254 lines
8.6 KiB
Python
254 lines
8.6 KiB
Python
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from datetime import datetime
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import dateutil.tz
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import numpy as np
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import pytest
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import pytz
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import pandas as pd
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from pandas import DatetimeIndex, Series
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import pandas._testing as tm
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def test_to_native_types_method_deprecated():
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index = pd.date_range(freq="1D", periods=3, start="2017-01-01")
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expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
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with tm.assert_produces_warning(FutureWarning):
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result = index.to_native_types()
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tm.assert_numpy_array_equal(result, expected)
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# Make sure slicing works
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expected = np.array(["2017-01-01", "2017-01-03"], dtype=object)
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with tm.assert_produces_warning(FutureWarning):
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result = index.to_native_types([0, 2])
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tm.assert_numpy_array_equal(result, expected)
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def test_to_native_types():
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index = pd.date_range(freq="1D", periods=3, start="2017-01-01")
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# First, with no arguments.
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expected = np.array(["2017-01-01", "2017-01-02", "2017-01-03"], dtype=object)
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result = index._format_native_types()
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tm.assert_numpy_array_equal(result, expected)
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# No NaN values, so na_rep has no effect
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result = index._format_native_types(na_rep="pandas")
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tm.assert_numpy_array_equal(result, expected)
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# Make sure date formatting works
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expected = np.array(["01-2017-01", "01-2017-02", "01-2017-03"], dtype=object)
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result = index._format_native_types(date_format="%m-%Y-%d")
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tm.assert_numpy_array_equal(result, expected)
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# NULL object handling should work
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index = DatetimeIndex(["2017-01-01", pd.NaT, "2017-01-03"])
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expected = np.array(["2017-01-01", "NaT", "2017-01-03"], dtype=object)
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result = index._format_native_types()
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tm.assert_numpy_array_equal(result, expected)
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expected = np.array(["2017-01-01", "pandas", "2017-01-03"], dtype=object)
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result = index._format_native_types(na_rep="pandas")
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tm.assert_numpy_array_equal(result, expected)
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class TestDatetimeIndexRendering:
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def test_dti_repr_short(self):
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dr = pd.date_range(start="1/1/2012", periods=1)
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repr(dr)
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dr = pd.date_range(start="1/1/2012", periods=2)
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repr(dr)
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dr = pd.date_range(start="1/1/2012", periods=3)
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repr(dr)
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@pytest.mark.parametrize("method", ["__repr__", "__str__"])
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def test_dti_representation(self, method):
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idxs = []
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idxs.append(DatetimeIndex([], freq="D"))
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idxs.append(DatetimeIndex(["2011-01-01"], freq="D"))
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idxs.append(DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D"))
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idxs.append(DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D"))
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idxs.append(
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DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"],
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freq="H",
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tz="Asia/Tokyo",
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)
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)
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idxs.append(
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DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern"
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)
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)
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idxs.append(
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DatetimeIndex(["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="UTC")
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)
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exp = []
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exp.append("DatetimeIndex([], dtype='datetime64[ns]', freq='D')")
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exp.append("DatetimeIndex(['2011-01-01'], dtype='datetime64[ns]', freq='D')")
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exp.append(
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"DatetimeIndex(['2011-01-01', '2011-01-02'], "
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"dtype='datetime64[ns]', freq='D')"
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)
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exp.append(
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"DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], "
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"dtype='datetime64[ns]', freq='D')"
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)
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exp.append(
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"DatetimeIndex(['2011-01-01 09:00:00+09:00', "
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"'2011-01-01 10:00:00+09:00', '2011-01-01 11:00:00+09:00']"
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", dtype='datetime64[ns, Asia/Tokyo]', freq='H')"
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)
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exp.append(
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"DatetimeIndex(['2011-01-01 09:00:00-05:00', "
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"'2011-01-01 10:00:00-05:00', 'NaT'], "
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"dtype='datetime64[ns, US/Eastern]', freq=None)"
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)
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exp.append(
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"DatetimeIndex(['2011-01-01 09:00:00+00:00', "
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"'2011-01-01 10:00:00+00:00', 'NaT'], "
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"dtype='datetime64[ns, UTC]', freq=None)"
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""
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)
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with pd.option_context("display.width", 300):
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for indx, expected in zip(idxs, exp):
