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721 lines
26 KiB
721 lines
26 KiB
import inspect
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import operator
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import numpy as np
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import pytest
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from pandas._typing import Dtype
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from pandas.core.dtypes.common import is_bool_dtype
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from pandas.core.dtypes.dtypes import NumpyEADtype
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from pandas.core.dtypes.missing import na_value_for_dtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.sorting import nargsort
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class BaseMethodsTests:
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"""Various Series and DataFrame methods."""
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def test_hash_pandas_object(self, data):
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# _hash_pandas_object should return a uint64 ndarray of the same length
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# as the data
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from pandas.core.util.hashing import _default_hash_key
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res = data._hash_pandas_object(
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encoding="utf-8", hash_key=_default_hash_key, categorize=False
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)
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assert res.dtype == np.uint64
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assert res.shape == data.shape
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def test_value_counts_default_dropna(self, data):
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# make sure we have consistent default dropna kwarg
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if not hasattr(data, "value_counts"):
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pytest.skip(f"value_counts is not implemented for {type(data)}")
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sig = inspect.signature(data.value_counts)
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kwarg = sig.parameters["dropna"]
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assert kwarg.default is True
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@pytest.mark.parametrize("dropna", [True, False])
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def test_value_counts(self, all_data, dropna):
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all_data = all_data[:10]
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if dropna:
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other = all_data[~all_data.isna()]
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else:
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other = all_data
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result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
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expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
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tm.assert_series_equal(result, expected)
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def test_value_counts_with_normalize(self, data):
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# GH 33172
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data = data[:10].unique()
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values = np.array(data[~data.isna()])
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ser = pd.Series(data, dtype=data.dtype)
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result = ser.value_counts(normalize=True).sort_index()
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if not isinstance(data, pd.Categorical):
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expected = pd.Series(
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[1 / len(values)] * len(values), index=result.index, name="proportion"
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)
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else:
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expected = pd.Series(0.0, index=result.index, name="proportion")
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expected[result > 0] = 1 / len(values)
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if getattr(data.dtype, "storage", "") == "pyarrow" or isinstance(
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data.dtype, pd.ArrowDtype
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):
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# TODO: avoid special-casing
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expected = expected.astype("double[pyarrow]")
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elif getattr(data.dtype, "storage", "") == "pyarrow_numpy":
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# TODO: avoid special-casing
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expected = expected.astype("float64")
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elif na_value_for_dtype(data.dtype) is pd.NA:
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# TODO(GH#44692): avoid special-casing
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expected = expected.astype("Float64")
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tm.assert_series_equal(result, expected)
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def test_count(self, data_missing):
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df = pd.DataFrame({"A": data_missing})
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result = df.count(axis="columns")
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expected = pd.Series([0, 1])
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tm.assert_series_equal(result, expected)
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def test_series_count(self, data_missing):
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# GH#26835
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ser = pd.Series(data_missing)
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result = ser.count()
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expected = 1
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assert result == expected
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def test_apply_simple_series(self, data):
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result = pd.Series(data).apply(id)
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assert isinstance(result, pd.Series)
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@pytest.mark.parametrize("na_action", [None, "ignore"])
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def test_map(self, data_missing, na_action):
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result = data_missing.map(lambda x: x, na_action=na_action)
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expected = data_missing.to_numpy()
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tm.assert_numpy_array_equal(result, expected)
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def test_argsort(self, data_for_sorting):
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result = pd.Series(data_for_sorting).argsort()
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# argsort result gets passed to take, so should be np.intp
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expected = pd.Series(np.array([2, 0, 1], dtype=np.intp))
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tm.assert_series_equal(result, expected)
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def test_argsort_missing_array(self, data_missing_for_sorting):
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result = data_missing_for_sorting.argsort()
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# argsort result gets passed to take, so should be np.intp
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expected = np.array([2, 0, 1], dtype=np.intp)
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tm.assert_numpy_array_equal(result, expected)
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def test_argsort_missing(self, data_missing_for_sorting):
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msg = "The behavior of Series.argsort in the presence of NA values"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = pd.Series(data_missing_for_sorting).argsort()
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expected = pd.Series(np.array([1, -1, 0], dtype=np.intp))
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tm.assert_series_equal(result, expected)
