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usr
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lib64
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python3.6
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pandas
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tests
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arrays
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string_
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/usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/string_
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/usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/string_/test_string.py
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import operator import numpy as np import pytest import pandas.util._test_decorators as td import pandas as pd import pandas._testing as tm def test_repr(): df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype="string")}) expected = " A\n0 a\n1 <NA>\n2 b" assert repr(df) == expected expected = "0 a\n1 <NA>\n2 b\nName: A, dtype: string" assert repr(df.A) == expected expected = "<StringArray>\n['a', <NA>, 'b']\nLength: 3, dtype: string" assert repr(df.A.array) == expected def test_none_to_nan(): a = pd.arrays.StringArray._from_sequence(["a", None, "b"]) assert a[1] is not None assert a[1] is pd.NA def test_setitem_validates(): a = pd.arrays.StringArray._from_sequence(["a", "b"]) with pytest.raises(ValueError, match="10"): a[0] = 10 with pytest.raises(ValueError, match="strings"): a[:] = np.array([1, 2]) def test_setitem_with_scalar_string(): # is_float_dtype considers some strings, like 'd', to be floats # which can cause issues. arr = pd.array(["a", "c"], dtype="string") arr[0] = "d" expected = pd.array(["d", "c"], dtype="string") tm.assert_extension_array_equal(arr, expected) @pytest.mark.parametrize( "input, method", [ (["a", "b", "c"], operator.methodcaller("capitalize")), (["a", "b", "c"], operator.methodcaller("capitalize")), (["a b", "a bc. de"], operator.methodcaller("capitalize")), ], ) def test_string_methods(input, method): a = pd.Series(input, dtype="string") b = pd.Series(input, dtype="object") result = method(a.str) expected = method(b.str) assert result.dtype.name == "string" tm.assert_series_equal(result.astype(object), expected) def test_astype_roundtrip(): s = pd.Series(pd.date_range("2000", periods=12)) s[0] = None result = s.astype("string").astype("datetime64[ns]") tm.assert_series_equal(result, s) def test_add(): a = pd.Series(["a", "b", "c", None, None], dtype="string") b = pd.Series(["x", "y", None, "z", None], dtype="string") result = a + b expected = pd.Series(["ax", "by", None, None, None], dtype="string") tm.assert_series_equal(result, expected) result = a.add(b) tm.assert_series_equal(result, expected) result = a.radd(b) expected = pd.Series(["xa", "yb", None, None, None], dtype="string") tm.assert_series_equal(result, expected) result = a.add(b, fill_value="-") expected = pd.Series(["ax", "by", "c-", "-z", None], dtype="string") tm.assert_series_equal(result, expected) def test_add_2d(): a = pd.array(["a", "b", "c"], dtype="string") b = np.array([["a", "b", "c"]], dtype=object) with pytest.raises(ValueError, match="3 != 1"): a + b s = pd.Series(a) with pytest.raises(ValueError, match="3 != 1"): s + b def test_add_sequence(): a = pd.array(["a", "b", None, None], dtype="string") other = ["x", None, "y", None] result = a + other expected = pd.array(["ax", None, None, None], dtype="string") tm.assert_extension_array_equal(result, expected) result = other + a expected = pd.array(["xa", None, None, None], dtype="string") tm.assert_extension_array_equal(result, expected) def test_mul(): a = pd.array(["a", "b", None], dtype="string") result = a * 2 expected = pd.array(["aa", "bb", None], dtype="string") tm.assert_extension_array_equal(result, expected) result = 2 * a tm.assert_extension_array_equal(result, expected) @pytest.mark.xfail(reason="GH-28527") def test_add_strings(): array = pd.array(["a", "b", "c", "d"], dtype="string") df = pd.DataFrame([["t", "u", "v", "w"]]) assert array.__add__(df) is NotImplemented result = array + df expected = pd.DataFrame([["at", "bu", "cv", "dw"]]).astype("string") tm.assert_frame_equal(result, expected) result = df + array expected = pd.DataFrame([["ta", "ub", "vc", "wd"]]).astype("string") tm.assert_frame_equal(result, expected) @pytest.mark.xfail(reason="GH-28527") def test_add_frame(): array = pd.array(["a", "b", np.nan, np.nan], dtype="string") df = pd.DataFrame([["x", np.nan, "y", np.nan]]) assert array.