[英]pyfakefs fixture causes pandas.read_csv() to fail in pytest
I am using pandas
read_csv()
function to read some CSV content and want to use "high" or "round_trip" floating-point precision.我正在使用pandas
read_csv()
function 读取一些 CSV 内容,并希望使用“高”或“round_trip”浮点精度。
The following works in the Python REPL or running a program with the python interpreter dircectly.以下工作在 Python REPL 中或直接使用 python 解释器运行程序。
import pandas as pd
from io import StringIO
df = pd.read_csv( StringIO('0.00042119645,3.4,8.8244e-5\r\n'), float_precision='round_trip' )
If I put in a pytest
test it also works:)如果我输入pytest
测试它也有效:)
However, if I use the pyfakes
fixture then it fails !!但是,如果我使用pyfakes
fixture 那么它就会失败! For some reason the pd.read_csv()
function uses the python
engine instead of the default C
engine.出于某种原因, pd.read_csv()
function 使用python
引擎而不是默认的C
引擎。 This is true even if I explicitly set engine='c'
.即使我明确设置engine='c'
也是如此。
The error reported is:报错是:
ValueError("The 'float_precision' option is not supported with the 'python' engine")
import pandas as pd
from io import StringIO
def test_pandas_read_csv(
#mocker, #! pytest-mock test fixture
fs, #! pyfakefs test fixture
):
try :
df = pd.read_csv( StringIO('0.00042119645,3.4,8.8244e-5\r\n'), float_precision='round_trip' )
assert True
except Exception as exc:
assert False
How do I use the pandas read_csv()
function with the default c
engine in my pytest
tests that also require the pyfakes
fixture?如何在我的pytest
测试中使用默认c
引擎的 pandas read_csv()
function 也需要pyfakes
夹具?
Here is my pytest code.这是我的 pytest 代码。 The tests fail - comment out the fs,
line to get it to pass all tests.测试失败 - 注释掉fs,
行以使其通过所有测试。
import pytest
import pandas as pd
from io import StringIO
class Test_Pandas_Read_CSV :
@pytest.mark.parametrize(
'csv_str,kwargs,exp_status',
[
(
('0.00042119645,3.4,8.8244e-5\r\n'), #! csv_str
dict( #! kwargs
float_precision='round_trip',
# engine='c',
),
True, #! exp_status
),
(
('0.00042119645,3.4,8.8244e-5\r\n'), #! csv_str
dict( #! kwargs
float_precision='round_trip',
engine='c',
),
True, #! exp_status
),
(
('0.00042119645,3.4,8.8244e-5\r\n'), #! csv_str
dict( #! kwargs
float_precision='round_trip',
engine='python',
),
False, #! exp_status
),
]
)
def test_pandas_read_csv(
self,
# mocker, #! pytest-mock test fixture
fs, #! pyfakefs test fixture
csv_str : str,
kwargs,
exp_status : bool,
) :
try :
df = pd.read_csv( StringIO(csv_str), **kwargs )
status = True
except Exception as exc:
status = False
assert status == exp_status
My work-around involves mocking the pandas.read_csv() call with a function that first pops the float_precision key from the keyword arguments, and then calls the proper/original pandas.read_csv() function. I wrapped that in a customized pyfakes fixture to do the mocking My work-around involves mocking the pandas.read_csv() call with a function that first pops the float_precision key from the keyword arguments, and then calls the proper/original pandas.read_csv() function. I wrapped that in a customized pyfakes fixture to做 mocking
Though this makes the tests pass, I am concerned that the real C based functions aren't being called and thus the results may vary from what would run on the real target.虽然这使测试通过,但我担心没有调用真正的基于 C 的函数,因此结果可能与在真实目标上运行的结果不同。
orig_pandas_read_csv = pd.read_csv
def mock_pandas_read_csv( *args, **kwargs ):
kwargs.pop('float_precision', None)
return orig_pandas_read_csv( *args, **kwargs )
@pytest.fixture
def my_fs(mocker, fs):
mocker.patch.object( pd, 'read_csv', new=mock_pandas_read_csv )
yield fs
...
def test_pandas_read_csv(
self,
# mocker, #! pytest-mock test fixture
my_fs, #! pyfakefs test fixture (with mocked pandas functions)
csv_str : str,
kwargs,
exp_status : bool,
) :
try :
df = pd.read_csv( StringIO(csv_str), **kwargs )
status = True
except Exception as exc:
status = False
assert status == exp_status
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