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_repr():
# GH18203
result = repr(Grouper(key="A", freq="H"))
expected = ("TimeGrouper(key='A', freq=<Hour>, axis=0, sort=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, "
"closed='left', label='left', how='mean', "
"convention='e', origin=Timestamp('2000-01-01 00:00:00'))")
assert result == expected
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 = normal_result.append(pad)
expected = expected.sort_index()
expected.index = date_range(start='2013-01-01',
freq='D',
periods=5,
name='key')
assert_series_equal(expected, dt_result)
assert dt_result.index.name == 'key'
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 = normal_result.append(pad)
expected = expected.sort_index()
expected.index = date_range(start='2013-01-01',
freq='D',
periods=5,
name='key')
assert_frame_equal(expected, dt_result)
assert dt_result.index.name == 'key'
def test_aggregate_normal(resample_method):
"""Check TimeGrouper's aggregation is identical as normal groupby."""
if resample_method == 'ohlc':
pytest.xfail(reason='DataError: No numeric types to aggregate')
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)
# if TimeGrouper is used included, 'nth' doesn't work yet
"""
def test_aggregate_nth(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 = 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)
def test_repr():
# GH18203
result = repr(Grouper(key="A", freq="H"))
expected = ("TimeGrouper(key='A', freq=<Hour>, axis=0, sort=True, "
"closed='left', label='left', how='mean', "
"convention='e', base=0)")
assert result == expected
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()
expected = df.groupby(Grouper(key="date")).apply(sumfunc_value)
result = df_dt.groupby(Grouper(freq="M", key="date")).apply(sumfunc_value)
tm.assert_series_equal(
result.reset_index(drop=True), expected.reset_index(drop=True)
)
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="6M"))
assert g.group_keys
assert isinstance(g.grouper, BinGrouper)
groups = g.groups
assert isinstance(groups, dict)
assert len(groups) == 3
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 = normal_result.append(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_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 = normal_result.append(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_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="100Y")
with tm.assert_produces_warning(FutureWarning, match="`squeeze` parameter"):
gb = df.groupby(tdg, squeeze=True)
# 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
res = gb.apply(lambda x: x["Quantity"] * 2)
key = Timestamp("2013-12-31")
ordering = df["Date"].sort_values().dropna().index
mi = MultiIndex.from_product([[key], ordering], names=["Date", None])
ex_values = df["Quantity"].take(ordering).values * 2
expected = Series(ex_values, index=mi, name="Quantity")
tm.assert_series_equal(res, expected)
def test_aggregate_normal(resample_method):
"""Check TimeGrouper's aggregation is identical as normal groupby."""
if resample_method == "ohlc":
pytest.xfail(reason="DataError: No numeric types to aggregate")
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)
# if TimeGrouper is used included, 'nth' doesn't work yet
"""
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"])
expected = DataFrame(
{"Quantity": 0},
index=date_range("20130901",
"20131205",
freq="5D",
name="Date",
inclusive="left"),
)
expected.iloc[[0, 6, 18], 0] = np.array([24, 6, 9], dtype="int64")
msg = "The default value of numeric_only"
with tm.assert_produces_warning(FutureWarning, match=msg):
result1 = df.resample("5D").sum()
tm.assert_frame_equal(result1, expected)
df_sorted = df.sort_index()
with tm.assert_produces_warning(FutureWarning, match=msg):
result2 = df_sorted.groupby(Grouper(freq="5D")).sum()
tm.assert_frame_equal(result2, expected)
with tm.assert_produces_warning(FutureWarning, match=msg):
result3 = df.groupby(Grouper(freq="5D")).sum()
tm.assert_frame_equal(result3, expected)
def test_apply_iteration():
# #2300
N = 1000
ind = pd.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)
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_apply_iteration():
# #2300
N = 1000
ind = pd.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)
def test_resampler_is_iterable(series):
# GH 15314
freq = "H"
tg = Grouper(freq=freq, convention="start")
grouped = series.groupby(tg)
resampled = series.resample(freq)
for (rk, rv), (gk, gv) in zip(resampled, grouped):
assert rk == gk
tm.assert_series_equal(rv, gv)
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_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
assert_series_equal(result, expected)
result = test_series.resample('A').count()
expected.index = result.index
assert_series_equal(result, expected)
def test_custom_grouper(index):
dti = index
s = Series(np.array([1] * len(dti)), index=dti, dtype='int64')
b = Grouper(freq=Minute(5))
g = s.groupby(b)
# check all cython functions work
funcs = ['add', 'mean', 'prod', 'ohlc', 'min', 'max', 'var']
for f in funcs:
g._cython_agg_general(f)
b = Grouper(freq=Minute(5), closed='right', label='right')
g = s.groupby(b)
# check all cython functions work
funcs = ['add', 'mean', 'prod', 'ohlc', 'min', 'max', 'var']
for f in funcs:
g._cython_agg_general(f)
assert g.ngroups == 2593
assert notna(g.mean()).all()
# construct expected val
arr = [1] + [5] * 2592
idx = dti[0:-1:5]
idx = idx.append(dti[-1:])
expect = Series(arr, index=idx)
# GH2763 - return in put dtype if we can
result = g.agg(np.sum)
assert_series_equal(result, expect)
df = DataFrame(np.random.rand(len(dti), 10),
index=dti, dtype='float64')
r = df.groupby(b).agg(np.sum)
assert len(r.columns) == 10
assert len(r.index) == 2593
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_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_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 = pd.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()
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value)
result = (df_dt.groupby(Grouper(freq='M', key='date'))
.apply(sumfunc_value))
assert_series_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
def test_resample_frame_basic():
df = tm.makeTimeDataFrame()
b = Grouper(freq="M")
g = df.groupby(b)
# check all cython functions work
funcs = ["add", "mean", "prod", "min", "max", "var"]
for f in funcs:
g._cython_agg_general(f)
result = df.resample("A").mean()
tm.assert_series_equal(result["A"], df["A"].resample("A").mean())
result = df.resample("M").mean()
tm.assert_series_equal(result["A"], df["A"].resample("M").mean())
df.resample("M", kind="period").mean()
df.resample("W-WED", kind="period").mean()
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_resample_frame_basic():
df = tm.makeTimeDataFrame()
b = Grouper(freq='M')
g = df.groupby(b)
# check all cython functions work
funcs = ['add', 'mean', 'prod', 'min', 'max', 'var']
for f in funcs:
g._cython_agg_general(f)
result = df.resample('A').mean()
assert_series_equal(result['A'], df['A'].resample('A').mean())
result = df.resample('M').mean()
assert_series_equal(result['A'], df['A'].resample('M').mean())
df.resample('M', kind='period').mean()
df.resample('W-WED', kind='period').mean()
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_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 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
def test_resample_ohlc(series):
s = series
grouper = Grouper(freq=Minute(5))
expect = s.groupby(grouper).agg(lambda x: x[-1])
result = s.resample("5Min").ohlc()
assert len(result) == len(expect)
assert len(result.columns) == 4
xs = result.iloc[-2]
assert xs["open"] == s[-6]
assert xs["high"] == s[-6:-1].max()
assert xs["low"] == s[-6:-1].min()
assert xs["close"] == s[-2]
xs = result.iloc[0]
assert xs["open"] == s[0]
assert xs["high"] == s[:5].max()
assert xs["low"] == s[:5].min()
assert xs["close"] == s[4]