__add__(df) is NotImplemented result = array + df expected = pd.DataFrame([["ax", np.nan, np.nan, np.nan]]).astype("string") tm.assert_frame_equal(result, expected) result = df + array expected = pd.DataFrame([["xa", np.nan, np.nan, np.nan]]).astype("string") tm.assert_frame_equal(result, expected) def test_comparison_methods_scalar(all_compare_operators): op_name = all_compare_operators a = pd.array(["a", None, "c"], dtype="string") other = "a" result = getattr(a, op_name)(other) expected = np.array([getattr(item, op_name)(other) for item in a], dtype=object) expected = pd.array(expected, dtype="boolean") tm.assert_extension_array_equal(result, expected) result = getattr(a, op_name)(pd.NA) expected = pd.array([None, None, None], dtype="boolean") tm.assert_extension_array_equal(result, expected) def test_comparison_methods_array(all_compare_operators): op_name = all_compare_operators a = pd.array(["a", None, "c"], dtype="string") other = [None, None, "c"] result = getattr(a, op_name)(other) expected = np.empty_like(a, dtype="object") expected[-1] = getattr(other[-1], op_name)(a[-1]) expected = pd.array(expected, dtype="boolean") tm.assert_extension_array_equal(result, expected) result = getattr(a, op_name)(pd.NA) expected = pd.array([None, None, None], dtype="boolean") tm.assert_extension_array_equal(result, expected) def test_constructor_raises(): with pytest.raises(ValueError, match="sequence of strings"): pd.arrays.StringArray(np.array(["a", "b"], dtype="S1")) with pytest.raises(ValueError, match="sequence of strings"): pd.arrays.StringArray(np.array([])) with pytest.raises(ValueError, match="strings or pandas.NA"): pd.arrays.StringArray(np.array(["a", np.nan], dtype=object)) with pytest.raises(ValueError, match="strings or pandas.NA"): pd.arrays.StringArray(np.array(["a", None], dtype=object)) with pytest.raises(ValueError, match="strings or pandas.NA"): pd.arrays.StringArray(np.array(["a", pd.NaT], dtype=object)) @pytest.mark.parametrize("copy", [True, False]) def test_from_sequence_no_mutate(copy): nan_arr = np.array(["a", np.nan], dtype=object) na_arr = np.array(["a", pd.NA], dtype=object) result = pd.arrays.StringArray._from_sequence(nan_arr, copy=copy) expected = pd.arrays.StringArray(na_arr) tm.assert_extension_array_equal(result, expected) expected = nan_arr if copy else na_arr tm.assert_numpy_array_equal(nan_arr, expected) def test_astype_int(): arr = pd.array(["1", pd.NA, "3"], dtype="string") result = arr.astype("Int64") expected = pd.array([1, pd.NA, 3], dtype="Int64") tm.assert_extension_array_equal(result, expected) @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.xfail(reason="Not implemented StringArray.sum") def test_reduce(skipna): arr = pd.Series(["a", "b", "c"], dtype="string") result = arr.sum(skipna=skipna) assert result == "abc" @pytest.mark.parametrize("method", ["min", "max"]) @pytest.mark.parametrize("skipna", [True, False]) def test_min_max(method, skipna): arr = pd.Series(["a", "b", "c", None], dtype="string") result = getattr(arr, method)(skipna=skipna) if skipna: expected = "a" if method == "min" else "c" assert result == expected else: assert result is pd.NA @pytest.mark.parametrize("method", ["min", "max"]) @pytest.mark.parametrize( "arr", [ pd.Series(["a", "b", "c", None], dtype="string"), pd.array(["a", "b", "c", None], dtype="string"), ], ) def test_min_max_numpy(method, arr): result = getattr(np, method)(arr) expected = "a" if method == "min" else "c" assert result == expected @pytest.mark.parametrize("skipna", [True, False]) @pytest.mark.xfail(reason="Not implemented StringArray.sum") def test_reduce_missing(skipna): arr = pd.Series([None, "a", None, "b", "c", None], dtype="string") result = arr.sum(skipna=skipna) if skipna: assert result == "abc" else: assert pd.isna(result) @td.skip_if_no("pyarrow", min_version="0.15.0") def test_arrow_array(): # protocol added in 0.15.0 import pyarrow as pa data = pd.array(["a", "b", "c"], dtype="string") arr = pa.array(data) expected = pa.array(list(data), type=pa.string(), from_pandas=True) assert arr.equals(expected) @td.skip_if_no("pyarrow", min_version="0.15.1.dev") def test_arrow_roundtrip(): # roundtrip possible from arrow 1.0.0 import pyarrow as pa data = pd.array(["a", "b", None], dtype="string") df = pd.DataFrame({"a": data}) table = pa.table(df) assert table.field("a").type == "string" result = table.to_pandas() assert isinstance(result["a"].dtype, pd.StringDtype) tm.assert_frame_equal(result, df) # ensure the missing value is represented by NA and not np.nan or None assert result.loc[2, "a"] is pd.NA def test_value_counts_na(): arr = pd.array(["a", "b", "a", pd.NA], dtype="string") result = arr.value_counts(dropna=False) expected = pd.Series([2, 1, 1], index=["a", "b", pd.NA], dtype="Int64") tm.assert_series_equal(result, expected) result = arr.value_counts(dropna=True) expected = pd.Series([2, 1], index=["a", "b"], dtype="Int64") tm.assert_series_equal(result, expected) @pytest.mark.parametrize( "values, expected", [ (pd.array(["a", "b", "c"]), np.array([False, False, False])), (pd.array(["a", "b", None]), np.array([False, False, True])), ], ) def test_use_inf_as_na(values, expected): # https://github.com/pandas-dev/pandas/issues/33655 with pd.option_context("mode.use_inf_as_na", True): result = values.isna() tm.assert_numpy_array_equal(result, expected) result = pd.Series(values).isna() expected = pd.Series(expected) tm.assert_series_equal(result, expected) result = pd.DataFrame(values).isna() expected = pd.DataFrame(expected) tm.assert_frame_equal(result, expected) def test_memory_usage(): # GH 33963 series = pd.Series(["a", "b", "c"], dtype="string") assert 0 < series.nbytes <= series.memory_usage() < series.memory_usage(deep=True) @pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64]) def test_astype_from_float_dtype(dtype): # https://github.com/pandas-dev/pandas/issues/36451 s = pd.Series([0.1], dtype=dtype) result = s.astype("string") expected = pd.Series(["0.1"], dtype="string") tm.assert_series_equal(result, expected)
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