mirror of
https://github.com/PiBrewing/craftbeerpi4.git
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508 lines
15 KiB
Python
508 lines
15 KiB
Python
from datetime import datetime, timedelta
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Index,
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MultiIndex,
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Series,
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date_range,
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option_context,
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period_range,
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timedelta_range,
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)
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import pandas._testing as tm
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class TestSeriesRepr:
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def test_multilevel_name_print(self):
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index = MultiIndex(
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levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
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codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
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names=["first", "second"],
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)
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s = Series(range(len(index)), index=index, name="sth")
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expected = [
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"first second",
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"foo one 0",
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" two 1",
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" three 2",
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"bar one 3",
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" two 4",
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"baz two 5",
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" three 6",
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"qux one 7",
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" two 8",
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" three 9",
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"Name: sth, dtype: int64",
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]
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expected = "\n".join(expected)
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assert repr(s) == expected
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def test_name_printing(self):
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# Test small Series.
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s = Series([0, 1, 2])
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s.name = "test"
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assert "Name: test" in repr(s)
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s.name = None
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assert "Name:" not in repr(s)
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# Test big Series (diff code path).
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s = Series(range(1000))
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s.name = "test"
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assert "Name: test" in repr(s)
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s.name = None
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assert "Name:" not in repr(s)
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s = Series(index=date_range("20010101", "20020101"), name="test", dtype=object)
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assert "Name: test" in repr(s)
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def test_repr(self, datetime_series, string_series, object_series):
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str(datetime_series)
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str(string_series)
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str(string_series.astype(int))
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str(object_series)
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str(Series(tm.randn(1000), index=np.arange(1000)))
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str(Series(tm.randn(1000), index=np.arange(1000, 0, step=-1)))
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# empty
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str(Series(dtype=object))
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# with NaNs
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string_series[5:7] = np.NaN
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str(string_series)
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# with Nones
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ots = datetime_series.astype("O")
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ots[::2] = None
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repr(ots)
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# various names
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for name in [
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"",
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1,
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1.2,
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"foo",
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"\u03B1\u03B2\u03B3",
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"loooooooooooooooooooooooooooooooooooooooooooooooooooong",
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("foo", "bar", "baz"),
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(1, 2),
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("foo", 1, 2.3),
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("\u03B1", "\u03B2", "\u03B3"),
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("\u03B1", "bar"),
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]:
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string_series.name = name
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repr(string_series)
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biggie = Series(
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tm.randn(1000), index=np.arange(1000), name=("foo", "bar", "baz")
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)
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repr(biggie)
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# 0 as name
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ser = Series(np.random.randn(100), name=0)
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rep_str = repr(ser)
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assert "Name: 0" in rep_str
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# tidy repr
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ser = Series(np.random.randn(1001), name=0)
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rep_str = repr(ser)
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assert "Name: 0" in rep_str
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ser = Series(["a\n\r\tb"], name="a\n\r\td", index=["a\n\r\tf"])
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assert "\t" not in repr(ser)
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assert "\r" not in repr(ser)
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assert "a\n" not in repr(ser)
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# with empty series (#4651)
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s = Series([], dtype=np.int64, name="foo")
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assert repr(s) == "Series([], Name: foo, dtype: int64)"
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s = Series([], dtype=np.int64, name=None)
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assert repr(s) == "Series([], dtype: int64)"
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def test_tidy_repr(self):
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a = Series(["\u05d0"] * 1000)
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a.name = "title1"
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repr(a) # should not raise exception
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def test_repr_bool_fails(self, capsys):
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s = Series([DataFrame(np.random.randn(2, 2)) for i in range(5)])
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# It works (with no Cython exception barf)!
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repr(s)
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captured = capsys.readouterr()
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assert captured.err == ""
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def test_repr_name_iterable_indexable(self):
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s = Series([1, 2, 3], name=np.int64(3))
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# it works!
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repr(s)
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s.name = ("\u05d0",) * 2
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repr(s)
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def test_repr_should_return_str(self):
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# https://docs.python.org/3/reference/datamodel.html#object.__repr__
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# ...The return value must be a string object.
