import re
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import IntervalArray
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class TestSeriesReplace:
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def test_replace_explicit_none(self):
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# GH#36984 if the user explicitly passes value=None, give it to them
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ser = pd.Series([0, 0, ""], dtype=object)
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result = ser.replace("", None)
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expected = pd.Series([0, 0, None], dtype=object)
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tm.assert_series_equal(result, expected)
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# Cast column 2 to object to avoid implicit cast when setting entry to ""
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df = pd.DataFrame(np.zeros((3, 3))).astype({2: object})
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df.iloc[2, 2] = ""
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result = df.replace("", None)
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expected = pd.DataFrame(
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{
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0: np.zeros(3),
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1: np.zeros(3),
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2: np.array([0.0, 0.0, None], dtype=object),
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}
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)
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assert expected.iloc[2, 2] is None
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tm.assert_frame_equal(result, expected)
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# GH#19998 same thing with object dtype
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ser = pd.Series([10, 20, 30, "a", "a", "b", "a"])
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result = ser.replace("a", None)
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expected = pd.Series([10, 20, 30, None, None, "b", None])
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assert expected.iloc[-1] is None
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tm.assert_series_equal(result, expected)
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def test_replace_noop_doesnt_downcast(self):
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# GH#44498
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ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
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res = ser.replace({np.nan: None}) # should be a no-op
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tm.assert_series_equal(res, ser)
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assert res.dtype == object
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# same thing but different calling convention
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res = ser.replace(np.nan, None)
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tm.assert_series_equal(res, ser)
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assert res.dtype == object
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def test_replace(self):
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N = 100
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ser = pd.Series(np.random.randn(N))
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ser[0:4] = np.nan
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ser[6:10] = 0
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# replace list with a single value
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return_value = ser.replace([np.nan], -1, inplace=True)
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assert return_value is None
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exp = ser.fillna(-1)
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tm.assert_series_equal(ser, exp)
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rs = ser.replace(0.0, np.nan)
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ser[ser == 0.0] = np.nan
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tm.assert_series_equal(rs, ser)
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ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), dtype=object)
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ser[:5] = np.nan
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ser[6:10] = "foo"
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ser[20:30] = "bar"
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# replace list with a single value
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rs = ser.replace([np.nan, "foo", "bar"], -1)
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assert (rs[:5] == -1).all()
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assert (rs[6:10] == -1).all()
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assert (rs[20:30] == -1).all()
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assert (pd.isna(ser[:5])).all()
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# replace with different values
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rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
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assert (rs[:5] == -1).all()
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assert (rs[6:10] == -2).all()
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assert (rs[20:30] == -3).all()
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assert (pd.isna(ser[:5])).all()
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# replace with different values with 2 lists
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rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
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tm.assert_series_equal(rs, rs2)
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# replace inplace
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return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
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assert return_value is None
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assert (ser[:5] == -1).all()
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assert (ser[6:10] == -1).all()
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assert (ser[20:30] == -1).all()
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def test_replace_nan_with_inf(self):
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ser = pd.Series([np.nan, 0, np.inf])
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tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
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ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT])
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tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
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filled = ser.copy()
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filled[4] = 0
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tm.assert_series_equal(ser.replace(np.inf, 0), filled)
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def test_replace_listlike_value_listlike_target(self, datetime_series):
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ser = pd.Series(datetime_series.index)
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tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
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# malformed
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msg = r"Replacement lists must match in length\. Expecting 3 got 2"
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with pytest.raises(ValueError, match=msg):
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ser.replace([1, 2, 3], [np.nan, 0])
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# ser is dt64 so can't hold 1 or 2, so this replace is a no-op
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result = ser.replace([1, 2], [np.nan, 0])
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tm.assert_series_equal(result, ser)
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ser = pd.Series([0, 1, 2, 3, 4])
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result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
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tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0]))
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def test_replace_gh5319(self):
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# API change from 0.12?
