# One-sample test for proportion - python

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1

I want to do "One-sample test for proportion" with Python. I found this document one sample proportion ztest example but I don't understand how to use it. For example, what are count and nobs. In the 2 examples, example1 gives single number for count and nobs, however, example2 gives 2 numbers.

For result, I'd like to know the p-value that the event happen rate is higher than 60%

Example1

``````>>> count = 5
>>> nobs = 83
>>> value = .05
>>> stat, pval = proportions_ztest(count, nobs, value)
>>> print('{0:0.3f}'.format(pval))
0.695
``````

Example2

``````>>> import numpy as np
>>> from statsmodels.stats.proportion import proportions_ztest
>>> count = np.array([5, 12])
>>> nobs = np.array([83, 99])
>>> stat, pval = proportions_ztest(counts, nobs)
>>> print('{0:0.3f}'.format(pval))
0.159
``````

My data looks like this

``````Yes No
1   0
1   0
1   0
0   1
0   1
1   0
1   0
0   1
0   1
0   1
0   1
0   1
``````

Can you help explain how to use it and give some examples?

Thank you!

11

In case of example 1:

`nobs` is the total number of trials, i.e. the number of rows in your list.

`count` is the number of successful trials, i.e. the number of `Yes` events in your list.

`value` is the proportion to test against, i.e. `0.6` based on your question text.

The null hypothesis here is that the single sample given by these values was drawn from a distribution with proportion equal to the specified `value`.

In case of example 2:

There are two independent samples, the first entry of the `nobs` and `count` vectors represent the first sample, the second ones the second sample. `value` is then omitted and the null hypothesis will be that the two samples have equal true proportion.

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