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test_datetime.py
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test_datetime.py
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from datetime import datetime, timedelta
from dateutil import tz
import numpy as np
import pandas as pd
from pandas import DataFrame, Index, Series, Timestamp, date_range
from pandas.util import testing as tm
class TestDatetimeIndex(object):
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):
# 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'), np.nan, np.nan],
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'), np.nan, np.nan],
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 = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D')
assert result == expected
result = df.loc[5]
assert 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
tm.assert_series_equal(ser[sel], ser)
# setitem
result = ser.copy()
result[sel] = 1
expected = Series(1, index=index)
tm.assert_series_equal(result, expected)
# .loc getitem
tm.assert_series_equal(ser.loc[sel], ser)
# .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.).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')
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')]
exp = Series([np.nan, 0.2, np.nan],
index=pd.DatetimeIndex(keys, name='idx'), name='s')
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
tm.assert_series_equal(ser.loc[keys], exp, check_index_type=True)
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')]
exp = Series([np.nan, 0.2, np.nan],
index=pd.PeriodIndex(keys, name='idx'), name='s')
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = ser.loc[keys]
tm.assert_series_equal(result, exp)
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)