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# GH 5319
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ser = pd.Series([0, np.nan, 2, 3, 4])
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expected = ser.ffill()
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result = ser.replace([np.nan])
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tm.assert_series_equal(result, expected)
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ser = pd.Series([0, np.nan, 2, 3, 4])
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expected = ser.ffill()
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result = ser.replace(np.nan)
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tm.assert_series_equal(result, expected)
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def test_replace_datetime64(self):
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# GH 5797
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ser = pd.Series(pd.date_range("20130101", periods=5))
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expected = ser.copy()
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expected.loc[2] = pd.Timestamp("20120101")
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result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")})
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tm.assert_series_equal(result, expected)
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result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101"))
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tm.assert_series_equal(result, expected)
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def test_replace_nat_with_tz(self):
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# GH 11792: Test with replacing NaT in a list with tz data
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ts = pd.Timestamp("2015/01/01", tz="UTC")
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s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")])
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result = s.replace([np.nan, pd.NaT], pd.Timestamp.min)
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expected = pd.Series([pd.Timestamp.min, ts], dtype=object)
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tm.assert_series_equal(expected, result)
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def test_replace_timedelta_td64(self):
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tdi = pd.timedelta_range(0, periods=5)
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ser = pd.Series(tdi)
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# Using a single dict argument means we go through replace_list
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result = ser.replace({ser[1]: ser[3]})
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expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]])
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tm.assert_series_equal(result, expected)
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def test_replace_with_single_list(self):
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ser = pd.Series([0, 1, 2, 3, 4])
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result = ser.replace([1, 2, 3])
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tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4]))
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s = ser.copy()
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return_value = s.replace([1, 2, 3], inplace=True)
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assert return_value is None
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tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4]))
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# make sure things don't get corrupted when fillna call fails
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s = ser.copy()
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msg = (
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r"Invalid fill method\. Expecting pad \(ffill\) or backfill "
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r"\(bfill\)\. Got crash_cymbal"
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)
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with pytest.raises(ValueError, match=msg):
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return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal")
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assert return_value is None
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tm.assert_series_equal(s, ser)
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def test_replace_mixed_types(self):
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ser = pd.Series(np.arange(5), dtype="int64")
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def check_replace(to_rep, val, expected):
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sc = ser.copy()
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result = ser.replace(to_rep, val)
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return_value = sc.replace(to_rep, val, inplace=True)
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assert return_value is None
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tm.assert_series_equal(expected, result)
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tm.assert_series_equal(expected, sc)
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# 3.0 can still be held in our int64 series, so we do not upcast GH#44940
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tr, v = [3], [3.0]
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check_replace(tr, v, ser)
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# Note this matches what we get with the scalars 3 and 3.0
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check_replace(tr[0], v[0], ser)
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# MUST upcast to float
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e = pd.Series([0, 1, 2, 3.5, 4])
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tr, v = [3], [3.5]
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check_replace(tr, v, e)
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# casts to object
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e = pd.Series([0, 1, 2, 3.5, "a"])
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tr, v = [3, 4], [3.5, "a"]
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check_replace(tr, v, e)
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# again casts to object
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e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")])
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tr, v = [3, 4], [3.5, pd.Timestamp("20130101")]
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check_replace(tr, v, e)
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# casts to object
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e = pd.Series([0, 1, 2, 3.5, True], dtype="object")
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tr, v = [3, 4], [3.5, True]
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check_replace(tr, v, e)
