I wrote a library called Parametrize From File that can load test parameters from data files like this. But I'm not sure that I fully understand your example. If this was your data file...
| user |
password |
item number |
item name |
| A |
B |
C |
D |
| E |
F |
G |
H |
...would these be the tests you want to run?
@pytest.mark.parametrize(
'user, password',
[('A', 'B'), ('E', 'F')],
)
def test_1(user, password):
assert ...
@pytest.mark.parametrize(
'iterm_number, item_name',
[('C', 'D'), ('G', 'H')],
)
def test_2(user, password):
assert ...
In other words, are the user/password columns completely unrelated to the item_number/item_name columns? If no, I'm misunderstanding your question. If yes, this isn't very scalable. It's easy to imagine writing 100 tests, each with 2+ parameters, for a total of >200 columns! This format also breaks the convention that every value in a row should be related in some way. I'd recommend either putting the parameters for each test into their own file/worksheet, or using a file format that better matches the list-of-tuples/list-of-dicts structure expected by pytest, e.g. YAML, TOML, NestedText, etc.
With all that said, here's how you would load parameters from an xlsx file using Parametrize From File:
import pandas as pd
from collections import defaultdict
import parametrize_from_file as pff
def load_xlsx(path):
"""
Load an xlsx file and return the data structure expected by Parametrize
From File, which is a map of test names to test parameters. In this case,
the xlsx file doesn't specify any test names, so we use a `defaultdict` to
make all the parameters available to any test.
"""
df = pd.read_excel(path)
return defaultdict(lambda: df)
def get_cols(cols):
"""
Extract specific columns from the parameters loaded from the xlsx file.
The parameters are loaded as a pandas DataFrame, and need to be converted
into a list of dicts in order to be understood by Parametrize From File.
"""
def _get_cols(df):
return df[cols].to_dict('records')
return _get_cols
# Use the function we defined above to load xlsx files.
pff.add_loader('.xlsx', load_xlsx)
@pff.parametrize(preprocess=get_cols(['user', 'password']))
def test_1(user, password):
pass
@pff.parametrize(preprocess=get_cols(['item_number', 'item_name']))
def test_2(item_number, item_name):
pass
Note that this code would be much simpler if the parameters were organized in one of the formats I recommended above.
