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test_partial_slicing.py
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""" test partial slicing on Series/Frame """
from datetime import datetime
import operator as op
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame, DatetimeIndex, Index, Series, Timedelta, Timestamp, date_range)
from pandas.core.indexing import IndexingError
from pandas.util import testing as tm
class TestSlicing(object):
def test_dti_slicing(self):
dti = date_range(start='1/1/2005', end='12/1/2005', freq='M')
dti2 = dti[[1, 3, 5]]
v1 = dti2[0]
v2 = dti2[1]
v3 = dti2[2]
assert v1 == Timestamp('2/28/2005')
assert v2 == Timestamp('4/30/2005')
assert v3 == Timestamp('6/30/2005')
# don't carry freq through irregular slicing
assert dti2.freq is None
def test_slice_keeps_name(self):
# GH4226
st = pd.Timestamp('2013-07-01 00:00:00', tz='America/Los_Angeles')
et = pd.Timestamp('2013-07-02 00:00:00', tz='America/Los_Angeles')
dr = pd.date_range(st, et, freq='H', name='timebucket')
assert dr[1:].name == dr.name
def test_slice_with_negative_step(self):
ts = Series(np.arange(20),
date_range('2014-01-01', periods=20, freq='MS'))
SLC = pd.IndexSlice
def assert_slices_equivalent(l_slc, i_slc):
tm.assert_series_equal(ts[l_slc], ts.iloc[i_slc])
tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc])
tm.assert_series_equal(ts.loc[l_slc], ts.iloc[i_slc])
assert_slices_equivalent(SLC[Timestamp('2014-10-01')::-1], SLC[9::-1])
assert_slices_equivalent(SLC['2014-10-01'::-1], SLC[9::-1])
assert_slices_equivalent(SLC[:Timestamp('2014-10-01'):-1], SLC[:8:-1])
assert_slices_equivalent(SLC[:'2014-10-01':-1], SLC[:8:-1])
assert_slices_equivalent(SLC['2015-02-01':'2014-10-01':-1],
SLC[13:8:-1])
assert_slices_equivalent(SLC[Timestamp('2015-02-01'):Timestamp(
'2014-10-01'):-1], SLC[13:8:-1])
assert_slices_equivalent(SLC['2015-02-01':Timestamp('2014-10-01'):-1],
SLC[13:8:-1])
assert_slices_equivalent(SLC[Timestamp('2015-02-01'):'2014-10-01':-1],
SLC[13:8:-1])
assert_slices_equivalent(SLC['2014-10-01':'2015-02-01':-1], SLC[:0])
def test_slice_with_zero_step_raises(self):
ts = Series(np.arange(20),
date_range('2014-01-01', periods=20, freq='MS'))
with pytest.raises(ValueError, match='slice step cannot be zero'):
ts[::0]
with pytest.raises(ValueError, match='slice step cannot be zero'):
ts.loc[::0]
with pytest.raises(ValueError, match='slice step cannot be zero'):
ts.loc[::0]
def test_slice_bounds_empty(self):
# GH#14354
empty_idx = date_range(freq='1H', periods=0, end='2015')
right = empty_idx._maybe_cast_slice_bound('2015-01-02', 'right', 'loc')
exp = Timestamp('2015-01-02 23:59:59.999999999')
assert right == exp
left = empty_idx._maybe_cast_slice_bound('2015-01-02', 'left', 'loc')
exp = Timestamp('2015-01-02 00:00:00')
assert left == exp
def test_slice_duplicate_monotonic(self):
# https://github.com/pandas-dev/pandas/issues/16515
idx = pd.DatetimeIndex(['2017', '2017'])
result = idx._maybe_cast_slice_bound('2017-01-01', 'left', 'loc')
expected = Timestamp('2017-01-01')
assert result == expected
def test_monotone_DTI_indexing_bug(self):
