# coding=utf-8 # pylint: disable-msg=E1101,W0612 from datetime import datetime, timedelta import numpy as np import pandas.compat as compat from pandas.compat import lrange, range, u import pandas as pd from pandas import ( Categorical, DataFrame, Index, Series, date_range, option_context, period_range, timedelta_range) from pandas.core.base import StringMixin from pandas.core.index import MultiIndex import pandas.util.testing as tm from .common import TestData class TestSeriesRepr(TestData): def test_multilevel_name_print(self): index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', 'three']], codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], names=['first', 'second']) s = Series(lrange(0, len(index)), index=index, name='sth') expected = ["first second", "foo one 0", " two 1", " three 2", "bar one 3", " two 4", "baz two 5", " three 6", "qux one 7", " two 8", " three 9", "Name: sth, dtype: int64"] expected = "\n".join(expected) assert repr(s) == expected def test_name_printing(self): # Test small Series. s = Series([0, 1, 2]) s.name = "test" assert "Name: test" in repr(s) s.name = None assert "Name:" not in repr(s) # Test big Series (diff code path). s = Series(lrange(0, 1000)) s.name = "test" assert "Name: test" in repr(s) s.name = None assert "Name:" not in repr(s) s = Series(index=date_range('20010101', '20020101'), name='test') assert "Name: test" in repr(s) def test_repr(self): str(self.ts) str(self.series) str(self.series.astype(int)) str(self.objSeries) str(Series(tm.randn(1000), index=np.arange(1000))) str(Series(tm.randn(1000), index=np.arange(1000, 0, step=-1))) # empty str(self.empty) # with NaNs self.series[5:7] = np.NaN str(self.series) # with Nones ots = self.ts.astype('O') ots[::2] = None repr(ots) # various names for name in ['', 1, 1.2, 'foo', u('\u03B1\u03B2\u03B3'), 'loooooooooooooooooooooooooooooooooooooooooooooooooooong', ('foo', 'bar', 'baz'), (1, 2), ('foo', 1, 2.3), (u('\u03B1'), u('\u03B2'), u('\u03B3')), (u('\u03B1'), 'bar')]: self.series.name = name repr(self.series) biggie = Series(tm.randn(1000), index=np.arange(1000), name=('foo', 'bar', 'baz')) repr(biggie) # 0 as name ser = Series(np.random.randn(100), name=0) rep_str = repr(ser) assert "Name: 0" in rep_str # tidy repr ser = Series(np.random.randn(1001), name=0) rep_str = repr(ser) assert "Name: 0" in rep_str ser = Series(["a\n\r\tb"], name="a\n\r\td", index=["a\n\r\tf"]) assert "\t" not in repr(ser) assert "\r" not in repr(ser) assert "a\n" not in repr(ser) # with empty series (#4651) s = Series([], dtype=np.int64, name='foo') assert repr(s) == 'Series([], Name: foo, dtype: int64)' s = Series([], dtype=np.int64, name=None) assert repr(s) == 'Series([], dtype: int64)' def test_tidy_repr(self): a = Series([u("\u05d0")] * 1000) a.name = 'title1' repr(a) # should not raise exception def test_repr_bool_fails(self, capsys): s = Series([DataFrame(np.random.randn(2, 2)) for i in range(5)]) # It works (with no Cython exception barf)! repr(s) captured = capsys.readouterr() assert captured.err == '' def test_repr_name_iterable_indexable(self): s = Series([1, 2, 3], name=np.int64(3)) # it works! repr(s) s.name = (u("\u05d0"), ) * 2 repr(s) def test_repr_should_return_str(self): # https://docs.python.org/3/reference/datamodel.html#object.__repr__ # ...The return value must be a string object. # (str on py2.x, str (unicode) on py3) data = [8, 5, 3, 5] index1 = [u("\u03c3"), u("\u03c4"), u("\u03c5"), u("\u03c6")] df = Series(data, index=index1) assert type(df.