# Perform Chi-Square Test in Python

The Chi-square test is used to determine independence between two categorical data variables. We will perform this test in Python using the `SciPy`

module in this tutorial.

We will use the `chi2_contingency()`

function from the SciPy module to perform the test. Let us start by importing the `SciPy`

module.

## Perform Chi-Square Test in Python

Import SciPy:

```
from scipy.stats import chi2_contingency
```

The `chi2_contingency`

function takes a contingency table in the 2D format as an input. A contingency table is used in statistics to summarize the relationship between categorical variables.

So let us create this contingency table.

```
data = [[207, 282, 241], [234, 242, 232]]
```

Let us pass this array to the function.

```
stat, p, dof1, expected = chi2_contingency(data)
```

The `chi2_contingency()`

function will return a tuple containing test statistics, the p-value, degrees of freedom, and the expected table. We will compare the obtained p-value with the alpha value of 0.05.

Let’s now interpret the p-value using the below code.

```
alpha = 0.05
print("p val is " + str(p))
if p <= alpha:
print('Dependent')
else:
print('Independent')
```

The output for the above code would be:

```
p val is 0.1031971404730939
Independent
```

If the p-value is greater than the alpha value, which is 0.05, both variables are not significantly related and can be considered independent.

In our case, we have a p-value greater than alpha, and therefore we can conclude that both our variables are independent. Therefore, we can perform the chi-square test in Python using the above technique.

**Preet Sanghavi**