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expected = df.groupby("X")["Y"].count()
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tm.assert_series_equal(result, expected)
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def test_aggregate_float64_no_int64():
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# see gh-11199
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df = DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 2, 2, 4, 5], "c": [1, 2, 3, 4, 5]})
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expected = DataFrame({"a": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
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expected.index.name = "b"
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result = df.groupby("b")[["a"]].mean()
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tm.assert_frame_equal(result, expected)
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expected = DataFrame({"a": [1, 2.5, 4, 5], "c": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
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expected.index.name = "b"
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result = df.groupby("b")[["a", "c"]].mean()
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tm.assert_frame_equal(result, expected)
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def test_aggregate_api_consistency():
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# GH 9052
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# make sure that the aggregates via dict
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# are consistent
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df = DataFrame(
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{
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"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
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"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
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"C": np.random.randn(8) + 1.0,
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"D": np.arange(8),
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}
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)
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grouped = df.groupby(["A", "B"])
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c_mean = grouped["C"].mean()
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c_sum = grouped["C"].sum()
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d_mean = grouped["D"].mean()
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d_sum = grouped["D"].sum()
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result = grouped["D"].agg(["sum", "mean"])
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expected = pd.concat([d_sum, d_mean], axis=1)
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expected.columns = ["sum", "mean"]
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tm.assert_frame_equal(result, expected, check_like=True)
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result = grouped.agg([np.sum, np.mean])
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expected = pd.concat([c_sum, c_mean, d_sum, d_mean], axis=1)
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expected.columns = MultiIndex.from_product([["C", "D"], ["sum", "mean"]])
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tm.assert_frame_equal(result, expected, check_like=True)
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result = grouped[["D", "C"]].agg([np.sum, np.mean])
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expected = pd.concat([d_sum, d_mean, c_sum, c_mean], axis=1)
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expected.columns = MultiIndex.from_product([["D", "C"], ["sum", "mean"]])
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tm.assert_frame_equal(result, expected, check_like=True)
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result = grouped.agg({"C": "mean", "D": "sum"})
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expected = pd.concat([d_sum, c_mean], axis=1)
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tm.assert_frame_equal(result, expected, check_like=True)
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result = grouped.agg({"C": ["mean", "sum"], "D": ["mean", "sum"]})
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expected = pd.concat([c_mean, c_sum, d_mean, d_sum], axis=1)
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expected.columns = MultiIndex.from_product([["C", "D"], ["mean", "sum"]])
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msg = r"Column\(s\) \['r', 'r2'\] do not exist"
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with pytest.raises(KeyError, match=msg):
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grouped[["D", "C"]].agg({"r": np.sum, "r2": np.mean})
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def test_agg_dict_renaming_deprecation():
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# 15931
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df = DataFrame({"A": [1, 1, 1, 2, 2], "B": range(5), "C": range(5)})
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msg = r"nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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df.groupby("A").agg(
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{"B": {"foo": ["sum", "max"]}, "C": {"bar": ["count", "min"]}}
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)
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msg = r"Column\(s\) \['ma'\] do not exist"
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with pytest.raises(KeyError, match=msg):
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df.groupby("A")[["B", "C"]].agg({"ma": "max"})
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msg = r"nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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df.groupby("A").B.agg({"foo": "count"})
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def test_agg_compat():
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# GH 12334
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df = DataFrame(
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{
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"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
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"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
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"C": np.random.randn(8) + 1.0,
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"D": np.arange(8),
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}
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)
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g = df.groupby(["A", "B"])
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msg = r"nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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g["D"].agg({"C": ["sum", "std"]})
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with pytest.raises(SpecificationError, match=msg):
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g["D"].agg({"C": "sum", "D": "std"})
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def test_agg_nested_dicts():
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# API change for disallowing these types of nested dicts
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df = DataFrame(
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{
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"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
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"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
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"C": np.random.randn(8) + 1.0,
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"D": np.arange(8),
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}
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)
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g = df.groupby(["A", "B"])
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msg = r"nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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g.aggregate({"r1": {"C": ["mean", "sum"]}, "r2": {"D": ["mean", "sum"]}})
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with pytest.raises(SpecificationError, match=msg):
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g.agg({"C": {"ra": ["mean", "std"]}, "D": {"rb": ["mean", "std"]}})
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# same name as the original column
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# GH9052
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with pytest.raises(SpecificationError, match=msg):
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g["D"].agg({"result1": np.sum, "result2": np.mean})
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with pytest.raises(SpecificationError, match=msg):
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g["D"].agg({"D": np.sum, "result2": np.mean})
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def test_agg_item_by_item_raise_typeerror():
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df = DataFrame(np.random.randint(10, size=(20, 10)))
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def raiseException(df):
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pprint_thing("----------------------------------------")
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pprint_thing(df.to_string())
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raise TypeError("test")
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with pytest.raises(TypeError, match="test"):
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with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"):
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df.groupby(0).agg(raiseException)
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def test_series_agg_multikey():
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ts = tm.makeTimeSeries()
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grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
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result = grouped.agg(np.sum)
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expected = grouped.sum()
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tm.assert_series_equal(result, expected)
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def test_series_agg_multi_pure_python():
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data = DataFrame(
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{
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"A": [
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"foo",
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"foo",
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"foo",
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"foo",
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"bar",