__repr__() == str) # both py2 / 3 def test_repr_max_rows(self): # GH 6863 with pd.option_context('max_rows', None): str(Series(range(1001))) # should not raise exception def test_unicode_string_with_unicode(self): df = Series([u("\u05d0")], name=u("\u05d1")) if compat.PY3: str(df) else: compat.text_type(df) def test_bytestring_with_unicode(self): df = Series([u("\u05d0")], name=u("\u05d1")) if compat.PY3: bytes(df) else: str(df) def test_timeseries_repr_object_dtype(self): index = Index([datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], dtype=object) ts = Series(np.random.randn(len(index)), index) repr(ts) ts = tm.makeTimeSeries(1000) assert repr(ts).splitlines()[-1].startswith('Freq:') ts2 = ts.iloc[np.random.randint(0, len(ts) - 1, 400)] repr(ts2).splitlines()[-1] def test_latex_repr(self): result = r"""\begin{tabular}{ll} \toprule {} & 0 \\ \midrule 0 & $\alpha$ \\ 1 & b \\ 2 & c \\ \bottomrule \end{tabular} """ with option_context('display.latex.escape', False, 'display.latex.repr', True): s = Series([r'$\alpha$', 'b', 'c']) assert result == s._repr_latex_() assert s._repr_latex_() is None def test_index_repr_in_frame_with_nan(self): # see gh-25061 i = Index([1, np.nan]) s = Series([1, 2], index=i) exp = """1.0 1\nNaN 2\ndtype: int64""" assert repr(s) == exp class TestCategoricalRepr(object): def test_categorical_repr_unicode(self): # GH#21002 if len(index) > 60, sys.getdefaultencoding()=='ascii', # and we are working in PY2, then rendering a Categorical could raise # UnicodeDecodeError by trying to decode when it shouldn't class County(StringMixin): name = u'San Sebastián' state = u'PR' def __unicode__(self): return self.name + u', ' + self.state cat = pd.Categorical([County() for n in range(61)]) idx = pd.Index(cat) ser = idx.to_series() if compat.PY3: # no reloading of sys, just check that the default (utf8) works # as expected repr(ser) str(ser) else: # set sys.defaultencoding to ascii, then change it back after # the test with tm.set_defaultencoding('ascii'): repr(ser) str(ser) def test_categorical_repr(self): a = Series(Categorical([1, 2, 3, 4])) exp = u("0 1\n1 2\n2 3\n3 4\n" + "dtype: category\nCategories (4, int64): [1, 2, 3, 4]") assert exp == a.__unicode__() a = Series(Categorical(["a", "b"] * 25)) exp = u("0 a\n1 b\n" + " ..\n" + "48 a\n49 b\n" + "Length: 50, dtype: category\nCategories (2, object): [a, b]") with option_context("display.max_rows", 5): assert exp == repr(a) levs = list("abcdefghijklmnopqrstuvwxyz") a = Series(Categorical(["a", "b"], categories=levs, ordered=True)) exp = u("0 a\n1 b\n" + "dtype: category\n" "Categories (26, object): [a < b < c < d ... w < x < y < z]") assert exp == a.__unicode__() def test_categorical_series_repr(self): s = Series(Categorical([1, 2, 3])) exp = """0 1 1 2 2 3 dtype: category Categories (3, int64): [1, 2, 3]""" assert repr(s) == exp s = Series(Categorical(np.arange(10))) exp = """0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 dtype: category Categories (10, int64): [0, 1, 2, 3, ..., 6, 7, 8, 9]""" assert repr(s) == exp def test_categorical_series_repr_ordered(self): s = Series(Categorical([1, 2, 3], ordered=True)) exp = """0 1 1 2 2 3 dtype: category Categories (3, int64): [1 < 2 < 3]""" assert repr(s) == exp s = Series(Categorical(np.arange(10), ordered=True)) exp = """0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 dtype: category Categories (10, int64): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]""" assert repr(s) == exp def test_categorical_series_repr_datetime(self): idx = date_range('2011-01-01 09:00', freq='H', periods=5) s = Series(Categorical(idx)) exp = """0 2011-01-01 09:00:00 1 2011-01-01 10:00:00 2 2011-01-01 11:00:00 3 2011-01-01 12:00:00 4 2011-01-01 13:00:00 dtype: category Categories (5, datetime64[ns]): [2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]""" # noqa assert repr(s) == exp idx = date_range('2011-01-01 09:00', freq='H', periods=5, tz='US/Eastern') s = Series(Categorical(idx)) exp = """0 2011-01-01 09:00:00-05:00 1 2011-01-01 10:00:00-05:00 