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# test an object with dates + floats + integers + strings
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dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D"))
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result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"])
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expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object)
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tm.assert_series_equal(result, expected)
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def test_replace_bool_with_string_no_op(self):
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s = pd.Series([True, False, True])
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result = s.replace("fun", "in-the-sun")
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tm.assert_series_equal(s, result)
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def test_replace_bool_with_string(self):
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# nonexistent elements
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s = pd.Series([True, False, True])
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result = s.replace(True, "2u")
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expected = pd.Series(["2u", False, "2u"])
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tm.assert_series_equal(expected, result)
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def test_replace_bool_with_bool(self):
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s = pd.Series([True, False, True])
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result = s.replace(True, False)
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expected = pd.Series([False] * len(s))
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tm.assert_series_equal(expected, result)
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def test_replace_with_dict_with_bool_keys(self):
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s = pd.Series([True, False, True])
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result = s.replace({"asdf": "asdb", True: "yes"})
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expected = pd.Series(["yes", False, "yes"])
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tm.assert_series_equal(result, expected)
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def test_replace_Int_with_na(self, any_int_ea_dtype):
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# GH 38267
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result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA)
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expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
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tm.assert_series_equal(result, expected)
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result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA)
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result.replace(1, pd.NA, inplace=True)
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tm.assert_series_equal(result, expected)
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def test_replace2(self):
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N = 100
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ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), dtype=object)
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ser[:5] = np.nan
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ser[6:10] = "foo"
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ser[20:30] = "bar"
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# replace list with a single value
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rs = ser.replace([np.nan, "foo", "bar"], -1)
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assert (rs[:5] == -1).all()
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assert (rs[6:10] == -1).all()
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assert (rs[20:30] == -1).all()
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assert (pd.isna(ser[:5])).all()
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# replace with different values
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rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
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assert (rs[:5] == -1).all()
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assert (rs[6:10] == -2).all()
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assert (rs[20:30] == -3).all()
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assert (pd.isna(ser[:5])).all()
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# replace with different values with 2 lists
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rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
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tm.assert_series_equal(rs, rs2)
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# replace inplace
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return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
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assert return_value is None
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assert (ser[:5] == -1).all()
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assert (ser[6:10] == -1).all()
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assert (ser[20:30] == -1).all()
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@pytest.mark.parametrize("inplace", [True, False])
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def test_replace_cascade(self, inplace):
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# Test that replaced values are not replaced again
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# GH #50778
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ser = pd.Series([1, 2, 3])
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expected = pd.Series([2, 3, 4])
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res = ser.replace([1, 2, 3], [2, 3, 4], inplace=inplace)
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if inplace:
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tm.assert_series_equal(ser, expected)
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else:
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tm.assert_series_equal(res, expected)
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def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype):
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# GH 32621, GH#44940
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ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype)
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expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype)
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result = ser.replace({"one": "1", "two": "2"})
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tm.assert_series_equal(expected, result)
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def test_replace_with_empty_dictlike(self):
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# GH 15289
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s = pd.Series(list("abcd"))
