962 lines
30 KiB
Python
962 lines
30 KiB
Python
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from datetime import datetime
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import numpy as np
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import pytest
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from pandas._libs import lib
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import pandas as pd
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from pandas import (
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DataFrame,
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NamedAgg,
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Series,
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)
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import pandas._testing as tm
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from pandas.core.indexes.datetimes import date_range
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dti = date_range(start=datetime(2005, 1, 1), end=datetime(2005, 1, 10), freq="Min")
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test_series = Series(np.random.rand(len(dti)), dti)
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_test_frame = DataFrame({"A": test_series, "B": test_series, "C": np.arange(len(dti))})
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@pytest.fixture
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def test_frame():
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return _test_frame.copy()
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def test_str():
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r = test_series.resample("H")
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assert (
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"DatetimeIndexResampler [freq=<Hour>, axis=0, closed=left, "
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"label=left, convention=start, origin=start_day]" in str(r)
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)
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r = test_series.resample("H", origin="2000-01-01")
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assert (
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"DatetimeIndexResampler [freq=<Hour>, axis=0, closed=left, "
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"label=left, convention=start, origin=2000-01-01 00:00:00]" in str(r)
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)
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def test_api():
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r = test_series.resample("H")
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result = r.mean()
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assert isinstance(result, Series)
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assert len(result) == 217
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r = test_series.to_frame().resample("H")
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result = r.mean()
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assert isinstance(result, DataFrame)
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assert len(result) == 217
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def test_groupby_resample_api():
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# GH 12448
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# .groupby(...).resample(...) hitting warnings
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# when appropriate
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df = DataFrame(
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{
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"date": date_range(start="2016-01-01", periods=4, freq="W"),
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"group": [1, 1, 2, 2],
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"val": [5, 6, 7, 8],
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}
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).set_index("date")
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# replication step
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i = (
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date_range("2016-01-03", periods=8).tolist()
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+ date_range("2016-01-17", periods=8).tolist()
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)
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index = pd.MultiIndex.from_arrays([[1] * 8 + [2] * 8, i], names=["group", "date"])
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expected = DataFrame({"val": [5] * 7 + [6] + [7] * 7 + [8]}, index=index)
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result = df.groupby("group").apply(lambda x: x.resample("1D").ffill())[["val"]]
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tm.assert_frame_equal(result, expected)
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def test_groupby_resample_on_api():
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# GH 15021
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# .groupby(...).resample(on=...) results in an unexpected
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# keyword warning.
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df = DataFrame(
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{
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"key": ["A", "B"] * 5,
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"dates": date_range("2016-01-01", periods=10),
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"values": np.random.randn(10),
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}
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)
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msg = "The default value of numeric_only"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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expected = df.set_index("dates").groupby("key").resample("D").mean()
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result = df.groupby("key").resample("D", on="dates").mean()
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tm.assert_frame_equal(result, expected)
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def test_resample_group_keys():
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df = DataFrame({"A": 1, "B": 2}, index=date_range("2000", periods=10))
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g = df.resample("5D")
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expected = df.copy()
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with tm.assert_produces_warning(FutureWarning, match="Not prepending group keys"):
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result = g.apply(lambda x: x)
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tm.assert_frame_equal(result, expected)
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# no warning
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g = df.resample("5D", group_keys=False)
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with tm.assert_produces_warning(None):
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result = g.apply(lambda x: x)
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tm.assert_frame_equal(result, expected)
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# no warning, group keys
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expected.index = pd.MultiIndex.from_arrays(
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[pd.to_datetime(["2000-01-01", "2000-01-06"]).repeat(5), expected.index]
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)
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g = df.resample("5D", group_keys=True)
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with tm.assert_produces_warning(None):
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result = g.apply(lambda x: x)
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tm.assert_frame_equal(result, expected)
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def test_pipe(test_frame):
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# GH17905
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# series
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r = test_series.resample("H")
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expected = r.max() - r.mean()
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result = r.pipe(lambda x: x.max() - x.mean())
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tm.assert_series_equal(result, expected)
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# dataframe
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r = test_frame.resample("H")
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expected = r.max() - r.mean()
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result = r.pipe(lambda x: x.max() - x.mean())
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tm.assert_frame_equal(result, expected)
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def test_getitem(test_frame):
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r = test_frame.resample("H")
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tm.assert_index_equal(r._selected_obj.columns, test_frame.columns)
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r = test_frame.resample("H")["B"]
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assert r._selected_obj.name == test_frame.columns[1]
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# technically this is allowed
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r = test_frame.resample("H")["A", "B"]
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tm.assert_index_equal(r._selected_obj.columns, test_frame.columns[[0, 1]])
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r = test_frame.resample("H")["A", "B"]
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tm.assert_index_equal(r._selected_obj.columns, test_frame.columns[[0, 1]])
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@pytest.mark.parametrize("key", [["D"], ["A", "D"]])
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def test_select_bad_cols(key, test_frame):
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g = test_frame.resample("H")
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# 'A' should not be referenced as a bad column...
