How to apply multivariate normal pdf function in Python with my own data

Question

I use the following code to produce random data and plot the distribution of probability densities. How can I do the same with my own data as shown below?

Code

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats

mean, cov = [0, 1], [(1, .5), (.5, 1)]
data = np.random.multivariate_normal(mean, cov, 200)
df = pd.DataFrame(data, columns=["X", "Y"])
x, y = np.random.multivariate_normal(mean, cov, 1000).T

g = sns.jointplot(x=x, y=y, data=df, kind="kde", n_levels=75, color="m")
g.plot_joint(plt.scatter, c="black", s=30, linewidth=1, marker="+")
g.ax_joint.collections[0].set_alpha(0)
g.set_axis_labels("X", "Y");

My own data sample

        X       Y
0       1       8
1       7       8
2       7       9
3       5       8
4       7       7
5       9       9
6       1       3
4       6       8
5       9       7
6       9       6
7       8       2
8       1       9
9       0       10
10      22      2
11      4       45
12      9       8

I tried this but I am getting strange values

import numpy as np

mean = np.mean(data1['X'], axis=0)
cov = np.cov(data1['Y'], rowvar=0)
X = multivariate_normal.pdf(data1['X'], mean=2.5, cov=0.5)

mean = np.mean(data1['X'], axis=0)
cov = np.cov(data1['Y'], rowvar=0)
Y = multivariate_normal.pdf(data1['Y'], mean=2.5, cov=0.5)

df = np.concatenate( (X.reshape(-1,1),Y.reshape(-1,1)) , axis=1)
df = pd.DataFrame(df)
df =  df.rename({0: 'X', 1: 'Y'}, axis=1) 

g = sns.jointplot(x=X, y=Y, data=df, kind="kde", n_levels=75, color="r")
g.plot_joint(plt.scatter, c="black", s=30, linewidth=1, marker="+")
g.ax_joint.collections[0].set_alpha(0)
g.set_axis_labels("X", "Y");

Answer 1

This solution worked.

import numpy as np

mean = np.mean(data1['X'], axis=0)
cov = np.cov(data1['Y'], rowvar=0)
X = multivariate_normal.pdf(data1['X'], mean=2.5, cov=0.5)

mean = np.mean(data1['X'], axis=0)
cov = np.cov(data1['Y'], rowvar=0)
Y = multivariate_normal.pdf(data1['Y'], mean=2.5, cov=0.5)

df = np.concatenate( (X.reshape(-1,1),Y.reshape(-1,1)) , axis=1)
df = pd.DataFrame(df)
df =  df.rename({0: 'X', 1: 'Y'}, axis=1) 

g = sns.jointplot(x=X, y=Y, data=df, kind="kde", n_levels=75, color="r")
g.plot_joint(plt.scatter, c="black", s=30, linewidth=1, marker="+")
g.ax_joint.collections[0].set_alpha(0)
g.set_axis_labels("X", "Y");