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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Series,
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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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from pandas.api.types import is_scalar
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class TestSeriesSearchSorted:
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def test_searchsorted(self):
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ser = Series([1, 2, 3])
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result = ser.searchsorted(1, side="left")
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assert is_scalar(result)
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assert result == 0
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result = ser.searchsorted(1, side="right")
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assert is_scalar(result)
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assert result == 1
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def test_searchsorted_numeric_dtypes_scalar(self):
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ser = Series([1, 2, 90, 1000, 3e9])
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res = ser.searchsorted(30)
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assert is_scalar(res)
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assert res == 2
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res = ser.searchsorted([30])
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exp = np.array([2], dtype=np.intp)
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tm.assert_numpy_array_equal(res, exp)
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def test_searchsorted_numeric_dtypes_vector(self):
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ser = Series([1, 2, 90, 1000, 3e9])
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res = ser.searchsorted([91, 2e6])
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exp = np.array([3, 4], dtype=np.intp)
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tm.assert_numpy_array_equal(res, exp)
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def test_searchsorted_datetime64_scalar(self):
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ser = Series(date_range("20120101", periods=10, freq="2D"))
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val = Timestamp("20120102")
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res = ser.searchsorted(val)
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assert is_scalar(res)
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assert res == 1
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def test_searchsorted_datetime64_scalar_mixed_timezones(self):
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# GH 30086
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ser = Series(date_range("20120101", periods=10, freq="2D", tz="UTC"))
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val = Timestamp("20120102", tz="America/New_York")
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res = ser.searchsorted(val)
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assert is_scalar(res)
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assert res == 1
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def test_searchsorted_datetime64_list(self):
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ser = Series(date_range("20120101", periods=10, freq="2D"))
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vals = [Timestamp("20120102"), Timestamp("20120104")]
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res = ser.searchsorted(vals)
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exp = np.array([1, 2], dtype=np.intp)
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tm.assert_numpy_array_equal(res, exp)
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def test_searchsorted_sorter(self):
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# GH8490
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ser = Series([3, 1, 2])
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res = ser.searchsorted([0, 3], sorter=np.argsort(ser))
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exp = np.array([0, 2], dtype=np.intp)
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tm.assert_numpy_array_equal(res, exp)
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def test_searchsorted_dataframe_fail(self):
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# GH#49620
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ser = Series([1, 2, 3, 4, 5])
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vals = pd.DataFrame([[1, 2], [3, 4]])
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msg = "Value must be 1-D array-like or scalar, DataFrame is not supported"
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with pytest.raises(ValueError, match=msg):
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ser.searchsorted(vals)
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