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def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
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# GH 24382
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is_bool = data_for_sorting.dtype._is_boolean
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exp_argmax = 1
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exp_argmax_repeated = 3
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if is_bool:
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# See data_for_sorting docstring
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exp_argmax = 0
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exp_argmax_repeated = 1
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# data_for_sorting -> [B, C, A] with A < B < C
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assert data_for_sorting.argmax() == exp_argmax
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assert data_for_sorting.argmin() == 2
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# with repeated values -> first occurrence
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data = data_for_sorting.take([2, 0, 0, 1, 1, 2])
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assert data.argmax() == exp_argmax_repeated
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assert data.argmin() == 0
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# with missing values
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# data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
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assert data_missing_for_sorting.argmax() == 0
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assert data_missing_for_sorting.argmin() == 2
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@pytest.mark.parametrize("method", ["argmax", "argmin"])
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def test_argmin_argmax_empty_array(self, method, data):
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# GH 24382
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err_msg = "attempt to get"
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with pytest.raises(ValueError, match=err_msg):
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getattr(data[:0], method)()
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@pytest.mark.parametrize("method", ["argmax", "argmin"])
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def test_argmin_argmax_all_na(self, method, data, na_value):
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# all missing with skipna=True is the same as empty
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err_msg = "attempt to get"
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data_na = type(data)._from_sequence([na_value, na_value], dtype=data.dtype)
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with pytest.raises(ValueError, match=err_msg):
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getattr(data_na, method)()
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@pytest.mark.parametrize(
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"op_name, skipna, expected",
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[
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("idxmax", True, 0),
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("idxmin", True, 2),
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("argmax", True, 0),
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("argmin", True, 2),
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("idxmax", False, np.nan),
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("idxmin", False, np.nan),
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("argmax", False, -1),
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("argmin", False, -1),
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],
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)
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def test_argreduce_series(
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self, data_missing_for_sorting, op_name, skipna, expected
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):
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# data_missing_for_sorting -> [B, NA, A] with A < B and NA missing.
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warn = None
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msg = "The behavior of Series.argmax/argmin"
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if op_name.startswith("arg") and expected == -1:
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warn = FutureWarning
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if op_name.startswith("idx") and np.isnan(expected):
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warn = FutureWarning
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msg = f"The behavior of Series.{op_name}"
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ser = pd.Series(data_missing_for_sorting)
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with tm.assert_produces_warning(warn, match=msg):
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result = getattr(ser, op_name)(skipna=skipna)
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tm.assert_almost_equal(result, expected)
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def test_argmax_argmin_no_skipna_notimplemented(self, data_missing_for_sorting):
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# GH#38733
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data = data_missing_for_sorting
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with pytest.raises(NotImplementedError, match=""):
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data.argmin(skipna=False)
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with pytest.raises(NotImplementedError, match=""):
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data.argmax(skipna=False)
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@pytest.mark.parametrize(
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"na_position, expected",
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[
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("last", np.array([2, 0, 1], dtype=np.dtype("intp"))),
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("first", np.array([1, 2, 0], dtype=np.dtype("intp"))),
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],
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)
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def test_nargsort(self, data_missing_for_sorting, na_position, expected):
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# GH 25439
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result = nargsort(data_missing_for_sorting, na_position=na_position)
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("ascending", [True, False])
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def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
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ser = pd.Series(data_for_sorting)
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result = ser.sort_values(ascending=ascending, key=sort_by_key)
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expected = ser.iloc[[2, 0, 1]]
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if not ascending:
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# GH 35922. Expect stable sort
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if ser.nunique() == 2:
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expected = ser.iloc[[0, 1, 2]]
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else:
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expected = ser.iloc[[1, 0, 2]]
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ascending", [True, False])
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def test_sort_values_missing(
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self, data_missing_for_sorting, ascending, sort_by_key
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):
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ser = pd.Series(data_missing_for_sorting)
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result = ser.sort_values(ascending=ascending, key=sort_by_key)
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if ascending:
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expected = ser.iloc[[2, 0, 1]]
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else:
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expected = ser.iloc[[0, 2, 1]]
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("ascending", [True, False])
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def test_sort_values_frame(self, data_for_sorting, ascending):
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df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
