mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-11-27 17:28:02 +01:00
381 lines
12 KiB
Python
381 lines
12 KiB
Python
from datetime import date, datetime, timedelta
|
|
|
|
from dateutil import tz
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index, Series, Timestamp, date_range
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestDatetimeIndex:
|
|
def test_setitem_with_datetime_tz(self):
|
|
# 16889
|
|
# support .loc with alignment and tz-aware DatetimeIndex
|
|
mask = np.array([True, False, True, False])
|
|
|
|
idx = date_range("20010101", periods=4, tz="UTC")
|
|
df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64")
|
|
|
|
result = df.copy()
|
|
result.loc[mask, :] = df.loc[mask, :]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.copy()
|
|
result.loc[mask] = df.loc[mask]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
idx = date_range("20010101", periods=4)
|
|
df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64")
|
|
|
|
result = df.copy()
|
|
result.loc[mask, :] = df.loc[mask, :]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.copy()
|
|
result.loc[mask] = df.loc[mask]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_indexing_with_datetime_tz(self):
|
|
|
|
# GH#8260
|
|
# support datetime64 with tz
|
|
|
|
idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo")
|
|
dr = date_range("20130110", periods=3)
|
|
df = DataFrame({"A": idx, "B": dr})
|
|
df["C"] = idx
|
|
df.iloc[1, 1] = pd.NaT
|
|
df.iloc[1, 2] = pd.NaT
|
|
|
|
# indexing
|
|
result = df.iloc[1]
|
|
expected = Series(
|
|
[Timestamp("2013-01-02 00:00:00-0500", tz="US/Eastern"), pd.NaT, pd.NaT],
|
|
index=list("ABC"),
|
|
dtype="object",
|
|
name=1,
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
result = df.loc[1]
|
|
expected = Series(
|
|
[Timestamp("2013-01-02 00:00:00-0500", tz="US/Eastern"), pd.NaT, pd.NaT],
|
|
index=list("ABC"),
|
|
dtype="object",
|
|
name=1,
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# indexing - fast_xs
|
|
df = DataFrame({"a": date_range("2014-01-01", periods=10, tz="UTC")})
|
|
result = df.iloc[5]
|
|
expected = Series(
|
|
[Timestamp("2014-01-06 00:00:00+0000", tz="UTC")], index=["a"], name=5
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.loc[5]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# indexing - boolean
|
|
result = df[df.a > df.a[3]]
|
|
expected = df.iloc[4:]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# indexing - setting an element
|
|
df = DataFrame(
|
|
data=pd.to_datetime(["2015-03-30 20:12:32", "2015-03-12 00:11:11"]),
|
|
columns=["time"],
|
|
)
|
|
df["new_col"] = ["new", "old"]
|
|
df.time = df.set_index("time").index.tz_localize("UTC")
|
|
v = df[df.new_col == "new"].set_index("time").index.tz_convert("US/Pacific")
|
|
|
|
# trying to set a single element on a part of a different timezone
|
|
# this converts to object
|
|
df2 = df.copy()
|
|
df2.loc[df2.new_col == "new", "time"] = v
|
|
|
|
expected = Series([v[0], df.loc[1, "time"]], name="time")
|
|
tm.assert_series_equal(df2.time, expected)
|
|
|
|
v = df.loc[df.new_col == "new", "time"] + pd.Timedelta("1s")
|
|
df.loc[df.new_col == "new", "time"] = v
|
|
tm.assert_series_equal(df.loc[df.new_col == "new", "time"], v)
|
|
|
|
def test_consistency_with_tz_aware_scalar(self):
|
|
# xef gh-12938
|
|
# various ways of indexing the same tz-aware scalar
|
|
df = Series([Timestamp("2016-03-30 14:35:25", tz="Europe/Brussels")]).to_frame()
|
|
|
|
df = pd.concat([df, df]).reset_index(drop=True)
|
|
expected = Timestamp("2016-03-30 14:35:25+0200", tz="Europe/Brussels")
|
|
|
|
result = df[0][0]
|
|
assert result == expected
|
|
|
|
result = df.iloc[0, 0]
|
|
assert result == expected
|
|
|
|
