528 lines
18 KiB
Python
528 lines
18 KiB
Python
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"""
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test setting *parts* of objects both positionally and label based
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TODO: these should be split among the indexer tests
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"""
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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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from pandas import DataFrame, Index, Series, date_range
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import pandas._testing as tm
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class TestPartialSetting:
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def test_partial_setting(self):
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# GH2578, allow ix and friends to partially set
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# series
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s_orig = Series([1, 2, 3])
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s = s_orig.copy()
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s[5] = 5
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expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5])
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tm.assert_series_equal(s, expected)
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s = s_orig.copy()
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s.loc[5] = 5
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expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5])
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tm.assert_series_equal(s, expected)
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s = s_orig.copy()
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s[5] = 5.0
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expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5])
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tm.assert_series_equal(s, expected)
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s = s_orig.copy()
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s.loc[5] = 5.0
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expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5])
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tm.assert_series_equal(s, expected)
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# iloc/iat raise
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s = s_orig.copy()
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with pytest.raises(IndexError):
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s.iloc[3] = 5.0
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with pytest.raises(IndexError):
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s.iat[3] = 5.0
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# ## frame ##
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df_orig = DataFrame(
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np.arange(6).reshape(3, 2), columns=["A", "B"], dtype="int64"
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)
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# iloc/iat raise
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df = df_orig.copy()
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with pytest.raises(IndexError):
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df.iloc[4, 2] = 5.0
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with pytest.raises(IndexError):
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df.iat[4, 2] = 5.0
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# row setting where it exists
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expected = DataFrame(dict({"A": [0, 4, 4], "B": [1, 5, 5]}))
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df = df_orig.copy()
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df.iloc[1] = df.iloc[2]
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tm.assert_frame_equal(df, expected)
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expected = DataFrame(dict({"A": [0, 4, 4], "B": [1, 5, 5]}))
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df = df_orig.copy()
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df.loc[1] = df.loc[2]
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tm.assert_frame_equal(df, expected)
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# like 2578, partial setting with dtype preservation
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expected = DataFrame(dict({"A": [0, 2, 4, 4], "B": [1, 3, 5, 5]}))
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df = df_orig.copy()
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df.loc[3] = df.loc[2]
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tm.assert_frame_equal(df, expected)
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# single dtype frame, overwrite
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expected = DataFrame(dict({"A": [0, 2, 4], "B": [0, 2, 4]}))
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df = df_orig.copy()
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df.loc[:, "B"] = df.loc[:, "A"]
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tm.assert_frame_equal(df, expected)
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# mixed dtype frame, overwrite
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expected = DataFrame(dict({"A": [0, 2, 4], "B": Series([0, 2, 4])}))
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df = df_orig.copy()
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df["B"] = df["B"].astype(np.float64)
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df.loc[:, "B"] = df.loc[:, "A"]
