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| import numpy as np
| import pytest
|
| import pandas as pd
| from pandas import (
| DataFrame,
| Series,
| date_range,
| )
| import pandas._testing as tm
|
|
| def check(result, expected=None):
| if expected is not None:
| tm.assert_frame_equal(result, expected)
| result.dtypes
| str(result)
|
|
| class TestDataFrameNonuniqueIndexes:
| def test_setattr_columns_vs_construct_with_columns(self):
| # assignment
| # GH 3687
| arr = np.random.randn(3, 2)
| idx = list(range(2))
| df = DataFrame(arr, columns=["A", "A"])
| df.columns = idx
| expected = DataFrame(arr, columns=idx)
| check(df, expected)
|
| def test_setattr_columns_vs_construct_with_columns_datetimeindx(self):
| idx = date_range("20130101", periods=4, freq="Q-NOV")
| df = DataFrame(
| [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=["a", "a", "a", "a"]
| )
| df.columns = idx
| expected = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=idx)
| check(df, expected)
|
| def test_insert_with_duplicate_columns(self):
| # insert
| df = DataFrame(
| [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]],
| columns=["foo", "bar", "foo", "hello"],
| )
| df["string"] = "bah"
| expected = DataFrame(
| [[1, 1, 1, 5, "bah"], [1, 1, 2, 5, "bah"], [2, 1, 3, 5, "bah"]],
| columns=["foo", "bar", "foo", "hello", "string"],
| )
| check(df, expected)
| with pytest.raises(ValueError, match="Length of value"):
| df.insert(0, "AnotherColumn", range(len(df.index) - 1))
|
| # insert same dtype
| df["foo2"] = 3
| expected = DataFrame(
| [[1, 1, 1, 5, "bah", 3], [1, 1, 2, 5, "bah", 3], [2, 1, 3, 5, "bah", 3]],
| columns=["foo", "bar", "foo", "hello", "string", "foo2"],
| )
| check(df, expected)
|
| # set (non-dup)
| df["foo2"] = 4
| expected = DataFrame(
| [[1, 1, 1, 5, "bah", 4], [1, 1, 2, 5, "bah", 4], [2, 1, 3, 5, "bah", 4]],
| columns=["foo", "bar", "foo", "hello", "string", "foo2"],
| )
| check(df, expected)
| df["foo2"] = 3
|
| # delete (non dup)
| del df["bar"]
| expected = DataFrame(
| [[1, 1, 5, "bah", 3], [1, 2, 5, "bah", 3], [2, 3, 5, "bah", 3]],
| columns=["foo", "foo", "hello", "string", "foo2"],
| )
| check(df, expected)
|
| # try to delete again (its not consolidated)
| del df["hello"]
| expected = DataFrame(
| [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
| columns=["foo", "foo", "string", "foo2"],
| )
| check(df, expected)
|
| # consolidate
| df = df._consolidate()
| expected = DataFrame(
| [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
| columns=["foo", "foo", "string", "foo2"],
| )
| check(df, expected)
|
| # insert
| df.insert(2, "new_col", 5.0)
| expected = DataFrame(
| [[1, 1, 5.0, "bah", 3], [1, 2, 5.0, "bah", 3], [2, 3, 5.0, "bah", 3]],
| columns=["foo", "foo", "new_col", "string", "foo2"],
| )
| check(df, expected)
|
| # insert a dup
| with pytest.raises(ValueError, match="cannot insert"):
| df.insert(2, "new_col", 4.0)
|
| df.insert(2, "new_col", 4.0, allow_duplicates=True)
| expected = DataFrame(
| [
| [1, 1, 4.0, 5.0, "bah", 3],
| [1, 2, 4.0, 5.0, "bah", 3],
| [2, 3, 4.0, 5.0, "bah", 3],
| ],
| columns=["foo", "foo", "new_col", "new_col", "string", "foo2"],
| )
| check(df, expected)
|
| # delete (dup)
| del df["foo"]
| expected = DataFrame(
