"""
test with the TimeGrouper / grouping with datetimes
"""
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
import pytz
import pandas as pd
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
offsets,
)
import pandas._testing as tm
from pandas.core.groupby.grouper import Grouper
from pandas.core.groupby.ops import BinGrouper
@pytest.fixture
def frame_for_truncated_bingrouper():
"""
DataFrame used by groupby_with_truncated_bingrouper, made into
a separate fixture for easier reuse in
test_groupby_apply_timegrouper_with_nat_apply_squeeze
"""
df = DataFrame(
{
"Quantity": [18, 3, 5, 1, 9, 3],
"Date": [
Timestamp(2013, 9, 1, 13, 0),
Timestamp(2013, 9, 1, 13, 5),
Timestamp(2013, 10, 1, 20, 0),
Timestamp(2013, 10, 3, 10, 0),
pd.NaT,
Timestamp(2013, 9, 2, 14, 0),
],
}
)
return df
@pytest.fixture
def groupby_with_truncated_bingrouper(frame_for_truncated_bingrouper):
"""
GroupBy object such that gb._grouper is a BinGrouper and
len(gb._grouper.result_index) < len(gb._grouper.group_keys_seq)
Aggregations on this groupby should have
dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date")
As either the index or an index level.
"""
df = frame_for_truncated_bingrouper
tdg = Grouper(key="Date", freq="5D")
gb = df.groupby(tdg)
# check we're testing the case we're interested in
assert len(gb._grouper.result_index) != len(gb._grouper.group_keys_seq)
return gb
class TestGroupBy:
def test_groupby_with_timegrouper(self):
# GH 4161
# TimeGrouper requires a sorted index
# also verifies that the resultant index has the correct name
df_original = DataFrame(
{
"Buyer": "Carl Carl Carl Carl Joe Carl".split(),
"Quantity": [18, 3, 5, 1, 9, 3],
"Date": [
datetime(2013, 9, 1, 13, 0),
datetime(2013, 9, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 3, 10, 0),
datetime(2013, 12, 2, 12, 0),
datetime(2013, 9, 2, 14, 0),
],
}
)
# GH 6908 change target column's order
df_reordered = df_original.sort_values(by="Quantity")
for df in [df_original, df_reordered]:
df = df.set_index(["Date"])
exp_dti = date_range(
"20130901",
"20131205",
freq="5D",
name="Date",
inclusive="left",
unit=df.index.unit,
)
expected = DataFrame(
{"Buyer": 0, "Quantity": 0},
index=exp_dti,
)
# Cast to object to avoid implicit cast when setting entry to "CarlCarlCarl"
expected = expected.astype({"Buyer": object})
expected.iloc[0, 0] = "CarlCarlCarl"
expected.iloc[6, 0] = "CarlCarl"
expected.iloc[18, 0] = "Joe"
expected.iloc[[0, 6, 18], 1] = np.array([24, 6, 9], dtype="int64")
result1 = df.resample("5D").sum()
tm.assert_frame_equal(result1, expected)
df_sorted = df.sort_index()
result2 = df_sorted.groupby(Grouper(freq="5D")).sum()
tm.assert_frame_equal(result2, expected)
result3 = df.groupby(Grouper(freq="5D")).sum()
tm.assert_frame_equal(result3, expected)
@pytest.mark.parametrize("should_sort", [True, False])
def test_groupby_with_timegrouper_methods(self, should_sort):
# GH 3881
# make sure API of timegrouper conforms
df = DataFrame(
{
"Branch": "A A A A A B".split(),
"Buyer": "Carl Mark Carl Joe Joe Carl".split(),
"Quantity": [1, 3, 5, 8, 9, 3],
"Date": [
datetime(2013, 1, 1, 13, 0),
datetime(2013, 1, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 2, 10, 0),
datetime(2013, 12, 2, 12, 0),
datetime(2013, 12, 2, 14, 0),
],
}
)
if should_sort:
df = df.sort_values(by="Quantity", ascending=False)
df = df.set_index("Date", drop=False)
g = df.groupby(Grouper(freq="6ME"))
assert g.group_keys
assert isinstance(g._grouper, BinGrouper)
groups = g.groups
assert isinstance(groups, dict)
assert len(groups) == 3
def test_timegrouper_with_reg_groups(self):
# GH 3794
# allow combination of timegrouper/reg groups
df_original = DataFrame(
{
