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