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# will have to rethink regex if you change message!
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msg = r"^\"Columns not found: 'D'\"$"
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with pytest.raises(KeyError, match=msg):
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g[key]
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def test_attribute_access(test_frame):
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r = test_frame.resample("H")
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tm.assert_series_equal(r.A.sum(), r["A"].sum())
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@pytest.mark.parametrize("attr", ["groups", "ngroups", "indices"])
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def test_api_compat_before_use(attr):
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# make sure that we are setting the binner
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# on these attributes
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rng = date_range("1/1/2012", periods=100, freq="S")
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ts = Series(np.arange(len(rng)), index=rng)
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rs = ts.resample("30s")
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# before use
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getattr(rs, attr)
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# after grouper is initialized is ok
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rs.mean()
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getattr(rs, attr)
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def tests_skip_nuisance(test_frame):
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df = test_frame
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df["D"] = "foo"
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r = df.resample("H")
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result = r[["A", "B"]].sum()
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expected = pd.concat([r.A.sum(), r.B.sum()], axis=1)
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tm.assert_frame_equal(result, expected)
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expected = r[["A", "B", "C"]].sum()
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msg = "The default value of numeric_only"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = r.sum()
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tm.assert_frame_equal(result, expected)
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def test_downsample_but_actually_upsampling():
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# this is reindex / asfreq
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rng = date_range("1/1/2012", periods=100, freq="S")
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ts = Series(np.arange(len(rng), dtype="int64"), index=rng)
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result = ts.resample("20s").asfreq()
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expected = Series(
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[0, 20, 40, 60, 80],
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index=date_range("2012-01-01 00:00:00", freq="20s", periods=5),
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)
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tm.assert_series_equal(result, expected)
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def test_combined_up_downsampling_of_irregular():
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# since we are really doing an operation like this
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# ts2.resample('2s').mean().ffill()
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# preserve these semantics
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rng = date_range("1/1/2012", periods=100, freq="S")
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ts = Series(np.arange(len(rng)), index=rng)
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ts2 = ts.iloc[[0, 1, 2, 3, 5, 7, 11, 15, 16, 25, 30]]
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result = ts2.resample("2s").mean().ffill()
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expected = Series(
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[
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0.5,
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2.5,
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5.0,
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7.0,
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7.0,
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11.0,
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11.0,
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15.0,
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16.0,
