100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
|
import pytest
|
||
|
|
|
||
|
|
from pandas import (
|
||
|
|
Index,
|
||
|
|
Series,
|
||
|
|
)
|
||
|
|
import pandas._testing as tm
|
||
|
|
from pandas.api.types import is_bool_dtype
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, index, drop_labels, axis, expected_data, expected_index",
|
||
|
|
[
|
||
|
|
# Unique Index
|
||
|
|
([1, 2], ["one", "two"], ["two"], 0, [1], ["one"]),
|
||
|
|
([1, 2], ["one", "two"], ["two"], "rows", [1], ["one"]),
|
||
|
|
([1, 1, 2], ["one", "two", "one"], ["two"], 0, [1, 2], ["one", "one"]),
|
||
|
|
# GH 5248 Non-Unique Index
|
||
|
|
([1, 1, 2], ["one", "two", "one"], "two", 0, [1, 2], ["one", "one"]),
|
||
|
|
([1, 1, 2], ["one", "two", "one"], ["one"], 0, [1], ["two"]),
|
||
|
|
([1, 1, 2], ["one", "two", "one"], "one", 0, [1], ["two"]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_drop_unique_and_non_unique_index(
|
||
|
|
data, index, axis, drop_labels, expected_data, expected_index
|
||
|
|
):
|
||
|
|
ser = Series(data=data, index=index)
|
||
|
|
result = ser.drop(drop_labels, axis=axis)
|
||
|
|
expected = Series(data=expected_data, index=expected_index)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, index, drop_labels, axis, error_type, error_desc",
|
||
|
|
[
|
||
|
|
# single string/tuple-like
|
||
|
|
(range(3), list("abc"), "bc", 0, KeyError, "not found in axis"),
|
||
|
|
# bad axis
|
||
|
|
(range(3), list("abc"), ("a",), 0, KeyError, "not found in axis"),
|
||
|
|
(range(3), list("abc"), "one", "columns", ValueError, "No axis named columns"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_drop_exception_raised(data, index, drop_labels, axis, error_type, error_desc):
|
||
|
|
ser = Series(data, index=index)
|
||
|
|
with pytest.raises(error_type, match=error_desc):
|
||
|
|
ser.drop(drop_labels, axis=axis)
|
||
|
|
|
||
|
|
|
||
|
|
def test_drop_with_ignore_errors():
|
||
|
|
# errors='ignore'
|
||
|
|
ser = Series(range(3), index=list("abc"))
|
||
|
|
result = ser.drop("bc", errors="ignore")
|
||
|
|
tm.assert_series_equal(result, ser)
|
||
|
|
result = ser.drop(["a", "d"], errors="ignore")
|
||
|
|
expected = ser.iloc[1:]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# GH 8522
|
||
|
|
ser = Series([2, 3], index=[True, False])
|
||
|
|
assert is_bool_dtype(ser.index)
|
||
|
|
assert ser.index.dtype == bool
|
||
|
|
result = ser.drop(True)
|
||
|
|
expected = Series([3], index=[False])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 3]])
|
||
|
|
@pytest.mark.parametrize("drop_labels", [[], [1], [3]])
|
||
|
|
def test_drop_empty_list(index, drop_labels):
|
||
|
|
# GH 21494
|
||
|
|
expected_index = [i for i in index if i not in drop_labels]
|
||
|
|
series = Series(index=index, dtype=object).drop(drop_labels)
|
||
|
|
expected = Series(index=expected_index, dtype=object)
|
||
|
|
tm.assert_series_equal(series, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, index, drop_labels",
|
||
|
|
[
|
||
|
|
(None, [1, 2, 3], [1, 4]),
|
||
|
|
(None, [1, 2, 2], [1, 4]),
|
||
|
|
([2, 3], [0, 1], [False, True]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_drop_non_empty_list(data, index, drop_labels):
|
||
|
|
# GH 21494 and GH 16877
|
||
|
|
dtype = object if data is None else None
|
||
|
|
ser = Series(data=data, index=index, dtype=dtype)
|
||
|
|
with pytest.raises(KeyError, match="not found in axis"):
|
||
|
|
ser.drop(drop_labels)
|
||
|
|
|
||
|
|
|
||
|
|
def test_drop_index_ea_dtype(any_numeric_ea_dtype):
|
||
|
|
# GH#45860
|
||
|
|
df = Series(100, index=Index([1, 2, 2], dtype=any_numeric_ea_dtype))
|
||
|
|
idx = Index([df.index[1]])
|
||
|
|
result = df.drop(idx)
|
||
|
|
expected = Series(100, index=Index([1], dtype=any_numeric_ea_dtype))
|
||
|
|
tm.assert_series_equal(result, expected)
|