# GH 19362
# Testing accessing the first element in a montononic descending
# partial string indexing.
df = pd.DataFrame(list(range(5)))
date_list = ['2018-01-02', '2017-02-10', '2016-03-10',
'2015-03-15', '2014-03-16']
date_index = pd.to_datetime(date_list)
df['date'] = date_index
expected = pd.DataFrame({0: list(range(5)), 'date': date_index})
tm.assert_frame_equal(df, expected)
df = pd.DataFrame({'A': [1, 2, 3]},
index=pd.date_range('20170101',
periods=3)[::-1])
expected = pd.DataFrame({'A': 1},
index=pd.date_range('20170103',
periods=1))
tm.assert_frame_equal(df.loc['2017-01-03'], expected)
def test_slice_year(self):
dti = date_range(freq='B', start=datetime(2005, 1, 1), periods=500)
s = Series(np.arange(len(dti)), index=dti)
result = s['2005']
expected = s[s.index.year == 2005]
tm.assert_series_equal(result, expected)
df = DataFrame(np.random.rand(len(dti), 5), index=dti)
result = df.loc['2005']
expected = df[df.index.year == 2005]
tm.assert_frame_equal(result, expected)
rng = date_range('1/1/2000', '1/1/2010')
result = rng.get_loc('2009')
expected = slice(3288, 3653)
assert result == expected
def test_slice_quarter(self):
dti = date_range(freq='D', start=datetime(2000, 6, 1), periods=500)
s = Series(np.arange(len(dti)), index=dti)
assert len(s['2001Q1']) == 90
df = DataFrame(np.random.rand(len(dti), 5), index=dti)
assert len(df.loc['1Q01']) == 90
def test_slice_month(self):
dti = date_range(freq='D', start=datetime(2005, 1, 1), periods=500)
s = Series(np.arange(len(dti)), index=dti)
assert len(s['2005-11']) == 30
df = DataFrame(np.random.rand(len(dti), 5), index=dti)
assert len(df.loc['2005-11']) == 30
tm.assert_series_equal(s['2005-11'], s['11-2005'])
def test_partial_slice(self):
rng = date_range(freq='D', start=datetime(2005, 1, 1), periods=500)
s = Series(np.arange(len(rng)), index=rng)
result = s['2005-05':'2006-02']
expected = s['20050501':'20060228']
tm.assert_series_equal(result, expected)
result = s['2005-05':]
expected = s['20050501':]
tm.assert_series_equal(result, expected)
result = s[:'2006-02']
expected = s[:'20060228']
tm.assert_series_equal(result, expected)
result = s['2005-1-1']
assert result == s.iloc[0]
with pytest.raises(KeyError, match=r"^'2004-12-31'$"):
s['2004-12-31']
def test_partial_slice_daily(self):
rng = date_range(freq='H', start=datetime(2005, 1, 31), periods=500)
s = Series(np.arange(len(rng)), index=rng)
result = s['2005-1-31']
tm.assert_series_equal(result, s.iloc[:24])
with pytest.raises(KeyError, match=r"^'2004-12-31 00'$"):
s['2004-12-31 00']
def test_partial_slice_hourly(self):
rng = date_range(freq='T', start=datetime(2005, 1, 1, 20, 0, 0),
periods=500)
s = Series(np.arange(len(rng)), index=rng)
result = s['2005-1-1']
tm.assert_series_equal(result, s.iloc[:60 * 4])
result = s['2005-1-1 20']
tm.assert_series_equal(result, s.iloc[:60])
assert s['2005-1-1 20:00'] == s.iloc[0]
with pytest.raises(KeyError, match=r"^'2004-12-31 00:15'$"):
s['2004-12-31 00:15']
def test_partial_slice_minutely(self):
rng = date_range(freq='S', start=datetime(2005, 1, 1, 23, 59, 0),
periods=500)
s = Series(np.arange(len(rng)), index=rng)
result = s['2005-1-1 23:59']
tm.assert_series_equal(result, s.iloc[:60])
result = s['2005-1-1']
tm.assert_series_equal(result, s.iloc[:60])
assert s[Timestamp('2005-1-1 23:59:00')] == s.iloc[0]
with pytest.raises(KeyError, match=r"^'2004-12-31 00:00:00'$"):
s['2004-12-31 00:00:00']
def test_partial_slice_second_precision(self):
rng = date_range(start=datetime(2005, 1, 1, 0, 0, 59,
microsecond=999990),
periods=20, freq='US')
s = Series(np.arange(20), rng)
tm.assert_series_equal(s['2005-1-1 00:00'], s.iloc[:10])
tm.assert_series_equal(s['2005-1-1 00:00:59'], s.iloc[:10])
tm.assert_series_equal(s['2005-1-1 00:01'], s.iloc[10:])
tm.assert_series_equal(s['2005-1-1 00:01:00'], s.iloc[10:])
assert s[Timestamp('2005-1-1 00:00:59.999990')] == s.iloc[0]
with pytest.raises(KeyError, match='2005-1-1 00:00:00'):
s['2005-1-1 00:00:00']
def test_partial_slicing_dataframe(self):
