Inzynierka/Lib/site-packages/pandas/tests/io/parser/test_mangle_dupes.py
2023-06-02 12:51:02 +02:00

166 lines
4.5 KiB
Python

"""
Tests that duplicate columns are handled appropriately when parsed by the
CSV engine. In general, the expected result is that they are either thoroughly
de-duplicated (if mangling requested) or ignored otherwise.
"""
from io import StringIO
import pytest
from pandas import DataFrame
import pandas._testing as tm
skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
@skip_pyarrow
def test_basic(all_parsers):
parser = all_parsers
data = "a,a,b,b,b\n1,2,3,4,5"
result = parser.read_csv(StringIO(data), sep=",")
expected = DataFrame([[1, 2, 3, 4, 5]], columns=["a", "a.1", "b", "b.1", "b.2"])
tm.assert_frame_equal(result, expected)
@skip_pyarrow
def test_basic_names(all_parsers):
# See gh-7160
parser = all_parsers
data = "a,b,a\n0,1,2\n3,4,5"
expected = DataFrame([[0, 1, 2], [3, 4, 5]], columns=["a", "b", "a.1"])
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
def test_basic_names_raise(all_parsers):
# See gh-7160
parser = all_parsers
data = "0,1,2\n3,4,5"
with pytest.raises(ValueError, match="Duplicate names"):
parser.read_csv(StringIO(data), names=["a", "b", "a"])
@skip_pyarrow
@pytest.mark.parametrize(
"data,expected",
[
("a,a,a.1\n1,2,3", DataFrame([[1, 2, 3]], columns=["a", "a.2", "a.1"])),
(
"a,a,a.1,a.1.1,a.1.1.1,a.1.1.1.1\n1,2,3,4,5,6",
DataFrame(
[[1, 2, 3, 4, 5, 6]],
columns=["a", "a.2", "a.1", "a.1.1", "a.1.1.1", "a.1.1.1.1"],
),
),
(
"a,a,a.3,a.1,a.2,a,a\n1,2,3,4,5,6,7",
DataFrame(
[[1, 2, 3, 4, 5, 6, 7]],
columns=["a", "a.4", "a.3", "a.1", "a.2", "a.5", "a.6"],
),
),
],
)
def test_thorough_mangle_columns(all_parsers, data, expected):
# see gh-17060
parser = all_parsers
result = parser.read_csv(StringIO(data))
tm.assert_frame_equal(result, expected)
@skip_pyarrow
@pytest.mark.parametrize(
"data,names,expected",
[
(
"a,b,b\n1,2,3",
["a.1", "a.1", "a.1.1"],
DataFrame(
[["a", "b", "b"], ["1", "2", "3"]], columns=["a.1", "a.1.1", "a.1.1.1"]
),
),
(
"a,b,c,d,e,f\n1,2,3,4,5,6",
["a", "a", "a.1", "a.1.1", "a.1.1.1", "a.1.1.1.1"],
DataFrame(
[["a", "b", "c", "d", "e", "f"], ["1", "2", "3", "4", "5", "6"]],
columns=["a", "a.1", "a.1.1", "a.1.1.1", "a.1.1.1.1", "a.1.1.1.1.1"],
),
),
(
"a,b,c,d,e,f,g\n1,2,3,4,5,6,7",
["a", "a", "a.3", "a.1", "a.2", "a", "a"],
DataFrame(
[
["a", "b", "c", "d", "e", "f", "g"],
["1", "2", "3", "4", "5", "6", "7"],
],
columns=["a", "a.1", "a.3", "a.1.1", "a.2", "a.2.1", "a.3.1"],
),
),
],
)
def test_thorough_mangle_names(all_parsers, data, names, expected):
# see gh-17095
parser = all_parsers
with pytest.raises(ValueError, match="Duplicate names"):
parser.read_csv(StringIO(data), names=names)
@skip_pyarrow
def test_mangled_unnamed_placeholders(all_parsers):
# xref gh-13017
orig_key = "0"
parser = all_parsers
orig_value = [1, 2, 3]
df = DataFrame({orig_key: orig_value})
# This test recursively updates `df`.
for i in range(3):
expected = DataFrame()
for j in range(i + 1):
col_name = "Unnamed: 0" + f".{1*j}" * min(j, 1)
expected.insert(loc=0, column=col_name, value=[0, 1, 2])
expected[orig_key] = orig_value
df = parser.read_csv(StringIO(df.to_csv()))
tm.assert_frame_equal(df, expected)
@skip_pyarrow
def test_mangle_dupe_cols_already_exists(all_parsers):
# GH#14704
parser = all_parsers
data = "a,a,a.1,a,a.3,a.1,a.1.1\n1,2,3,4,5,6,7"
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4, 5, 6, 7]],
columns=["a", "a.2", "a.1", "a.4", "a.3", "a.1.2", "a.1.1"],
)
tm.assert_frame_equal(result, expected)
@skip_pyarrow
def test_mangle_dupe_cols_already_exists_unnamed_col(all_parsers):
# GH#14704
parser = all_parsers
data = ",Unnamed: 0,,Unnamed: 2\n1,2,3,4"
result = parser.read_csv(StringIO(data))
expected = DataFrame(
[[1, 2, 3, 4]],
columns=["Unnamed: 0.1", "Unnamed: 0", "Unnamed: 2.1", "Unnamed: 2"],
)
tm.assert_frame_equal(result, expected)