FM / period

/usr/local/lib64/python3.6/site-packages/pandas/tests/indexes/period/

/usr/local/lib64/python3.6/site-packages/pandas/tests/indexes/period

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_asfreq.py	5432	0644	edit dl rm
test_astype.py	6189	0644	edit dl rm
test_constructors.py	19831	0644	edit dl rm
test_factorize.py	1267	0644	edit dl rm
test_fillna.py	1104	0644	edit dl rm
test_formats.py	6971	0644	edit dl rm
test_indexing.py	29017	0644	edit dl rm
test_join.py	1410	0644	edit dl rm
test_monotonic.py	1245	0644	edit dl rm
test_ops.py	12642	0644	edit dl rm
test_partial_slicing.py	5323	0644	edit dl rm
test_period.py	21462	0644	edit dl rm
test_period_range.py	3954	0644	edit dl rm
test_scalar_compat.py	1123	0644	edit dl rm
test_searchsorted.py	2622	0644	edit dl rm
test_setops.py	12087	0644	edit dl rm
test_shift.py	4398	0644	edit dl rm
test_tools.py	1006	0644	edit dl rm
test_to_timestamp.py	3612	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/indexes/period/test_partial_slicing.py (5323B)

import numpy as np
import pytest

from pandas import DataFrame, Series, date_range, period_range
import pandas._testing as tm

class TestPeriodIndex:
    def test_pindex_slice_index(self):
        pi = period_range(start="1/1/10", end="12/31/12", freq="M")
        s = Series(np.random.rand(len(pi)), index=pi)
        res = s["2010"]
        exp = s[0:12]
        tm.assert_series_equal(res, exp)
        res = s["2011"]
        exp = s[12:24]
        tm.assert_series_equal(res, exp)

@pytest.mark.parametrize("make_range", [date_range, period_range])
    def test_range_slice_day(self, make_range):
        # GH#6716
        idx = make_range(start="2013/01/01", freq="D", periods=400)

msg = "slice indices must be integers or None or have an __index__ method"
        # slices against index should raise IndexError
        values = [
            "2014",
            "2013/02",
            "2013/01/02",
            "2013/02/01 9H",
            "2013/02/01 09:00",
        ]
        for v in values:
            with pytest.raises(TypeError, match=msg):
                idx[v:]

s = Series(np.random.rand(len(idx)), index=idx)

tm.assert_series_equal(s["2013/01/02":], s[1:])
        tm.assert_series_equal(s["2013/01/02":"2013/01/05"], s[1:5])
        tm.assert_series_equal(s["2013/02":], s[31:])
        tm.assert_series_equal(s["2014":], s[365:])

invalid = ["2013/02/01 9H", "2013/02/01 09:00"]
        for v in invalid:
            with pytest.raises(TypeError, match=msg):
                idx[v:]

@pytest.mark.parametrize("make_range", [date_range, period_range])
    def test_range_slice_seconds(self, make_range):
        # GH#6716
        idx = make_range(start="2013/01/01 09:00:00", freq="S", periods=4000)
        msg = "slice indices must be integers or None or have an __index__ method"

# slices against index should raise IndexError
        values = [
            "2014",
            "2013/02",
            "2013/01/02",
            "2013/02/01 9H",
            "2013/02/01 09:00",
        ]
        for v in values:
            with pytest.raises(TypeError, match=msg):
                idx[v:]

s = Series(np.random.rand(len(idx)), index=idx)

tm.assert_series_equal(s["2013/01/01 09:05":"2013/01/01 09:10"], s[300:660])
        tm.assert_series_equal(s["2013/01/01 10:00":"2013/01/01 10:05"], s[3600:3960])
        tm.assert_series_equal(s["2013/01/01 10H":], s[3600:])
        tm.assert_series_equal(s[:"2013/01/01 09:30"], s[:1860])
        for d in ["2013/01/01", "2013/01", "2013"]:
            tm.assert_series_equal(s[d:], s)