"Branch": "A A A A A A A B".split(),
"Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
"Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
"Date": [
datetime(2013, 1, 1, 13, 0),
datetime(2013, 1, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 2, 10, 0),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 2, 10, 0),
datetime(2013, 12, 2, 12, 0),
datetime(2013, 12, 2, 14, 0),
],
}
).set_index("Date")
df_sorted = df_original.sort_values(by="Quantity", ascending=False)
for df in [df_original, df_sorted]:
expected = DataFrame(
{
"Buyer": "Carl Joe Mark".split(),
"Quantity": [10, 18, 3],
"Date": [
datetime(2013, 12, 31, 0, 0),
datetime(2013, 12, 31, 0, 0),
datetime(2013, 12, 31, 0, 0),
],
}
).set_index(["Date", "Buyer"])
msg = "The default value of numeric_only"
result = df.groupby([Grouper(freq="YE"), "Buyer"]).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
expected = DataFrame(
{
"Buyer": "Carl Mark Carl Joe".split(),
"Quantity": [1, 3, 9, 18],
"Date": [
datetime(2013, 1, 1, 0, 0),
datetime(2013, 1, 1, 0, 0),
datetime(2013, 7, 1, 0, 0),
datetime(2013, 7, 1, 0, 0),
],
}
).set_index(["Date", "Buyer"])
result = df.groupby([Grouper(freq="6MS"), "Buyer"]).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
df_original = DataFrame(
{
"Branch": "A A A A A A A B".split(),
"Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
"Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
"Date": [
datetime(2013, 10, 1, 13, 0),
datetime(2013, 10, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 2, 10, 0),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 2, 10, 0),
datetime(2013, 10, 2, 12, 0),
datetime(2013, 10, 2, 14, 0),
],
}
).set_index("Date")
df_sorted = df_original.sort_values(by="Quantity", ascending=False)
for df in [df_original, df_sorted]:
expected = DataFrame(
{
"Buyer": "Carl Joe Mark Carl Joe".split(),
"Quantity": [6, 8, 3, 4, 10],
"Date": [
datetime(2013, 10, 1, 0, 0),
datetime(2013, 10, 1, 0, 0),
datetime(2013, 10, 1, 0, 0),
datetime(2013, 10, 2, 0, 0),
datetime(2013, 10, 2, 0, 0),
],
}
).set_index(["Date", "Buyer"])
result = df.groupby([Grouper(freq="1D"), "Buyer"]).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
result = df.groupby([Grouper(freq="1ME"), "Buyer"]).sum(numeric_only=True)
expected = DataFrame(
{
"Buyer": "Carl Joe Mark".split(),
"Quantity": [10, 18, 3],
"Date": [
datetime(2013, 10, 31, 0, 0),
datetime(2013, 10, 31, 0, 0),
datetime(2013, 10, 31, 0, 0),
],
}
).set_index(["Date", "Buyer"])
tm.assert_frame_equal(result, expected)
# passing the name
df = df.reset_index()
result = df.groupby([Grouper(freq="1ME", key="Date"), "Buyer"]).sum(
numeric_only=True
)
tm.assert_frame_equal(result, expected)
with pytest.raises(KeyError, match="'The grouper name foo is not found'"):
df.groupby([Grouper(freq="1ME", key="foo"), "Buyer"]).sum()
# passing the level
df = df.set_index("Date")
result = df.groupby([Grouper(freq="1ME", level="Date"), "Buyer"]).sum(
numeric_only=True
)
tm.assert_frame_equal(result, expected)
result = df.groupby([Grouper(freq="1ME", level=0), "Buyer"]).sum(
numeric_only=True
)
tm.assert_frame_equal(result, expected)
with pytest.raises(ValueError, match="The level foo is not valid"):
df.groupby([Grouper(freq="1ME", level="foo"), "Buyer"]).sum()
# multi names
df = df.copy()
df["Date"] = df.index + offsets.MonthEnd(2)
result = df.groupby([Grouper(freq="1ME", key="Date"), "Buyer"]).sum(
numeric_only=True
)
expected = DataFrame(
{
"Buyer": "Carl Joe Mark".split(),
"Quantity": [10, 18, 3],
"Date": [
datetime(2013, 11, 30, 0, 0),
datetime(2013, 11, 30, 0, 0),
datetime(2013, 11, 30, 0, 0),
],
}
).set_index(["Date", "Buyer"])
tm.assert_frame_equal(result, expected)
