# Sklearn and PCA. Why is max n_row == max n_components?

I posted my question on stack overflow, but there someone suggested that I should try it here. What I'm doing now :)

OK, first to my data. I have a word-bi-gram frequency matrix (1100 x 100658, dtype=int), where the first 5 columns contain information about the document. So every row is a document and every column a word-bi-gram like (of-the, on-the, and-that,...). I want to visualize the data, but before I do that, I want to reduce the dimension. So I thought I do that with PCA from sklearn. First I set the column labels with

myPandaDataFrame.columns = word-bi-grams


then I deleted some doc-columns, because I want to see what kind of information I can get if I only look at the proficiency.

del existing_df['SUBSET']
del existing_df['PROMPT']
del existing_df['L1']
del existing_df['ESSAYID']


then I set the proficiency column to be the index with

myPandaDataFrame.columns.set_index(['PROFICIENCY'], inplace=True, drop=True)


and then I did this

from sklearn.decomposition import PCA
x = 500
pcax = PCA(n_components=x)
PCA(copy=True, n_components=x, whiten=False)