426 lines
13 KiB
Python
426 lines
13 KiB
Python
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from textwrap import dedent
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas.util._test_decorators import async_mark
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import pandas as pd
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from pandas import DataFrame, Series, Timestamp
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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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test_frame = DataFrame(
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{"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)},
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index=date_range("1/1/2000", freq="s", periods=40),
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)
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@async_mark()
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@td.check_file_leaks
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async def test_tab_complete_ipython6_warning(ip):
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from IPython.core.completer import provisionalcompleter
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code = dedent(
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"""\
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import pandas._testing as tm
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s = tm.makeTimeSeries()
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rs = s.resample("D")
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"""
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)
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await ip.run_code(code)
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# GH 31324 newer jedi version raises Deprecation warning;
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# appears resolved 2021-02-02
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with tm.assert_produces_warning(None):
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with provisionalcompleter("ignore"):
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list(ip.Completer.completions("rs.", 1))
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def test_deferred_with_groupby():
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# GH 12486
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# support deferred resample ops with groupby
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data = [
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["2010-01-01", "A", 2],
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["2010-01-02", "A", 3],
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["2010-01-05", "A", 8],
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["2010-01-10", "A", 7],
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["2010-01-13", "A", 3],
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["2010-01-01", "B", 5],
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["2010-01-03", "B", 2],
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["2010-01-04", "B", 1],
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["2010-01-11", "B", 7],
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["2010-01-14", "B", 3],
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]
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df = DataFrame(data, columns=["date", "id", "score"])
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df.date = pd.to_datetime(df.date)
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def f(x):
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return x.set_index("date").resample("D").asfreq()
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expected = df.groupby("id").apply(f)
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result = df.set_index("date").groupby("id").resample("D").asfreq()
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tm.assert_frame_equal(result, expected)
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df = DataFrame(
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{
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"date": pd.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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def f(x):
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return x.resample("1D").ffill()
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expected = df.groupby("group").apply(f)
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result = df.groupby("group").resample("1D").ffill()
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tm.assert_frame_equal(result, expected)
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def test_getitem():
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g = test_frame.groupby("A")
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expected = g.B.apply(lambda x: x.resample("2s").mean())
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result = g.resample("2s").B.mean()
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tm.assert_series_equal(result, expected)
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result = g.B.resample("2s").mean()
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tm.assert_series_equal(result, expected)
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result = g.resample("2s").mean().B
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tm.assert_series_equal(result, expected)
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def test_getitem_multiple():
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# GH 13174
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# multiple calls after selection causing an issue with aliasing
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data = [{"id": 1, "buyer": "A"}, {"id": 2, "buyer": "B"}]
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df = DataFrame(data, index=pd.date_range("2016-01-01", periods=2))
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r = df.groupby("id").resample("1D")
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result = r["buyer"].count()
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expected = Series(
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[1, 1],
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index=pd.MultiIndex.from_tuples(
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[(1, Timestamp("2016-01-01")), (2, Timestamp("2016-01-02"))],
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names=["id", None],
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),
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name="buyer",
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)
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tm.assert_series_equal(result, expected)
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result = r["buyer"].count()
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tm.assert_series_equal(result, expected)
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def test_groupby_resample_on_api_with_getitem():
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# GH 17813
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df = DataFrame(
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{"id": list("aabbb"), "date": pd.date_range("1-1-2016", periods=5), "data": 1}
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)
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exp = df.set_index("date").groupby("id").resample("2D")["data"].sum()
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result = df.groupby("id").resample("2D", on="date")["data"].sum()
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tm.assert_series_equal(result, exp)
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def test_groupby_with_origin():
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# GH 31809
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freq = "1399min" # prime number that is smaller than 24h
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start, end = "1/1/2000 00:00:00", "1/31/2000 00:00"
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middle = "1/15/2000 00:00:00"
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rng = pd.date_range(start, end, freq="1231min") # prime number