@pytest.mark.parametrize("make_range", [date_range, period_range])
    def test_range_slice_outofbounds(self, make_range):
        # GH#5407
        idx = make_range(start="2013/10/01", freq="D", periods=10)

df = DataFrame(dict(units=[100 + i for i in range(10)]), index=idx)
        empty = DataFrame(index=type(idx)([], freq="D"), columns=["units"])
        empty["units"] = empty["units"].astype("int64")

tm.assert_frame_equal(df["2013/09/01":"2013/09/30"], empty)
        tm.assert_frame_equal(df["2013/09/30":"2013/10/02"], df.iloc[:2])
        tm.assert_frame_equal(df["2013/10/01":"2013/10/02"], df.iloc[:2])
        tm.assert_frame_equal(df["2013/10/02":"2013/09/30"], empty)
        tm.assert_frame_equal(df["2013/10/15":"2013/10/17"], empty)
        tm.assert_frame_equal(df["2013-06":"2013-09"], empty)
        tm.assert_frame_equal(df["2013-11":"2013-12"], empty)

def test_partial_slice_doesnt_require_monotonicity(self):
        # See also: DatetimeIndex test ofm the same name
        dti = date_range("2014-01-01", periods=30, freq="30D")
        pi = dti.to_period("D")

ser_montonic = Series(np.arange(30), index=pi)

shuffler = list(range(0, 30, 2)) + list(range(1, 31, 2))
        ser = ser_montonic[shuffler]
        nidx = ser.index

# Manually identified locations of year==2014
        indexer_2014 = np.array(
            [0, 1, 2, 3, 4, 5, 6, 15, 16, 17, 18, 19, 20], dtype=np.intp
        )
        assert (nidx[indexer_2014].year == 2014).all()
        assert not (nidx[~indexer_2014].year == 2014).any()

result = nidx.get_loc("2014")
        tm.assert_numpy_array_equal(result, indexer_2014)

expected = ser[indexer_2014]

with tm.assert_produces_warning(FutureWarning):
            result = nidx.get_value(ser, "2014")
        tm.assert_series_equal(result, expected)

result = ser.loc["2014"]
        tm.assert_series_equal(result, expected)

result = ser["2014"]
        tm.assert_series_equal(result, expected)

# Manually identified locations where ser.index is within Mat 2015
        indexer_may2015 = np.array([23], dtype=np.intp)
        assert nidx[23].year == 2015 and nidx[23].month == 5

result = nidx.get_loc("May 2015")
        tm.assert_numpy_array_equal(result, indexer_may2015)

expected = ser[indexer_may2015]

with tm.assert_produces_warning(FutureWarning):
            result = nidx.get_value(ser, "May 2015")
        tm.assert_series_equal(result, expected)

result = ser.loc["May 2015"]
        tm.assert_series_equal(result, expected)

result = ser["May 2015"]
        tm.assert_series_equal(result, expected)