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"bar",
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"bar",
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"bar",
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"foo",
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"foo",
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"foo",
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],
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"B": [
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"one",
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"one",
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"one",
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"two",
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"one",
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"one",
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"one",
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"two",
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"two",
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"two",
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"one",
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],
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"C": [
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"dull",
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"dull",
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"shiny",
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"dull",
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"dull",
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"shiny",
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"shiny",
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"dull",
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"shiny",
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"shiny",
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"shiny",
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],
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"D": np.random.randn(11),
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"E": np.random.randn(11),
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"F": np.random.randn(11),
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}
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)
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def bad(x):
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assert len(x.values.base) > 0
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return "foo"
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result = data.groupby(["A", "B"]).agg(bad)
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expected = data.groupby(["A", "B"]).agg(lambda x: "foo")
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tm.assert_frame_equal(result, expected)
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def test_agg_consistency():
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# agg with ([]) and () not consistent
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# GH 6715
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def P1(a):
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return np.percentile(a.dropna(), q=1)
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df = DataFrame(
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{
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"col1": [1, 2, 3, 4],
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"col2": [10, 25, 26, 31],
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"date": [
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dt.date(2013, 2, 10),
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dt.date(2013, 2, 10),
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dt.date(2013, 2, 11),
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dt.date(2013, 2, 11),
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],
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}
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)
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g = df.groupby("date")
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expected = g.agg([P1])
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expected.columns = expected.columns.levels[0]
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result = g.agg(P1)
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tm.assert_frame_equal(result, expected)
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def test_agg_callables():
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# GH 7929
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df = DataFrame({"foo": [1, 2], "bar": [3, 4]}).astype(np.int64)
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class fn_class:
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def __call__(self, x):
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return sum(x)
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equiv_callables = [
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sum,
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np.sum,
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lambda x: sum(x),
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lambda x: x.sum(),
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partial(sum),
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fn_class(),
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]
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expected = df.groupby("foo").agg(sum)
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for ecall in equiv_callables:
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result = df.groupby("foo").agg(ecall)
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tm.assert_frame_equal(result, expected)
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def test_agg_over_numpy_arrays():
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# GH 3788
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df = DataFrame(
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[
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[1, np.array([10, 20, 30])],
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[1, np.array([40, 50, 60])],
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[2, np.array([20, 30, 40])],
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],
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columns=["category", "arraydata"],
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)
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gb = df.groupby("category")
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expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]]
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expected_index = Index([1, 2], name="category")
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expected_column = ["arraydata"]
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expected = DataFrame(expected_data, index=expected_index, columns=expected_column)
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alt = gb.sum(numeric_only=False)
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tm.assert_frame_equal(alt, expected)
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result = gb.agg("sum", numeric_only=False)
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tm.assert_frame_equal(result, expected)
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# FIXME: the original version of this test called `gb.agg(sum)`
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# and that raises TypeError if `numeric_only=False` is passed
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@pytest.mark.parametrize("as_period", [True, False])
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def test_agg_tzaware_non_datetime_result(as_period):
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# discussed in GH#29589, fixed in GH#29641, operating on tzaware values
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# with function that is not dtype-preserving
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dti = date_range("2012-01-01", periods=4, tz="UTC")
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if as_period:
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dti = dti.tz_localize(None).to_period("D")
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df = DataFrame({"a": [0, 0, 1, 1], "b": dti})
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gb = df.groupby("a")
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# Case that _does_ preserve the dtype
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result = gb["b"].agg(lambda x: x.iloc[0])
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expected = Series(dti[::2], name="b")
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expected.index.name = "a"
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tm.assert_series_equal(result, expected)
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# Cases that do _not_ preserve the dtype
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result = gb["b"].agg(lambda x: x.iloc[0].year)
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expected = Series([2012, 2012], name="b")
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expected.index.name = "a"
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tm.assert_series_equal(result, expected)
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result = gb["b"].agg(lambda x: x.iloc[-1] - x.iloc[0])
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expected = Series([pd.Timedelta(days=1), pd.Timedelta(days=1)], name="b")
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expected.index.name = "a"
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if as_period:
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expected = Series([pd.offsets.Day(1), pd.offsets.Day(1)], name="b")
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expected.index.name = "a"
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tm.assert_series_equal(result, expected)
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def test_agg_timezone_round_trip():
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# GH 15426
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ts = pd.Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
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df = DataFrame({"a": 1, "b": [ts + dt.timedelta(minutes=nn) for nn in range(10)]})
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result1 = df.groupby("a")["b"].agg(np.min).iloc[0]
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result2 = df.groupby("a")["b"].agg(lambda x: np.min(x)).iloc[0]
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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(numeric_only=False)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# out column is non-numeric, so with numeric_only=True it is dropped
|
|
result2 = df.groupby(0).sum(numeric_only=True)
|
|
expected2 = expected[[]]
|
|
tm.assert_frame_equal(result2, expected2)
|
|
|
|
|
|
@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)
|