399 lines
11 KiB
Python
399 lines
11 KiB
Python
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import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Series,
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from_dummies,
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get_dummies,
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)
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import pandas._testing as tm
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@pytest.fixture
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def dummies_basic():
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return DataFrame(
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{
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"col1_a": [1, 0, 1],
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"col1_b": [0, 1, 0],
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"col2_a": [0, 1, 0],
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"col2_b": [1, 0, 0],
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"col2_c": [0, 0, 1],
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},
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)
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@pytest.fixture
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def dummies_with_unassigned():
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return DataFrame(
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{
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"col1_a": [1, 0, 0],
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"col1_b": [0, 1, 0],
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"col2_a": [0, 1, 0],
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"col2_b": [0, 0, 0],
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"col2_c": [0, 0, 1],
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},
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)
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def test_error_wrong_data_type():
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dummies = [0, 1, 0]
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with pytest.raises(
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TypeError,
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match=r"Expected 'data' to be a 'DataFrame'; Received 'data' of type: list",
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):
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from_dummies(dummies)
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def test_error_no_prefix_contains_unassigned():
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dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, 0]})
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with pytest.raises(
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ValueError,
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match=(
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r"Dummy DataFrame contains unassigned value\(s\); "
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r"First instance in row: 2"
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),
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):
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from_dummies(dummies)
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def test_error_no_prefix_wrong_default_category_type():
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dummies = DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]})
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with pytest.raises(
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TypeError,
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match=(
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r"Expected 'default_category' to be of type 'None', 'Hashable', or 'dict'; "
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r"Received 'default_category' of type: list"
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),
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):
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from_dummies(dummies, default_category=["c", "d"])
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def test_error_no_prefix_multi_assignment():
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dummies = DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]})
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with pytest.raises(
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ValueError,
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match=(
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r"Dummy DataFrame contains multi-assignment\(s\); "
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r"First instance in row: 2"
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),
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):
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from_dummies(dummies)
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def test_error_no_prefix_contains_nan():
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dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, np.nan]})
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with pytest.raises(
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ValueError, match=r"Dummy DataFrame contains NA value in column: 'b'"
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):
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from_dummies(dummies)
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def test_error_contains_non_dummies():
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dummies = DataFrame(
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{"a": [1, 6, 3, 1], "b": [0, 1, 0, 2], "c": ["c1", "c2", "c3", "c4"]}
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)
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with pytest.raises(
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TypeError,
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match=r"Passed DataFrame contains non-dummy data",
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):
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from_dummies(dummies)
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def test_error_with_prefix_multiple_seperators():
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dummies = DataFrame(
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{
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"col1_a": [1, 0, 1],
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"col1_b": [0, 1, 0],
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"col2-a": [0, 1, 0],
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"col2-b": [1, 0, 1],
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},
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)
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with pytest.raises(
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ValueError,
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match=(r"Separator not specified for column: col2-a"),
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):
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from_dummies(dummies, sep="_")
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def test_error_with_prefix_sep_wrong_type(dummies_basic):
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with pytest.raises(
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TypeError,
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match=(
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r"Expected 'sep' to be of type 'str' or 'None'; "
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r"Received 'sep' of type: list"
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),
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):
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from_dummies(dummies_basic, sep=["_"])