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tm.assert_series_equal(s, s.replace({}))
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empty_series = pd.Series([])
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tm.assert_series_equal(s, s.replace(empty_series))
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def test_replace_string_with_number(self):
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# GH 15743
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s = pd.Series([1, 2, 3])
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result = s.replace("2", np.nan)
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expected = pd.Series([1, 2, 3])
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tm.assert_series_equal(expected, result)
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def test_replace_replacer_equals_replacement(self):
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# GH 20656
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# make sure all replacers are matching against original values
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s = pd.Series(["a", "b"])
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expected = pd.Series(["b", "a"])
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result = s.replace({"a": "b", "b": "a"})
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tm.assert_series_equal(expected, result)
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def test_replace_unicode_with_number(self):
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# GH 15743
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s = pd.Series([1, 2, 3])
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result = s.replace("2", np.nan)
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expected = pd.Series([1, 2, 3])
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tm.assert_series_equal(expected, result)
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def test_replace_mixed_types_with_string(self):
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# Testing mixed
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s = pd.Series([1, 2, 3, "4", 4, 5])
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result = s.replace([2, "4"], np.nan)
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expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
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tm.assert_series_equal(expected, result)
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@pytest.mark.parametrize(
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"categorical, numeric",
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[
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(pd.Categorical(["A"], categories=["A", "B"]), [1]),
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(pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]),
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],
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)
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def test_replace_categorical(self, categorical, numeric):
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# GH 24971, GH#23305
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ser = pd.Series(categorical)
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result = ser.replace({"A": 1, "B": 2})
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expected = pd.Series(numeric).astype("category")
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if 2 not in expected.cat.categories:
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# i.e. categories should be [1, 2] even if there are no "B"s present
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# GH#44940
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expected = expected.cat.add_categories(2)
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tm.assert_series_equal(expected, result)
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def test_replace_categorical_single(self):
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# GH 26988
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dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
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s = pd.Series(dti)
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c = s.astype("category")
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expected = c.copy()
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expected = expected.cat.add_categories("foo")
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expected[2] = "foo"
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expected = expected.cat.remove_unused_categories()
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assert c[2] != "foo"
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result = c.replace(c[2], "foo")
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tm.assert_series_equal(expected, result)
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assert c[2] != "foo" # ensure non-inplace call does not alter original
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return_value = c.replace(c[2], "foo", inplace=True)
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assert return_value is None
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tm.assert_series_equal(expected, c)
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first_value = c[0]
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return_value = c.replace(c[1], c[0], inplace=True)
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assert return_value is None
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assert c[0] == c[1] == first_value # test replacing with existing value
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def test_replace_with_no_overflowerror(self):
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# GH 25616
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# casts to object without Exception from OverflowError
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s = pd.Series([0, 1, 2, 3, 4])
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result = s.replace([3], ["100000000000000000000"])
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expected = pd.Series([0, 1, 2, "100000000000000000000", 4])
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tm.assert_series_equal(result, expected)
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s = pd.Series([0, "100000000000000000000", "100000000000000000001"])
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result = s.replace(["100000000000000000000"], [1])
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expected = pd.Series([0, 1, "100000000000000000001"])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"ser, to_replace, exp",
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[
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([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]),
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(["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]),
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],
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)