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tm.assert_frame_equal(df, expected)
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# single dtype frame, partial setting
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expected = df_orig.copy()
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expected["C"] = df["A"]
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df = df_orig.copy()
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df.loc[:, "C"] = df.loc[:, "A"]
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tm.assert_frame_equal(df, expected)
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# mixed frame, partial setting
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expected = df_orig.copy()
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expected["C"] = df["A"]
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df = df_orig.copy()
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df.loc[:, "C"] = df.loc[:, "A"]
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tm.assert_frame_equal(df, expected)
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# GH 8473
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dates = date_range("1/1/2000", periods=8)
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df_orig = DataFrame(
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np.random.randn(8, 4), index=dates, columns=["A", "B", "C", "D"]
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)
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expected = pd.concat(
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[df_orig, DataFrame({"A": 7}, index=[dates[-1] + dates.freq])], sort=True
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)
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df = df_orig.copy()
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df.loc[dates[-1] + dates.freq, "A"] = 7
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tm.assert_frame_equal(df, expected)
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df = df_orig.copy()
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df.at[dates[-1] + dates.freq, "A"] = 7
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tm.assert_frame_equal(df, expected)
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exp_other = DataFrame({0: 7}, index=[dates[-1] + dates.freq])
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expected = pd.concat([df_orig, exp_other], axis=1)
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df = df_orig.copy()
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df.loc[dates[-1] + dates.freq, 0] = 7
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tm.assert_frame_equal(df, expected)
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df = df_orig.copy()
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df.at[dates[-1] + dates.freq, 0] = 7
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tm.assert_frame_equal(df, expected)
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def test_partial_setting_mixed_dtype(self):
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# in a mixed dtype environment, try to preserve dtypes
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# by appending
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df = DataFrame([[True, 1], [False, 2]], columns=["female", "fitness"])
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s = df.loc[1].copy()
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s.name = 2
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expected = df.append(s)
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df.loc[2] = df.loc[1]
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tm.assert_frame_equal(df, expected)
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# columns will align
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df = DataFrame(columns=["A", "B"])
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df.loc[0] = Series(1, index=range(4))
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tm.assert_frame_equal(df, DataFrame(columns=["A", "B"], index=[0]))
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# columns will align
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df = DataFrame(columns=["A", "B"])
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df.loc[0] = Series(1, index=["B"])
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exp = DataFrame([[np.nan, 1]], columns=["A", "B"], index=[0], dtype="float64")
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tm.assert_frame_equal(df, exp)
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# list-like must conform
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df = DataFrame(columns=["A", "B"])
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with pytest.raises(ValueError):
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df.loc[0] = [1, 2, 3]
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# TODO: #15657, these are left as object and not coerced
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df = DataFrame(columns=["A", "B"])
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df.loc[3] = [6, 7]
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exp = DataFrame([[6, 7]], index=[3], columns=["A", "B"], dtype="object")
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tm.assert_frame_equal(df, exp)
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def test_series_partial_set(self):
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# partial set with new index
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# Regression from GH4825
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ser = Series([0.1, 0.2], index=[1, 2])
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# loc equiv to .reindex