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def test_error_with_prefix_contains_unassigned(dummies_with_unassigned):
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with pytest.raises(
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ValueError,
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match=(
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r"Dummy DataFrame contains unassigned value\(s\); "
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r"First instance in row: 2"
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),
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):
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from_dummies(dummies_with_unassigned, sep="_")
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def test_error_with_prefix_default_category_wrong_type(dummies_with_unassigned):
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with pytest.raises(
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TypeError,
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match=(
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r"Expected 'default_category' to be of type 'None', 'Hashable', or 'dict'; "
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r"Received 'default_category' of type: list"
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),
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):
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from_dummies(dummies_with_unassigned, sep="_", default_category=["x", "y"])
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def test_error_with_prefix_default_category_dict_not_complete(
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dummies_with_unassigned,
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):
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with pytest.raises(
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ValueError,
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match=(
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r"Length of 'default_category' \(1\) did not match "
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r"the length of the columns being encoded \(2\)"
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),
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):
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from_dummies(dummies_with_unassigned, sep="_", default_category={"col1": "x"})
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def test_error_with_prefix_contains_nan(dummies_basic):
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dummies_basic.loc[2, "col2_c"] = np.nan
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with pytest.raises(
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ValueError, match=r"Dummy DataFrame contains NA value in column: 'col2_c'"
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):
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from_dummies(dummies_basic, sep="_")
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def test_error_with_prefix_contains_non_dummies(dummies_basic):
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dummies_basic.loc[2, "col2_c"] = "str"
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with pytest.raises(TypeError, match=r"Passed DataFrame contains non-dummy data"):
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from_dummies(dummies_basic, sep="_")
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def test_error_with_prefix_double_assignment():
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dummies = DataFrame(
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{
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"col1_a": [1, 0, 1],
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"col1_b": [1, 1, 0],
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"col2_a": [0, 1, 0],
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"col2_b": [1, 0, 0],
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"col2_c": [0, 0, 1],
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},
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)
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with pytest.raises(
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ValueError,
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match=(
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r"Dummy DataFrame contains multi-assignment\(s\); "
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r"First instance in row: 0"
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),
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):
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from_dummies(dummies, sep="_")
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def test_roundtrip_series_to_dataframe():
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categories = Series(["a", "b", "c", "a"])
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dummies = get_dummies(categories)
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result = from_dummies(dummies)
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expected = DataFrame({"": ["a", "b", "c", "a"]})
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tm.assert_frame_equal(result, expected)
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def test_roundtrip_single_column_dataframe():
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categories = DataFrame({"": ["a", "b", "c", "a"]})
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dummies = get_dummies(categories)
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result = from_dummies(dummies, sep="_")
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expected = categories
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tm.assert_frame_equal(result, expected)
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def test_roundtrip_with_prefixes():
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categories = DataFrame({"col1": ["a", "b", "a"], "col2": ["b", "a", "c"]})
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dummies = get_dummies(categories)
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result = from_dummies(dummies, sep="_")
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expected = categories
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_string_cats_basic():
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dummies = DataFrame({"a": [1, 0, 0, 1], "b": [0, 1, 0, 0], "c": [0, 0, 1, 0]})
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expected = DataFrame({"": ["a", "b", "c", "a"]})
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_string_cats_basic_bool_values():
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dummies = DataFrame(
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{
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"a": [True, False, False, True],
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"b": [False, True, False, False],
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"c": [False, False, True, False],
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}
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)
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expected = DataFrame({"": ["a", "b", "c", "a"]})
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_string_cats_basic_mixed_bool_values():
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dummies = DataFrame(
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{"a": [1, 0, 0, 1], "b": [False, True, False, False], "c": [0, 0, 1, 0]}
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)
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expected = DataFrame({"": ["a", "b", "c", "a"]})
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_int_cats_basic():
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dummies = DataFrame(
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{1: [1, 0, 0, 0], 25: [0, 1, 0, 0], 2: [0, 0, 1, 0], 5: [0, 0, 0, 1]}
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)