# error as we have both a level and a name!
msg = "The Grouper cannot specify both a key and a level!"
with pytest.raises(ValueError, match=msg):
df.groupby(
[Grouper(freq="1ME", key="Date", level="Date"), "Buyer"]
).sum()
# single groupers
expected = DataFrame(
[[31]],
columns=["Quantity"],
index=DatetimeIndex(
[datetime(2013, 10, 31, 0, 0)], freq=offsets.MonthEnd(), name="Date"
),
)
result = df.groupby(Grouper(freq="1ME")).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
result = df.groupby([Grouper(freq="1ME")]).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
expected.index = expected.index.shift(1)
assert expected.index.freq == offsets.MonthEnd()
result = df.groupby(Grouper(freq="1ME", key="Date")).sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
result = df.groupby([Grouper(freq="1ME", key="Date")]).sum(
numeric_only=True
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("freq", ["D", "ME", "YE", "QE-APR"])
def test_timegrouper_with_reg_groups_freq(self, freq):
# GH 6764 multiple grouping with/without sort
df = DataFrame(
{
"date": pd.to_datetime(
[
"20121002",
"20121007",
"20130130",
"20130202",
"20130305",
"20121002",
"20121207",
"20130130",
"20130202",
"20130305",
"20130202",
"20130305",
]
),
"user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
"whole_cost": [
1790,
364,
280,
259,
201,
623,
90,
312,
359,
301,
359,
801,
],
"cost1": [12, 15, 10, 24, 39, 1, 0, 90, 45, 34, 1, 12],
}
).set_index("date")
expected = (
df.groupby("user_id")["whole_cost"]
.resample(freq)
.sum(min_count=1) # XXX
.dropna()
.reorder_levels(["date", "user_id"])
.sort_index()
.astype("int64")
)
expected.name = "whole_cost"
result1 = (
df.sort_index().groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
)
tm.assert_series_equal(result1, expected)
result2 = df.groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
tm.assert_series_equal(result2, expected)
def test_timegrouper_get_group(self):
# GH 6914
df_original = DataFrame(
{
"Buyer": "Carl Joe Joe Carl Joe Carl".split(),
"Quantity": [18, 3, 5, 1, 9, 3],
"Date": [
datetime(2013, 9, 1, 13, 0),
datetime(2013, 9, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 3, 10, 0),
datetime(2013, 12, 2, 12, 0),
datetime(2013, 9, 2, 14, 0),
],
}
)
df_reordered = df_original.sort_values(by="Quantity")
# single grouping
expected_list = [
df_original.iloc[[0, 1, 5]],
df_original.iloc[[2, 3]],
df_original.iloc[[4]],
]
dt_list = ["2013-09-30", "2013-10-31", "2013-12-31"]
for df in [df_original, df_reordered]:
grouped = df.groupby(Grouper(freq="ME", key="Date"))
for t, expected in zip(dt_list, expected_list):
dt = Timestamp(t)
result = grouped.get_group(dt)
tm.assert_frame_equal(result, expected)
# multiple grouping
expected_list = [
df_original.iloc[[1]],
df_original.iloc[[3]],
df_original.iloc[[4]],
]
g_list = [("Joe", "2013-09-30"), ("Carl", "2013-10-31"), ("Joe", "2013-12-31")]
for df in [df_original, df_reordered]:
grouped = df.groupby(["Buyer", Grouper(freq="ME", key="Date")])
for (b, t), expected in zip(g_list, expected_list):
dt = Timestamp(t)
result = grouped.get_group((b, dt))
tm.assert_frame_equal(result, expected)
# with index
df_original = df_original.set_index("Date")
df_reordered = df_original.sort_values(by="Quantity")
expected_list = [
df_original.iloc[[0, 1, 5]],
df_original.iloc[[2, 3]],
df_original.iloc[[4]],
]
for df in [df_original, df_reordered]:
grouped = df.groupby(Grouper(freq="ME"))
for t, expected in zip(dt_list, expected_list):
dt = Timestamp(t)
result = grouped.get_group(dt)
tm.assert_frame_equal(result, expected)