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expected = Series([np.nan, 0.2, np.nan], index=[3, 2, 3])
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with pytest.raises(KeyError, match="with any missing labels"):
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result = ser.loc[[3, 2, 3]]
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result = ser.reindex([3, 2, 3])
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tm.assert_series_equal(result, expected, check_index_type=True)
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expected = Series([np.nan, 0.2, np.nan, np.nan], index=[3, 2, 3, "x"])
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with pytest.raises(KeyError, match="with any missing labels"):
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result = ser.loc[[3, 2, 3, "x"]]
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result = ser.reindex([3, 2, 3, "x"])
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tm.assert_series_equal(result, expected, check_index_type=True)
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expected = Series([0.2, 0.2, 0.1], index=[2, 2, 1])
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result = ser.loc[[2, 2, 1]]
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tm.assert_series_equal(result, expected, check_index_type=True)
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expected = Series([0.2, 0.2, np.nan, 0.1], index=[2, 2, "x", 1])
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with pytest.raises(KeyError, match="with any missing labels"):
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result = ser.loc[[2, 2, "x", 1]]
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result = ser.reindex([2, 2, "x", 1])
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tm.assert_series_equal(result, expected, check_index_type=True)
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# raises as nothing in in the index
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msg = (
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r"\"None of \[Int64Index\(\[3, 3, 3\], dtype='int64'\)\] are"
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r" in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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ser.loc[[3, 3, 3]]
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expected = Series([0.2, 0.2, np.nan], index=[2, 2, 3])
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[[2, 2, 3]]
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result = ser.reindex([2, 2, 3])
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tm.assert_series_equal(result, expected, check_index_type=True)
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s = Series([0.1, 0.2, 0.3], index=[1, 2, 3])
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expected = Series([0.3, np.nan, np.nan], index=[3, 4, 4])
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with pytest.raises(KeyError, match="with any missing labels"):
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s.loc[[3, 4, 4]]
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result = s.reindex([3, 4, 4])
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tm.assert_series_equal(result, expected, check_index_type=True)
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s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
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expected = Series([np.nan, 0.3, 0.3], index=[5, 3, 3])
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with pytest.raises(KeyError, match="with any missing labels"):
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s.loc[[5, 3, 3]]
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result = s.reindex([5, 3, 3])
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tm.assert_series_equal(result, expected, check_index_type=True)
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s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
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expected = Series([np.nan, 0.4, 0.4], index=[5, 4, 4])
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with pytest.raises(KeyError, match="with any missing labels"):
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s.loc[[5, 4, 4]]
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result = s.reindex([5, 4, 4])
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tm.assert_series_equal(result, expected, check_index_type=True)
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s = Series([0.1, 0.2, 0.3, 0.4], index=[4, 5, 6, 7])
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expected = Series([0.4, np.nan, np.nan], index=[7, 2, 2])
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with pytest.raises(KeyError, match="with any missing labels"):
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s.loc[[7, 2, 2]]
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result = s.reindex([7, 2, 2])
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tm.assert_series_equal(result, expected, check_index_type=True)
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s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4])
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expected = Series([0.4, np.nan, np.nan], index=[4, 5, 5])
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with pytest.raises(KeyError, match="with any missing labels"):
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s.loc[[4, 5, 5]]
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result = s.reindex([4, 5, 5])
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tm.assert_series_equal(result, expected, check_index_type=True)
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# iloc
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expected = Series([0.2, 0.2, 0.1, 0.1], index=[2, 2, 1, 1])