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# (str on py2.x, str (unicode) on py3)
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data = [8, 5, 3, 5]
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index1 = ["\u03c3", "\u03c4", "\u03c5", "\u03c6"]
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df = Series(data, index=index1)
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assert type(df.__repr__() == str) # both py2 / 3
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def test_repr_max_rows(self):
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# GH 6863
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with pd.option_context("max_rows", None):
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str(Series(range(1001))) # should not raise exception
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def test_unicode_string_with_unicode(self):
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df = Series(["\u05d0"], name="\u05d1")
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str(df)
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def test_str_to_bytes_raises(self):
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# GH 26447
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df = Series(["abc"], name="abc")
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msg = "^'str' object cannot be interpreted as an integer$"
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with pytest.raises(TypeError, match=msg):
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bytes(df)
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def test_timeseries_repr_object_dtype(self):
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index = Index(
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[datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], dtype=object
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)
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ts = Series(np.random.randn(len(index)), index)
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repr(ts)
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ts = tm.makeTimeSeries(1000)
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assert repr(ts).splitlines()[-1].startswith("Freq:")
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ts2 = ts.iloc[np.random.randint(0, len(ts) - 1, 400)]
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repr(ts2).splitlines()[-1]
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def test_latex_repr(self):
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result = r"""\begin{tabular}{ll}
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\toprule
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{} & 0 \\
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\midrule
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0 & $\alpha$ \\
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1 & b \\
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2 & c \\
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\bottomrule
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\end{tabular}
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"""
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with option_context("display.latex.escape", False, "display.latex.repr", True):
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s = Series([r"$\alpha$", "b", "c"])
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assert result == s._repr_latex_()
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assert s._repr_latex_() is None
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def test_index_repr_in_frame_with_nan(self):
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# see gh-25061
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i = Index([1, np.nan])
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s = Series([1, 2], index=i)
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exp = """1.0 1\nNaN 2\ndtype: int64"""
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assert repr(s) == exp
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def test_format_pre_1900_dates(self):
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rng = date_range("1/1/1850", "1/1/1950", freq="A-DEC")
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rng.format()
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ts = Series(1, index=rng)
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repr(ts)
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def test_series_repr_nat(self):
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series = Series([0, 1000, 2000, pd.NaT.value], dtype="M8[ns]")
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result = repr(series)
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expected = (
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"0 1970-01-01 00:00:00.000000\n"
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"1 1970-01-01 00:00:00.000001\n"
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"2 1970-01-01 00:00:00.000002\n"
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"3 NaT\n"
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"dtype: datetime64[ns]"
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)
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assert result == expected
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class TestCategoricalRepr:
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def test_categorical_repr_unicode(self):
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# see gh-21002
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class County:
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name = "San Sebastián"
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state = "PR"
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def __repr__(self) -> str:
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return self.name + ", " + self.state
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cat = Categorical([County() for _ in range(61)])
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idx = Index(cat)
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ser = idx.to_series()
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repr(ser)
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str(ser)
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def test_categorical_repr(self):
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a = Series(Categorical([1, 2, 3, 4]))
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exp = (
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"0 1\n1 2\n2 3\n3 4\n"
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+ "dtype: category\nCategories (4, int64): [1, 2, 3, 4]"
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)
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assert exp == a.__str__()
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a = Series(Categorical(["a", "b"] * 25))
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exp = (
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"0 a\n1 b\n"
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+ " ..\n"
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+ "48 a\n49 b\n"
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+ "Length: 50, dtype: category\nCategories (2, object): ['a', 'b']"
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)
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with option_context("display.max_rows", 5):
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assert exp == repr(a)
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levs = list("abcdefghijklmnopqrstuvwxyz")
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a = Series(Categorical(["a", "b"], categories=levs, ordered=True))
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exp = (
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"0 a\n1 b\n" + "dtype: category\n"
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"Categories (26, object): ['a' < 'b' < 'c' < 'd' ... 'w' < 'x' < 'y' < 'z']"
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)
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assert exp == a.__str__()
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def test_categorical_series_repr(self):
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s = Series(Categorical([1, 2, 3]))
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exp = """0 1
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1 2
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2 3
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dtype: category
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Categories (3, int64): [1, 2, 3]"""
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assert repr(s) == exp
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s = Series(Categorical(np.arange(10)))
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exp = """0 0
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1 1
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2 2
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3 3
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4 4
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5 5
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6 6
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7 7
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8 8
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9 9
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dtype: category
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Categories (10, int64): [0, 1, 2, 3, ..., 6, 7, 8, 9]"""
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assert repr(s) == exp
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def test_categorical_series_repr_ordered(self):
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s = Series(Categorical([1, 2, 3], ordered=True))
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exp = """0 1
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1 2
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2 3
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dtype: category
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Categories (3, int64): [1 < 2 < 3]"""
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assert repr(s) == exp
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s = Series(Categorical(np.arange(10), ordered=True))
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exp = """0 0
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1 1
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2 2
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3 3
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4 4
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5 5
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6 6
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7 7
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8 8
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9 9
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dtype: category
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Categories (10, int64): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]"""
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assert repr(s) == exp
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def test_categorical_series_repr_datetime(self):
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idx = date_range("2011-01-01 09:00", freq="H", periods=5)
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s = Series(Categorical(idx))
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exp = """0 2011-01-01 09:00:00
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1 2011-01-01 10:00:00
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2 2011-01-01 11:00:00