2 2011-01-01 11:00:00-05:00 3 2011-01-01 12:00:00-05:00 4 2011-01-01 13:00:00-05:00 dtype: category Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]""" # noqa assert repr(s) == exp def test_categorical_series_repr_datetime_ordered(self): idx = date_range('2011-01-01 09:00', freq='H', periods=5) s = Series(Categorical(idx, ordered=True)) exp = """0 2011-01-01 09:00:00 1 2011-01-01 10:00:00 2 2011-01-01 11:00:00 3 2011-01-01 12:00:00 4 2011-01-01 13:00:00 dtype: category Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 < 2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa assert repr(s) == exp idx = date_range('2011-01-01 09:00', freq='H', periods=5, tz='US/Eastern') s = Series(Categorical(idx, ordered=True)) exp = """0 2011-01-01 09:00:00-05:00 1 2011-01-01 10:00:00-05:00 2 2011-01-01 11:00:00-05:00 3 2011-01-01 12:00:00-05:00 4 2011-01-01 13:00:00-05:00 dtype: category Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 < 2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 < 2011-01-01 13:00:00-05:00]""" # noqa assert repr(s) == exp def test_categorical_series_repr_period(self): idx = period_range('2011-01-01 09:00', freq='H', periods=5) s = Series(Categorical(idx)) exp = """0 2011-01-01 09:00 1 2011-01-01 10:00 2 2011-01-01 11:00 3 2011-01-01 12:00 4 2011-01-01 13:00 dtype: category Categories (5, period[H]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00]""" # noqa assert repr(s) == exp idx = period_range('2011-01', freq='M', periods=5) s = Series(Categorical(idx)) exp = """0 2011-01 1 2011-02 2 2011-03 3 2011-04 4 2011-05 dtype: category Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]""" assert repr(s) == exp def test_categorical_series_repr_period_ordered(self): idx = period_range('2011-01-01 09:00', freq='H', periods=5) s = Series(Categorical(idx, ordered=True)) exp = """0 2011-01-01 09:00 1 2011-01-01 10:00 2 2011-01-01 11:00 3 2011-01-01 12:00 4 2011-01-01 13:00 dtype: category Categories (5, period[H]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 < 2011-01-01 13:00]""" # noqa assert repr(s) == exp idx = period_range('2011-01', freq='M', periods=5) s = Series(Categorical(idx, ordered=True)) exp = """0 2011-01 1 2011-02 2 2011-03 3 2011-04 4 2011-05 dtype: category Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]""" assert repr(s) == exp def test_categorical_series_repr_timedelta(self): idx = timedelta_range('1 days', periods=5) s = Series(Categorical(idx)) exp = """0 1 days 1 2 days 2 3 days 3 4 days 4 5 days dtype: category Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]""" assert repr(s) == exp idx = timedelta_range('1 hours', periods=10) s = Series(Categorical(idx)) exp = """0 0 days 01:00:00 1 1 days 01:00:00 2 2 days 01:00:00 3 3 days 01:00:00 4 4 days 01:00:00 5 5 days 01:00:00 6 6 days 01:00:00 7 7 days 01:00:00 8 8 days 01:00:00 9 9 days 01:00:00 dtype: category Categories (10, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00]""" # noqa assert repr(s) == exp def test_categorical_series_repr_timedelta_ordered(self): idx = timedelta_range('1 days', periods=5) s = Series(Categorical(idx, ordered=True)) exp = """0 1 days 1 2 days 2 3 days 3 4 days 4 5 days dtype: category Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]""" # noqa assert repr(s) == exp idx = timedelta_range('1 hours', periods=10) s = Series(Categorical(idx, ordered=True)) exp = """0 0 days 01:00:00 1 1 days 01:00:00 2 2 days 01:00:00 3 3 days 01:00:00 4 4 days 01:00:00 5 5 days 01:00:00 6 6 days 01:00:00 7 7 days 01:00:00 8 8 days 01:00:00 9 9 days 01:00:00 dtype: category Categories (10, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 < 3 days 01:00:00 ... 6 days 01:00:00 < 7 days 01:00:00 < 8 days 01:00:00 < 9 days 01:00:00]""" # noqa assert repr(s) == exp