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357 lines
11 KiB
357 lines
11 KiB
from datetime import datetime
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from operator import methodcaller
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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Timestamp,
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)
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import pandas._testing as tm
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from pandas.core.groupby.grouper import Grouper
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from pandas.core.indexes.datetimes import date_range
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test_series = Series(np.random.randn(1000), index=date_range("1/1/2000", periods=1000))
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def test_apply():
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grouper = Grouper(freq="A", label="right", closed="right")
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grouped = test_series.groupby(grouper)
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def f(x):
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return x.sort_values()[-3:]
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applied = grouped.apply(f)
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expected = test_series.groupby(lambda x: x.year).apply(f)
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applied.index = applied.index.droplevel(0)
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expected.index = expected.index.droplevel(0)
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tm.assert_series_equal(applied, expected)
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def test_count():
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test_series[::3] = np.nan
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expected = test_series.groupby(lambda x: x.year).count()
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grouper = Grouper(freq="A", label="right", closed="right")
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result = test_series.groupby(grouper).count()
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expected.index = result.index
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tm.assert_series_equal(result, expected)
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result = test_series.resample("A").count()
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expected.index = result.index
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tm.assert_series_equal(result, expected)
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def test_numpy_reduction():
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result = test_series.resample("A", closed="right").prod()
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expected = test_series.groupby(lambda x: x.year).agg(np.prod)
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expected.index = result.index
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tm.assert_series_equal(result, expected)
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def test_apply_iteration():
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# #2300
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N = 1000
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ind = date_range(start="2000-01-01", freq="D", periods=N)
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df = DataFrame({"open": 1, "close": 2}, index=ind)
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tg = Grouper(freq="M")
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_, grouper, _ = tg._get_grouper(df)
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# Errors
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grouped = df.groupby(grouper, group_keys=False)
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def f(df):
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return df["close"] / df["open"]
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# it works!
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result = grouped.apply(f)
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tm.assert_index_equal(result.index, df.index)
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@pytest.mark.parametrize(
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"name, func",
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[
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("Int64Index", tm.makeIntIndex),
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("Index", tm.makeUnicodeIndex),
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("Float64Index", tm.makeFloatIndex),
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("MultiIndex", lambda m: tm.makeCustomIndex(m, 2)),
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],
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)
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def test_fails_on_no_datetime_index(name, func):
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n = 2
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index = func(n)
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df = DataFrame({"a": np.random.randn(n)}, index=index)
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msg = (
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"Only valid with DatetimeIndex, TimedeltaIndex "
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f"or PeriodIndex, but got an instance of '{name}'"
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)
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with pytest.raises(TypeError, match=msg):
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df.groupby(Grouper(freq="D"))
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def test_aaa_group_order():
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# GH 12840
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# check TimeGrouper perform stable sorts
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n = 20
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data = np.random.randn(n, 4)
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df = DataFrame(data, columns=["A", "B", "C", "D"])
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df["key"] = [
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datetime(2013, 1, 1),
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datetime(2013, 1, 2),
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datetime(2013, 1, 3),
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datetime(2013, 1, 4),
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datetime(2013, 1, 5),
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] * 4
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grouped = df.groupby(Grouper(key="key", freq="D"))
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tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)), df[::5])
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tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)), df[1::5])
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tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)), df[2::5])
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tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)), df[3::5])
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tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)), df[4::5])
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def test_aggregate_normal(resample_method):
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"""Check TimeGrouper's aggregation is identical as normal groupby."""