| [[4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3]],
| columns=["new_col", "new_col", "string", "foo2"],
| )
| tm.assert_frame_equal(df, expected)
|
| def test_dup_across_dtypes(self):
| # dup across dtypes
| df = DataFrame(
| [[1, 1, 1.0, 5], [1, 1, 2.0, 5], [2, 1, 3.0, 5]],
| columns=["foo", "bar", "foo", "hello"],
| )
| check(df)
|
| df["foo2"] = 7.0
| expected = DataFrame(
| [[1, 1, 1.0, 5, 7.0], [1, 1, 2.0, 5, 7.0], [2, 1, 3.0, 5, 7.0]],
| columns=["foo", "bar", "foo", "hello", "foo2"],
| )
| check(df, expected)
|
| result = df["foo"]
| expected = DataFrame([[1, 1.0], [1, 2.0], [2, 3.0]], columns=["foo", "foo"])
| check(result, expected)
|
| # multiple replacements
| df["foo"] = "string"
| expected = DataFrame(
| [
| ["string", 1, "string", 5, 7.0],
| ["string", 1, "string", 5, 7.0],
| ["string", 1, "string", 5, 7.0],
| ],
| columns=["foo", "bar", "foo", "hello", "foo2"],
| )
| check(df, expected)
|
| del df["foo"]
| expected = DataFrame(
| [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "hello", "foo2"]
| )
| check(df, expected)
|
| def test_column_dups_indexes(self):
| # check column dups with index equal and not equal to df's index
| df = DataFrame(
| np.random.randn(5, 3),
| index=["a", "b", "c", "d", "e"],
| columns=["A", "B", "A"],
| )
| for index in [df.index, pd.Index(list("edcba"))]:
| this_df = df.copy()
| expected_ser = Series(index.values, index=this_df.index)
| expected_df = DataFrame(
| {"A": expected_ser, "B": this_df["B"]},
| columns=["A", "B", "A"],
| )
| this_df["A"] = index
| check(this_df, expected_df)
|
| def test_changing_dtypes_with_duplicate_columns(self):
| # multiple assignments that change dtypes
| # the location indexer is a slice
| # GH 6120
| df = DataFrame(np.random.randn(5, 2), columns=["that", "that"])
| expected = DataFrame(1.0, index=range(5), columns=["that", "that"])
|
| df["that"] = 1.0
| check(df, expected)
|
| df = DataFrame(np.random.rand(5, 2), columns=["that", "that"])
| expected = DataFrame(1, index=range(5), columns=["that", "that"])
|
| df["that"] = 1
| check(df, expected)
|
| def test_dup_columns_comparisons(self):
| # equality
| df1 = DataFrame([[1, 2], [2, np.nan], [3, 4], [4, 4]], columns=["A", "B"])
| df2 = DataFrame([[0, 1], [2, 4], [2, np.nan], [4, 5]], columns=["A", "A"])
|
| # not-comparing like-labelled
| msg = (
| r"Can only compare identically-labeled \(both index and columns\) "
| "DataFrame objects"
| )
| with pytest.raises(ValueError, match=msg):
| df1 == df2
|
| df1r = df1.reindex_like(df2)
| result = df1r == df2
| expected = DataFrame(
| [[False, True], [True, False], [False, False], [True, False]],
| columns=["A", "A"],
| )
| tm.assert_frame_equal(result, expected)
|
| def test_mixed_column_selection(self):
| # mixed column selection
| # GH 5639
| dfbool = DataFrame(
| {
| "one": Series([True, True, False], index=["a", "b", "c"]),
| "two": Series([False, False, True, False], index=["a", "b", "c", "d"]),
| "three": Series([False, True, True, True], index=["a", "b", "c", "d"]),
| }
| )
| expected = pd.concat([dfbool["one"], dfbool["three"], dfbool["one"]], axis=1)
| result = dfbool[["one", "three", "one"]]
| check(result, expected)
|
| def test_multi_axis_dups(self):
| # multi-axis dups
| # GH 6121
| df = DataFrame(
| np.arange(25.0).reshape(5, 5),