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ts = Series(np.random.randn(len(rng)), index=rng)
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ts2 = ts[middle:end]
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# proves that grouper without a fixed origin does not work
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# when dealing with unusual frequencies
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simple_grouper = pd.Grouper(freq=freq)
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count_ts = ts.groupby(simple_grouper).agg("count")
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count_ts = count_ts[middle:end]
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count_ts2 = ts2.groupby(simple_grouper).agg("count")
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with pytest.raises(AssertionError):
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tm.assert_index_equal(count_ts.index, count_ts2.index)
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# test origin on 1970-01-01 00:00:00
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origin = Timestamp(0)
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adjusted_grouper = pd.Grouper(freq=freq, origin=origin)
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adjusted_count_ts = ts.groupby(adjusted_grouper).agg("count")
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adjusted_count_ts = adjusted_count_ts[middle:end]
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adjusted_count_ts2 = ts2.groupby(adjusted_grouper).agg("count")
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tm.assert_series_equal(adjusted_count_ts, adjusted_count_ts2)
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# test origin on 2049-10-18 20:00:00
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origin_future = Timestamp(0) + pd.Timedelta("1399min") * 30_000
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adjusted_grouper2 = pd.Grouper(freq=freq, origin=origin_future)
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adjusted2_count_ts = ts.groupby(adjusted_grouper2).agg("count")
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adjusted2_count_ts = adjusted2_count_ts[middle:end]
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adjusted2_count_ts2 = ts2.groupby(adjusted_grouper2).agg("count")
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tm.assert_series_equal(adjusted2_count_ts, adjusted2_count_ts2)
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# both grouper use an adjusted timestamp that is a multiple of 1399 min
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# they should be equals even if the adjusted_timestamp is in the future
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tm.assert_series_equal(adjusted_count_ts, adjusted2_count_ts2)
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def test_nearest():
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# GH 17496
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# Resample nearest
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index = pd.date_range("1/1/2000", periods=3, freq="T")
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result = Series(range(3), index=index).resample("20s").nearest()
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expected = Series(
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[0, 0, 1, 1, 1, 2, 2],
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index=pd.DatetimeIndex(
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[
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"2000-01-01 00:00:00",
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"2000-01-01 00:00:20",
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"2000-01-01 00:00:40",
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"2000-01-01 00:01:00",
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"2000-01-01 00:01:20",
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"2000-01-01 00:01:40",
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"2000-01-01 00:02:00",
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],
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dtype="datetime64[ns]",
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freq="20S",
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),
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)
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tm.assert_series_equal(result, expected)
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def test_methods():
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g = test_frame.groupby("A")
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r = g.resample("2s")
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for f in ["first", "last", "median", "sem", "sum", "mean", "min", "max"]:
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.resample("2s"), f)())
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tm.assert_frame_equal(result, expected)
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for f in ["size"]:
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.resample("2s"), f)())
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tm.assert_series_equal(result, expected)
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for f in ["count"]:
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.resample("2s"), f)())
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tm.assert_frame_equal(result, expected)
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# series only
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for f in ["nunique"]:
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result = getattr(r.B, f)()
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expected = g.B.apply(lambda x: getattr(x.resample("2s"), f)())
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tm.assert_series_equal(result, expected)
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for f in ["nearest", "backfill", "ffill", "asfreq"]:
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.resample("2s"), f)())
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tm.assert_frame_equal(result, expected)
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result = r.ohlc()
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expected = g.apply(lambda x: x.resample("2s").ohlc())
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tm.assert_frame_equal(result, expected)
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for f in ["std", "var"]:
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result = getattr(r, f)(ddof=1)
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expected = g.apply(lambda x: getattr(x.resample("2s"), f)(ddof=1))
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tm.assert_frame_equal(result, expected)
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def test_apply():
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g = test_frame.groupby("A")
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r = g.resample("2s")
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# reduction
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expected = g.resample("2s").sum()
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def f(x):
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return x.resample("2s").sum()
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result = r.apply(f)
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tm.assert_frame_equal(result, expected)
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def f(x):
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return x.resample("2s").apply(lambda y: y.sum())
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result = g.apply(f)
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tm.assert_frame_equal(result, expected)
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def test_apply_with_mutated_index():
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# GH 15169
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index = pd.date_range("1-1-2015", "12-31-15", freq="D")
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df = DataFrame(data={"col1": np.random.rand(len(index))}, index=index)
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def f(x):
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s = Series([1, 2], index=["a", "b"])