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result = df.sort_values(["A", "B"])
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expected = pd.DataFrame(
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{"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("keep", ["first", "last", False])
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def test_duplicated(self, data, keep):
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arr = data.take([0, 1, 0, 1])
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result = arr.duplicated(keep=keep)
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if keep == "first":
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expected = np.array([False, False, True, True])
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elif keep == "last":
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expected = np.array([True, True, False, False])
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else:
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expected = np.array([True, True, True, True])
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("box", [pd.Series, lambda x: x])
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@pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
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def test_unique(self, data, box, method):
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duplicated = box(data._from_sequence([data[0], data[0]], dtype=data.dtype))
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result = method(duplicated)
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assert len(result) == 1
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assert isinstance(result, type(data))
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assert result[0] == duplicated[0]
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def test_factorize(self, data_for_grouping):
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codes, uniques = pd.factorize(data_for_grouping, use_na_sentinel=True)
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is_bool = data_for_grouping.dtype._is_boolean
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if is_bool:
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# only 2 unique values
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expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 0], dtype=np.intp)
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expected_uniques = data_for_grouping.take([0, 4])
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else:
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expected_codes = np.array([0, 0, -1, -1, 1, 1, 0, 2], dtype=np.intp)
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expected_uniques = data_for_grouping.take([0, 4, 7])
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tm.assert_numpy_array_equal(codes, expected_codes)
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tm.assert_extension_array_equal(uniques, expected_uniques)
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def test_factorize_equivalence(self, data_for_grouping):
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codes_1, uniques_1 = pd.factorize(data_for_grouping, use_na_sentinel=True)
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codes_2, uniques_2 = data_for_grouping.factorize(use_na_sentinel=True)
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tm.assert_numpy_array_equal(codes_1, codes_2)
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tm.assert_extension_array_equal(uniques_1, uniques_2)
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assert len(uniques_1) == len(pd.unique(uniques_1))
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assert uniques_1.dtype == data_for_grouping.dtype
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def test_factorize_empty(self, data):
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codes, uniques = pd.factorize(data[:0])
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expected_codes = np.array([], dtype=np.intp)
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expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
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tm.assert_numpy_array_equal(codes, expected_codes)
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tm.assert_extension_array_equal(uniques, expected_uniques)
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def test_fillna_copy_frame(self, data_missing):
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arr = data_missing.take([1, 1])
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df = pd.DataFrame({"A": arr})
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df_orig = df.copy()
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filled_val = df.iloc[0, 0]
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result = df.fillna(filled_val)
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result.iloc[0, 0] = filled_val
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tm.assert_frame_equal(df, df_orig)
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def test_fillna_copy_series(self, data_missing):
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arr = data_missing.take([1, 1])
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ser = pd.Series(arr, copy=False)
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ser_orig = ser.copy()
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filled_val = ser[0]
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result = ser.fillna(filled_val)
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result.iloc[0] = filled_val
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tm.assert_series_equal(ser, ser_orig)
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def test_fillna_length_mismatch(self, data_missing):
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msg = "Length of 'value' does not match."
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with pytest.raises(ValueError, match=msg):
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data_missing.fillna(data_missing.take([1]))
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# Subclasses can override if we expect e.g Sparse[bool], boolean, pyarrow[bool]
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_combine_le_expected_dtype: Dtype = NumpyEADtype("bool")
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def test_combine_le(self, data_repeated):
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# GH 20825
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# Test that combine works when doing a <= (le) comparison
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orig_data1, orig_data2 = data_repeated(2)
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s1 = pd.Series(orig_data1)
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s2 = pd.Series(orig_data2)
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result = s1.combine(s2, lambda x1, x2: x1 <= x2)
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expected = pd.Series(
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pd.array(
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[a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
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dtype=self._combine_le_expected_dtype,
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)
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)
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tm.assert_series_equal(result, expected)
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val = s1.iloc[0]
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result = s1.combine(val, lambda x1, x2: x1 <= x2)
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expected = pd.Series(
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pd.array(
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[a <= val for a in list(orig_data1)],
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dtype=self._combine_le_expected_dtype,
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)
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)
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tm.assert_series_equal(result, expected)
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def test_combine_add(self, data_repeated):
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# GH 20825
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orig_data1, orig_data2 = data_repeated(2)
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s1 = pd.Series(orig_data1)
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s2 = pd.Series(orig_data2)
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# Check if the operation is supported pointwise for our scalars. If not,
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# we will expect Series.combine to raise as well.