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16.0,
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16.0,
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16.0,
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25.0,
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25.0,
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25.0,
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30.0,
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],
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index=pd.DatetimeIndex(
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[
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"2012-01-01 00:00:00",
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"2012-01-01 00:00:02",
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"2012-01-01 00:00:04",
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"2012-01-01 00:00:06",
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"2012-01-01 00:00:08",
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"2012-01-01 00:00:10",
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"2012-01-01 00:00:12",
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"2012-01-01 00:00:14",
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"2012-01-01 00:00:16",
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"2012-01-01 00:00:18",
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"2012-01-01 00:00:20",
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"2012-01-01 00:00:22",
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"2012-01-01 00:00:24",
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"2012-01-01 00:00:26",
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"2012-01-01 00:00:28",
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"2012-01-01 00:00:30",
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],
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dtype="datetime64[ns]",
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freq="2S",
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),
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)
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tm.assert_series_equal(result, expected)
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def test_transform_series():
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r = test_series.resample("20min")
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expected = test_series.groupby(pd.Grouper(freq="20min")).transform("mean")
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result = r.transform("mean")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("on", [None, "date"])
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def test_transform_frame(on):
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# GH#47079
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index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
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index.name = "date"
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df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index)
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expected = df.groupby(pd.Grouper(freq="20min")).transform("mean")
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if on == "date":
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# Move date to being a column; result will then have a RangeIndex
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expected = expected.reset_index(drop=True)
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df = df.reset_index()
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r = df.resample("20min", on=on)
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result = r.transform("mean")
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tm.assert_frame_equal(result, expected)
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def test_fillna():
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# need to upsample here
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rng = date_range("1/1/2012", periods=10, freq="2S")
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ts = Series(np.arange(len(rng), dtype="int64"), index=rng)
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r = ts.resample("s")
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expected = r.ffill()
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result = r.fillna(method="ffill")
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tm.assert_series_equal(result, expected)
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expected = r.bfill()
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result = r.fillna(method="bfill")
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tm.assert_series_equal(result, expected)
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msg = (
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r"Invalid fill method\. Expecting pad \(ffill\), backfill "