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return s
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expected = df.groupby(pd.Grouper(freq="M")).apply(f)
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result = df.resample("M").apply(f)
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tm.assert_frame_equal(result, expected)
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# A case for series
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expected = df["col1"].groupby(pd.Grouper(freq="M")).apply(f)
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result = df["col1"].resample("M").apply(f)
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tm.assert_series_equal(result, expected)
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def test_apply_columns_multilevel():
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# GH 16231
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cols = pd.MultiIndex.from_tuples([("A", "a", "", "one"), ("B", "b", "i", "two")])
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ind = date_range(start="2017-01-01", freq="15Min", periods=8)
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df = DataFrame(np.array([0] * 16).reshape(8, 2), index=ind, columns=cols)
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agg_dict = {col: (np.sum if col[3] == "one" else np.mean) for col in df.columns}
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result = df.resample("H").apply(lambda x: agg_dict[x.name](x))
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expected = DataFrame(
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np.array([0] * 4).reshape(2, 2),
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index=date_range(start="2017-01-01", freq="1H", periods=2),
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columns=pd.MultiIndex.from_tuples(
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[("A", "a", "", "one"), ("B", "b", "i", "two")]
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),
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)
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tm.assert_frame_equal(result, expected)
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def test_resample_groupby_with_label():
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# GH 13235
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index = date_range("2000-01-01", freq="2D", periods=5)
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df = DataFrame(index=index, data={"col0": [0, 0, 1, 1, 2], "col1": [1, 1, 1, 1, 1]})
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result = df.groupby("col0").resample("1W", label="left").sum()
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mi = [
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np.array([0, 0, 1, 2]),
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pd.to_datetime(
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np.array(["1999-12-26", "2000-01-02", "2000-01-02", "2000-01-02"])
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),
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]
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mindex = pd.MultiIndex.from_arrays(mi, names=["col0", None])
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expected = DataFrame(
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data={"col0": [0, 0, 2, 2], "col1": [1, 1, 2, 1]}, index=mindex
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)
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tm.assert_frame_equal(result, expected)
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def test_consistency_with_window():
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# consistent return values with window
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df = test_frame
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expected = pd.Int64Index([1, 2, 3], name="A")
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result = df.groupby("A").resample("2s").mean()
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assert result.index.nlevels == 2
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tm.assert_index_equal(result.index.levels[0], expected)
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result = df.groupby("A").rolling(20).mean()
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assert result.index.nlevels == 2
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tm.assert_index_equal(result.index.levels[0], expected)
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def test_median_duplicate_columns():
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# GH 14233
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df = DataFrame(
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np.random.randn(20, 3),
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columns=list("aaa"),
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index=pd.date_range("2012-01-01", periods=20, freq="s"),
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)
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df2 = df.copy()
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df2.columns = ["a", "b", "c"]
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expected = df2.resample("5s").median()
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result = df.resample("5s").median()
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expected.columns = result.columns
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tm.assert_frame_equal(result, expected)
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def test_apply_to_one_column_of_df():
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# GH: 36951
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df = DataFrame(
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{"col": range(10), "col1": range(10, 20)},
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index=pd.date_range("2012-01-01", periods=10, freq="20min"),
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)
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result = df.resample("H").apply(lambda group: group.col.sum())
|
||
|
|
expected = Series(
|
||
|
|
[3, 12, 21, 9], index=pd.date_range("2012-01-01", periods=4, freq="H")
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
result = df.resample("H").apply(lambda group: group["col"].sum())
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_resample_groupby_agg():
|
||
|
|
# GH: 33548
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"cat": [
|
||
|
|
"cat_1",
|
||
|
|
"cat_1",
|
||
|
|
"cat_2",
|
||
|
|
"cat_1",
|
||
|
|
"cat_2",
|
||
|
|
"cat_1",
|
||
|
|
"cat_2",
|
||
|
|
"cat_1",
|
||
|
|
],
|
||
|
|
"num": [5, 20, 22, 3, 4, 30, 10, 50],
|
||
|
|
"date": [
|
||
|
|
"2019-2-1",
|
||
|
|
"2018-02-03",
|
||
|
|
"2020-3-11",
|
||
|
|
"2019-2-2",
|
||
|
|
"2019-2-2",
|
||
|
|
"2018-12-4",
|
||
|
|
"2020-3-11",
|
||
|
|
"2020-12-12",
|
||
|
|
],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
df["date"] = pd.to_datetime(df["date"])
|
||
|
|
|
||
|
|
resampled = df.groupby("cat").resample("Y", on="date")
|
||
|
|
expected = resampled.sum()
|
||
|
|
result = resampled.agg({"num": "sum"})
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("consolidate", [True, False])
|
||
|
|
def test_resample_groupby_agg_object_dtype_all_nan(consolidate):
|
||
|
|
# https://github.com/pandas-dev/pandas/issues/39329
|
||
|
|
|
||
|
|
dates = pd.date_range("2020-01-01", periods=15, freq="D")
|
||
|
|
df1 = DataFrame({"key": "A", "date": dates, "col1": range(15), "col_object": "val"})
|
||
|
|
df2 = DataFrame({"key": "B", "date": dates, "col1": range(15)})
|
||
|
|
df = pd.concat([df1, df2], ignore_index=True)
|
||
|
|
if consolidate:
|
||
|
|
df = df._consolidate()
|
||
|
|
|
||
|
|
result = df.groupby(["key"]).resample("W", on="date").min()
|
||
|
|
idx = pd.MultiIndex.from_arrays(
|
||
|
|
[
|
||
|
|
["A"] * 3 + ["B"] * 3,
|
||
|
|
pd.to_datetime(["2020-01-05", "2020-01-12", "2020-01-19"] * 2),
|
||
|
|
],
|
||
|
|
names=["key", "date"],
|
||
|
|
)
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"key": ["A"] * 3 + ["B"] * 3,
|
||
|
|
"date": pd.to_datetime(["2020-01-01", "2020-01-06", "2020-01-13"] * 2),
|
||
|
|
"col1": [0, 5, 12] * 2,
|
||
|
|
"col_object": ["val"] * 3 + [np.nan] * 3,
|
||
|
|
},
|
||
|
|
index=idx,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|