522 lines
16 KiB
Python
522 lines
16 KiB
Python
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from datetime import (
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datetime,
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timedelta,
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)
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from io import StringIO
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import numpy as np
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import pytest
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from pandas._config import using_pyarrow_string_dtype
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from pandas import (
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NA,
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Categorical,
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CategoricalIndex,
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DataFrame,
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IntervalIndex,
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MultiIndex,
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NaT,
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PeriodIndex,
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Series,
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Timestamp,
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date_range,
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option_context,
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period_range,
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)
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import pandas._testing as tm
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class TestDataFrameRepr:
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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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cols = ["\u03c8"]
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df = DataFrame(data, columns=cols, index=index1)
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assert type(df.__repr__()) is str # noqa: E721
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ser = df[cols[0]]
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assert type(ser.__repr__()) is str # noqa: E721
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def test_repr_bytes_61_lines(self):
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# GH#12857
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lets = list("ACDEFGHIJKLMNOP")
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words = np.random.default_rng(2).choice(lets, (1000, 50))
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df = DataFrame(words).astype("U1")
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assert (df.dtypes == object).all()
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# smoke tests; at one point this raised with 61 but not 60
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repr(df)
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repr(df.iloc[:60, :])
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repr(df.iloc[:61, :])
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def test_repr_unicode_level_names(self, frame_or_series):
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index = MultiIndex.from_tuples([(0, 0), (1, 1)], names=["\u0394", "i1"])
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obj = DataFrame(np.random.default_rng(2).standard_normal((2, 4)), index=index)
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obj = tm.get_obj(obj, frame_or_series)
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repr(obj)
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def test_assign_index_sequences(self):
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# GH#2200
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df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}).set_index(
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["a", "b"]
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)
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index = list(df.index)
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index[0] = ("faz", "boo")
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df.index = index
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repr(df)
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# this travels an improper code path
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index[0] = ["faz", "boo"]
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df.index = index
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repr(df)
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def test_repr_with_mi_nat(self):
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df = DataFrame({"X": [1, 2]}, index=[[NaT, Timestamp("20130101")], ["a", "b"]])
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result = repr(df)
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expected = " X\nNaT a 1\n2013-01-01 b 2"
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assert result == expected
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def test_repr_with_different_nulls(self):
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# GH45263
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df = DataFrame([1, 2, 3, 4], [True, None, np.nan, NaT])
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result = repr(df)
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expected = """ 0
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True 1
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None 2
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NaN 3
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NaT 4"""
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assert result == expected
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def test_repr_with_different_nulls_cols(self):
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# GH45263
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d = {np.nan: [1, 2], None: [3, 4], NaT: [6, 7], True: [8, 9]}
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df = DataFrame(data=d)
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result = repr(df)
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expected = """ NaN None NaT True
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0 1 3 6 8
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1 2 4 7 9"""
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assert result == expected
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def test_multiindex_na_repr(self):
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# only an issue with long columns
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df3 = DataFrame(
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{
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"A" * 30: {("A", "A0006000", "nuit"): "A0006000"},
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"B" * 30: {("A", "A0006000", "nuit"): np.nan},
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"C" * 30: {("A", "A0006000", "nuit"): np.nan},
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"D" * 30: {("A", "A0006000", "nuit"): np.nan},
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"E" * 30: {("A", "A0006000", "nuit"): "A"},
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"F" * 30: {("A", "A0006000", "nuit"): np.nan},
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}
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)
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idf = df3.set_index(["A" * 30, "C" * 30])
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repr(idf)
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def test_repr_name_coincide(self):
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index = MultiIndex.from_tuples(
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[("a", 0, "foo"), ("b", 1, "bar")], names=["a", "b", "c"]
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)
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df = DataFrame({"value": [0, 1]}, index=index)
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lines = repr(df).split("\n")
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assert lines[2].startswith("a 0 foo")
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def test_repr_to_string(
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self,
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multiindex_year_month_day_dataframe_random_data,
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multiindex_dataframe_random_data,
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):
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ymd = multiindex_year_month_day_dataframe_random_data
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frame = multiindex_dataframe_random_data
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repr(frame)
