我正在尝试使用产生参数值的函数对pytest测试进行参数化。我希望它为每个值分别运行测试。这是示例代码：
def provider():
yield pytest.param([1, 2])
class Test:
@pytest.mark.parametrize("a", provider())
def test(self, a, logger):
logger.info("a: {}".format(a))
@pytest.mark.parametrize("a", [1, 2])
def test_2(self, a, logger):
logger.info("a: {}".format(a))
是否可以使用提供程序功能对测试进行参数设置，以使其像test_2一样工作。
以下是我来自上述测试的日志：
2019-12-09 14:39:36 test[a0] start
a: [1, 2]
2019-12-09 14:39:36 test[a0] passed
2019-12-09 14:39:36 test_2[1] start
a: 1
2019-12-09 14:39:36 test_2[1] passed
2019-12-09 14:39:36 test_2[2] start
a: 2
2019-12-09 14:39:36 test_2[2] passed
答案 0 :(得分：1)
您的函数# Imports
import plotly.graph_objs as go
import pandas as pd
import numpy as np
dfi=pd.DataFrame({'date': {0: '2020.01.01',
1: '2020.01.01',
2: '2020.01.01',
3: '2020.01.01',
4: '2020.01.01',
5: '2020.01.01',
6: '2020.02.01',
7: '2020.02.01',
8: '2020.02.01',
9: '2020.02.01',
10: '2020.02.01',
11: '2020.02.01',
12: '2020.03.01',
13: '2020.03.01',
14: '2020.03.01',
15: '2020.03.01',
16: '2020.03.01',
17: '2020.03.01'},
'sub_id': {0: 1233,
1: 1233,
2: 1233,
3: 3424,
4: 3424,
5: 3424,
6: 1233,
7: 1233,
8: 1233,
9: 3424,
10: 3424,
11: 3424,
12: 1233,
13: 1233,
14: 1233,
15: 3424,
16: 3424,
17: 3424},
'stat_type': {0: 'link_clicks',
1: 'transaction',
2: 'customer_signups',
3: 'link_clicks',
4: 'transaction',
5: 'customer_signups',
6: 'link_clicks',
7: 'transaction',
8: 'customer_signups',
9: 'link_clicks',
10: 'transaction',
11: 'customer_signups',
12: 'link_clicks',
13: 'transaction',
14: 'customer_signups',
15: 'link_clicks',
16: 'transaction',
17: 'customer_signups'},
'value': {0: 12,
1: 50,
2: 9,
3: 24,
4: 100,
5: 18,
6: 14,
7: 24,
8: 39,
9: 20,
10: 10,
11: 8,
12: 4,
13: 2,
14: 3,
15: 2,
16: 1,
17: 1}})
# change some types
dfi['date']=pd.to_datetime(dfi['date'])
dfi['sub_id']=dfi['sub_id'].astype(str)
df=dfi
# split df by stat_type and organize them in a dict
groups = df['stat_type'].unique().tolist()
dfs={}
for g in groups:
dfs[str(g)]=df[df['stat_type']==g]
# pivot data to get different sub_id across dates
dfp={}
for df in dfs:
dfp[df]=dfs[df].pivot(index='date', columns='sub_id', values='value')
# one trace for each column per dataframe
fig=go.Figure()
# set up the first trace
fig.add_trace(go.Scatter(x=dfp['link_clicks'].index,
y=dfp['link_clicks']['1233'],
visible=True)
)
fig.add_trace(go.Scatter(x=dfp['link_clicks'].index,
y=dfp['link_clicks']['3424'],
visible=True)
)
# plotly start
# buttons for menu 1, names
updatemenu=[]
buttons=[]
# button with one option for each dataframe
for df in dfp.keys():
buttons.append(dict(method='restyle',
label=df,
visible=True,
args=[{'y':[dfp[str(df)]['1233'].values, dfp[str(df)]['3424'].values],
'x':[dfp[str(df)].index],
'type':'scatter'}],
)
)
# some adjustments to the updatemenus
updatemenu=[]
your_menu=dict()
updatemenu.append(your_menu)
updatemenu[0]['buttons']=buttons
updatemenu[0]['direction']='down'
updatemenu[0]['showactive']=True
# add dropdown menus to the figure
fig.update_layout(showlegend=False, updatemenus=updatemenu)
# add notations to the dropdown menus
fig.update_layout(
annotations=[
go.layout.Annotation(text="<b>stat_type:</b>",
x=-0.3, xref="paper",
y=1.1, yref="paper",
align="left", showarrow=False),
]
)
fig.show()
产生一个参数。您必须遍历列表并产生每个项目。
provider()根据您的确切需求，您可以将其简化为：
def provider():
for param in [1, 2]:
yield pytest.param(param)
甚至只是：
def provider():
yield from [1, 2]