我有下面的代码，在这里我试图将一个样本比例ztest应用于数据中每一行的值。我在数据框df中有以下示例数据。我正在尝试将每个值的比例与从计数值中获得的比例和从obs值中获得的试验次数进行比较。我想要每个记录的p值。相反，我似乎对所有记录都获得一个p值。我在下面有几行期望的输出来说明我的意思。有人可以指出我做错了什么以及如何解决吗？还是建议采用更轻松的方式？看来似乎应该有一种方法可以对付熊猫。
# code:
def pvl(x):
return sm.stats.proportions_ztest(x['count'],
x['value'],
x['obs'],
alternative='larger')[1]
df['pval']=df.apply(pvl,
axis=1
)
# sample data:
print(df)
count value obs
211.0 0.013354 15800.0
18.0 0.001139 15800.0
310.0 0.019620 15800.0
114.0 0.007215 15800.0
85.0 0.005380 15800.0
# sample output:
count value obs pval
211.0 0.013354 15800.0 0.5
18.0 0.001139 15800.0 0.5
310.0 0.019620 15800.0 0.5
114.0 0.007215 15800.0 0.5
85.0 0.005380 15800.0 0.5
# desired output:
count value obs pval
211.0 0.013354 15800.0 0.49
18.0 0.001139 15800.0 4.1454796845134295e-41
310.0 0.019620 15800.0 0.9999999999965842
您所拥有的应该起作用，但是我相信您的示例中x['obs']应该在x['value']之前。我在下面更新了。
def pvl(x):
_, pval = sm.stats.proportions_ztest(x['count'],
x['obs'],
x['value'],
alternative='larger')
return pval
Documenation：
Signature:
sm.stats.proportions_ztest(
count,
nobs,
value=None,
alternative='two-sided',
prop_var=False,
)
Docstring:
Test for proportions based on normal (z) test
Parameters
----------
count : integer or array_like
the number of successes in nobs trials. If this is array_like, then
the assumption is that this represents the number of successes for
each independent sample
nobs : integer or array-like
the number of trials or observations, with the same length as
count.
value : float, array_like or None, optional
This is the value of the null hypothesis equal to the proportion in the
case of a one sample test. In the case of a two-sample test, the
null hypothesis is that prop[0] - prop[1] = value, where prop is the
proportion in the two samples. If not provided value = 0 and the null
is prop[0] = prop[1]
alternative : string in ['two-sided', 'smaller', 'larger']
The alternative hypothesis can be either two-sided or one of the one-
sided tests, smaller means that the alternative hypothesis is
``prop < value`` and larger means ``prop > value``. In the two sample
test, smaller means that the alternative hypothesis is ``p1 < p2`` and
larger means ``p1 > p2`` where ``p1`` is the proportion of the first
sample and ``p2`` of the second one.
prop_var : False or float in (0, 1)
If prop_var is false, then the variance of the proportion estimate is
calculated based on the sample proportion. Alternatively, a proportion
can be specified to calculate this variance. Common use case is to
use the proportion under the Null hypothesis to specify the variance
of the proportion estimate.
Returns
-------
zstat : float
test statistic for the z-test
p-value : float
p-value for the z-test
Examples
--------
>>> count = 5
>>> nobs = 83
>>> value = .05
>>> stat, pval = proportions_ztest(count, nobs, value)
>>> print('{0:0.3f}'.format(pval))
0.695