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r"\(bfill\) or nearest\. Got 0"
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)
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with pytest.raises(ValueError, match=msg):
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r.fillna(0)
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@pytest.mark.parametrize(
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"func",
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[
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lambda x: x.resample("20min", group_keys=False),
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lambda x: x.groupby(pd.Grouper(freq="20min"), group_keys=False),
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],
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ids=["resample", "groupby"],
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)
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def test_apply_without_aggregation(func):
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# both resample and groupby should work w/o aggregation
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t = func(test_series)
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result = t.apply(lambda x: x)
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tm.assert_series_equal(result, test_series)
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def test_apply_without_aggregation2():
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grouped = test_series.to_frame(name="foo").resample("20min", group_keys=False)
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result = grouped["foo"].apply(lambda x: x)
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tm.assert_series_equal(result, test_series.rename("foo"))
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def test_agg_consistency():
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# make sure that we are consistent across
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# similar aggregations with and w/o selection list
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df = DataFrame(
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np.random.randn(1000, 3),
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index=date_range("1/1/2012", freq="S", periods=1000),
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columns=["A", "B", "C"],
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)
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r = df.resample("3T")
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msg = r"Column\(s\) \['r1', 'r2'\] do not exist"
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with pytest.raises(KeyError, match=msg):
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r.agg({"r1": "mean", "r2": "sum"})
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def test_agg_consistency_int_str_column_mix():
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# GH#39025
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df = DataFrame(
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np.random.randn(1000, 2),
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index=date_range("1/1/2012", freq="S", periods=1000),
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columns=[1, "a"],
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)
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r = df.resample("3T")
|
||
|
|
|
||
|
|
msg = r"Column\(s\) \[2, 'b'\] do not exist"
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
r.agg({2: "mean", "b": "sum"})
|
||
|
|
|
||
|
|
|
||
|
|
# TODO(GH#14008): once GH 14008 is fixed, move these tests into
|
||
|
|
# `Base` test class
|
||
|
|
|
||
|
|
|
||
|
|
def test_agg():
|
||
|
|
# test with all three Resampler apis and TimeGrouper
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
|
||
|
|
index.name = "date"
|
||
|
|
df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index)
|
||
|
|
df_col = df.reset_index()
|
||
|
|
df_mult = df_col.copy()
|
||
|
|
df_mult.index = pd.MultiIndex.from_arrays(
|
||
|
|
[range(10), df.index], names=["index", "date"]
|
||
|
|
)
|
||
|
|
r = df.resample("2D")
|
||
|
|
cases = [
|
||
|
|
r,
|
||
|
|
df_col.resample("2D", on="date"),
|
||
|
|
df_mult.resample("2D", level="date"),
|
||
|
|
df.groupby(pd.Grouper(freq="2D")),
|
||
|
|
]
|
||
|
|
|
||
|
|
a_mean = r["A"].mean()
|
||
|
|
a_std = r["A"].std()
|
||
|
|
a_sum = r["A"].sum()
|
||
|
|
b_mean = r["B"].mean()
|
||
|
|
b_std = r["B"].std()
|
||
|
|
b_sum = r["B"].sum()
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
|
||
|
|
expected.columns = pd.MultiIndex.from_product([["A", "B"], ["mean", "std"]])
|
||
|
|
for t in cases:
|
||
|
|
# In case 2, "date" is an index and a column, so agg still tries to agg
|
||
|
|
warn = FutureWarning if t == cases[2] else None
|
||