def test_timegrouper_apply_return_type_series(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = DataFrame({"date": ["10/10/2000", "11/10/2000"], "value": [10, 13]})
df_dt = df.copy()
df_dt["date"] = pd.to_datetime(df_dt["date"])
def sumfunc_series(x):
return Series([x["value"].sum()], ("sum",))
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = df.groupby(Grouper(key="date")).apply(sumfunc_series)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
result = df_dt.groupby(Grouper(freq="ME", key="date")).apply(sumfunc_series)
tm.assert_frame_equal(
result.reset_index(drop=True), expected.reset_index(drop=True)
)
def test_timegrouper_apply_return_type_value(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = DataFrame({"date": ["10/10/2000", "11/10/2000"], "value": [10, 13]})
df_dt = df.copy()
df_dt["date"] = pd.to_datetime(df_dt["date"])
def sumfunc_value(x):
return x.value.sum()
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = df.groupby(Grouper(key="date")).apply(sumfunc_value)
with tm.assert_produces_warning(DeprecationWarning, match=msg):
result = df_dt.groupby(Grouper(freq="ME", key="date")).apply(sumfunc_value)
tm.assert_series_equal(
result.reset_index(drop=True), expected.reset_index(drop=True)
)
def test_groupby_groups_datetimeindex(self):
# GH#1430
periods = 1000
ind = date_range(start="2012/1/1", freq="5min", periods=periods)
df = DataFrame(
{"high": np.arange(periods), "low": np.arange(periods)}, index=ind
)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
# it works!
groups = grouped.groups
assert isinstance(next(iter(groups.keys())), datetime)
def test_groupby_groups_datetimeindex2(self):
# GH#11442
index = date_range("2015/01/01", periods=5, name="date")
df = DataFrame({"A": [5, 6, 7, 8, 9], "B": [1, 2, 3, 4, 5]}, index=index)
result = df.groupby(level="date").groups
dates = ["2015-01-05", "2015-01-04", "2015-01-03", "2015-01-02", "2015-01-01"]
expected = {
Timestamp(date): DatetimeIndex([date], name="date") for date in dates
}
tm.assert_dict_equal(result, expected)
grouped = df.groupby(level="date")
for date in dates:
result = grouped.get_group(date)
data = [[df.loc[date, "A"], df.loc[date, "B"]]]
expected_index = DatetimeIndex(
[date], name="date", freq="D", dtype=index.dtype
)
expected = DataFrame(data, columns=list("AB"), index=expected_index)
tm.assert_frame_equal(result, expected)
def test_groupby_groups_datetimeindex_tz(self):
# GH 3950
dates = [
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
]
df = DataFrame(
{
"label": ["a", "a", "a", "b", "b", "b"],
"datetime": dates,
"value1": np.arange(6, dtype="int64"),
"value2": [1, 2] * 3,
}
)
df["datetime"] = df["datetime"].apply(lambda d: Timestamp(d, tz="US/Pacific"))
exp_idx1 = DatetimeIndex(
[
"2011-07-19 07:00:00",
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
"2011-07-19 09:00:00",
],
tz="US/Pacific",
name="datetime",
)
exp_idx2 = Index(["a", "b"] * 3, name="label")
exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
expected = DataFrame(
{"value1": [0, 3, 1, 4, 2, 5], "value2": [1, 2, 2, 1, 1, 2]},
index=exp_idx,
columns=["value1", "value2"],
)
result = df.groupby(["datetime", "label"]).sum()
tm.assert_frame_equal(result, expected)
# by level
didx = DatetimeIndex(dates, tz="Asia/Tokyo")
df = DataFrame(
{"value1": np.arange(6, dtype="int64"), "value2": [1, 2, 3, 1, 2, 3]},
index=didx,
)
exp_idx = DatetimeIndex(
["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
tz="Asia/Tokyo",
)
expected = DataFrame(
{"value1": [3, 5, 7], "value2": [2, 4, 6]},
index=exp_idx,
columns=["value1", "value2"],
)
result = df.groupby(level=0).sum()
tm.assert_frame_equal(result, expected)
def test_frame_datetime64_handling_groupby(self):