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result = ser.iloc[[1, 1, 0, 0]]
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tm.assert_series_equal(result, expected, check_index_type=True)
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def test_series_partial_set_with_name(self):
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# GH 11497
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idx = Index([1, 2], dtype="int64", name="idx")
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ser = Series([0.1, 0.2], index=idx, name="s")
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# loc
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[[3, 2, 3]]
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[[3, 2, 3, "x"]]
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exp_idx = Index([2, 2, 1], dtype="int64", name="idx")
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expected = Series([0.2, 0.2, 0.1], index=exp_idx, name="s")
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result = ser.loc[[2, 2, 1]]
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tm.assert_series_equal(result, expected, check_index_type=True)
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[[2, 2, "x", 1]]
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# raises as nothing in in the index
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msg = (
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r"\"None of \[Int64Index\(\[3, 3, 3\], dtype='int64',"
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r" name='idx'\)\] are in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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ser.loc[[3, 3, 3]]
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with pytest.raises(KeyError, match="with any missing labels"):
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ser.loc[[2, 2, 3]]
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idx = Index([1, 2, 3], dtype="int64", name="idx")
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with pytest.raises(KeyError, match="with any missing labels"):
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Series([0.1, 0.2, 0.3], index=idx, name="s").loc[[3, 4, 4]]
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idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
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with pytest.raises(KeyError, match="with any missing labels"):
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Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 3, 3]]
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idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
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with pytest.raises(KeyError, match="with any missing labels"):
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Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 4, 4]]
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idx = Index([4, 5, 6, 7], dtype="int64", name="idx")
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with pytest.raises(KeyError, match="with any missing labels"):
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Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[7, 2, 2]]
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idx = Index([1, 2, 3, 4], dtype="int64", name="idx")
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with pytest.raises(KeyError, match="with any missing labels"):
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Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[4, 5, 5]]
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# iloc
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exp_idx = Index([2, 2, 1, 1], dtype="int64", name="idx")
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expected = Series([0.2, 0.2, 0.1, 0.1], index=exp_idx, name="s")
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result = ser.iloc[[1, 1, 0, 0]]
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tm.assert_series_equal(result, expected, check_index_type=True)
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def test_partial_set_invalid(self):
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# GH 4940
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# allow only setting of 'valid' values
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orig = tm.makeTimeDataFrame()
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df = orig.copy()
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# don't allow not string inserts
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with pytest.raises(TypeError):
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df.loc[100.0, :] = df.iloc[0]
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|
||
|
|
with pytest.raises(TypeError):
|
||
|
|
df.loc[100, :] = df.iloc[0]
|
||
|
|
|
||
|
|
# allow object conversion here
|
||
|
|
df = orig.copy()
|
||
|
|
df.loc["a", :] = df.iloc[0]
|
||
|
|
exp = orig.append(Series(df.iloc[0], name="a"))
|
||
|
|
tm.assert_frame_equal(df, exp)
|
||
|
|
tm.assert_index_equal(df.index, Index(orig.index.tolist() + ["a"]))
|
||
|
|
assert df.index.dtype == "object"
|
||
|
|
|
||
|
|
def test_partial_set_empty_series(self):
|
||
|
|
|
||
|
|
# GH5226
|
||
|
|
|
||
|
|
# partially set with an empty object series
|
||
|
|
s = Series(dtype=object)
|
||
|
|
s.loc[1] = 1
|
||
|
|
tm.assert_series_equal(s, Series([1], index=[1]))
|
||
|
|
s.loc[3] = 3
|
||
|
|
tm.assert_series_equal(s, Series([1, 3], index=[1, 3]))
|
||
|
|
|
||
|
|
s = Series(dtype=object)
|
||
|
|
s.loc[1] = 1.0
|
||
|
|
tm.assert_series_equal(s, Series([1.0], index=[1]))
|
||
|
|
s.loc[3] = 3.0
|
||
|
|
tm.assert_series_equal(s, Series([1.0, 3.0], index=[1, 3]))
|
||
|
|
|
||
|
|
s = Series(dtype=object)