result = df.loc[0, 0]
|
|
assert result == expected
|
|
|
|
result = df.iat[0, 0]
|
|
assert result == expected
|
|
|
|
result = df.at[0, 0]
|
|
assert result == expected
|
|
|
|
result = df[0].loc[0]
|
|
assert result == expected
|
|
|
|
result = df[0].at[0]
|
|
assert result == expected
|
|
|
|
def test_indexing_with_datetimeindex_tz(self):
|
|
|
|
# GH 12050
|
|
# indexing on a series with a datetimeindex with tz
|
|
index = date_range("2015-01-01", periods=2, tz="utc")
|
|
|
|
ser = Series(range(2), index=index, dtype="int64")
|
|
|
|
# list-like indexing
|
|
|
|
for sel in (index, list(index)):
|
|
# getitem
|
|
result = ser[sel]
|
|
expected = ser.copy()
|
|
if sel is not index:
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# setitem
|
|
result = ser.copy()
|
|
result[sel] = 1
|
|
expected = Series(1, index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# .loc getitem
|
|
result = ser.loc[sel]
|
|
expected = ser.copy()
|
|
if sel is not index:
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# .loc setitem
|
|
result = ser.copy()
|
|
result.loc[sel] = 1
|
|
expected = Series(1, index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# single element indexing
|
|
|
|
# getitem
|
|
assert ser[index[1]] == 1
|
|
|
|
# setitem
|
|
result = ser.copy()
|
|
result[index[1]] = 5
|
|
expected = Series([0, 5], index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# .loc getitem
|
|
assert ser.loc[index[1]] == 1
|
|
|
|
# .loc setitem
|
|
result = ser.copy()
|
|
result.loc[index[1]] = 5
|
|
expected = Series([0, 5], index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_partial_setting_with_datetimelike_dtype(self):
|
|
|
|
# GH9478
|
|
# a datetimeindex alignment issue with partial setting
|
|
df = DataFrame(
|
|
np.arange(6.0).reshape(3, 2),
|
|
columns=list("AB"),
|
|
index=date_range("1/1/2000", periods=3, freq="1H"),
|
|
)
|
|
expected = df.copy()
|
|
expected["C"] = [expected.index[0]] + [pd.NaT, pd.NaT]
|
|
|
|
mask = df.A < 1
|
|
df.loc[mask, "C"] = df.loc[mask].index
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_datetime(self):
|
|
|
|
# GH 9516
|
|
dt1 = Timestamp("20130101 09:00:00")
|
|
dt2 = Timestamp("20130101 10:00:00")
|
|
|
|
for conv in [
|
|
lambda x: x,
|
|
lambda x: x.to_datetime64(),
|
|
lambda x: x.to_pydatetime(),
|
|
lambda x: np.datetime64(x),
|
|
]:
|
|
|
|
df = DataFrame()
|
|
df.loc[conv(dt1), "one"] = 100
|
|
df.loc[conv(dt2), "one"] = 200
|
|
|
|
expected = DataFrame({"one": [100.0, 200.0]}, index=[dt1, dt2])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_series_partial_set_datetime(self):
|
|
# GH 11497
|
|
|
|
idx = date_range("2011-01-01", "2011-01-02", freq="D", name="idx")
|
|
ser = Series([0.1, 0.2], index=idx, name="s")
|
|
|
|
result = ser.loc[[Timestamp("2011-01-01"), Timestamp("2011-01-02")]]
|
|
exp = Series([0.1, 0.2], index=idx, name="s")
|
|
exp.index = exp.index._with_freq(None)
|
|
tm.assert_series_equal(result, exp, check_index_type=True)
|
|
|
|
keys = [
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-01"),
|
|
]
|
|
exp = Series(
|
|
[0.2, 0.2, 0.1], index=pd.DatetimeIndex(keys, name="idx"), name="s"
|
|
)
|
|
tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True)
|
|
|
|
keys = [
|
|
Timestamp("2011-01-03"),
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-03"),
|
|
]
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
|
ser.loc[keys]
|
|
|
|
def test_series_partial_set_period(self):
|
|
# GH 11497
|
|
|
|
idx = pd.period_range("2011-01-01", "2011-01-02", freq="D", name="idx")
|
|
ser = Series([0.1, 0.2], index=idx, name="s")
|
|
|
|
result = ser.loc[
|
|
[pd.Period("2011-01-01", freq="D"), pd.Period("2011-01-02", freq="D")]
|
|
]
|
|