# it works!
df = DataFrame(
[(3, np.datetime64("2012-07-03")), (3, np.datetime64("2012-07-04"))],
columns=["a", "date"],
)
result = df.groupby("a").first()
assert result["date"][3] == Timestamp("2012-07-03")
def test_groupby_multi_timezone(self):
# combining multiple / different timezones yields UTC
df = DataFrame(
{
"value": range(5),
"date": [
"2000-01-28 16:47:00",
"2000-01-29 16:48:00",
"2000-01-30 16:49:00",
"2000-01-31 16:50:00",
"2000-01-01 16:50:00",
],
"tz": [
"America/Chicago",
"America/Chicago",
"America/Los_Angeles",
"America/Chicago",
"America/New_York",
],
}
)
result = df.groupby("tz", group_keys=False).date.apply(
lambda x: pd.to_datetime(x).dt.tz_localize(x.name)
)
expected = Series(
[
Timestamp("2000-01-28 16:47:00-0600", tz="America/Chicago"),
Timestamp("2000-01-29 16:48:00-0600", tz="America/Chicago"),
Timestamp("2000-01-30 16:49:00-0800", tz="America/Los_Angeles"),
Timestamp("2000-01-31 16:50:00-0600", tz="America/Chicago"),
Timestamp("2000-01-01 16:50:00-0500", tz="America/New_York"),
],
name="date",
dtype=object,
)
tm.assert_series_equal(result, expected)
tz = "America/Chicago"
res_values = df.groupby("tz").date.get_group(tz)
result = pd.to_datetime(res_values).dt.tz_localize(tz)
exp_values = Series(
["2000-01-28 16:47:00", "2000-01-29 16:48:00", "2000-01-31 16:50:00"],
index=[0, 1, 3],
name="date",
)
expected = pd.to_datetime(exp_values).dt.tz_localize(tz)
tm.assert_series_equal(result, expected)
def test_groupby_groups_periods(self):
dates = [
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
]
df = DataFrame(
{
"label": ["a", "a", "a", "b", "b", "b"],
"period": [pd.Period(d, freq="h") for d in dates],
"value1": np.arange(6, dtype="int64"),
"value2": [1, 2] * 3,
}
)
exp_idx1 = pd.PeriodIndex(
[
"2011-07-19 07:00:00",
"2011-07-19 07:00:00",
"2011-07-19 08:00:00",
"2011-07-19 08:00:00",
"2011-07-19 09:00:00",
"2011-07-19 09:00:00",
],
freq="h",
name="period",
)
exp_idx2 = Index(["a", "b"] * 3, name="label")
exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
expected = DataFrame(
{"value1": [0, 3, 1, 4, 2, 5], "value2": [1, 2, 2, 1, 1, 2]},
index=exp_idx,
columns=["value1", "value2"],
)
result = df.groupby(["period", "label"]).sum()
tm.assert_frame_equal(result, expected)
# by level
didx = pd.PeriodIndex(dates, freq="h")
df = DataFrame(
{"value1": np.arange(6, dtype="int64"), "value2": [1, 2, 3, 1, 2, 3]},
index=didx,
)
exp_idx = pd.PeriodIndex(
["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
freq="h",
)
expected = DataFrame(
{"value1": [3, 5, 7], "value2": [2, 4, 6]},
index=exp_idx,
columns=["value1", "value2"],
)
result = df.groupby(level=0).sum()
tm.assert_frame_equal(result, expected)
def test_groupby_first_datetime64(self):
df = DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)])
df[1] = df[1].astype("M8[ns]")
assert issubclass(df[1].dtype.type, np.datetime64)
result = df.groupby(level=0).first()
got_dt = result[1].dtype
assert issubclass(got_dt.type, np.datetime64)
result = df[1].groupby(level=0).first()
got_dt = result.dtype
assert issubclass(got_dt.type, np.datetime64)
def test_groupby_max_datetime64(self):
# GH 5869
# datetimelike dtype conversion from int
df = DataFrame({"A": Timestamp("20130101"), "B": np.arange(5)})
