451 lines
16 KiB
Python
451 lines
16 KiB
Python
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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class TestSeriesReplace:
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def test_replace(self, datetime_series):
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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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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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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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# 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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# 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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s = pd.Series(np.arange(5), dtype="int64")
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def check_replace(to_rep, val, expected):
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sc = s.copy()
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r = s.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, r)
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tm.assert_series_equal(expected, sc)
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# MUST upcast to float
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e = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0])
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tr, v = [3], [3.0]
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check_replace(tr, v, e)
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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_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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def test_replace_with_dictlike_and_string_dtype(self):
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# GH 32621
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s = pd.Series(["one", "two", np.nan], dtype="string")
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expected = pd.Series(["1", "2", np.nan])
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result = s.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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with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
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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",), 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
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# Do not check if dtypes are equal due to a known issue that
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# Categorical.replace sometimes coerces to object (GH 23305)
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s = pd.Series(categorical)
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result = s.replace({"A": 1, "B": 2})
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expected = pd.Series(numeric)
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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):
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# GH#33340
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ser = pd.Series([1, 2, "A", pd.Timestamp.now(), 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 test_replace_with_compiled_regex(self):
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# https://github.com/pandas-dev/pandas/issues/35680
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s = pd.Series(["a", "b", "c"])
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regex = re.compile("^a$")
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result = s.replace({regex: "z"}, regex=True)
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expected = pd.Series(["z", "b", "c"])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("pattern", ["^.$", "."])
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def test_str_replace_regex_default_raises_warning(self, pattern):
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# https://github.com/pandas-dev/pandas/pull/24809
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s = pd.Series(["a", "b", "c"])
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msg = r"The default value of regex will change from True to False"
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if len(pattern) == 1:
|
|
msg += r".*single character regular expressions.*not.*literal strings"
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False) as w:
|
|
s.str.replace(pattern, "")
|
|
assert re.match(msg, str(w[0].message))
|