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def test_replace_commutative(self, ser, to_replace, exp):
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# GH 16051
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# DataFrame.replace() overwrites when values are non-numeric
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series = pd.Series(ser)
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expected = pd.Series(exp)
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result = series.replace(to_replace)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])]
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)
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def test_replace_no_cast(self, ser, exp):
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# GH 9113
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# BUG: replace int64 dtype with bool coerces to int64
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series = pd.Series(ser)
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result = series.replace(2, True)
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expected = pd.Series(exp)
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tm.assert_series_equal(result, expected)
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def test_replace_invalid_to_replace(self):
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# GH 18634
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# API: replace() should raise an exception if invalid argument is given
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series = pd.Series(["a", "b", "c "])
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msg = (
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r"Expecting 'to_replace' to be either a scalar, array-like, "
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r"dict or None, got invalid type.*"
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)
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with pytest.raises(TypeError, match=msg):
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series.replace(lambda x: x.strip())
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@pytest.mark.parametrize("frame", [False, True])
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def test_replace_nonbool_regex(self, frame):
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obj = pd.Series(["a", "b", "c "])
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if frame:
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obj = obj.to_frame()
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msg = "'to_replace' must be 'None' if 'regex' is not a bool"
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with pytest.raises(ValueError, match=msg):
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obj.replace(to_replace=["a"], regex="foo")
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@pytest.mark.parametrize("frame", [False, True])
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def test_replace_empty_copy(self, frame):
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obj = pd.Series([], dtype=np.float64)
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if frame:
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obj = obj.to_frame()
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res = obj.replace(4, 5, inplace=True)
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assert res is None
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res = obj.replace(4, 5, inplace=False)
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tm.assert_equal(res, obj)
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assert res is not obj
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def test_replace_only_one_dictlike_arg(self, fixed_now_ts):
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# GH#33340
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ser = pd.Series([1, 2, "A", fixed_now_ts, True])
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to_replace = {0: 1, 2: "A"}
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value = "foo"
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msg = "Series.replace cannot use dict-like to_replace and non-None value"
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with pytest.raises(ValueError, match=msg):
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ser.replace(to_replace, value)
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to_replace = 1
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value = {0: "foo", 2: "bar"}
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msg = "Series.replace cannot use dict-value and non-None to_replace"
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with pytest.raises(ValueError, match=msg):
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ser.replace(to_replace, value)
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def test_replace_extension_other(self, frame_or_series):
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# https://github.com/pandas-dev/pandas/issues/34530
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obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64"))
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result = obj.replace("", "") # no exception
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# should not have changed dtype
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tm.assert_equal(obj, result)
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def _check_replace_with_method(self, ser: pd.Series):
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df = ser.to_frame()
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res = ser.replace(ser[1], method="pad")
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expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype)
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tm.assert_series_equal(res, expected)
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res_df = df.replace(ser[1], method="pad")
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tm.assert_frame_equal(res_df, expected.to_frame())
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ser2 = ser.copy()
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res2 = ser2.replace(ser[1], method="pad", inplace=True)
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assert res2 is None
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tm.assert_series_equal(ser2, expected)
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res_df2 = df.replace(ser[1], method="pad", inplace=True)
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assert res_df2 is None
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tm.assert_frame_equal(df, expected.to_frame())
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def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype):
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arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype)
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ser = pd.Series(arr)
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self._check_replace_with_method(ser)
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@pytest.mark.parametrize("as_categorical", [True, False])