exp = Series([0.1, 0.2], index=idx, name="s")
|
|
tm.assert_series_equal(result, exp, check_index_type=True)
|
|
|
|
keys = [
|
|
pd.Period("2011-01-02", freq="D"),
|
|
pd.Period("2011-01-02", freq="D"),
|
|
pd.Period("2011-01-01", freq="D"),
|
|
]
|
|
exp = Series([0.2, 0.2, 0.1], index=pd.PeriodIndex(keys, name="idx"), name="s")
|
|
tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True)
|
|
|
|
keys = [
|
|
pd.Period("2011-01-03", freq="D"),
|
|
pd.Period("2011-01-02", freq="D"),
|
|
pd.Period("2011-01-03", freq="D"),
|
|
]
|
|
with pytest.raises(KeyError, match="with any missing labels"):
|
|
ser.loc[keys]
|
|
|
|
def test_nanosecond_getitem_setitem_with_tz(self):
|
|
# GH 11679
|
|
data = ["2016-06-28 08:30:00.123456789"]
|
|
index = pd.DatetimeIndex(data, dtype="datetime64[ns, America/Chicago]")
|
|
df = DataFrame({"a": [10]}, index=index)
|
|
result = df.loc[df.index[0]]
|
|
expected = Series(10, index=["a"], name=df.index[0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[df.index[0], "a"] = -1
|
|
expected = DataFrame(-1, index=index, columns=["a"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_across_dst(self):
|
|
# GH 21846
|
|
idx = pd.date_range(
|
|
"2017-10-29 01:30:00", tz="Europe/Berlin", periods=5, freq="30 min"
|
|
)
|
|
series2 = pd.Series([0, 1, 2, 3, 4], index=idx)
|
|
|
|
t_1 = pd.Timestamp(
|
|
"2017-10-29 02:30:00+02:00", tz="Europe/Berlin", freq="30min"
|
|
)
|
|
t_2 = pd.Timestamp(
|
|
"2017-10-29 02:00:00+01:00", tz="Europe/Berlin", freq="30min"
|
|
)
|
|
result = series2.loc[t_1:t_2]
|
|
expected = pd.Series([2, 3], index=idx[2:4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = series2[t_1]
|
|
expected = 2
|
|
assert result == expected
|
|
|
|
def test_loc_incremental_setitem_with_dst(self):
|
|
# GH 20724
|
|
base = datetime(2015, 11, 1, tzinfo=tz.gettz("US/Pacific"))
|
|
idxs = [base + timedelta(seconds=i * 900) for i in range(16)]
|
|
result = pd.Series([0], index=[idxs[0]])
|
|
for ts in idxs:
|
|
result.loc[ts] = 1
|
|
expected = pd.Series(1, index=idxs)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_with_existing_dst(self):
|
|
# GH 18308
|
|
start = pd.Timestamp("2017-10-29 00:00:00+0200", tz="Europe/Madrid")
|
|
end = pd.Timestamp("2017-10-29 03:00:00+0100", tz="Europe/Madrid")
|
|
ts = pd.Timestamp("2016-10-10 03:00:00", tz="Europe/Madrid")
|
|
idx = pd.date_range(start, end, closed="left", freq="H")
|
|
result = pd.DataFrame(index=idx, columns=["value"])
|
|
result.loc[ts, "value"] = 12
|
|
expected = pd.DataFrame(
|
|
[np.nan] * len(idx) + [12],
|
|
index=idx.append(pd.DatetimeIndex([ts])),
|
|
columns=["value"],
|
|
dtype=object,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_str_slicing(self):
|
|
ix = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M")
|
|
ser = ix.to_series()
|
|
result = ser.loc[:"2017-12"]
|
|
expected = ser.iloc[:-1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_label_slicing(self):
|
|
ix = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M")
|
|
ser = ix.to_series()
|
|
result = ser.loc[: ix[-2]]
|
|
expected = ser.iloc[:-1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"slice_, positions",
|
|
[
|
|
[slice(date(2018, 1, 1), None), [0, 1, 2]],
|
|
[slice(date(2019, 1, 2), None), [2]],
|
|
[slice(date(2020, 1, 1), None), []],
|
|
[slice(None, date(2020, 1, 1)), [0, 1, 2]],
|
|
[slice(None, date(2019, 1, 1)), [0]],
|
|
],
|
|
)
|
|
def test_getitem_slice_date(self, slice_, positions):
|
|
# https://github.com/pandas-dev/pandas/issues/31501
|
|
s = pd.Series(
|
|
[0, 1, 2],
|
|
pd.DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]),
|
|
)
|
|
result = s[slice_]
|
|
expected = s.take(positions)
|
|
tm.assert_series_equal(result, expected)
|