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expected = DataFrame({"": [1, 25, 2, 5]}, dtype="object")
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_float_cats_basic():
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dummies = DataFrame(
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{1.0: [1, 0, 0, 0], 25.0: [0, 1, 0, 0], 2.5: [0, 0, 1, 0], 5.84: [0, 0, 0, 1]}
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)
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expected = DataFrame({"": [1.0, 25.0, 2.5, 5.84]}, dtype="object")
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_mixed_cats_basic():
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dummies = DataFrame(
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{
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1.23: [1, 0, 0, 0, 0],
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"c": [0, 1, 0, 0, 0],
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2: [0, 0, 1, 0, 0],
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False: [0, 0, 0, 1, 0],
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None: [0, 0, 0, 0, 1],
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}
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)
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expected = DataFrame({"": [1.23, "c", 2, False, None]}, dtype="object")
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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def test_no_prefix_string_cats_contains_get_dummies_NaN_column():
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dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, 0], "NaN": [0, 0, 1]})
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expected = DataFrame({"": ["a", "b", "NaN"]})
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result = from_dummies(dummies)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"default_category, expected",
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[
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pytest.param(
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"c",
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DataFrame({"": ["a", "b", "c"]}),
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id="default_category is a str",
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),
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pytest.param(
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1,
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DataFrame({"": ["a", "b", 1]}),
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id="default_category is a int",
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),
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pytest.param(
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1.25,
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DataFrame({"": ["a", "b", 1.25]}),
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id="default_category is a float",
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),
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pytest.param(
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0,
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DataFrame({"": ["a", "b", 0]}),
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id="default_category is a 0",
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),
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pytest.param(
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False,
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DataFrame({"": ["a", "b", False]}),
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id="default_category is a bool",
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),
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pytest.param(
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(1, 2),
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DataFrame({"": ["a", "b", (1, 2)]}),
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id="default_category is a tuple",
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),
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],
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)
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def test_no_prefix_string_cats_default_category(default_category, expected):
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dummies = DataFrame({"a": [1, 0, 0], "b": [0, 1, 0]})
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result = from_dummies(dummies, default_category=default_category)
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tm.assert_frame_equal(result, expected)
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def test_with_prefix_basic(dummies_basic):
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expected = DataFrame({"col1": ["a", "b", "a"], "col2": ["b", "a", "c"]})
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result = from_dummies(dummies_basic, sep="_")
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tm.assert_frame_equal(result, expected)
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def test_with_prefix_contains_get_dummies_NaN_column():
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dummies = DataFrame(
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{
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"col1_a": [1, 0, 0],
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"col1_b": [0, 1, 0],
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"col1_NaN": [0, 0, 1],
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"col2_a": [0, 1, 0],
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"col2_b": [0, 0, 0],
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"col2_c": [0, 0, 1],
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"col2_NaN": [1, 0, 0],
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},
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)
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expected = DataFrame({"col1": ["a", "b", "NaN"], "col2": ["NaN", "a", "c"]})
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result = from_dummies(dummies, sep="_")
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"default_category, expected",
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[
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pytest.param(
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"x",
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DataFrame({"col1": ["a", "b", "x"], "col2": ["x", "a", "c"]}),
|
||
|
|
id="default_category is a str",
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
0,
|
||
|
|
DataFrame({"col1": ["a", "b", 0], "col2": [0, "a", "c"]}),
|
||
|
|
id="default_category is a 0",
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
False,
|
||
|
|
DataFrame({"col1": ["a", "b", False], "col2": [False, "a", "c"]}),
|
||
|
|
id="default_category is a False",
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
{"col2": 1, "col1": 2.5},
|
||
|
|
DataFrame({"col1": ["a", "b", 2.5], "col2": [1, "a", "c"]}),
|
||
|
|
id="default_category is a dict with int and float values",
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
{"col2": None, "col1": False},
|
||
|
|
DataFrame({"col1": ["a", "b", False], "col2": [None, "a", "c"]}),
|
||
|
|
id="default_category is a dict with bool and None values",
|
||
|
|
),
|
||
|
|
pytest.param(
|
||
|
|
{"col2": (1, 2), "col1": [1.25, False]},
|
||
|
|
DataFrame({"col1": ["a", "b", [1.25, False]], "col2": [(1, 2), "a", "c"]}),
|
||
|
|
id="default_category is a dict with list and tuple values",
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_with_prefix_default_category(
|
||
|
|
dummies_with_unassigned, default_category, expected
|
||
|
|
):
|
||
|
|
result = from_dummies(
|
||
|
|
dummies_with_unassigned, sep="_", default_category=default_category
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|