|
|
with tm.assert_produces_warning(
|
||
|
|
warn,
|
||
|
|
match=r"\['date'\] did not aggregate successfully",
|
||
|
|
):
|
||
|
|
# .var on dt64 column raises and is dropped
|
||
|
|
result = t.aggregate([np.mean, np.std])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, b_std], axis=1)
|
||
|
|
for t in cases:
|
||
|
|
result = t.aggregate({"A": np.mean, "B": np.std})
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
result = t.aggregate(A=("A", np.mean), B=("B", np.std))
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
result = t.aggregate(A=NamedAgg("A", np.mean), B=NamedAgg("B", np.std))
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_std], axis=1)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples([("A", "mean"), ("A", "std")])
|
||
|
|
for t in cases:
|
||
|
|
result = t.aggregate({"A": ["mean", "std"]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_sum], axis=1)
|
||
|
|
expected.columns = ["mean", "sum"]
|
||
|
|
for t in cases:
|
||
|
|
result = t["A"].aggregate(["mean", "sum"])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = t["A"].aggregate(mean="mean", sum="sum")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
msg = "nested renamer is not supported"
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t.aggregate({"A": {"mean": "mean", "sum": "sum"}})
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_sum, b_mean, b_sum], axis=1)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
||
|
|
[("A", "mean"), ("A", "sum"), ("B", "mean2"), ("B", "sum2")]
|
||
|
|
)
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t.aggregate(
|
||
|
|
{
|
||
|
|
"A": {"mean": "mean", "sum": "sum"},
|
||
|
|
"B": {"mean2": "mean", "sum2": "sum"},
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_std, b_mean, b_std], axis=1)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
||
|
|
[("A", "mean"), ("A", "std"), ("B", "mean"), ("B", "std")]
|
||
|
|
)
|
||
|
|
for t in cases:
|
||
|
|
result = t.aggregate({"A": ["mean", "std"], "B": ["mean", "std"]})
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
expected = pd.concat([a_mean, a_sum, b_mean, b_sum], axis=1)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
||
|
|
[
|
||
|
|
("r1", "A", "mean"),
|
||
|
|
("r1", "A", "sum"),
|
||
|
|
("r2", "B", "mean"),
|
||
|
|
("r2", "B", "sum"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def test_agg_misc():
|
||
|
|
# test with all three Resampler apis and TimeGrouper
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
|
||
|
|
index.name = "date"
|
||
|
|
df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index)
|
||
|
|
df_col = df.reset_index()
|
||
|
|
df_mult = df_col.copy()
|
||
|
|
df_mult.index = pd.MultiIndex.from_arrays(
|
||
|
|
[range(10), df.index], names=["index", "date"]
|
||
|
|
)
|
||
|
|
|
||
|
|
r = df.resample("2D")
|
||
|
|
cases = [
|
||
|
|
r,
|
||
|
|
df_col.resample("2D", on="date"),
|
||
|
|
df_mult.resample("2D", level="date"),
|
||
|
|
df.groupby(pd.Grouper(freq="2D")),
|
||
|
|
]
|
||
|
|
|
||
|
|
# passed lambda
|
||
|
|
for t in cases:
|
||
|
|
result = t.agg({"A": np.sum, "B": lambda x: np.std(x, ddof=1)})
|
||
|
|
rcustom = t["B"].apply(lambda x: np.std(x, ddof=1))
|
||
|
|
expected = pd.concat([r["A"].sum(), rcustom], axis=1)
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
result = t.agg(A=("A", np.sum), B=("B", lambda x: np.std(x, ddof=1)))
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
result = t.agg(
|
||
|
|
A=NamedAgg("A", np.sum), B=NamedAgg("B", lambda x: np.std(x, ddof=1))
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
# agg with renamers
|
||
|
|
expected = pd.concat(
|
||
|
|
[t["A"].sum(), t["B"].sum(), t["A"].mean(), t["B"].mean()], axis=1
|
||
|
|
)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
||
|
|
[("result1", "A"), ("result1", "B"), ("result2", "A"), ("result2", "B")]
|
||
|
|
)
|
||
|
|
|
||
|
|
msg = r"Column\(s\) \['result1', 'result2'\] do not exist"
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
t[["A", "B"]].agg({"result1": np.sum, "result2": np.mean})
|
||
|
|
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
t[["A", "B"]].agg(A=("result1", np.sum), B=("result2", np.mean))
|
||
|
|
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
t[["A", "B"]].agg(
|
||
|
|
A=NamedAgg("result1", np.sum), B=NamedAgg("result2", np.mean)
|
||
|
|
)
|
||
|
|
|
||
|
|
# agg with different hows
|
||
|
|
expected = pd.concat(
|
||
|
|
[t["A"].sum(), t["A"].std(), t["B"].mean(), t["B"].std()], axis=1
|
||
|
|
)
|
||
|
|
expected.columns = pd.MultiIndex.from_tuples(
|
||
|
|
[("A", "sum"), ("A", "std"), ("B", "mean"), ("B", "std")]
|
||
|
|
)
|
||
|
|
for t in cases:
|
||
|
|
result = t.agg({"A": ["sum", "std"], "B": ["mean", "std"]})
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
# equivalent of using a selection list / or not
|
||
|
|
for t in cases:
|
||
|
|