# GH14856
# Test various combinations of string slicing resolution vs.
# index resolution
# - If string resolution is less precise than index resolution,
# string is considered a slice
# - If string resolution is equal to or more precise than index
# resolution, string is considered an exact match
formats = ['%Y', '%Y-%m', '%Y-%m-%d', '%Y-%m-%d %H',
'%Y-%m-%d %H:%M', '%Y-%m-%d %H:%M:%S']
resolutions = ['year', 'month', 'day', 'hour', 'minute', 'second']
for rnum, resolution in enumerate(resolutions[2:], 2):
# we check only 'day', 'hour', 'minute' and 'second'
unit = Timedelta("1 " + resolution)
middate = datetime(2012, 1, 1, 0, 0, 0)
index = DatetimeIndex([middate - unit,
middate, middate + unit])
values = [1, 2, 3]
df = DataFrame({'a': values}, index, dtype=np.int64)
assert df.index.resolution == resolution
# Timestamp with the same resolution as index
# Should be exact match for Series (return scalar)
# and raise KeyError for Frame
for timestamp, expected in zip(index, values):
ts_string = timestamp.strftime(formats[rnum])
# make ts_string as precise as index
result = df['a'][ts_string]
assert isinstance(result, np.int64)
assert result == expected
msg = r"^'{}'$".format(ts_string)
with pytest.raises(KeyError, match=msg):
df[ts_string]
# Timestamp with resolution less precise than index
for fmt in formats[:rnum]:
for element, theslice in [[0, slice(None, 1)],
[1, slice(1, None)]]:
ts_string = index[element].strftime(fmt)
# Series should return slice
result = df['a'][ts_string]
expected = df['a'][theslice]
tm.assert_series_equal(result, expected)
# Frame should return slice as well
result = df[ts_string]
expected = df[theslice]
tm.assert_frame_equal(result, expected)
# Timestamp with resolution more precise than index
# Compatible with existing key
# Should return scalar for Series
# and raise KeyError for Frame
for fmt in formats[rnum + 1:]:
ts_string = index[1].strftime(fmt)
result = df['a'][ts_string]
assert isinstance(result, np.int64)
assert result == 2
msg = r"^'{}'$".format(ts_string)
with pytest.raises(KeyError, match=msg):
df[ts_string]
# Not compatible with existing key
# Should raise KeyError
for fmt, res in list(zip(formats, resolutions))[rnum + 1:]:
ts = index[1] + Timedelta("1 " + res)
ts_string = ts.strftime(fmt)
msg = r"^'{}'$".format(ts_string)
with pytest.raises(KeyError, match=msg):
df['a'][ts_string]
with pytest.raises(KeyError, match=msg):
df[ts_string]
def test_partial_slicing_with_multiindex(self):
# GH 4758
# partial string indexing with a multi-index buggy
df = DataFrame({'ACCOUNT': ["ACCT1", "ACCT1", "ACCT1", "ACCT2"],
'TICKER': ["ABC", "MNP", "XYZ", "XYZ"],
'val': [1, 2, 3, 4]},
index=date_range("2013-06-19 09:30:00",
periods=4, freq='5T'))
df_multi = df.set_index(['ACCOUNT', 'TICKER'], append=True)
expected = DataFrame([
[1]
], index=Index(['ABC'], name='TICKER'), columns=['val'])
result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1')]
tm.assert_frame_equal(result, expected)
expected = df_multi.loc[
(pd.Timestamp('2013-06-19 09:30:00', tz=None), 'ACCT1', 'ABC')]
result = df_multi.loc[('2013-06-19 09:30:00', 'ACCT1', 'ABC')]
tm.assert_series_equal(result, expected)
