# create a matrix that shows similarity

I have a dataset like this: data

Person Salary GPA IQ
Alisson 1 1 1
Simon 1 2 2
Michael 2 3 2
Dani 2 1 2
Brian 1 2 2
David 1 1 2
Ilan 1 1 2
Juan 2 1 2
Julius 3 3 3
Philipp 3 3 3
Joshua 2 1 3
Rick 2 1 1
Moises 3 1 1
Fabian 3 1 1
Isaac 2 2 1
Kurt 2 2 2

I now would like to create a heatmap which shows how similar this people are to each other. So if two people have in all three variables (GPA,IQ and Salary) three same number, then they get the nnumber 1 in similarity. If they have only two similar numbers in this three variables then they get another color. But I don’t know how can I visualise that.

### >Solution :

You can create the similarity matrix using `pandas` and `scipy`. And then plot the heatmap using `seaborn`. I used @root answer to create the similarity matrix.

Here is the code:

``````from scipy.spatial.distance import euclidean, pdist, squareform
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

data = {'Person': ['Alisson', 'Simon', 'Michael', 'Dani', 'Brian', 'David', 'Ilan', 'Juan', 'Julius', 'Philipp', 'Joshua', 'Rick', 'Moises', 'Fabian', 'Isaac', 'Kurt'],
'Salary': [1, 1, 2, 2, 1, 1, 1, 2, 3, 3, 2, 2, 3, 3, 2, 2],
'GPA': [1, 2, 3, 1, 2, 1, 1, 1, 3, 3, 1, 1, 1, 1, 2, 2],
'IQ': [1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 2]}

df = pd.DataFrame(data)
df = df.set_index("Person")

def similarity_func(u, v):
return 1/(1+euclidean(u,v))
dists = pdist(df, similarity_func)
similarity_matrix = pd.DataFrame(squareform(dists), columns=df.index, index=df.index)

fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(similarity_matrix, annot=True, cmap='YlGnBu', linewidths=.5, ax=ax)
``````

Output: 