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repr(ymd)
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repr(frame.T)
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repr(ymd.T)
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buf = StringIO()
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frame.to_string(buf=buf)
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ymd.to_string(buf=buf)
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frame.T.to_string(buf=buf)
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ymd.T.to_string(buf=buf)
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def test_repr_empty(self):
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# empty
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repr(DataFrame())
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# empty with index
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frame = DataFrame(index=np.arange(1000))
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repr(frame)
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def test_repr_mixed(self, float_string_frame):
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# mixed
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repr(float_string_frame)
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@pytest.mark.slow
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def test_repr_mixed_big(self):
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# big mixed
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biggie = DataFrame(
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{
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"A": np.random.default_rng(2).standard_normal(200),
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"B": [str(i) for i in range(200)],
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},
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index=range(200),
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)
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biggie.loc[:20, "A"] = np.nan
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biggie.loc[:20, "B"] = np.nan
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repr(biggie)
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@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="/r in")
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def test_repr(self):
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# columns but no index
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no_index = DataFrame(columns=[0, 1, 3])
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repr(no_index)
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df = DataFrame(["a\n\r\tb"], columns=["a\n\r\td"], index=["a\n\r\tf"])
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assert "\t" not in repr(df)
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assert "\r" not in repr(df)
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assert "a\n" not in repr(df)
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def test_repr_dimensions(self):
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df = DataFrame([[1, 2], [3, 4]])
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with option_context("display.show_dimensions", True):
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assert "2 rows x 2 columns" in repr(df)
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with option_context("display.show_dimensions", False):
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assert "2 rows x 2 columns" not in repr(df)
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with option_context("display.show_dimensions", "truncate"):
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assert "2 rows x 2 columns" not in repr(df)
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@pytest.mark.slow
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def test_repr_big(self):
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# big one
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biggie = DataFrame(np.zeros((200, 4)), columns=range(4), index=range(200))
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repr(biggie)
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def test_repr_unsortable(self):
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# columns are not sortable
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unsortable = DataFrame(
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{
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"foo": [1] * 50,
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datetime.today(): [1] * 50,
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"bar": ["bar"] * 50,
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datetime.today() + timedelta(1): ["bar"] * 50,
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},
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index=np.arange(50),
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)
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repr(unsortable)
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def test_repr_float_frame_options(self, float_frame):
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repr(float_frame)
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with option_context("display.precision", 3):
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repr(float_frame)
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with option_context("display.max_rows", 10, "display.max_columns", 2):
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repr(float_frame)
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with option_context("display.max_rows", 1000, "display.max_columns", 1000):
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repr(float_frame)
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def test_repr_unicode(self):
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uval = "\u03c3\u03c3\u03c3\u03c3"
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df = DataFrame({"A": [uval, uval]})
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result = repr(df)
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ex_top = " A"
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assert result.split("\n")[0].rstrip() == ex_top
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df = DataFrame({"A": [uval, uval]})
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result = repr(df)
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assert result.split("\n")[0].rstrip() == ex_top
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def test_unicode_string_with_unicode(self):
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df = DataFrame({"A": ["\u05d0"]})
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str(df)
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def test_repr_unicode_columns(self):
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df = DataFrame({"\u05d0": [1, 2, 3], "\u05d1": [4, 5, 6], "c": [7, 8, 9]})
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repr(df.columns) # should not raise UnicodeDecodeError
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def test_str_to_bytes_raises(self):
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# GH 26447
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df = DataFrame({"A": ["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_very_wide_repr(self):
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df = DataFrame(
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np.random.default_rng(2).standard_normal((10, 20)),
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columns=np.array(["a" * 10] * 20, dtype=object),
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)
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repr(df)
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def test_repr_column_name_unicode_truncation_bug(self):
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# #1906
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df = DataFrame(
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{
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"Id": [7117434],
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"StringCol": (
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"Is it possible to modify drop plot code"
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"so that the output graph is displayed "
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"in iphone simulator, Is it possible to "
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"modify drop plot code so that the "
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"output graph is \xe2\x80\xa8displayed "
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"in iphone simulator.Now we are adding "
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"the CSV file externally. I want to Call "
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"the File through the code.."
