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| from itertools import product
|
| import numpy as np
| import pytest
|
| import pandas as pd
| import pandas._testing as tm
|
| # Each test case consists of a tuple with the data and dtype to create the
| # test Series, the default dtype for the expected result (which is valid
| # for most cases), and the specific cases where the result deviates from
| # this default. Those overrides are defined as a dict with (keyword, val) as
| # dictionary key. In case of multiple items, the last override takes precedence.
|
| test_cases = [
| (
| # data
| [1, 2, 3],
| # original dtype
| np.dtype("int32"),
| # default expected dtype
| "Int32",
| # exceptions on expected dtype
| {("convert_integer", False): np.dtype("int32")},
| ),
| (
| [1, 2, 3],
| np.dtype("int64"),
| "Int64",
| {("convert_integer", False): np.dtype("int64")},
| ),
| (
| ["x", "y", "z"],
| np.dtype("O"),
| pd.StringDtype(),
| {("convert_string", False): np.dtype("O")},
| ),
| (
| [True, False, np.nan],
| np.dtype("O"),
| pd.BooleanDtype(),
| {("convert_boolean", False): np.dtype("O")},
| ),
| (
| ["h", "i", np.nan],
| np.dtype("O"),
| pd.StringDtype(),
| {("convert_string", False): np.dtype("O")},
| ),
| ( # GH32117
| ["h", "i", 1],
| np.dtype("O"),
| np.dtype("O"),
| {},
| ),
| (
| [10, np.nan, 20],
| np.dtype("float"),
| "Int64",
| {
| ("convert_integer", False, "convert_floating", True): "Float64",
| ("convert_integer", False, "convert_floating", False): np.dtype("float"),
| },
| ),
| (
| [np.nan, 100.5, 200],
| np.dtype("float"),
| "Float64",
| {("convert_floating", False): np.dtype("float")},
| ),
| (
| [3, 4, 5],
| "Int8",
| "Int8",
| {},
| ),
| (
| [[1, 2], [3, 4], [5]],
| None,
| np.dtype("O"),
| {},
| ),
| (
| [4, 5, 6],
| np.dtype("uint32"),
| "UInt32",
| {("convert_integer", False): np.dtype("uint32")},
| ),
| (
| [-10, 12, 13],
| np.dtype("i1"),
| "Int8",
| {("convert_integer", False): np.dtype("i1")},
| ),
| (
| [1.2, 1.3],
| np.dtype("float32"),
| "Float32",
| {("convert_floating", False): np.dtype("float32")},
| ),
| (
| [1, 2.0],
| object,
| "Int64",
| {
| ("convert_integer", False): "Float64",
| ("convert_integer", False, "convert_floating", False): np.dtype("float"),
| ("infer_objects", False): np.dtype("object"),
| },
| ),
| (
| [1, 2.5],
| object,
| "Float64",
| {
| ("convert_floating", False): np.dtype("float"),
| ("infer_objects", False): np.dtype("object"),
| },
| ),
| (["a", "b"], pd.CategoricalDtype(), pd.CategoricalDtype(), {}),
| (
| pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]),
| pd.DatetimeTZDtype(tz="UTC"),
| pd.DatetimeTZDtype(tz="UTC"),
| {},
| ),
| (
| pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]),
| "datetime64[ns]",
| np.dtype("datetime64[ns]"),
| {},
| ),
| (
| pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]),
| object,
| np.dtype("datetime64[ns]"),
| {("infer_objects", False): np.dtype("object")},
| ),
| (pd.period_range("1/1/2011", freq="M", periods=3), None, pd.PeriodDtype("M"), {}),
| (
| pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)]),
| None,
| pd.IntervalDtype("int64", "right"),
| {},
| ),
| ]
|
|
| class TestSeriesConvertDtypes:
| @pytest.mark.parametrize(
| "data, maindtype, expected_default, expected_other",
| test_cases,
| )
| @pytest.mark.parametrize("params", product(*[(True, False)] * 5))
| def test_convert_dtypes(
| self, data, maindtype, params, expected_default, expected_other
| ):
| if (
| hasattr(data, "dtype")
| and data.dtype == "M8[ns]"
| and isinstance(maindtype, pd.DatetimeTZDtype)
| ):
| # this astype is deprecated in favor of tz_localize
| msg = "Cannot use .astype to convert from timezone-naive dtype"
| with pytest.raises(TypeError, match=msg):
| pd.Series(data, dtype=maindtype)
| return
|
| if maindtype is not None:
| series = pd.Series(data, dtype=maindtype)
| else:
| series = pd.Series(data)
|
| result = series.convert_dtypes(*params)
|
| param_names = [
| "infer_objects",
| "convert_string",
| "convert_integer",
| "convert_boolean",
| "convert_floating",
| ]
| params_dict = dict(zip(param_names, params))
|
| expected_dtype = expected_default
| for spec, dtype in expected_other.items():
| if all(params_dict[key] is val for key, val in zip(spec[::2], spec[1::2])):
| expected_dtype = dtype
|
| expected = pd.Series(data, dtype=expected_dtype)
| tm.assert_series_equal(result, expected)
|
| # Test that it is a copy
| copy = series.copy(deep=True)
|
| result[result.notna()] = np.nan
|
| # Make sure original not changed
| tm.assert_series_equal(series, copy)
|
| def test_convert_string_dtype(self, nullable_string_dtype):
| # https://github.com/pandas-dev/pandas/issues/31731 -> converting columns
| # that are already string dtype
| df = pd.DataFrame(
| {"A": ["a", "b", pd.NA], "B": ["ä", "ö", "ü"]}, dtype=nullable_string_dtype
| )
| result = df.convert_dtypes()
| tm.assert_frame_equal(df, result)
|
| def test_convert_bool_dtype(self):
| # GH32287
| df = pd.DataFrame({"A": pd.array([True])})
| tm.assert_frame_equal(df, df.convert_dtypes())
|
| def test_convert_byte_string_dtype(self):
| # GH-43183
| byte_str = b"binary-string"
|
| df = pd.DataFrame(data={"A": byte_str}, index=[0])
| result = df.convert_dtypes()
| expected = df
| tm.assert_frame_equal(result, expected)
|
| @pytest.mark.parametrize(
| "infer_objects, dtype", [(True, "Int64"), (False, "object")]
| )
| def test_convert_dtype_object_with_na(self, infer_objects, dtype):
| # GH#48791
| ser = pd.Series([1, pd.NA])
| result = ser.convert_dtypes(infer_objects=infer_objects)
| expected = pd.Series([1, pd.NA], dtype=dtype)
| tm.assert_series_equal(result, expected)
|
| @pytest.mark.parametrize(
| "infer_objects, dtype", [(True, "Float64"), (False, "object")]
| )
| def test_convert_dtype_object_with_na_float(self, infer_objects, dtype):
| # GH#48791
| ser = pd.Series([1.5, pd.NA])
| result = ser.convert_dtypes(infer_objects=infer_objects)
| expected = pd.Series([1.5, pd.NA], dtype=dtype)
| tm.assert_series_equal(result, expected)
|
|