result = t[["A", "B"]].agg({"A": ["sum", "std"], "B": ["mean", "std"]})
|
||
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
||
|
|
|
||
|
|
msg = "nested renamer is not supported"
|
||
|
|
|
||
|
|
# series like aggs
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t["A"].agg({"A": ["sum", "std"]})
|
||
|
|
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t["A"].agg({"A": ["sum", "std"], "B": ["mean", "std"]})
|
||
|
|
|
||
|
|
# errors
|
||
|
|
# invalid names in the agg specification
|
||
|
|
msg = r"Column\(s\) \['B'\] do not exist"
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
t[["A"]].agg({"A": ["sum", "std"], "B": ["mean", "std"]})
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"func", [["min"], ["mean", "max"], {"A": "sum"}, {"A": "prod", "B": "median"}]
|
||
|
|
)
|
||
|
|
def test_multi_agg_axis_1_raises(func):
|
||
|
|
# GH#46904
|
||
|
|
np.random.seed(1234)
|
||
|
|
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
|
||
|
|
index.name = "date"
|
||
|
|
df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index).T
|
||
|
|
res = df.resample("M", axis=1)
|
||
|
|
with pytest.raises(NotImplementedError, match="axis other than 0 is not supported"):
|
||
|
|
res.agg(func)
|
||
|
|
|
||
|
|
|
||
|
|
def test_agg_nested_dicts():
|
||
|
|
|
||
|
|
np.random.seed(1234)
|
||
|
|
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
|
||
|
|
index.name = "date"
|
||
|
|
df = DataFrame(np.random.rand(10, 2), columns=list("AB"), index=index)
|
||
|
|
df_col = df.reset_index()
|
||
|
|
df_mult = df_col.copy()
|
||
|
|
df_mult.index = pd.MultiIndex.from_arrays(
|
||
|
|
[range(10), df.index], names=["index", "date"]
|
||
|
|
)
|
||
|
|
r = df.resample("2D")
|
||
|
|
cases = [
|
||
|
|
r,
|
||
|
|
df_col.resample("2D", on="date"),
|
||
|
|
df_mult.resample("2D", level="date"),
|
||
|
|
df.groupby(pd.Grouper(freq="2D")),
|
||
|
|
]
|
||
|
|
|
||
|
|
msg = "nested renamer is not supported"
|
||
|
|
for t in cases:
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t.aggregate({"r1": {"A": ["mean", "sum"]}, "r2": {"B": ["mean", "sum"]}})
|
||
|
|
|
||
|
|
for t in cases:
|
||
|
|
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t[["A", "B"]].agg(
|
||
|
|
{"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}}
|
||
|
|
)
|
||
|
|
|
||
|
|
with pytest.raises(pd.errors.SpecificationError, match=msg):
|
||
|
|
t.agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}})
|
||
|
|
|
||
|
|
|
||
|
|
def test_try_aggregate_non_existing_column():
|
||
|
|
# GH 16766
|
||
|
|
data = [
|
||
|
|
{"dt": datetime(2017, 6, 1, 0), "x": 1.0, "y": 2.0},
|
||
|
|
{"dt": datetime(2017, 6, 1, 1), "x": 2.0, "y": 2.0},
|
||
|
|
{"dt": datetime(2017, 6, 1, 2), "x": 3.0, "y": 1.5},
|
||
|
|
]
|
||
|
|
df = DataFrame(data).set_index("dt")
|
||
|
|
|
||
|
|
# Error as we don't have 'z' column
|
||
|
|
msg = r"Column\(s\) \['z'\] do not exist"
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
df.resample("30T").agg({"x": ["mean"], "y": ["median"], "z": ["sum"]})
|
||
|
|
|
||
|
|
|
||
|
|
def test_selection_api_validation():
|
||
|
|
# GH 13500
|
||
|
|
index = date_range(datetime(2005, 1, 1), datetime(2005, 1, 10), freq="D")
|
||
|
|
|
||
|
|
rng = np.arange(len(index), dtype=np.int64)
|
||
|
|
df = DataFrame(
|
||
|
|
{"date": index, "a": rng},
|
||
|
|
index=pd.MultiIndex.from_arrays([rng, index], names=["v", "d"]),
|
||
|
|
)
|
||
|
|
df_exp = DataFrame({"a": rng}, index=index)
|
||
|
|
|
||
|
|
# non DatetimeIndex
|
||
|
|
msg = (
|
||
|
|
"Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, "
|
||
|
|
"but got an instance of 'Int64Index'"
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
df.resample("2D", level="v")
|
||
|
|
|
||
|
|
msg = "The Grouper cannot specify both a key and a level!"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.resample("2D", on="date", level="d")
|
||
|
|
|
||
|
|
msg = "unhashable type: 'list'"
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
df.resample("2D", on=["a", "date"])
|
||
|
|
|
||
|
|
msg = r"\"Level \['a', 'date'\] not found\""
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
df.resample("2D", level=["a", "date"])
|
||
|
|
|
||
|
|
# upsampling not allowed
|
||
|
|
msg = (
|
||
|
|
"Upsampling from level= or on= selection is not supported, use "
|
||
|
|
r"\.set_index\(\.\.\.\) to explicitly set index to datetime-like"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.resample("2D", level="d").asfreq()
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
df.resample("2D", on="date").asfreq()
|
||
|
|
|
||
|
|
exp = df_exp.resample("2D").sum()
|
||
|
|
exp.index.name = "date"
|
||
|
|
result = df.resample("2D", on="date").sum()
|
||
|
|
tm.assert_frame_equal(exp, result)
|
||
|
|
|
||
|
|
exp.index.name = "d"
|
||
|
|
msg = "The default value of numeric_only"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
result = df.resample("2D", level="d").sum()
|
||
|
|