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try:
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with np.errstate(over="ignore"):
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expected = pd.Series(
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orig_data1._from_sequence(
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[a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
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)
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)
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except TypeError:
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# If the operation is not supported pointwise for our scalars,
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# then Series.combine should also raise
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with pytest.raises(TypeError):
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s1.combine(s2, lambda x1, x2: x1 + x2)
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return
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result = s1.combine(s2, lambda x1, x2: x1 + x2)
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tm.assert_series_equal(result, expected)
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val = s1.iloc[0]
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result = s1.combine(val, lambda x1, x2: x1 + x2)
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expected = pd.Series(
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orig_data1._from_sequence([a + val for a in list(orig_data1)])
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)
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tm.assert_series_equal(result, expected)
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def test_combine_first(self, data):
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# https://github.com/pandas-dev/pandas/issues/24147
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a = pd.Series(data[:3])
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b = pd.Series(data[2:5], index=[2, 3, 4])
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result = a.combine_first(b)
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expected = pd.Series(data[:5])
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tm.assert_series_equal(result, expected)
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|
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@pytest.mark.parametrize("frame", [True, False])
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|
@pytest.mark.parametrize(
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"periods, indices",
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[(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
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)
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def test_container_shift(self, data, frame, periods, indices):
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# https://github.com/pandas-dev/pandas/issues/22386
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subset = data[:5]
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data = pd.Series(subset, name="A")
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expected = pd.Series(subset.take(indices, allow_fill=True), name="A")
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if frame:
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result = data.to_frame(name="A").assign(B=1).shift(periods)
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expected = pd.concat(
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[expected, pd.Series([1] * 5, name="B").shift(periods)], axis=1
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)
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compare = tm.assert_frame_equal
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else:
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result = data.shift(periods)
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compare = tm.assert_series_equal
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compare(result, expected)
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def test_shift_0_periods(self, data):
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# GH#33856 shifting with periods=0 should return a copy, not same obj
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result = data.shift(0)
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assert data[0] != data[1] # otherwise below is invalid
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data[0] = data[1]
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assert result[0] != result[1] # i.e. not the same object/view
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@pytest.mark.parametrize("periods", [1, -2])