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data = np.random.randn(20, 4)
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normal_df = DataFrame(data, columns=["A", "B", "C", "D"])
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normal_df["key"] = [1, 2, 3, 4, 5] * 4
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dt_df = DataFrame(data, columns=["A", "B", "C", "D"])
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dt_df["key"] = [
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datetime(2013, 1, 1),
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datetime(2013, 1, 2),
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datetime(2013, 1, 3),
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datetime(2013, 1, 4),
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datetime(2013, 1, 5),
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] * 4
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normal_grouped = normal_df.groupby("key")
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dt_grouped = dt_df.groupby(Grouper(key="key", freq="D"))
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expected = getattr(normal_grouped, resample_method)()
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dt_result = getattr(dt_grouped, resample_method)()
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expected.index = date_range(start="2013-01-01", freq="D", periods=5, name="key")
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tm.assert_equal(expected, dt_result)
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# if TimeGrouper is used included, 'nth' doesn't work yet
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"""
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for func in ['nth']:
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expected = getattr(normal_grouped, func)(3)
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expected.index = date_range(start='2013-01-01',
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freq='D', periods=5, name='key')
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dt_result = getattr(dt_grouped, func)(3)
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tm.assert_frame_equal(expected, dt_result)
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"""
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@pytest.mark.parametrize(
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"method, method_args, unit",
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[
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("sum", {}, 0),
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("sum", {"min_count": 0}, 0),
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("sum", {"min_count": 1}, np.nan),
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("prod", {}, 1),
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("prod", {"min_count": 0}, 1),
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("prod", {"min_count": 1}, np.nan),
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],
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)
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def test_resample_entirely_nat_window(method, method_args, unit):
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s = Series([0] * 2 + [np.nan] * 2, index=date_range("2017", periods=4))
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result = methodcaller(method, **method_args)(s.resample("2d"))
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expected = Series(
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[0.0, unit], index=pd.DatetimeIndex(["2017-01-01", "2017-01-03"], freq="2D")
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"func, fill_value",
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[("min", np.nan), ("max", np.nan), ("sum", 0), ("prod", 1), ("count", 0)],
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)
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def test_aggregate_with_nat(func, fill_value):
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# check TimeGrouper's aggregation is identical as normal groupby
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# if NaT is included, 'var', 'std', 'mean', 'first','last'
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# and 'nth' doesn't work yet
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n = 20
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data = np.random.randn(n, 4).astype("int64")
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normal_df = DataFrame(data, columns=["A", "B", "C", "D"])
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normal_df["key"] = [1, 2, np.nan, 4, 5] * 4
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dt_df = DataFrame(data, columns=["A", "B", "C", "D"])
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dt_df["key"] = [
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datetime(2013, 1, 1),
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datetime(2013, 1, 2),
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pd.NaT,
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datetime(2013, 1, 4),
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datetime(2013, 1, 5),
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] * 4
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normal_grouped = normal_df.groupby("key")
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dt_grouped = dt_df.groupby(Grouper(key="key", freq="D"))
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normal_result = getattr(normal_grouped, func)()
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dt_result = getattr(dt_grouped, func)()
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pad = DataFrame([[fill_value] * 4], index=[3], columns=["A", "B", "C", "D"])
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expected = pd.concat([normal_result, pad])
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expected = expected.sort_index()
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dti = date_range(start="2013-01-01", freq="D", periods=5, name="key")
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expected.index = dti._with_freq(None) # TODO: is this desired?