|
||
|
|
s.loc["foo"] = 1
|
||
|
|
tm.assert_series_equal(s, Series([1], index=["foo"]))
|
||
|
|
s.loc["bar"] = 3
|
||
|
|
tm.assert_series_equal(s, Series([1, 3], index=["foo", "bar"]))
|
||
|
|
s.loc[3] = 4
|
||
|
|
tm.assert_series_equal(s, Series([1, 3, 4], index=["foo", "bar", 3]))
|
||
|
|
|
||
|
|
def test_partial_set_empty_frame(self):
|
||
|
|
|
||
|
|
# partially set with an empty object
|
||
|
|
# frame
|
||
|
|
df = DataFrame()
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.loc[1] = 1
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.loc[1] = Series([1], index=["foo"])
|
||
|
|
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
df.loc[:, 1] = 1
|
||
|
|
|
||
|
|
# these work as they don't really change
|
||
|
|
# anything but the index
|
||
|
|
# GH5632
|
||
|
|
expected = DataFrame(columns=["foo"], index=Index([], dtype="object"))
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame(index=Index([], dtype="object"))
|
||
|
|
df["foo"] = Series([], dtype="object")
|
||
|
|
return df
|
||
|
|
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame()
|
||
|
|
df["foo"] = Series(df.index)
|
||
|
|
return df
|
||
|
|
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame()
|
||
|
|
df["foo"] = df.index
|
||
|
|
return df
|
||
|
|
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
expected = DataFrame(columns=["foo"], index=Index([], dtype="int64"))
|
||
|
|
expected["foo"] = expected["foo"].astype("float64")
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame(index=Index([], dtype="int64"))
|
||
|
|
df["foo"] = []
|
||
|
|
return df
|
||
|
|
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame(index=Index([], dtype="int64"))
|
||
|
|
df["foo"] = Series(np.arange(len(df)), dtype="float64")
|
||
|
|
return df
|
||
|
|
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
def f():
|
||
|
|
df = DataFrame(index=Index([], dtype="int64"))
|
||
|
|
df["foo"] = range(len(df))
|
||
|
|
return df
|
||
|
|
|
||
|
|
expected = DataFrame(columns=["foo"], index=Index([], dtype="int64"))
|
||
|
|
expected["foo"] = expected["foo"].astype("float64")
|
||
|
|
tm.assert_frame_equal(f(), expected)
|
||
|
|
|
||
|
|
df = DataFrame()
|
||
|
|
tm.assert_index_equal(df.columns, Index([], dtype=object))
|
||
|
|
df2 = DataFrame()
|
||
|
|
df2[1] = Series([1], index=["foo"])
|
||
|
|
df.loc[:, 1] = Series([1], index=["foo"])
|
||
|
|
tm.assert_frame_equal(df, DataFrame([[1]], index=["foo"], columns=[1]))
|
||
|
|
tm.assert_frame_equal(df, df2)
|
||
|
|
|
||
|
|
# no index to start
|
||
|
|
expected = DataFrame({0: Series(1, index=range(4))}, columns=["A", "B", 0])
|
||
|
|
|
||
|
|
df = DataFrame(columns=["A", "B"])
|
||
|
|
df[0] = Series(1, index=range(4))
|
||
|
|
df.dtypes
|
||
|
|
str(df)
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
df = DataFrame(columns=["A", "B"])
|
||
|
|
df.loc[:, 0] = Series(1, index=range(4))
|
||
|
|
df.dtypes
|
||
|
|
str(df)
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
def test_partial_set_empty_frame_row(self):
|
||
|
|
# GH5720, GH5744
|
||
|
|
# don't create rows when empty
|
||
|
|
expected = DataFrame(columns=["A", "B", "New"], index=Index([], dtype="int64"))
|
||
|
|
expected["A"] = expected["A"].astype("int64")
|
||
|
|
expected["B"] = expected["B"].astype("float64")
|
||
|
|
expected["New"] = expected["New"].astype("float64")
|
||
|
|
|
||
|
|
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
|
||
|
|
y = df[df.A > 5]
|
||
|
|
y["New"] = np.nan
|
||
|
|
tm.assert_frame_equal(y, expected)
|
||
|
|
# tm.assert_frame_equal(y,expected)
|
||
|
|
|
||
|
|
expected = DataFrame(columns=["a", "b", "c c", "d"])
|
||
|
|
expected["d"] = expected["d"].astype("int64")
|
||
|
|
df = DataFrame(columns=["a", "b", "c c"])
|
||
|
|
df["d"] = 3
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
tm.assert_series_equal(df["c c"], Series(name="c c", dtype=object))
|
||
|
|
|
||
|
|
# reindex columns is ok
|
||
|
|
df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
|
||
|
|
y = df[df.A > 5]
|
||
|
|
result = y.reindex(columns=["A", "B", "C"])
|
||
|
|
expected = DataFrame(columns=["A", "B", "C"], index=Index([], dtype="int64"))
|
||
|
|
expected["A"] = expected["A"].astype("int64")
|
||
|
|
expected["B"] = expected["B"].astype("float64")
|
||
|
|
expected["C"] = expected["C"].astype("float64")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_partial_set_empty_frame_set_series(self):
|
||
|
|
# GH 5756
|
||
|
|
# setting with empty Series
|
||
|
|
df = DataFrame(Series(dtype=object))
|
||
|
|
tm.assert_frame_equal(df, DataFrame({0: Series(dtype=object)}))
|
||
|
|
|
||
|
|
df = DataFrame(Series(name="foo", dtype=object))
|
||
|
|
tm.assert_frame_equal(df, DataFrame({"foo": Series(dtype=object)}))
|
||
|
|
|
||
|
|
def test_partial_set_empty_frame_empty_copy_assignment(self):
|
||
|
|
# GH 5932
|
||
|
|
# copy on empty with assignment fails
|
||
|
|
df = DataFrame(index=[0])
|
||
|
|
df = df.copy()
|
||
|
|
df["a"] = 0
|
||
|
|
expected = DataFrame(0, index=[0], columns=["a"])
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
def test_partial_set_empty_frame_empty_consistencies(self):
|
||
|
|
# GH 6171
|
||
|
|
# consistency on empty frames
|
||
|
|
df = DataFrame(columns=["x", "y"])
|
||
|
|
df["x"] = [1, 2]
|
||
|
|
expected = DataFrame(dict(x=[1, 2], y=[np.nan, np.nan]))
|
||
|
|
tm.assert_frame_equal(df, expected, check_dtype=False)
|
||
|
|
|
||
|
|
df = DataFrame(columns=["x", "y"])
|
||
|
|
df["x"] = ["1", "2"]
|
||
|
|
expected = DataFrame(dict(x=["1", "2"], y=[np.nan, np.nan]), dtype=object)
|
||
|
|
tm.assert_frame_equal(df, expected)
|
||
|
|
|
||
|
|
df = DataFrame(columns=["x", "y"])
|
||
|
|
df.loc[0, "x"] = 1
|
||
|
|
expected = DataFrame(dict(x=[1], y=[np.nan]))
|
||
|
|
tm.assert_frame_equal(df, expected, check_dtype=False)
|