# Guidance needed with dimension reduction for clustering - some numerical, lots of categorical data

I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with binary values which originated from cols with categorical data. (used pd.get_dummies() for this)

Now I'd like to reduce the dimensions. I'm already running

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
pca.fit(df)


for three hours and I asked my self if my approach was correct. I also saw two variants of PCA, one for sparse data. Does it mean that it doesn't make sense to run PCA in such a mixed scenario?

As I was up to now busy with cleaning and transforming my data, I'd like to understand what a good strategy would be to eliminate irrelevant columns.

I'd appreciate some hints to move forward.