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tm.assert_frame_equal(expected, dt_result)
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assert dt_result.index.name == "key"
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def test_aggregate_with_nat_size():
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# GH 9925
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n = 20
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data = np.random.randn(n, 4).astype("int64")
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normal_df = DataFrame(data, columns=["A", "B", "C", "D"])
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normal_df["key"] = [1, 2, np.nan, 4, 5] * 4
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dt_df = DataFrame(data, columns=["A", "B", "C", "D"])
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dt_df["key"] = [
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datetime(2013, 1, 1),
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datetime(2013, 1, 2),
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pd.NaT,
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datetime(2013, 1, 4),
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datetime(2013, 1, 5),
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] * 4
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normal_grouped = normal_df.groupby("key")
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dt_grouped = dt_df.groupby(Grouper(key="key", freq="D"))
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normal_result = normal_grouped.size()
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dt_result = dt_grouped.size()
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pad = Series([0], index=[3])
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expected = pd.concat([normal_result, pad])
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expected = expected.sort_index()
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expected.index = date_range(
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start="2013-01-01", freq="D", periods=5, name="key"
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)._with_freq(None)
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tm.assert_series_equal(expected, dt_result)
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assert dt_result.index.name == "key"
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def test_repr():
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# GH18203
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result = repr(Grouper(key="A", freq="H"))
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expected = (
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"TimeGrouper(key='A', freq=<Hour>, axis=0, sort=True, "
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"closed='left', label='left', how='mean', "
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"convention='e', origin='start_day')"
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)
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assert result == expected
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result = repr(Grouper(key="A", freq="H", origin="2000-01-01"))
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expected = (
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"TimeGrouper(key='A', freq=<Hour>, axis=0, sort=True, "
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"closed='left', label='left', how='mean', "
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"convention='e', origin=Timestamp('2000-01-01 00:00:00'))"
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)
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assert result == expected
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@pytest.mark.parametrize(
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"method, method_args, expected_values",
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[
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("sum", {}, [1, 0, 1]),
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("sum", {"min_count": 0}, [1, 0, 1]),
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("sum", {"min_count": 1}, [1, np.nan, 1]),
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("sum", {"min_count": 2}, [np.nan, np.nan, np.nan]),
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("prod", {}, [1, 1, 1]),
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("prod", {"min_count": 0}, [1, 1, 1]),
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("prod", {"min_count": 1}, [1, np.nan, 1]),
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("prod", {"min_count": 2}, [np.nan, np.nan, np.nan]),
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],
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)
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def test_upsample_sum(method, method_args, expected_values):
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s = Series(1, index=date_range("2017", periods=2, freq="H"))
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resampled = s.resample("30T")
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index = pd.DatetimeIndex(
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["2017-01-01T00:00:00", "2017-01-01T00:30:00", "2017-01-01T01:00:00"],
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freq="30T",
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)
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result = methodcaller(method, **method_args)(resampled)
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expected = Series(expected_values, index=index)
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tm.assert_series_equal(result, expected)
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def test_groupby_resample_interpolate():
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# GH 35325
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d = {"price": [10, 11, 9], "volume": [50, 60, 50]}
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df = DataFrame(d)
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df["week_starting"] = date_range("01/01/2018", periods=3, freq="W")
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result = (
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df.set_index("week_starting")
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.groupby("volume")
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.resample("1D")
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.interpolate(method="linear")
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)
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msg = "containing strings is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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expected_ind = pd.MultiIndex.from_tuples(
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[
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(50, "2018-01-07"),
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(50, Timestamp("2018-01-08")),
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(50, Timestamp("2018-01-09")),
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(50, Timestamp("2018-01-10")),
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(50, Timestamp("2018-01-11")),
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(50, Timestamp("2018-01-12")),
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(50, Timestamp("2018-01-13")),
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(50, Timestamp("2018-01-14")),
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(50, Timestamp("2018-01-15")),
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(50, Timestamp("2018-01-16")),
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(50, Timestamp("2018-01-17")),
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(50, Timestamp("2018-01-18")),
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(50, Timestamp("2018-01-19")),
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(50, Timestamp("2018-01-20")),
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(50, Timestamp("2018-01-21")),
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(60, Timestamp("2018-01-14")),
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],
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names=["volume", "week_starting"],
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)
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expected = DataFrame(
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data={
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"price": [
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10.0,
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9.928571428571429,
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9.857142857142858,
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9.785714285714286,
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9.714285714285714,
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9.642857142857142,
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9.571428571428571,
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9.5,
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9.428571428571429,
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9.357142857142858,
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9.285714285714286,
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9.214285714285714,
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9.142857142857142,
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9.071428571428571,
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9.0,
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11.0,
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],
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"volume": [50.0] * 15 + [60],
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},
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index=expected_ind,
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)
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tm.assert_frame_equal(result, expected)
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