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),
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}
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)
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with option_context("display.max_columns", 20):
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assert "StringCol" in repr(df)
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def test_latex_repr(self):
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pytest.importorskip("jinja2")
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expected = r"""\begin{tabular}{llll}
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\toprule
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& 0 & 1 & 2 \\
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\midrule
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0 & $\alpha$ & b & c \\
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1 & 1 & 2 & 3 \\
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\bottomrule
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\end{tabular}
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"""
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with option_context(
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"styler.format.escape", None, "styler.render.repr", "latex"
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):
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df = DataFrame([[r"$\alpha$", "b", "c"], [1, 2, 3]])
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result = df._repr_latex_()
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assert result == expected
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# GH 12182
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assert df._repr_latex_() is None
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def test_repr_with_datetimeindex(self):
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df = DataFrame({"A": [1, 2, 3]}, index=date_range("2000", periods=3))
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result = repr(df)
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expected = " A\n2000-01-01 1\n2000-01-02 2\n2000-01-03 3"
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assert result == expected
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def test_repr_with_intervalindex(self):
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# https://github.com/pandas-dev/pandas/pull/24134/files
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df = DataFrame(
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{"A": [1, 2, 3, 4]}, index=IntervalIndex.from_breaks([0, 1, 2, 3, 4])
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)
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result = repr(df)
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expected = " A\n(0, 1] 1\n(1, 2] 2\n(2, 3] 3\n(3, 4] 4"
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assert result == expected
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def test_repr_with_categorical_index(self):
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df = DataFrame({"A": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
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result = repr(df)
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expected = " A\na 1\nb 2\nc 3"
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assert result == expected
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def test_repr_categorical_dates_periods(self):
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# normal DataFrame
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dt = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern")
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p = period_range("2011-01", freq="M", periods=5)
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df = DataFrame({"dt": dt, "p": p})
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exp = """ dt p
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0 2011-01-01 09:00:00-05:00 2011-01
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1 2011-01-01 10:00:00-05:00 2011-02
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2 2011-01-01 11:00:00-05:00 2011-03
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3 2011-01-01 12:00:00-05:00 2011-04
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4 2011-01-01 13:00:00-05:00 2011-05"""
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assert repr(df) == exp
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df2 = DataFrame({"dt": Categorical(dt), "p": Categorical(p)})
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assert repr(df2) == exp
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@pytest.mark.parametrize("arg", [np.datetime64, np.timedelta64])
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@pytest.mark.parametrize(
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"box, expected",
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[[Series, "0 NaT\ndtype: object"], [DataFrame, " 0\n0 NaT"]],
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)
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def test_repr_np_nat_with_object(self, arg, box, expected):
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# GH 25445
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result = repr(box([arg("NaT")], dtype=object))
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assert result == expected
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def test_frame_datetime64_pre1900_repr(self):
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df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="YE-DEC")})