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result = getattr(indx, method)()
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assert result == expected
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def test_dti_representation_to_series(self):
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idx1 = DatetimeIndex([], freq="D")
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idx2 = DatetimeIndex(["2011-01-01"], freq="D")
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idx3 = DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D")
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idx4 = DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
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idx5 = DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"],
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freq="H",
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tz="Asia/Tokyo",
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)
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idx6 = DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern"
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)
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idx7 = DatetimeIndex(["2011-01-01 09:00", "2011-01-02 10:15"])
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exp1 = """Series([], dtype: datetime64[ns])"""
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exp2 = "0 2011-01-01\ndtype: datetime64[ns]"
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exp3 = "0 2011-01-01\n1 2011-01-02\ndtype: datetime64[ns]"
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exp4 = (
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"0 2011-01-01\n"
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"1 2011-01-02\n"
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"2 2011-01-03\n"
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"dtype: datetime64[ns]"
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)
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exp5 = (
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"0 2011-01-01 09:00:00+09:00\n"
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"1 2011-01-01 10:00:00+09:00\n"
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"2 2011-01-01 11:00:00+09:00\n"
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"dtype: datetime64[ns, Asia/Tokyo]"
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)
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exp6 = (
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"0 2011-01-01 09:00:00-05:00\n"
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"1 2011-01-01 10:00:00-05:00\n"
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"2 NaT\n"
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"dtype: datetime64[ns, US/Eastern]"
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)
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exp7 = (
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"0 2011-01-01 09:00:00\n"
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"1 2011-01-02 10:15:00\n"
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"dtype: datetime64[ns]"
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)
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with pd.option_context("display.width", 300):
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for idx, expected in zip(
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[idx1, idx2, idx3, idx4, idx5, idx6, idx7],
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[exp1, exp2, exp3, exp4, exp5, exp6, exp7],
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):
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result = repr(Series(idx))
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assert result == expected
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def test_dti_summary(self):
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# GH#9116
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idx1 = DatetimeIndex([], freq="D")
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idx2 = DatetimeIndex(["2011-01-01"], freq="D")
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idx3 = DatetimeIndex(["2011-01-01", "2011-01-02"], freq="D")
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idx4 = DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
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idx5 = DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"],
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freq="H",
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tz="Asia/Tokyo",
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)
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idx6 = DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 10:00", pd.NaT], tz="US/Eastern"
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)
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exp1 = "DatetimeIndex: 0 entries\nFreq: D"
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exp2 = "DatetimeIndex: 1 entries, 2011-01-01 to 2011-01-01\nFreq: D"
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exp3 = "DatetimeIndex: 2 entries, 2011-01-01 to 2011-01-02\nFreq: D"
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exp4 = "DatetimeIndex: 3 entries, 2011-01-01 to 2011-01-03\nFreq: D"
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exp5 = (
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"DatetimeIndex: 3 entries, 2011-01-01 09:00:00+09:00 "
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"to 2011-01-01 11:00:00+09:00\n"
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"Freq: H"
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)
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exp6 = """DatetimeIndex: 3 entries, 2011-01-01 09:00:00-05:00 to NaT"""
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for idx, expected in zip(
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[idx1, idx2, idx3, idx4, idx5, idx6], [exp1, exp2, exp3, exp4, exp5, exp6]
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):
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result = idx._summary()
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assert result == expected
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def test_dti_business_repr(self):
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# only really care that it works
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repr(pd.bdate_range(datetime(2009, 1, 1), datetime(2010, 1, 1)))
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def test_dti_business_summary(self):
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rng = pd.bdate_range(datetime(2009, 1, 1), datetime(2010, 1, 1))
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rng._summary()
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rng[2:2]._summary()
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def test_dti_business_summary_pytz(self):
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pd.bdate_range("1/1/2005", "1/1/2009", tz=pytz.utc)._summary()
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def test_dti_business_summary_dateutil(self):
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pd.bdate_range("1/1/2005", "1/1/2009", tz=dateutil.tz.tzutc())._summary()
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def test_dti_custom_business_repr(self):
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# only really care that it works
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repr(pd.bdate_range(datetime(2009, 1, 1), datetime(2010, 1, 1), freq="C"))
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def test_dti_custom_business_summary(self):
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rng = pd.bdate_range(datetime(2009, 1, 1), datetime(2010, 1, 1), freq="C")
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rng._summary()
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rng[2:2]._summary()
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def test_dti_custom_business_summary_pytz(self):
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pd.bdate_range("1/1/2005", "1/1/2009", freq="C", tz=pytz.utc)._summary()
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def test_dti_custom_business_summary_dateutil(self):
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pd.bdate_range(
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"1/1/2005", "1/1/2009", freq="C", tz=dateutil.tz.tzutc()
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)._summary()
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