# TODO: can we retain second reso in .apply here?
expected = df.groupby("A")["A"].apply(lambda x: x.max()).astype("M8[s]")
result = df.groupby("A")["A"].max()
tm.assert_series_equal(result, expected)
def test_groupby_datetime64_32_bit(self):
# GH 6410 / numpy 4328
# 32-bit under 1.9-dev indexing issue
df = DataFrame({"A": range(2), "B": [Timestamp("2000-01-1")] * 2})
result = df.groupby("A")["B"].transform("min")
expected = Series([Timestamp("2000-01-1")] * 2, name="B")
tm.assert_series_equal(result, expected)
def test_groupby_with_timezone_selection(self):
# GH 11616
# Test that column selection returns output in correct timezone.
df = DataFrame(
{
"factor": np.random.default_rng(2).integers(0, 3, size=60),
"time": date_range("01/01/2000 00:00", periods=60, freq="s", tz="UTC"),
}
)
df1 = df.groupby("factor").max()["time"]
df2 = df.groupby("factor")["time"].max()
tm.assert_series_equal(df1, df2)
def test_timezone_info(self):
# see gh-11682: Timezone info lost when broadcasting
# scalar datetime to DataFrame
df = DataFrame({"a": [1], "b": [datetime.now(pytz.utc)]})
assert df["b"][0].tzinfo == pytz.utc
df = DataFrame({"a": [1, 2, 3]})
df["b"] = datetime.now(pytz.utc)
assert df["b"][0].tzinfo == pytz.utc
def test_datetime_count(self):
df = DataFrame(
{"a": [1, 2, 3] * 2, "dates": date_range("now", periods=6, freq="min")}
)
result = df.groupby("a").dates.count()
expected = Series([2, 2, 2], index=Index([1, 2, 3], name="a"), name="dates")
tm.assert_series_equal(result, expected)
def test_first_last_max_min_on_time_data(self):
# GH 10295
# Verify that NaT is not in the result of max, min, first and last on
# Dataframe with datetime or timedelta values.
df_test = DataFrame(
{
"dt": [
np.nan,
"2015-07-24 10:10",
"2015-07-25 11:11",
"2015-07-23 12:12",
np.nan,
],
"td": [
np.nan,
timedelta(days=1),
timedelta(days=2),
timedelta(days=3),
np.nan,
],
}
)
df_test.dt = pd.to_datetime(df_test.dt)
df_test["group"] = "A"
df_ref = df_test[df_test.dt.notna()]
grouped_test = df_test.groupby("group")
grouped_ref = df_ref.groupby("group")
tm.assert_frame_equal(grouped_ref.max(), grouped_test.max())
tm.assert_frame_equal(grouped_ref.min(), grouped_test.min())
tm.assert_frame_equal(grouped_ref.first(), grouped_test.first())
tm.assert_frame_equal(grouped_ref.last(), grouped_test.last())
def test_nunique_with_timegrouper_and_nat(self):
# GH 17575
test = DataFrame(
{
"time": [
Timestamp("2016-06-28 09:35:35"),
pd.NaT,
Timestamp("2016-06-28 16:46:28"),
],
"data": ["1", "2", "3"],
}
)
grouper = Grouper(key="time", freq="h")
result = test.groupby(grouper)["data"].nunique()
expected = test[test.time.notnull()].groupby(grouper)["data"].nunique()
expected.index = expected.index._with_freq(None)
tm.assert_series_equal(result, expected)
def test_scalar_call_versus_list_call(self):
# Issue: 17530
data_frame = {
"location": ["shanghai", "beijing", "shanghai"],
"time": Series(
["2017-08-09 13:32:23", "2017-08-11 23:23:15", "2017-08-11 22:23:15"],
dtype="datetime64[ns]",
),
"value": [1, 2, 3],
}
data_frame = DataFrame(data_frame).set_index("time")
grouper = Grouper(freq="D")
grouped = data_frame.groupby(grouper)
result = grouped.count()
