Handling large number of dummified columns using PCA

When I did dummification its columns increase to  2770 columns so if I do the PCA on this huge columns there is no use. So how can I handle this? 

In my house data set,It consists of  80 columns in total and contains

43- categorical value and 37- numerical value.

I wanted to apply PCA on this data set. Before that, I wanted to do the scaling of the data set. So I had to convert the categorical value into numerical by doing dummification using following codes.

housedata=pd.get_dummies(housedata)

housedata.head()

scaler=StandardScaler()

scaler.fit(housedata)

housedata=scaler.transform(housedata)

housedata=pd.DataFrame(housedata)

housedata.head()27

When I did dummification its columns increase to  2770 columns so if I do the PCA on this huge columns there is no use. so how can I handle this? 

Training over 2770 parameters is a tedious task and this will create an overfitted model so what you can do. Create dummy variables for the features which have less number of categories. And then you can also perform PCA and see if model performance improves or not.