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def test_diff(self, data, periods):
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data = data[:5]
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if is_bool_dtype(data.dtype):
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op = operator.xor
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else:
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op = operator.sub
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try:
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# does this array implement ops?
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op(data, data)
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except Exception:
|
|
pytest.skip(f"{type(data)} does not support diff")
|
|
s = pd.Series(data)
|
|
result = s.diff(periods)
|
|
expected = pd.Series(op(data, data.shift(periods)))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
df = pd.DataFrame({"A": data, "B": [1.0] * 5})
|
|
result = df.diff(periods)
|
|
if periods == 1:
|
|
b = [np.nan, 0, 0, 0, 0]
|
|
else:
|
|
b = [0, 0, 0, np.nan, np.nan]
|
|
expected = pd.DataFrame({"A": expected, "B": b})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"periods, indices",
|
|
[[-4, [-1, -1]], [-1, [1, -1]], [0, [0, 1]], [1, [-1, 0]], [4, [-1, -1]]],
|
|
)
|
|
def test_shift_non_empty_array(self, data, periods, indices):
|
|
# https://github.com/pandas-dev/pandas/issues/23911
|
|
subset = data[:2]
|
|
result = subset.shift(periods)
|
|
expected = subset.take(indices, allow_fill=True)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("periods", [-4, -1, 0, 1, 4])
|
|
def test_shift_empty_array(self, data, periods):
|
|
# https://github.com/pandas-dev/pandas/issues/23911
|
|
empty = data[:0]
|
|
result = empty.shift(periods)
|
|
expected = empty
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_shift_zero_copies(self, data):
|
|
# GH#31502
|
|
result = data.shift(0)
|
|
assert result is not data
|
|
|
|
result = data[:0].shift(2)
|
|
assert result is not data
|
|
|
|
def test_shift_fill_value(self, data):
|
|
arr = data[:4]
|
|
fill_value = data[0]
|
|
result = arr.shift(1, fill_value=fill_value)
|
|
expected = data.take([0, 0, 1, 2])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
result = arr.shift(-2, fill_value=fill_value)
|
|
expected = data.take([2, 3, 0, 0])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_not_hashable(self, data):
|
|
# We are in general mutable, so not hashable
|
|
with pytest.raises(TypeError, match="unhashable type"):
|
|
hash(data)
|
|
|
|
def test_hash_pandas_object_works(self, data, as_frame):
|
|
# https://github.com/pandas-dev/pandas/issues/23066
|
|
data = pd.Series(data)
|
|
if as_frame:
|
|
data = data.to_frame()
|
|
a = pd.util.hash_pandas_object(data)
|
|
b = pd.util.hash_pandas_object(data)
|
|
tm.assert_equal(a, b)
|
|
|
|
def test_searchsorted(self, data_for_sorting, as_series):
|
|
if data_for_sorting.dtype._is_boolean:
|
|
return self._test_searchsorted_bool_dtypes(data_for_sorting, as_series)
|
|
|
|
b, c, a = data_for_sorting
|
|
arr = data_for_sorting.take([2, 0, 1]) # to get [a, b, c]
|
|
|
|
if as_series:
|
|
arr = pd.Series(arr)
|
|
assert arr.searchsorted(a) == 0
|
|
assert arr.searchsorted(a, side="right") == 1
|
|
|
|
assert arr.searchsorted(b) == 1
|
|
assert arr.searchsorted(b, side="right") == 2
|
|
|
|
assert arr.searchsorted(c) == 2
|
|
assert arr.searchsorted(c, side="right") == 3
|
|
|
|
result = arr.searchsorted(arr.take([0, 2]))
|
|
expected = np.array([0, 2], dtype=np.intp)
|
|
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
# sorter
|
|
sorter = np.array([1, 2, 0])
|
|
assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
|
|
|
|
def _test_searchsorted_bool_dtypes(self, data_for_sorting, as_series):
|
|
# We call this from test_searchsorted in cases where we have a
|
|
# boolean-like dtype. The non-bool test assumes we have more than 2
|
|
# unique values.
|
|
dtype = data_for_sorting.dtype
|
|
data_for_sorting = pd.array([True, False], dtype=dtype)
|
|
b, a = data_for_sorting
|
|
arr = type(data_for_sorting)._from_sequence([a, b])
|
|
|
|
if as_series:
|
|
arr = pd.Series(arr)
|
|
assert arr.searchsorted(a) == 0
|
|
assert arr.searchsorted(a, side="right") == 1
|
|
|
|
assert arr.searchsorted(b) == 1
|
|
assert arr.searchsorted(b, side="right") == 2
|
|
|
|
result = arr.searchsorted(arr.take([0, 1]))
|
|
expected = np.array([0, 1], dtype=np.intp)
|
|
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
# sorter
|
|
sorter = np.array([1, 0])
|
|
assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
|
|
|
|
def test_where_series(self, data, na_value, as_frame):
|
|
assert data[0] != data[1]
|
|
cls = type(data)
|
|
a, b = data[:2]
|
|
|
|
orig = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
|
|