|
||
|
|
# it works!
|
||
|
|
repr(df)
|
||
|
|
|
||
|
|
def test_frame_to_string_with_periodindex(self):
|
||
|
|
index = PeriodIndex(["2011-1", "2011-2", "2011-3"], freq="M")
|
||
|
|
frame = DataFrame(np.random.default_rng(2).standard_normal((3, 4)), index=index)
|
||
|
|
|
||
|
|
# it works!
|
||
|
|
frame.to_string()
|
||
|
|
|
||
|
|
def test_to_string_ea_na_in_multiindex(self):
|
||
|
|
# GH#47986
|
||
|
|
df = DataFrame(
|
||
|
|
{"a": [1, 2]},
|
||
|
|
index=MultiIndex.from_arrays([Series([NA, 1], dtype="Int64")]),
|
||
|
|
)
|
||
|
|
|
||
|
|
result = df.to_string()
|
||
|
|
expected = """ a
|
||
|
|
<NA> 1
|
||
|
|
1 2"""
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_datetime64tz_slice_non_truncate(self):
|
||
|
|
# GH 30263
|
||
|
|
df = DataFrame({"x": date_range("2019", periods=10, tz="UTC")})
|
||
|
|
expected = repr(df)
|
||
|
|
df = df.iloc[:, :5]
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_to_records_no_typeerror_in_repr(self):
|
||
|
|
# GH 48526
|
||
|
|
df = DataFrame([["a", "b"], ["c", "d"], ["e", "f"]], columns=["left", "right"])
|
||
|
|
df["record"] = df[["left", "right"]].to_records()
|
||
|
|
expected = """ left right record
|
||
|
|
0 a b [0, a, b]
|
||
|
|
1 c d [1, c, d]
|
||
|
|
2 e f [2, e, f]"""
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_to_records_with_na_record_value(self):
|
||
|
|
# GH 48526
|
||
|
|
df = DataFrame(
|
||
|
|
[["a", np.nan], ["c", "d"], ["e", "f"]], columns=["left", "right"]
|
||
|
|
)
|
||
|
|
df["record"] = df[["left", "right"]].to_records()
|
||
|
|
expected = """ left right record
|
||
|
|
0 a NaN [0, a, nan]
|
||
|
|
1 c d [1, c, d]
|
||
|
|
2 e f [2, e, f]"""
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_to_records_with_na_record(self):
|
||
|
|
# GH 48526
|
||
|
|
df = DataFrame(
|
||
|
|
[["a", "b"], [np.nan, np.nan], ["e", "f"]], columns=[np.nan, "right"]
|
||
|
|
)
|
||
|
|
df["record"] = df[[np.nan, "right"]].to_records()
|
||
|
|
expected = """ NaN right record
|
||
|
|
0 a b [0, a, b]
|
||
|
|
1 NaN NaN [1, nan, nan]
|
||
|
|
2 e f [2, e, f]"""
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_to_records_with_inf_as_na_record(self):
|
||
|
|
# GH 48526
|
||
|
|
expected = """ NaN inf record
|
||
|
|
0 inf b [0, inf, b]
|
||
|
|
1 NaN NaN [1, nan, nan]
|
||
|
|
2 e f [2, e, f]"""
|
||
|
|
msg = "use_inf_as_na option is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
with option_context("use_inf_as_na", True):
|
||
|
|
df = DataFrame(
|
||
|
|
[[np.inf, "b"], [np.nan, np.nan], ["e", "f"]],
|
||
|
|
columns=[np.nan, np.inf],
|
||
|
|
)
|
||
|
|
df["record"] = df[[np.nan, np.inf]].to_records()
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_to_records_with_inf_record(self):
|
||
|
|
# GH 48526
|
||
|
|
expected = """ NaN inf record
|
||
|
|
0 inf b [0, inf, b]
|
||
|
|
1 NaN NaN [1, nan, nan]
|
||
|
|
2 e f [2, e, f]"""
|
||
|
|
msg = "use_inf_as_na option is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
with option_context("use_inf_as_na", False):
|
||
|
|
df = DataFrame(
|
||
|
|
[[np.inf, "b"], [np.nan, np.nan], ["e", "f"]],
|
||
|
|
columns=[np.nan, np.inf],
|
||
|
|
)
|
||
|
|
df["record"] = df[[np.nan, np.inf]].to_records()
|
||
|
|
result = repr(df)
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_masked_ea_with_formatter(self):
|
||
|
|
# GH#39336
|
||
|
|
df = DataFrame(
|
||
|
|
{
|
||
|
|
"a": Series([0.123456789, 1.123456789], dtype="Float64"),
|
||
|
|
"b": Series([1, 2], dtype="Int64"),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
result = df.to_string(formatters=["{:.2f}".format, "{:.2f}".format])
|
||
|
|
expected = """ a b
|
||
|
|
0 0.12 1.00
|
||
|
|
1 1.12 2.00"""
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_repr_ea_columns(self, any_string_dtype):
|
||
|
|
# GH#54797
|
||
|
|
pytest.importorskip("pyarrow")
|
||
|
|
df = DataFrame({"long_column_name": [1, 2, 3], "col2": [4, 5, 6]})
|
||
|
|
df.columns = df.columns.astype(any_string_dtype)
|
||
|
|
expected = """ long_column_name col2
|
||
|
|
0 1 4
|
||
|
|
1 2 5
|
||
|
|
2 3 6"""
|
||
|
|
assert repr(df) == expected
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,output",
|
||
|
|
[
|
||
|
|
([2, complex("nan"), 1], [" 2.0+0.0j", " NaN+0.0j", " 1.0+0.0j"]),
|
||
|
|
([2, complex("nan"), -1], [" 2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]),
|
||
|
|
([-2, complex("nan"), -1], ["-2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]),
|
||
|
|
([-1.23j, complex("nan"), -1], ["-0.00-1.23j", " NaN+0.00j", "-1.00+0.00j"]),
|
||
|
|
([1.23j, complex("nan"), 1.23], [" 0.00+1.23j", " NaN+0.00j", " 1.23+0.00j"]),
|
||
|
|
(
|
||
|
|
[-1.23j, complex(np.nan, np.nan), 1],
|
||
|
|
["-0.00-1.23j", " NaN+ NaNj", " 1.00+0.00j"],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[-1.23j, complex(1.2, np.nan), 1],
|
||
|
|
["-0.00-1.23j", " 1.20+ NaNj", " 1.00+0.00j"],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[-1.23j, complex(np.nan, -1.2), 1],
|
||
|
|
["-0.00-1.23j", " NaN-1.20j", " 1.00+0.00j"],
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("as_frame", [True, False])
|
||
|
|
def test_repr_with_complex_nans(data, output, as_frame):
|
||
|
|
# GH#53762, GH#53841
|
||
|
|
obj = Series(np.array(data))
|
||
|
|
if as_frame:
|
||
|
|
obj = obj.to_frame(name="val")
|
||
|
|
reprs = [f"{i} {val}" for i, val in enumerate(output)]
|
||
|
|
expected = f"{'val': >{len(reprs[0])}}\n" + "\n".join(reprs)
|
||
|
|
else:
|
||
|
|
reprs = [f"{i} {val}" for i, val in enumerate(output)]
|
||
|
|
expected = "\n".join(reprs) + "\ndtype: complex128"
|
||
|
|
assert str(obj) == expected, f"\n{str(obj)}\n\n{expected}"
|