| index=["a", "b", "c", "d", "e"],
| columns=["A", "B", "C", "D", "E"],
| )
| z = df[["A", "C", "A"]].copy()
| expected = z.loc[["a", "c", "a"]]
|
| df = DataFrame(
| np.arange(25.0).reshape(5, 5),
| index=["a", "b", "c", "d", "e"],
| columns=["A", "B", "C", "D", "E"],
| )
| z = df[["A", "C", "A"]]
| result = z.loc[["a", "c", "a"]]
| check(result, expected)
|
| def test_columns_with_dups(self):
| # GH 3468 related
|
| # basic
| df = DataFrame([[1, 2]], columns=["a", "a"])
| df.columns = ["a", "a.1"]
| str(df)
| expected = DataFrame([[1, 2]], columns=["a", "a.1"])
| tm.assert_frame_equal(df, expected)
|
| df = DataFrame([[1, 2, 3]], columns=["b", "a", "a"])
| df.columns = ["b", "a", "a.1"]
| str(df)
| expected = DataFrame([[1, 2, 3]], columns=["b", "a", "a.1"])
| tm.assert_frame_equal(df, expected)
|
| def test_columns_with_dup_index(self):
| # with a dup index
| df = DataFrame([[1, 2]], columns=["a", "a"])
| df.columns = ["b", "b"]
| str(df)
| expected = DataFrame([[1, 2]], columns=["b", "b"])
| tm.assert_frame_equal(df, expected)
|
| def test_multi_dtype(self):
| # multi-dtype
| df = DataFrame(
| [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]],
| columns=["a", "a", "b", "b", "d", "c", "c"],
| )
| df.columns = list("ABCDEFG")
| str(df)
| expected = DataFrame(
| [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("ABCDEFG")
| )
| tm.assert_frame_equal(df, expected)
|
| def test_multi_dtype2(self):
| df = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a", "a", "a"])
| df.columns = ["a", "a.1", "a.2", "a.3"]
| str(df)
| expected = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a.1", "a.2", "a.3"])
| tm.assert_frame_equal(df, expected)
|
| def test_dups_across_blocks(self, using_array_manager):
| # dups across blocks
| df_float = DataFrame(np.random.randn(10, 3), dtype="float64")
| df_int = DataFrame(np.random.randn(10, 3).astype("int64"))
| df_bool = DataFrame(True, index=df_float.index, columns=df_float.columns)
| df_object = DataFrame("foo", index=df_float.index, columns=df_float.columns)
| df_dt = DataFrame(
| pd.Timestamp("20010101"), index=df_float.index, columns=df_float.columns
| )
| df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1)
|
| if not using_array_manager:
| assert len(df._mgr.blknos) == len(df.columns)
| assert len(df._mgr.blklocs) == len(df.columns)
|
| # testing iloc
| for i in range(len(df.columns)):
| df.iloc[:, i]
|
| def test_dup_columns_across_dtype(self):
| # dup columns across dtype GH 2079/2194
| vals = [[1, -1, 2.0], [2, -2, 3.0]]
| rs = DataFrame(vals, columns=["A", "A", "B"])
| xp = DataFrame(vals)
| xp.columns = ["A", "A", "B"]
| tm.assert_frame_equal(rs, xp)
|
| def test_set_value_by_index(self):
| # See gh-12344
| warn = None
| msg = "will attempt to set the values inplace"
|
| df = DataFrame(np.arange(9).reshape(3, 3).T)
| df.columns = list("AAA")
| expected = df.iloc[:, 2]
|
| with tm.assert_produces_warning(warn, match=msg):
| df.iloc[:, 0] = 3
| tm.assert_series_equal(df.iloc[:, 2], expected)
|
| df = DataFrame(np.arange(9).reshape(3, 3).T)
| df.columns = [2, float(2), str(2)]
| expected = df.iloc[:, 1]
|
| with tm.assert_produces_warning(warn, match=msg):
| df.iloc[:, 0] = 3
| tm.assert_series_equal(df.iloc[:, 1], expected)
|
|