ser = orig.copy()
|
|
cond = np.array([True, True, False, False])
|
|
|
|
if as_frame:
|
|
ser = ser.to_frame(name="a")
|
|
cond = cond.reshape(-1, 1)
|
|
|
|
result = ser.where(cond)
|
|
expected = pd.Series(
|
|
cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
|
|
)
|
|
|
|
if as_frame:
|
|
expected = expected.to_frame(name="a")
|
|
tm.assert_equal(result, expected)
|
|
|
|
ser.mask(~cond, inplace=True)
|
|
tm.assert_equal(ser, expected)
|
|
|
|
# array other
|
|
ser = orig.copy()
|
|
if as_frame:
|
|
ser = ser.to_frame(name="a")
|
|
cond = np.array([True, False, True, True])
|
|
other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
|
|
if as_frame:
|
|
other = pd.DataFrame({"a": other})
|
|
cond = pd.DataFrame({"a": cond})
|
|
result = ser.where(cond, other)
|
|
expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
|
|
if as_frame:
|
|
expected = expected.to_frame(name="a")
|
|
tm.assert_equal(result, expected)
|
|
|
|
ser.mask(~cond, other, inplace=True)
|
|
tm.assert_equal(ser, expected)
|
|
|
|
@pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
|
|
def test_repeat(self, data, repeats, as_series, use_numpy):
|
|
arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
|
|
if as_series:
|
|
arr = pd.Series(arr)
|
|
|
|
result = np.repeat(arr, repeats) if use_numpy else arr.repeat(repeats)
|
|
|
|
repeats = [repeats] * 3 if isinstance(repeats, int) else repeats
|
|
expected = [x for x, n in zip(arr, repeats) for _ in range(n)]
|
|
expected = type(data)._from_sequence(expected, dtype=data.dtype)
|
|
if as_series:
|
|
expected = pd.Series(expected, index=arr.index.repeat(repeats))
|
|
|
|
tm.assert_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"repeats, kwargs, error, msg",
|
|
[
|
|
(2, {"axis": 1}, ValueError, "axis"),
|
|
(-1, {}, ValueError, "negative"),
|
|
([1, 2], {}, ValueError, "shape"),
|
|
(2, {"foo": "bar"}, TypeError, "'foo'"),
|
|
],
|
|
)
|
|
def test_repeat_raises(self, data, repeats, kwargs, error, msg, use_numpy):
|
|
with pytest.raises(error, match=msg):
|
|
if use_numpy:
|
|
np.repeat(data, repeats, **kwargs)
|
|
else:
|
|
data.repeat(repeats, **kwargs)
|
|
|
|
def test_delete(self, data):
|
|
result = data.delete(0)
|
|
expected = data[1:]
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
result = data.delete([1, 3])
|
|
expected = data._concat_same_type([data[[0]], data[[2]], data[4:]])
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_insert(self, data):
|
|
# insert at the beginning
|
|
result = data[1:].insert(0, data[0])
|
|
tm.assert_extension_array_equal(result, data)
|
|
|
|
result = data[1:].insert(-len(data[1:]), data[0])
|
|
tm.assert_extension_array_equal(result, data)
|
|
|
|
# insert at the middle
|
|
result = data[:-1].insert(4, data[-1])
|
|
|
|
taker = np.arange(len(data))
|
|
taker[5:] = taker[4:-1]
|
|
taker[4] = len(data) - 1
|
|
expected = data.take(taker)
|
|
tm.assert_extension_array_equal(result, expected)
|
|
|
|
def test_insert_invalid(self, data, invalid_scalar):
|
|
item = invalid_scalar
|
|
|
|
with pytest.raises((TypeError, ValueError)):
|
|
data.insert(0, item)
|
|
|
|
with pytest.raises((TypeError, ValueError)):
|
|
data.insert(4, item)
|
|
|
|
with pytest.raises((TypeError, ValueError)):
|
|
data.insert(len(data) - 1, item)
|
|
|
|
def test_insert_invalid_loc(self, data):
|
|
ub = len(data)
|
|
|
|
with pytest.raises(IndexError):
|
|
data.insert(ub + 1, data[0])
|
|
|
|
with pytest.raises(IndexError):
|
|
data.insert(-ub - 1, data[0])
|
|
|
|
with pytest.raises(TypeError):
|
|
# we expect TypeError here instead of IndexError to match np.insert
|
|
data.insert(1.5, data[0])
|
|
|
|
@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
|
|
def test_equals(self, data, na_value, as_series, box):
|
|
data2 = type(data)._from_sequence([data[0]] * len(data), dtype=data.dtype)
|
|
data_na = type(data)._from_sequence([na_value] * len(data), dtype=data.dtype)
|
|
|
|
data = tm.box_expected(data, box, transpose=False)
|
|
data2 = tm.box_expected(data2, box, transpose=False)
|
|
data_na = tm.box_expected(data_na, box, transpose=False)
|
|
|
|
# we are asserting with `is True/False` explicitly, to test that the
|
|
# result is an actual Python bool, and not something "truthy"
|
|
|
|
assert data.equals(data) is True
|
|
assert data.equals(data.copy()) is True
|
|
|
|
# unequal other data
|
|
assert data.equals(data2) is False
|
|
assert data.equals(data_na) is False
|
|
|
|
# different length
|
|
assert data[:2].equals(data[:3]) is False
|
|
|
|
# empty are equal
|
|
assert data[:0].equals(data[:0]) is True
|
|
|
|
# other types
|
|
assert data.equals(None) is False
|
|
assert data[[0]].equals(data[0]) is False
|
|
|
|
def test_equals_same_data_different_object(self, data):
|
|
# https://github.com/pandas-dev/pandas/issues/34660
|
|
assert pd.Series(data).equals(pd.Series(data))
|