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def test_replace_interval_with_method(self, as_categorical):
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# in particular interval that can't hold NA
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idx = pd.IntervalIndex.from_breaks(range(4))
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ser = pd.Series(idx)
|
if as_categorical:
|
ser = ser.astype("category")
|
|
self._check_replace_with_method(ser)
|
|
@pytest.mark.parametrize("as_period", [True, False])
|
@pytest.mark.parametrize("as_categorical", [True, False])
|
def test_replace_datetimelike_with_method(self, as_period, as_categorical):
|
idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific")
|
if as_period:
|
idx = idx.tz_localize(None).to_period("D")
|
|
ser = pd.Series(idx)
|
ser.iloc[-2] = pd.NaT
|
if as_categorical:
|
ser = ser.astype("category")
|
|
self._check_replace_with_method(ser)
|
|
def test_replace_with_compiled_regex(self):
|
# https://github.com/pandas-dev/pandas/issues/35680
|
s = pd.Series(["a", "b", "c"])
|
regex = re.compile("^a$")
|
result = s.replace({regex: "z"}, regex=True)
|
expected = pd.Series(["z", "b", "c"])
|
tm.assert_series_equal(result, expected)
|
|
def test_pandas_replace_na(self):
|
# GH#43344
|
ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA], dtype="string")
|
regex_mapping = {
|
"AA": "CC",
|
"BB": "CC",
|
"EE": "CC",
|
"CC": "CC-REPL",
|
}
|
result = ser.replace(regex_mapping, regex=True)
|
exp = pd.Series(["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA], dtype="string")
|
tm.assert_series_equal(result, exp)
|
|
@pytest.mark.parametrize(
|
"dtype, input_data, to_replace, expected_data",
|
[
|
("bool", [True, False], {True: False}, [False, False]),
|
("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
|
("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
|
("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
|
("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
|
("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
|
(
|
pd.IntervalDtype("int64"),
|
IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
|
{pd.Interval(1, 2): pd.Interval(10, 20)},
|
IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
|
),
|
(
|
pd.IntervalDtype("float64"),
|
IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
|
{pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
|
IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
|
),
|
(
|
pd.PeriodDtype("M"),
|
[pd.Period("2020-05", freq="M")],
|
{pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
|
[pd.Period("2020-06", freq="M")],
|
),
|
],
|
)
|
def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
|
# GH#33484
|
ser = pd.Series(input_data, dtype=dtype)
|
result = ser.replace(to_replace)
|
expected = pd.Series(expected_data, dtype=dtype)
|
tm.assert_series_equal(result, expected)
|
|
def test_replace_string_dtype(self):
|
# GH#40732, GH#44940
|
ser = pd.Series(["one", "two", np.nan], dtype="string")
|
res = ser.replace({"one": "1", "two": "2"})
|
expected = pd.Series(["1", "2", np.nan], dtype="string")
|
tm.assert_series_equal(res, expected)
|
|
# GH#31644
|
ser2 = pd.Series(["A", np.nan], dtype="string")
|
res2 = ser2.replace("A", "B")
|
expected2 = pd.Series(["B", np.nan], dtype="string")
|
tm.assert_series_equal(res2, expected2)
|
|
ser3 = pd.Series(["A", "B"], dtype="string")
|
res3 = ser3.replace("A", pd.NA)
|
expected3 = pd.Series([pd.NA, "B"], dtype="string")
|
tm.assert_series_equal(res3, expected3)
|
|
def test_replace_string_dtype_list_to_replace(self):
|
# GH#41215, GH#44940
|
ser = pd.Series(["abc", "def"], dtype="string")
|
res = ser.replace(["abc", "any other string"], "xyz")
|
expected = pd.Series(["xyz", "def"], dtype="string")
|
tm.assert_series_equal(res, expected)
|
|
def test_replace_string_dtype_regex(self):
|
# GH#31644
|
ser = pd.Series(["A", "B"], dtype="string")
|
res = ser.replace(r".", "C", regex=True)
|
expected = pd.Series(["C", "C"], dtype="string")
|
tm.assert_series_equal(res, expected)
|
|
def test_replace_nullable_numeric(self):
|
# GH#40732, GH#44940
|
|
floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
|
assert floats.replace({1.0: 9}).dtype == floats.dtype
|
assert floats.replace(1.0, 9).dtype == floats.dtype
|
assert floats.replace({1.0: 9.0}).dtype == floats.dtype
|
assert floats.replace(1.0, 9.0).dtype == floats.dtype
|
|
res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0])
|
assert res.dtype == floats.dtype
|
|
ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
|
assert ints.replace({1: 9}).dtype == ints.dtype
|
assert ints.replace(1, 9).dtype == ints.dtype
|
assert ints.replace({1: 9.0}).dtype == ints.dtype
|
assert ints.replace(1, 9.0).dtype == ints.dtype
|
|
# nullable (for now) raises instead of casting
|
with pytest.raises(TypeError, match="Invalid value"):
|
ints.replace({1: 9.5})
|
with pytest.raises(TypeError, match="Invalid value"):
|
ints.replace(1, 9.5)
|
|
@pytest.mark.parametrize("regex", [False, True])
|
def test_replace_regex_dtype_series(self, regex):
|
# GH-48644
|
series = pd.Series(["0"])
|
expected = pd.Series([1])
|
result = series.replace(to_replace="0", value=1, regex=regex)
|
tm.assert_series_equal(result, expected)
|
|
def test_replace_different_int_types(self, any_int_numpy_dtype):
|
# GH#45311
|
labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype)
|
|
maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype)
|
map_dict = dict(zip(maps.values, maps.index))
|
|
result = labs.replace(map_dict)
|
expected = labs.replace({0: 0, 2: 1, 1: 2})
|
tm.assert_series_equal(result, expected)
|
|
@pytest.mark.parametrize("val", [2, np.nan, 2.0])
|
def test_replace_value_none_dtype_numeric(self, val):
|
# GH#48231
|
ser = pd.Series([1, val])
|
result = ser.replace(val, None)
|
expected = pd.Series([1, None], dtype=object)
|
tm.assert_series_equal(result, expected)
|
|
def test_replace_change_dtype_series(self):
|
# GH#25797
|
df = pd.DataFrame.from_dict({"Test": ["0.5", True, "0.6"]})
|
df["Test"] = df["Test"].replace([True], [np.nan])
|
expected = pd.DataFrame.from_dict({"Test": ["0.5", np.nan, "0.6"]})
|
tm.assert_frame_equal(df, expected)
|
|
df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
|
df["Test"] = df["Test"].replace([None], [np.nan])
|
tm.assert_frame_equal(df, expected)
|
|
df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
|
df["Test"] = df["Test"].fillna(np.nan)
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("dtype", ["object", "Int64"])
|
def test_replace_na_in_obj_column(self, dtype):
|
# GH#47480
|
ser = pd.Series([0, 1, pd.NA], dtype=dtype)
|
expected = pd.Series([0, 2, pd.NA], dtype=dtype)
|
result = ser.replace(to_replace=1, value=2)
|
tm.assert_series_equal(result, expected)
|
|
ser.replace(to_replace=1, value=2, inplace=True)
|
tm.assert_series_equal(ser, expected)
|
|
@pytest.mark.parametrize("val", [0, 0.5])
|
def test_replace_numeric_column_with_na(self, val):
|
# GH#50758
|
ser = pd.Series([val, 1])
|
expected = pd.Series([val, pd.NA])
|
result = ser.replace(to_replace=1, value=pd.NA)
|
tm.assert_series_equal(result, expected)
|
|
ser.replace(to_replace=1, value=pd.NA, inplace=True)
|
tm.assert_series_equal(ser, expected)
|