grouped = data_frame.groupby([grouper])
expected = grouped.count()
tm.assert_frame_equal(result, expected)
def test_grouper_period_index(self):
# GH 32108
periods = 2
index = pd.period_range(
start="2018-01", periods=periods, freq="M", name="Month"
)
period_series = Series(range(periods), index=index)
result = period_series.groupby(period_series.index.month).sum()
expected = Series(
range(periods), index=Index(range(1, periods + 1), name=index.name)
)
tm.assert_series_equal(result, expected)
def test_groupby_apply_timegrouper_with_nat_dict_returns(
self, groupby_with_truncated_bingrouper
):
# GH#43500 case where gb._grouper.result_index and gb._grouper.group_keys_seq
# have different lengths that goes through the `isinstance(values[0], dict)`
# path
gb = groupby_with_truncated_bingrouper
res = gb["Quantity"].apply(lambda x: {"foo": len(x)})
df = gb.obj
unit = df["Date"]._values.unit
dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date", unit=unit)
mi = MultiIndex.from_arrays([dti, ["foo"] * len(dti)])
expected = Series([3, 0, 0, 0, 0, 0, 2], index=mi, name="Quantity")
tm.assert_series_equal(res, expected)
def test_groupby_apply_timegrouper_with_nat_scalar_returns(
self, groupby_with_truncated_bingrouper
):
# GH#43500 Previously raised ValueError bc used index with incorrect
# length in wrap_applied_result
gb = groupby_with_truncated_bingrouper
res = gb["Quantity"].apply(lambda x: x.iloc[0] if len(x) else np.nan)
df = gb.obj
unit = df["Date"]._values.unit
dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date", unit=unit)
expected = Series(
[18, np.nan, np.nan, np.nan, np.nan, np.nan, 5],
index=dti._with_freq(None),
name="Quantity",
)
tm.assert_series_equal(res, expected)
def test_groupby_apply_timegrouper_with_nat_apply_squeeze(
self, frame_for_truncated_bingrouper
):
df = frame_for_truncated_bingrouper
# We need to create a GroupBy object with only one non-NaT group,
# so use a huge freq so that all non-NaT dates will be grouped together
tdg = Grouper(key="Date", freq="100YE")
gb = df.groupby(tdg)
# check that we will go through the singular_series path
# in _wrap_applied_output_series
assert gb.ngroups == 1
assert gb._selected_obj._get_axis(gb.axis).nlevels == 1
# function that returns a Series
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
res = gb.apply(lambda x: x["Quantity"] * 2)
dti = Index([Timestamp("2013-12-31")], dtype=df["Date"].dtype, name="Date")
expected = DataFrame(
[[36, 6, 6, 10, 2]],
index=dti,
columns=Index([0, 1, 5, 2, 3], name="Quantity"),
)
tm.assert_frame_equal(res, expected)
@pytest.mark.single_cpu
def test_groupby_agg_numba_timegrouper_with_nat(
self, groupby_with_truncated_bingrouper
):
pytest.importorskip("numba")
# See discussion in GH#43487
gb = groupby_with_truncated_bingrouper
result = gb["Quantity"].aggregate(
lambda values, index: np.nanmean(values), engine="numba"
)
expected = gb["Quantity"].aggregate("mean")
tm.assert_series_equal(result, expected)
result_df = gb[["Quantity"]].aggregate(
lambda values, index: np.nanmean(values), engine="numba"
)
expected_df = gb[["Quantity"]].aggregate("mean")
tm.assert_frame_equal(result_df, expected_df)