What is the meaning of isotropic gaussian blobs , which are generated by sklearn.datasets.makeblobs? Could someone explain the meaning of isotropic gaussian blobs which are generated by sklearn.datasets.make_blobs(). I am not getting its meaning and only found this Generate isotropic Gaussian blobs for clustering on sklearn documentation. Also I have gone through this question.
So,heres my doubt
from sklearn.datasets import make_blobs
# data set generate
X, y = make_blobs(n_samples = 100000, n_features = 2, centers = 2, random_state = 2, cluster_std = 1.5)

# scatter plot of blobs
plt.scatter(X[:, 0], X[:, 1], c = y, s = 50, cmap = 'RdBu')


# distribution of first feature
sns.histplot(x = X[:, 0], kde = True) 

As the the distribution followed by this feature is approximately Normal.

# distribuution of second feature
sns.histplot(x = X[ :, 1], kde = True, color = "green", alpha = 0.2 )

The distribution of the second feature is Bimodal which is not normal.

# overall distribution of values
sns.histplot(x = X.flatten(), color = "red", kde = True, alpha = .5)

Which is also not normal!

# Variance Covrariance Matrix of Features
np.cov(X[:, 0], X[:, 1])

Output
array([[ 3.55546911,  4.70526192],
       [ 4.70526192, 19.00023664]])

What does it actually mean by Gaussian here!. It might be a silly question so appologies in advance.

The make_blobs() function draws samples from a special Gaussian mixture model.  A general Gaussian mixture model with $k$ clusters has a density of the form
$$
p(x) = \sum_{i=1}^k \pi_i \mathcal{N}(\mu_i, \Sigma_i)
$$
where $\pi_i \ge 0$ are the weights of each cluster with $\sum_{i=1}^k \pi_i = 1$, $\mu_i$ are the cluster centers, and $\Sigma_i$ are the cluster covariances.  Here $\mathcal{N}(\mu_i, \Sigma_i)$ refers to the normal Gaussian density with mean $\mu_i$ and covariance $\Sigma_i$.
In particular, for the make_blobs() function, each cluster or component has equal probability of being sampled $\pi_i = 1/k$ and the cluster centers can be either specified or in the case of your code randomly generated by setting centers = 2.  Isotropic refers to the fact that the covariance matrices will all be diagonal
$$
\Sigma_i = \begin{bmatrix}
\sigma_i^2 & 0 \\
0 & \sigma_i^2
\end{bmatrix}
$$
with $\sigma_i$ being the standard deviation that is passed in.  By default, all clusters will have the same standard deviation.  A Gaussian mixture model is not Gaussian unless there is only one cluster, which is why your plots don't look Gaussian, but rather a combination of Gaussians.
