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