tm.assert_frame_equal(exp, result)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"col_name", ["t2", "t2x", "t2q", "T_2M", "t2p", "t2m", "t2m1", "T2M"]
|
||
|
|
)
|
||
|
|
def test_agg_with_datetime_index_list_agg_func(col_name):
|
||
|
|
# GH 22660
|
||
|
|
# The parametrized column names would get converted to dates by our
|
||
|
|
# date parser. Some would result in OutOfBoundsError (ValueError) while
|
||
|
|
# others would result in OverflowError when passed into Timestamp.
|
||
|
|
# We catch these errors and move on to the correct branch.
|
||
|
|
df = DataFrame(
|
||
|
|
list(range(200)),
|
||
|
|
index=date_range(
|
||
|
|
start="2017-01-01", freq="15min", periods=200, tz="Europe/Berlin"
|
||
|
|
),
|
||
|
|
columns=[col_name],
|
||
|
|
)
|
||
|
|
result = df.resample("1d").aggregate(["mean"])
|
||
|
|
expected = DataFrame(
|
||
|
|
[47.5, 143.5, 195.5],
|
||
|
|
index=date_range(start="2017-01-01", freq="D", periods=3, tz="Europe/Berlin"),
|
||
|
|
columns=pd.MultiIndex(levels=[[col_name], ["mean"]], codes=[[0], [0]]),
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_resample_agg_readonly():
|
||
|
|
# GH#31710 cython needs to allow readonly data
|
||
|
|
index = date_range("2020-01-01", "2020-01-02", freq="1h")
|
||
|
|
arr = np.zeros_like(index)
|
||
|
|
arr.setflags(write=False)
|
||
|
|
|
||
|
|
ser = Series(arr, index=index)
|
||
|
|
rs = ser.resample("1D")
|
||
|
|
|
||
|
|
expected = Series([pd.Timestamp(0), pd.Timestamp(0)], index=index[::24])
|
||
|
|
|
||
|
|
result = rs.agg("last")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = rs.agg("first")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = rs.agg("max")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = rs.agg("min")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"start,end,freq,data,resample_freq,origin,closed,exp_data,exp_end,exp_periods",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
"2000-10-01 23:30:00",
|
||
|
|
"2000-10-02 00:26:00",
|
||
|
|
"7min",
|
||
|
|
[0, 3, 6, 9, 12, 15, 18, 21, 24],
|
||
|
|
"17min",
|
||
|
|
"end",
|
||
|
|
None,
|
||
|
|
[0, 18, 27, 63],
|
||
|
|
"20001002 00:26:00",
|
||
|
|
4,
|
||
|
|
),
|
||
|
|
(
|
||
|
|
"20200101 8:26:35",
|
||
|
|
"20200101 9:31:58",
|
||
|
|
"77s",
|
||
|
|
[1] * 51,
|
||
|
|
"7min",
|
||
|
|
"end",
|
||
|
|
"right",
|
||
|
|
[1, 6, 5, 6, 5, 6, 5, 6, 5, 6],
|
||
|
|
"2020-01-01 09:30:45",
|
||
|
|
10,
|
||
|
|
),
|
||
|
|
(
|
||
|
|
"2000-10-01 23:30:00",
|
||
|
|
"2000-10-02 00:26:00",
|
||
|
|
"7min",
|
||
|
|
[0, 3, 6, 9, 12, 15, 18, 21, 24],
|
||
|
|
"17min",
|
||
|
|
"end",
|
||
|
|
"left",
|
||
|
|
[0, 18, 27, 39, 24],
|
||
|
|
"20001002 00:43:00",
|
||
|
|
5,
|
||
|
|
),
|
||
|
|
(
|
||
|
|
"2000-10-01 23:30:00",
|
||
|
|
"2000-10-02 00:26:00",
|
||
|
|
"7min",
|
||
|
|
[0, 3, 6, 9, 12, 15, 18, 21, 24],
|
||
|
|
"17min",
|
||
|
|
"end_day",
|
||
|
|
None,
|
||
|
|
[3, 15, 45, 45],
|
||
|
|
"2000-10-02 00:29:00",
|
||
|
|
4,
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_end_and_end_day_origin(
|
||
|
|
start,
|
||
|
|
end,
|
||
|
|
freq,
|
||
|
|
data,
|
||
|
|
resample_freq,
|
||
|
|
origin,
|
||
|
|
closed,
|
||
|
|
exp_data,
|
||
|
|
exp_end,
|
||
|
|
exp_periods,
|
||
|
|
):
|
||
|
|
rng = date_range(start, end, freq=freq)
|
||
|
|
ts = Series(data, index=rng)
|
||
|
|
|
||
|
|
res = ts.resample(resample_freq, origin=origin, closed=closed).sum()
|
||
|
|
expected = Series(
|
||
|
|
exp_data,
|
||
|
|
index=date_range(end=exp_end, freq=resample_freq, periods=exp_periods),
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_series_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
# expected_data is a string when op raises a ValueError
|
||
|
|
"method, numeric_only, expected_data",
|
||
|
|
[
|
||
|
|
("sum", True, {"num": [25]}),
|
||
|
|
("sum", False, {"cat": ["cat_1cat_2"], "num": [25]}),
|
||
|
|
("sum", lib.no_default, {"num": [25]}),
|
||
|
|
("prod", True, {"num": [100]}),
|
||
|
|
("prod", False, {"num": [100]}),
|
||
|
|
("prod", lib.no_default, {"num": [100]}),
|
||
|
|
("min", True, {"num": [5]}),
|
||
|
|
("min", False, {"cat": ["cat_1"], "num": [5]}),
|
||
|
|
("min", lib.no_default, {"cat": ["cat_1"], "num": [5]}),
|
||
|
|
("max", True, {"num": [20]}),
|
||
|
|
("max", False, {"cat": ["cat_2"], "num": [20]}),
|
||
|
|
("max", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
|
||
|
|
("first", True, {"num": [5]}),
|
||
|
|
("first", False, {"cat": ["cat_1"], "num": [5]}),
|
||
|
|
("first", lib.no_default, {"cat": ["cat_1"], "num": [5]}),
|
||
|
|
("last", True, {"num": [20]}),
|
||
|
|
("last", False, {"cat": ["cat_2"], "num": [20]}),
|
||
|
|
("last", lib.no_default, {"cat": ["cat_2"], "num": [20]}),
|
||
|
|
("mean", True, {"num": [12.5]}),
|
||
|
|
("mean", False, {"num": [12.5]}),
|
||
|
|
("mean", lib.no_default, {"num": [12.5]}),
|
||
|
|
("median", True, {"num": [12.5]}),
|
||
|
|
("median", False, {"num": [12.5]}),