# this is an IndexingError as we don't do partial string selection on
# multi-levels.
msg = "Too many indexers"
with pytest.raises(IndexingError, match=msg):
df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]
# GH 4294
# partial slice on a series mi
s = pd.DataFrame(np.random.rand(1000, 1000), index=pd.date_range(
'2000-1-1', periods=1000)).stack()
s2 = s[:-1].copy()
expected = s2['2000-1-4']
result = s2[pd.Timestamp('2000-1-4')]
tm.assert_series_equal(result, expected)
result = s[pd.Timestamp('2000-1-4')]
expected = s['2000-1-4']
tm.assert_series_equal(result, expected)
df2 = pd.DataFrame(s)
expected = df2.xs('2000-1-4')
result = df2.loc[pd.Timestamp('2000-1-4')]
tm.assert_frame_equal(result, expected)
def test_partial_slice_doesnt_require_monotonicity(self):
# For historical reasons.
s = pd.Series(np.arange(10), pd.date_range('2014-01-01', periods=10))
nonmonotonic = s[[3, 5, 4]]
expected = nonmonotonic.iloc[:0]
timestamp = pd.Timestamp('2014-01-10')
tm.assert_series_equal(nonmonotonic['2014-01-10':], expected)
with pytest.raises(KeyError,
match=r"Timestamp\('2014-01-10 00:00:00'\)"):
nonmonotonic[timestamp:]
tm.assert_series_equal(nonmonotonic.loc['2014-01-10':], expected)
with pytest.raises(KeyError,
match=r"Timestamp\('2014-01-10 00:00:00'\)"):
nonmonotonic.loc[timestamp:]
def test_loc_datetime_length_one(self):
# GH16071
df = pd.DataFrame(columns=['1'],
index=pd.date_range('2016-10-01T00:00:00',
'2016-10-01T23:59:59'))
result = df.loc[datetime(2016, 10, 1):]
tm.assert_frame_equal(result, df)
result = df.loc['2016-10-01T00:00:00':]
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize('datetimelike', [
Timestamp('20130101'), datetime(2013, 1, 1),
np.datetime64('2013-01-01T00:00', 'ns')])
@pytest.mark.parametrize('op,expected', [
(op.lt, [True, False, False, False]),
(op.le, [True, True, False, False]),
(op.eq, [False, True, False, False]),
(op.gt, [False, False, False, True])])
def test_selection_by_datetimelike(self, datetimelike, op, expected):
# GH issue #17965, test for ability to compare datetime64[ns] columns
# to datetimelike
df = DataFrame({'A': [pd.Timestamp('20120101'),
pd.Timestamp('20130101'),
np.nan, pd.Timestamp('20130103')]})
result = op(df.A, datetimelike)
expected = Series(expected, name='A')
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize('start', [
'2018-12-02 21:50:00+00:00', pd.Timestamp('2018-12-02 21:50:00+00:00'),
pd.Timestamp('2018-12-02 21:50:00+00:00').to_pydatetime()
])
@pytest.mark.parametrize('end', [
'2018-12-02 21:52:00+00:00', pd.Timestamp('2018-12-02 21:52:00+00:00'),
pd.Timestamp('2018-12-02 21:52:00+00:00').to_pydatetime()
])
def test_getitem_with_datestring_with_UTC_offset(self, start, end):
# GH 24076
idx = pd.date_range(start='2018-12-02 14:50:00-07:00',
end='2018-12-02 14:50:00-07:00', freq='1min')
df = pd.DataFrame(1, index=idx, columns=['A'])
result = df[start:end]
expected = df.iloc[0:3, :]
tm.assert_frame_equal(result, expected)
# GH 16785
start = str(start)
end = str(end)
with pytest.raises(ValueError, match="Both dates must"):
df[start:end[:-4] + '1:00']
with pytest.raises(ValueError, match="The index must be timezone"):
df = df.tz_localize(None)
df[start:end]