import numpy as np import pytest from pandas import DataFrame, Series, date_range, period_range import pandas._testing as tm class TestPeriodIndex: def test_pindex_slice_index(self): pi = period_range(start="1/1/10", end="12/31/12", freq="M") s = Series(np.random.rand(len(pi)), index=pi) res = s["2010"] exp = s[0:12] tm.assert_series_equal(res, exp) res = s["2011"] exp = s[12:24] tm.assert_series_equal(res, exp) @pytest.mark.parametrize("make_range", [date_range, period_range]) def test_range_slice_day(self, make_range): # GH#6716 idx = make_range(start="2013/01/01", freq="D", periods=400) msg = "slice indices must be integers or None or have an __index__ method" # slices against index should raise IndexError values = [ "2014", "2013/02", "2013/01/02", "2013/02/01 9H", "2013/02/01 09:00", ] for v in values: with pytest.raises(TypeError, match=msg): idx[v:] s = Series(np.random.rand(len(idx)), index=idx) tm.assert_series_equal(s["2013/01/02":], s[1:]) tm.assert_series_equal(s["2013/01/02":"2013/01/05"], s[1:5]) tm.assert_series_equal(s["2013/02":], s[31:]) tm.assert_series_equal(s["2014":], s[365:]) invalid = ["2013/02/01 9H", "2013/02/01 09:00"] for v in invalid: with pytest.raises(TypeError, match=msg): idx[v:] @pytest.mark.parametrize("make_range", [date_range, period_range]) def test_range_slice_seconds(self, make_range): # GH#6716 idx = make_range(start="2013/01/01 09:00:00", freq="S", periods=4000) msg = "slice indices must be integers or None or have an __index__ method" # slices against index should raise IndexError values = [ "2014", "2013/02", "2013/01/02", "2013/02/01 9H", "2013/02/01 09:00", ] for v in values: with pytest.raises(TypeError, match=msg): idx[v:] s = Series(np.random.rand(len(idx)), index=idx) tm.assert_series_equal(s["2013/01/01 09:05":"2013/01/01 09:10"], s[300:660]) tm.assert_series_equal(s["2013/01/01 10:00":"2013/01/01 10:05"], s[3600:3960]) tm.assert_series_equal(s["2013/01/01 10H":], s[3600:]) tm.assert_series_equal(s[:"2013/01/01 09:30"], s[:1860]) for d in ["2013/01/01", "2013/01", "2013"]: tm.assert_series_equal(s[d:], s) @pytest.mark.parametrize("make_range", [date_range, period_range]) def test_range_slice_outofbounds(self, make_range): # GH#5407 idx = make_range(start="2013/10/01", freq="D", periods=10) df = DataFrame(dict(units=[100 + i for i in range(10)]), index=idx) empty = DataFrame(index=type(idx)([], freq="D"), columns=["units"]) empty["units"] = empty["units"].astype("int64") tm.assert_frame_equal(df["2013/09/01":"2013/09/30"], empty) tm.assert_frame_equal(df["2013/09/30":"2013/10/02"], df.iloc[:2]) tm.assert_frame_equal(df["2013/10/01":"2013/10/02"], df.iloc[:2]) tm.assert_frame_equal(df["2013/10/02":"2013/09/30"], empty) tm.assert_frame_equal(df["2013/10/15":"2013/10/17"], empty) tm.assert_frame_equal(df["2013-06":"2013-09"], empty) tm.assert_frame_equal(df["2013-11":"2013-12"], empty) def test_partial_slice_doesnt_require_monotonicity(self): # See also: DatetimeIndex test ofm the same name dti = date_range("2014-01-01", periods=30, freq="30D") pi = dti.to_period("D") ser_montonic = Series(np.arange(30), index=pi) shuffler = list(range(0, 30, 2)) + list(range(1, 31, 2)) ser = ser_montonic[shuffler] nidx = ser.index # Manually identified locations of year==2014 indexer_2014 = np.array( [0, 1, 2, 3, 4, 5, 6, 15, 16, 17, 18, 19, 20], dtype=np.intp ) assert (nidx[indexer_2014].year == 2014).all() assert not (nidx[~indexer_2014].year == 2014).any() result = nidx.get_loc("2014") tm.assert_numpy_array_equal(result, indexer_2014) expected = ser[indexer_2014] with tm.assert_produces_warning(FutureWarning): result = nidx.get_value(ser, "2014") tm.assert_series_equal(result, expected) result = ser.loc["2014"] tm.assert_series_equal(result, expected) result = ser["2014"] tm.assert_series_equal(result, expected) # Manually identified locations where ser.index is within Mat 2015 indexer_may2015 = np.array([23], dtype=np.intp) assert nidx[23].year == 2015 and nidx[23].month == 5 result = nidx.get_loc("May 2015") tm.assert_numpy_array_equal(result, indexer_may2015) expected = ser[indexer_may2015] with tm.assert_produces_warning(FutureWarning): result = nidx.get_value(ser, "May 2015") tm.assert_series_equal(result, expected) result = ser.loc["May 2015"] tm.assert_series_equal(result, expected) result = ser["May 2015"] tm.assert_series_equal(result, expected)