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from datetime import timedelta
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
from pandas.core.indexes.timedeltas import timedelta_range
def test_asfreq_bug():
df = DataFrame(data=[1, 3], index=[timedelta(), timedelta(minutes=3)])
result = df.resample("1min").asfreq()
expected = DataFrame(
data=[1, np.nan, np.nan, 3],
index=timedelta_range("0 day", periods=4, freq="1min"),
)
tm.assert_frame_equal(result, expected)
def test_resample_with_nat():
# GH 13223
index = pd.to_timedelta(["0s", pd.NaT, "2s"])
result = DataFrame({"value": [2, 3, 5]}, index).resample("1s").mean()
expected = DataFrame(
{"value": [2.5, np.nan, 5.0]},
index=timedelta_range("0 day", periods=3, freq="1s"),
)
tm.assert_frame_equal(result, expected)
def test_resample_as_freq_with_subperiod():
# GH 13022
index = timedelta_range("00:00:00", "00:10:00", freq="5min")
df = DataFrame(data={"value": [1, 5, 10]}, index=index)
result = df.resample("2min").asfreq()
expected_data = {"value": [1, np.nan, np.nan, np.nan, np.nan, 10]}
expected = DataFrame(
data=expected_data, index=timedelta_range("00:00:00", "00:10:00", freq="2min")
)
tm.assert_frame_equal(result, expected)
def test_resample_with_timedeltas():
expected = DataFrame({"A": np.arange(1480)})
expected = expected.groupby(expected.index // 30).sum()
expected.index = timedelta_range("0 days", freq="30min", periods=50)
df = DataFrame(
{"A": np.arange(1480)}, index=pd.to_timedelta(np.arange(1480), unit="min")
)
result = df.resample("30min").sum()
tm.assert_frame_equal(result, expected)
s = df["A"]
result = s.resample("30min").sum()
tm.assert_series_equal(result, expected["A"])
def test_resample_single_period_timedelta():
s = Series(list(range(5)), index=timedelta_range("1 day", freq="s", periods=5))
result = s.resample("2s").sum()
expected = Series([1, 5, 4], index=timedelta_range("1 day", freq="2s", periods=3))
tm.assert_series_equal(result, expected)
def test_resample_timedelta_idempotency():
# GH 12072
index = timedelta_range("0", periods=9, freq="10ms")
series = Series(range(9), index=index)
result = series.resample("10ms").mean()
expected = series.astype(float)
tm.assert_series_equal(result, expected)
def test_resample_offset_with_timedeltaindex():
# GH 10530 & 31809
rng = timedelta_range(start="0s", periods=25, freq="s")
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
with_base = ts.resample("2s", offset="5s").mean()
without_base = ts.resample("2s").mean()
exp_without_base = timedelta_range(start="0s", end="25s", freq="2s")
exp_with_base = timedelta_range(start="5s", end="29s", freq="2s")
tm.assert_index_equal(without_base.index, exp_without_base)
tm.assert_index_equal(with_base.index, exp_with_base)
def test_resample_categorical_data_with_timedeltaindex():
# GH #12169
df = DataFrame({"Group_obj": "A"}, index=pd.to_timedelta(list(range(20)), unit="s"))
df["Group"] = df["Group_obj"].astype("category")
result = df.resample("10s").agg(lambda x: (x.value_counts().index[0]))
exp_tdi = pd.TimedeltaIndex(np.array([0, 10], dtype="m8[s]"), freq="10s").as_unit(
"ns"
)
expected = DataFrame(
{"Group_obj": ["A", "A"], "Group": ["A", "A"]},
index=exp_tdi,
)
expected = expected.reindex(["Group_obj", "Group"], axis=1)
expected["Group"] = expected["Group_obj"].astype("category")
tm.assert_frame_equal(result, expected)
def test_resample_timedelta_values():
# GH 13119
# check that timedelta dtype is preserved when NaT values are
# introduced by the resampling
times = timedelta_range("1 day", "6 day", freq="4D")
df = DataFrame({"time": times}, index=times)
times2 = timedelta_range("1 day", "6 day", freq="2D")
exp = Series(times2, index=times2, name="time")
exp.iloc[1] = pd.NaT
res = df.resample("2D").first()["time"]
tm.assert_series_equal(res, exp)
res = df["time"].resample("2D").first()
tm.assert_series_equal(res, exp)
@pytest.mark.parametrize(
"start, end, freq, resample_freq",
[
("8h", "21h59min50s", "10s", "3h"), # GH 30353 example
("3h", "22h", "1h", "5h"),
("527D", "5006D", "3D", "10D"),
("1D", "10D", "1D", "2D"), # GH 13022 example
# tests that worked before GH 33498:
("8h", "21h59min50s", "10s", "2h"),
("0h", "21h59min50s", "10s", "3h"),
("10D", "85D", "D", "2D"),
],
)
def test_resample_timedelta_edge_case(start, end, freq, resample_freq):
# GH 33498
# check that the timedelta bins does not contains an extra bin
idx = timedelta_range(start=start, end=end, freq=freq)
s = Series(np.arange(len(idx)), index=idx)
result = s.resample(resample_freq).min()
expected_index = timedelta_range(freq=resample_freq, start=start, end=end)
tm.assert_index_equal(result.index, expected_index)
assert result.index.freq == expected_index.freq
assert not np.isnan(result.iloc[-1])
@pytest.mark.parametrize("duplicates", [True, False])
def test_resample_with_timedelta_yields_no_empty_groups(duplicates):
# GH 10603
df = DataFrame(
np.random.default_rng(2).normal(size=(10000, 4)),
index=timedelta_range(start="0s", periods=10000, freq="3906250ns"),
)
if duplicates:
# case with non-unique columns
df.columns = ["A", "B", "A", "C"]
result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x))
expected = DataFrame(
[[768] * 4] * 12 + [[528] * 4],
index=timedelta_range(start="1s", periods=13, freq="3s"),
)
expected.columns = df.columns
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
def test_resample_quantile_timedelta(unit):
# GH: 29485
dtype = np.dtype(f"m8[{unit}]")
df = DataFrame(
{"value": pd.to_timedelta(np.arange(4), unit="s").astype(dtype)},
index=pd.date_range("20200101", periods=4, tz="UTC"),
)
result = df.resample("2D").quantile(0.99)
expected = DataFrame(
{
"value": [
pd.Timedelta("0 days 00:00:00.990000"),
pd.Timedelta("0 days 00:00:02.990000"),
]
},
index=pd.date_range("20200101", periods=2, tz="UTC", freq="2D"),
).astype(dtype)
tm.assert_frame_equal(result, expected)
def test_resample_closed_right():
# GH#45414
idx = pd.Index([pd.Timedelta(seconds=120 + i * 30) for i in range(10)])
ser = Series(range(10), index=idx)
result = ser.resample("min", closed="right", label="right").sum()
expected = Series(
[0, 3, 7, 11, 15, 9],
index=pd.TimedeltaIndex(
[pd.Timedelta(seconds=120 + i * 60) for i in range(6)], freq="min"
),
)
tm.assert_series_equal(result, expected)
@td.skip_if_no("pyarrow")
def test_arrow_duration_resample():
# GH 56371
idx = pd.Index(timedelta_range("1 day", periods=5), dtype="duration[ns][pyarrow]")
expected = Series(np.arange(5, dtype=np.float64), index=idx)
result = expected.resample("1D").mean()
tm.assert_series_equal(result, expected)
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