# Using Python to Find Correlation Between Categorical and Continuous Variables

### In this post, we'll learn how to find correlations between categorical and continuous variables using Python and Pandas.

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Join For FreeBefore making any machine learning model on a tabular dataset, normally we check whether there is a relation between the independent and target variables. This can be done by measuring the correlation between two variables. In Python, Pandas provides a function, `dataframe.corr()`

, to find the correlation between numeric variables only.

In this article, we will see how to find the correlation between categorical and continuous variables.

**Case 1: When an Independent Variable Only Has Two Values**

### Point Biserial Correlation

If a categorical variable only has two values (i.e. true/false), then we can convert it into a numeric datatype (0 and 1). Since it becomes a numeric variable, we can find out the correlation using the `dataframe.corr()`

function.

Let's create a dataframe which will consist of two columns: **Employee Type** **(EmpType) **and** Salary**.

Purposely, we will assign more salary to EmpType1. This way we will get some correlation between EmpType and Salary.

Create a dataframe with the following properties:

Mean (average) salary of

`EmpType1`

is 60 with a standard deviation of five.Mean (average) salary of

`EmpType2`

is 50 with a standard deviation of five.

```
import pandas as pd
import numpy as np
num1=np.random.normal(loc=60,scale=5,size=100)
df1=pd.DataFrame(num1,columns=['Salary'])
df1['Type']='EmpType1'
num2=np.random.normal(loc=50,scale=5,size=100)
df2=pd.DataFrame(num2,columns=['Salary'])
df2['Type']='EmpType2'
df=pd.concat([df1,df2],axis=0)
# Since Categorical variable 'Type' has only 2 values we will convert it into numeric (0 and 1) datatype.
df['TypeInt']=(df['Type']=='EmpType1').astype(int)
df.corr()
```

Output

Salary | TypeInt | |

Salary | 1 | 0.736262 |

TypeInt | 0.736262 | 1 |

The correlation between EmpType and Salary is 0.7. So we can determine it is correlated.

## Case 2: When Independent Variables Have More Than Two Values

### ANOVA (Analysis of Variance)

We will assign more salary to `EmpType1`

, an average salary to `EmpType2`

, and a low salary to `EmpType3`

. This way, we will get some correlation between EmpType and Salary.

The mean salary of

`EmpType1`

is 90 with a standard deviation of five.The mean salary of

`EmpType2`

is 70 with a standard deviation of five.The mean salary of

`EmpType3`

is 50 with a standard deviation of five.

```
num1=np.random.normal(loc=90,scale=5,size=100)
df1=pd.DataFrame(num1,columns=['Salary'])
df1['Type']='EmpType1'
num2=np.random.normal(loc=70,scale=5,size=100)
df2=pd.DataFrame(num2,columns=['Salary'])
df2['Type']='EmpType2'
num3=np.random.normal(loc=50,scale=5,size=100)
df3=pd.DataFrame(num3,columns=['Salary'])
df3['Type']='EmpType3'
df=pd.concat([df1,df2,df3],axis=0)
from scipy import stats
F, p = stats.f_oneway(df[df.Type=='EmpType1'].Salary,
df[df.Type=='EmpType2'].Salary,
df[df.Type=='EmpType3'].Salary)
print(F)
```

The output we get is: 1443.6261

- Since the mean salary of three employee types is 90, 70, and 50 (with a standard deviation of five) the
**F score is 1444**. - If the mean salary of three employee types is 60, 55, 50 the
**F score is 86**. - And if the mean salary of three employee types is 51, 50, 49 (almost the same) then
**F score will be close to 0**, - The greater the F score value the higher the correlation will be.

You can download and run full code from this link.

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