# Proportion Test in Python: Similar to R prop.test

**Proportion test** is used for comparing the proportions (e.g. number of successes) in two or more groups to determine if there are significant differences between these groups.

In Python, you can use the `chi2_contingency`

function from the scipy package to perform a proportion test
similar to the `prop.test`

function in R.

`chi2_contingency`

function performs a chi-squared test of independence (similar to the `prop.test`

function in R) based on proportions provided
in the contingency table.

`prop.test`

is exactly similar to the chi-squared test on a 2x2 contingency table.The basic syntax for `chi2_contingency`

for proportion test:

```
# import package
from scipy.stats import chi2_contingency
chi2, p, dof, exp_prop = chi2_contingency(contingency_table)
```

The `contingency_table`

contains the proportion for the two groups.

The following examples demonstrate how to use the proportion test (similar to `prop.test`

function in R) to compare the proportions
in two different groups.

## Sample dataset

Suppose a marketing survey is completed in two cities (A and B) with 500 individuals for a purchase of the product.

In city A, 300 individuals purchased the product, and in city B, 400 individuals purchased the product.

The number of successes in cities A and B are 300 and 400, respectively.

```
# import package
import numpy as np
# Create the contingency table
contingency_table = np.array([[300, 200],
[400, 100]])
```

## Hypothesis

We will test the following Null and Alternative hypotheses.

**Null Hypothesis (H0)**: No difference in the proportions of individuals who purchased the product of the two cities.

**Alternative Hypothesis (Ha)**: There is a difference between the proportions of individuals who purchased the product of the two cities.

## Proportion test using `chi2_contingency`

Now, perform the proportion test using the `chi2_contingency`

function from the scipy package. This function is similar to
`prop.test`

function in R.

```
# import package
from scipy.stats import chi2_contingency
# perform the chi-square test for proportion
chi2, p, dof, exp_prop = chi2_contingency(contingency_table)
print(chi2, p, dof, exp_prop)
# output
46.67142857142857 8.394401757688147e-12 1 [[350. 150.]
[350. 150.]]
```

The Chi-Square Statistic: 46.67; and *p* value: < 0.05

As the *p* value (< 0.05) is less than the significance level alpha (0.05), we reject the null hypothesis.

We conclude that there is a significant difference in the proportion of products purchased by individuals in two cities.

If you want to perform one-sample proportion test, please read our article on the one-sample proportion Z test.