758 lines
27 KiB
Python
758 lines
27 KiB
Python
""" test with the TimeGrouper / grouping with datetimes """
|
|
|
|
from datetime import datetime
|
|
from io import StringIO
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import pytz
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range
|
|
import pandas._testing as tm
|
|
from pandas.core.groupby.grouper import Grouper
|
|
from pandas.core.groupby.ops import BinGrouper
|
|
|
|
|
|
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"])
|
|
|
|
expected = DataFrame(
|
|
{"Quantity": 0},
|
|
index=date_range(
|
|
"20130901", "20131205", freq="5D", name="Date", closed="left"
|
|
),
|
|
)
|
|
expected.iloc[[0, 6, 18], 0] = 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(pd.Grouper(freq="5D")).sum()
|
|
tm.assert_frame_equal(result2, expected)
|
|
|
|
result3 = df.groupby(pd.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 = pd.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(pd.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_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"])
|
|
|
|
result = df.groupby([pd.Grouper(freq="A"), "Buyer"]).sum()
|
|
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([pd.Grouper(freq="6MS"), "Buyer"]).sum()
|
|
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([pd.Grouper(freq="1D"), "Buyer"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.groupby([pd.Grouper(freq="1M"), "Buyer"]).sum()
|
|
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([pd.Grouper(freq="1M", key="Date"), "Buyer"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
with pytest.raises(KeyError, match="'The grouper name foo is not found'"):
|
|
df.groupby([pd.Grouper(freq="1M", key="foo"), "Buyer"]).sum()
|
|
|
|
# passing the level
|
|
df = df.set_index("Date")
|
|
result = df.groupby([pd.Grouper(freq="1M", level="Date"), "Buyer"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
result = df.groupby([pd.Grouper(freq="1M", level=0), "Buyer"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
with pytest.raises(ValueError):
|
|
df.groupby([pd.Grouper(freq="1M", level="foo"), "Buyer"]).sum()
|
|
|
|
# multi names
|
|
df = df.copy()
|
|
df["Date"] = df.index + pd.offsets.MonthEnd(2)
|
|
result = df.groupby([pd.Grouper(freq="1M", key="Date"), "Buyer"]).sum()
|
|
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!
|
|
with pytest.raises(ValueError):
|
|
df.groupby(
|
|
[pd.Grouper(freq="1M", key="Date", level="Date"), "Buyer"]
|
|
).sum()
|
|
|
|
# single groupers
|
|
expected = DataFrame(
|
|
{"Quantity": [31], "Date": [datetime(2013, 10, 31, 0, 0)]}
|
|
).set_index("Date")
|
|
result = df.groupby(pd.Grouper(freq="1M")).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.groupby([pd.Grouper(freq="1M")]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
{"Quantity": [31], "Date": [datetime(2013, 11, 30, 0, 0)]}
|
|
).set_index("Date")
|
|
result = df.groupby(pd.Grouper(freq="1M", key="Date")).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.groupby([pd.Grouper(freq="1M", key="Date")]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("freq", ["D", "M", "A", "Q-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([pd.Grouper(freq=freq), "user_id"])["whole_cost"]
|
|
.sum()
|
|
)
|
|
tm.assert_series_equal(result1, expected)
|
|
|
|
result2 = df.groupby([pd.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(pd.Grouper(freq="M", key="Date"))
|
|
for t, expected in zip(dt_list, expected_list):
|
|
dt = pd.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", pd.Grouper(freq="M", key="Date")])
|
|
for (b, t), expected in zip(g_list, expected_list):
|
|
dt = pd.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(pd.Grouper(freq="M"))
|
|
for t, expected in zip(dt_list, expected_list):
|
|
dt = pd.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 = 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_series(x):
|
|
return pd.Series([x["value"].sum()], ("sum",))
|
|
|
|
expected = df.groupby(pd.Grouper(key="date")).apply(sumfunc_series)
|
|
result = df_dt.groupby(pd.Grouper(freq="M", 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 = 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)
|
|
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 = pd.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(list(groups.keys())[0], datetime)
|
|
|
|
# GH#11442
|
|
index = pd.date_range("2015/01/01", periods=5, name="date")
|
|
df = pd.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 = {
|
|
pd.Timestamp(date): pd.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 = pd.DatetimeIndex([date], name="date")
|
|
expected = pd.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 = pd.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 = pd.DatetimeIndex(dates, tz="Asia/Tokyo")
|
|
df = DataFrame(
|
|
{"value1": np.arange(6, dtype="int64"), "value2": [1, 2, 3, 1, 2, 3]},
|
|
index=didx,
|
|
)
|
|
|
|
exp_idx = pd.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
|
|
|
|
data = """0,2000-01-28 16:47:00,America/Chicago
|
|
1,2000-01-29 16:48:00,America/Chicago
|
|
2,2000-01-30 16:49:00,America/Los_Angeles
|
|
3,2000-01-31 16:50:00,America/Chicago
|
|
4,2000-01-01 16:50:00,America/New_York"""
|
|
|
|
df = pd.read_csv(StringIO(data), header=None, names=["value", "date", "tz"])
|
|
result = df.groupby("tz").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].view("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(dict(A=Timestamp("20130101"), B=np.arange(5)))
|
|
expected = df.groupby("A")["A"].apply(lambda x: x.max())
|
|
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": [pd.Timestamp("2000-01-1")] * 2})
|
|
result = df.groupby("A")["B"].transform(min)
|
|
expected = Series([pd.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.
|
|
np.random.seed(42)
|
|
df = pd.DataFrame(
|
|
{
|
|
"factor": np.random.randint(0, 3, size=60),
|
|
"time": pd.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 = pd.DataFrame({"a": [1], "b": [datetime.now(pytz.utc)]})
|
|
assert df["b"][0].tzinfo == pytz.utc
|
|
df = pd.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": pd.date_range("now", periods=6, freq="T")}
|
|
)
|
|
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.
|
|
from datetime import timedelta as td
|
|
|
|
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, td(days=1), td(days=2), td(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 = pd.DataFrame(
|
|
{
|
|
"time": [
|
|
Timestamp("2016-06-28 09:35:35"),
|
|
pd.NaT,
|
|
Timestamp("2016-06-28 16:46:28"),
|
|
],
|
|
"data": ["1", "2", "3"],
|
|
}
|
|
)
|
|
|
|
grouper = pd.Grouper(key="time", freq="h")
|
|
result = test.groupby(grouper)["data"].nunique()
|
|
expected = test[test.time.notnull()].groupby(grouper)["data"].nunique()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_scalar_call_versus_list_call(self):
|
|
# Issue: 17530
|
|
data_frame = {
|
|
"location": ["shanghai", "beijing", "shanghai"],
|
|
"time": pd.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 = pd.DataFrame(data_frame).set_index("time")
|
|
grouper = pd.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)
|