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3 2011-01-01 12:00:00
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4 2011-01-01 13:00:00
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dtype: category
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Categories (5, datetime64[ns]): [2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00,
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2011-01-01 12:00:00, 2011-01-01 13:00:00]""" # noqa
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assert repr(s) == exp
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idx = date_range("2011-01-01 09:00", freq="H", periods=5, tz="US/Eastern")
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s = Series(Categorical(idx))
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exp = """0 2011-01-01 09:00:00-05:00
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1 2011-01-01 10:00:00-05:00
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2 2011-01-01 11:00:00-05:00
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3 2011-01-01 12:00:00-05:00
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4 2011-01-01 13:00:00-05:00
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dtype: category
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Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,
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2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,
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2011-01-01 13:00:00-05:00]""" # noqa
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assert repr(s) == exp
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def test_categorical_series_repr_datetime_ordered(self):
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idx = date_range("2011-01-01 09:00", freq="H", periods=5)
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s = Series(Categorical(idx, ordered=True))
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exp = """0 2011-01-01 09:00:00
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1 2011-01-01 10:00:00
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2 2011-01-01 11:00:00
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3 2011-01-01 12:00:00
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4 2011-01-01 13:00:00
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dtype: category
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Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
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2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa
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assert repr(s) == exp
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idx = date_range("2011-01-01 09:00", freq="H", periods=5, tz="US/Eastern")
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s = Series(Categorical(idx, ordered=True))
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exp = """0 2011-01-01 09:00:00-05:00
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1 2011-01-01 10:00:00-05:00
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2 2011-01-01 11:00:00-05:00
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3 2011-01-01 12:00:00-05:00
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4 2011-01-01 13:00:00-05:00
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dtype: category
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Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
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2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
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2011-01-01 13:00:00-05:00]""" # noqa
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assert repr(s) == exp
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def test_categorical_series_repr_period(self):
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idx = period_range("2011-01-01 09:00", freq="H", periods=5)
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s = Series(Categorical(idx))
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exp = """0 2011-01-01 09:00
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1 2011-01-01 10:00
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2 2011-01-01 11:00
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3 2011-01-01 12:00
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4 2011-01-01 13:00
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dtype: category
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Categories (5, period[H]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00,
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2011-01-01 13:00]""" # noqa
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assert repr(s) == exp
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idx = period_range("2011-01", freq="M", periods=5)
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s = Series(Categorical(idx))
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exp = """0 2011-01
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1 2011-02
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2 2011-03
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3 2011-04
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4 2011-05
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dtype: category
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Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]"""
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assert repr(s) == exp
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def test_categorical_series_repr_period_ordered(self):
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idx = period_range("2011-01-01 09:00", freq="H", periods=5)
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s = Series(Categorical(idx, ordered=True))
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exp = """0 2011-01-01 09:00
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1 2011-01-01 10:00
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2 2011-01-01 11:00
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3 2011-01-01 12:00
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4 2011-01-01 13:00
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dtype: category
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Categories (5, period[H]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 <
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2011-01-01 13:00]""" # noqa
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assert repr(s) == exp
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idx = period_range("2011-01", freq="M", periods=5)
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s = Series(Categorical(idx, ordered=True))
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exp = """0 2011-01
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1 2011-02
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2 2011-03
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3 2011-04
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4 2011-05
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dtype: category
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Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]"""
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assert repr(s) == exp
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def test_categorical_series_repr_timedelta(self):
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idx = timedelta_range("1 days", periods=5)
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s = Series(Categorical(idx))
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exp = """0 1 days
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1 2 days
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2 3 days
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3 4 days
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4 5 days
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dtype: category
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Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]"""
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assert repr(s) == exp
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idx = timedelta_range("1 hours", periods=10)
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s = Series(Categorical(idx))
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exp = """0 0 days 01:00:00
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1 1 days 01:00:00
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2 2 days 01:00:00
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3 3 days 01:00:00
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4 4 days 01:00:00
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5 5 days 01:00:00
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6 6 days 01:00:00
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7 7 days 01:00:00
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8 8 days 01:00:00
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9 9 days 01:00:00
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dtype: category
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Categories (10, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00,
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3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00,
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8 days 01:00:00, 9 days 01:00:00]""" # noqa
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assert repr(s) == exp
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def test_categorical_series_repr_timedelta_ordered(self):
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idx = timedelta_range("1 days", periods=5)
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s = Series(Categorical(idx, ordered=True))
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exp = """0 1 days
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1 2 days
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2 3 days
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3 4 days
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4 5 days
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dtype: category
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Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]"""
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assert repr(s) == exp
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idx = timedelta_range("1 hours", periods=10)
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s = Series(Categorical(idx, ordered=True))
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exp = """0 0 days 01:00:00
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1 1 days 01:00:00
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2 2 days 01:00:00
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3 3 days 01:00:00
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4 4 days 01:00:00
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5 5 days 01:00:00
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6 6 days 01:00:00
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7 7 days 01:00:00
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8 8 days 01:00:00
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9 9 days 01:00:00
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dtype: category
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Categories (10, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 <
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3 days 01:00:00 ... 6 days 01:00:00 < 7 days 01:00:00 <
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8 days 01:00:00 < 9 days 01:00:00]""" # noqa
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assert repr(s) == exp
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