Python 参数化列表和数据框夹具的pytest

我正在

pytest

中为pandas

DataFrame

的自定义子类创建测试。我想测试我的类构造函数是否同时适用于列表和数据帧。我怎样才能参数化我的测试，使

test\u构造函数

既能接受

samp\u list

又能接受

samp\u df

，而不是有重复的

test\u构造函数和
test\u构造函数和
测试函数

@pytest.fixture(scope='module')
def sample():
    samp_list = [{'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 100},
    {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 100},
    {'timestamp': '2020-01-01', 'group': 'c', 'dollar_gains': 110},
    {'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 110},
    {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 90},
    {'timestamp': '2020-01-01', 'group': 'd', 'dollar_gains': 100}]

    samp_df = pd.DataFrame(samp_list)

    return samp_list, samp_df

def test_constructor(sample):
    print('hi')
    hist_dg = HistDollarGains(sample, 'group', 'timestamp')
    assert hist_dg.group == 'group'
    assert hist_dg.timestamp_col == 'timestamp'

这对你来说可能太晚了，但我今天遇到了一个类似的问题。似乎有一个选项可以将参数传递给fixture，并且它将针对传递的每个值运行

Martin Winkel在这里写了一篇很好的文章：

就你的情况而言，我认为你应该这样做：

import pandas as pd
import pytest

opt_dict = {'samp_list': [{'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 100},
                {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 100},
                {'timestamp': '2020-01-01', 'group': 'c', 'dollar_gains': 110},
                {'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 110},
                {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 90},
                {'timestamp': '2020-01-01', 'group': 'd', 'dollar_gains': 100}],
            'samp_df':pd.DataFrame([{'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 100},
                {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 100},
                {'timestamp': '2020-01-01', 'group': 'c', 'dollar_gains': 110},
                {'timestamp': '2020-01-01', 'group': 'a', 'dollar_gains': 110},
                {'timestamp': '2020-01-01', 'group': 'b', 'dollar_gains': 90},
                {'timestamp': '2020-01-01', 'group': 'd', 'dollar_gains': 100}]),
            'samp_bad_input':'this should result in a failed test'}

@pytest.fixture(params=opt_dict.keys())
def df_opt(request):
    '''
    I haven't looked into why request works without a definition; it is some kind of pytest built-in for use with fixtures
    '''
    return opt_dict[request.param]

def test_sample(df_opt):
    if isinstance(df_opt,list):
        sample_df =  pd.DataFrame(df_opt)
    else:
        sample_df = df_opt
    assert isinstance(sample_df,pd.DataFrame), "somehow, we didn't end up with a dataframe"