|
||
|
|
("median", lib.no_default, {"num": [12.5]}),
|
||
|
|
("std", True, {"num": [10.606601717798213]}),
|
||
|
|
("std", False, "could not convert string to float"),
|
||
|
|
("std", lib.no_default, {"num": [10.606601717798213]}),
|
||
|
|
("var", True, {"num": [112.5]}),
|
||
|
|
("var", False, "could not convert string to float"),
|
||
|
|
("var", lib.no_default, {"num": [112.5]}),
|
||
|
|
("sem", True, {"num": [7.5]}),
|
||
|
|
("sem", False, "could not convert string to float"),
|
||
|
|
("sem", lib.no_default, {"num": [7.5]}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_frame_downsample_method(method, numeric_only, expected_data):
|
||
|
|
# GH#46442 test if `numeric_only` behave as expected for DataFrameGroupBy
|
||
|
|
|
||
|
|
index = date_range("2018-01-01", periods=2, freq="D")
|
||
|
|
expected_index = date_range("2018-12-31", periods=1, freq="Y")
|
||
|
|
df = DataFrame({"cat": ["cat_1", "cat_2"], "num": [5, 20]}, index=index)
|
||
|
|
resampled = df.resample("Y")
|
||
|
|
if numeric_only is lib.no_default:
|
||
|
|
kwargs = {}
|
||
|
|
else:
|
||
|
|
kwargs = {"numeric_only": numeric_only}
|
||
|
|
|
||
|
|
func = getattr(resampled, method)
|
||
|
|
if numeric_only is lib.no_default and method not in (
|
||
|
|
"min",
|
||
|
|
"max",
|
||
|
|
"first",
|
||
|
|
"last",
|
||
|
|
"prod",
|
||
|
|
):
|
||
|
|
warn = FutureWarning
|
||
|
|
msg = (
|
||
|
|
f"default value of numeric_only in DataFrameGroupBy.{method} is deprecated"
|
||
|
|
)
|
||
|
|
elif method in ("prod", "mean", "median") and numeric_only is not True:
|
||
|
|
warn = FutureWarning
|
||
|
|
msg = f"Dropping invalid columns in DataFrameGroupBy.{method} is deprecated"
|
||
|
|
else:
|
||
|
|
warn = None
|
||
|
|
msg = ""
|
||
|
|
with tm.assert_produces_warning(warn, match=msg):
|
||
|
|
if isinstance(expected_data, str):
|
||
|
|
klass = TypeError if method == "var" else ValueError
|
||
|
|
with pytest.raises(klass, match=expected_data):
|
||
|
|
_ = func(**kwargs)
|
||
|
|
else:
|
||
|
|
result = func(**kwargs)
|
||
|
|
expected = DataFrame(expected_data, index=expected_index)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"method, numeric_only, expected_data",
|
||
|
|
[
|
||
|
|
("sum", True, ()),
|
||
|
|
("sum", False, ["cat_1cat_2"]),
|
||
|
|
("sum", lib.no_default, ["cat_1cat_2"]),
|
||
|
|
("prod", True, ()),
|
||
|
|
("prod", False, ()),
|
||
|
|
("prod", lib.no_default, ()),
|
||
|
|
("min", True, ()),
|
||
|
|
("min", False, ["cat_1"]),
|
||
|
|
("min", lib.no_default, ["cat_1"]),
|
||
|
|
("max", True, ()),
|
||
|
|
("max", False, ["cat_2"]),
|
||
|
|
("max", lib.no_default, ["cat_2"]),
|
||
|
|
("first", True, ()),
|
||
|
|
("first", False, ["cat_1"]),
|
||
|
|
("first", lib.no_default, ["cat_1"]),
|
||
|
|
("last", True, ()),
|
||
|
|
("last", False, ["cat_2"]),
|
||
|
|
("last", lib.no_default, ["cat_2"]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_series_downsample_method(method, numeric_only, expected_data):
|
||
|
|
# GH#46442 test if `numeric_only` behave as expected for SeriesGroupBy
|
||
|
|
|
||
|
|
index = date_range("2018-01-01", periods=2, freq="D")
|
||
|
|
expected_index = date_range("2018-12-31", periods=1, freq="Y")
|
||
|
|
df = Series(["cat_1", "cat_2"], index=index)
|
||
|
|
resampled = df.resample("Y")
|
||
|
|
|
||
|
|
func = getattr(resampled, method)
|
||
|
|
if numeric_only and numeric_only is not lib.no_default:
|
||
|
|
with tm.assert_produces_warning(
|
||
|
|
FutureWarning, match="This will raise a TypeError"
|
||
|
|
):
|
||
|
|
with pytest.raises(NotImplementedError, match="not implement numeric_only"):
|
||
|
|
func(numeric_only=numeric_only)
|
||
|
|
elif method == "prod":
|
||
|
|
with pytest.raises(TypeError, match="can't multiply sequence by non-int"):
|
||
|
|
func(numeric_only=numeric_only)
|
||
|
|
else:
|
||
|
|
result = func(numeric_only=numeric_only)
|
||
|
|
expected = Series(expected_data, index=expected_index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["agg", "apply", "transform"])
|
||
|
|
def test_numeric_only_warning_numpy(method):
|
||
|
|
# GH#50538
|
||
|
|
resampled = _test_frame.assign(D="x").resample("H")
|
||
|
|
if method == "transform":
|
||
|
|
msg = "The default value of numeric_only"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
getattr(resampled, method)(np.mean)
|
||
|
|
# Ensure users can pass numeric_only
|
||
|
|
result = getattr(resampled, method)(np.mean, numeric_only=True)
|
||
|
|
expected = resampled.transform("mean", numeric_only=True)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
else:
|
||
|
|
msg = "The operation <function mean.*failed"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
getattr(resampled, method)(np.mean)
|
||
|
|
# Ensure users can't pass numeric_only
|
||
|
|
with pytest.raises(TypeError, match="got an unexpected keyword argument"):
|
||
|
|
getattr(resampled, method)(np.mean, numeric_only=True)
|