pca - Need to perform Principal component analysis on a dataframe collection in python using numpy or sklearn -

i having 'dataframe collection' df data below. trying perform principal component analysis(pca) on dataframe collection using sklearn. getting typeerror

from sklearn.decomposition import pca df  # dataframe collection pca = pca(n_components=5) pca.fit(x) 

how convert dataframe collection array matrix sequence. think if convert array matrix, able pca

data:

{'ussp2 cmpn curncy':   0       0.297453  1       0.320505  2       0.345978  3       0.427871  name: (ussp2 cmpn curncy, px_last), length: 1747, dtype: float64,   'margdebt index':   0     0.095478  1     0.167469  2     0.186317  3     0.203729  name: (margdebt index, px_last), length: 79, dtype: float64,   'sl% smt% index':   0     0.163636  1     0.000000  2     0.000000  3     0.363636  name: (sl% smt% index, px_last), dtype: float64,   'ffsraiws index':   0     0.157234  1     0.278174  2     0.530603  3     0.526519  name: (ffsraiws index, px_last), dtype: float64,   'usphnsa index':   0     0.107330  1     0.213351  2     0.544503  3     0.460733  name: (usphnsa index, px_last), length: 79, dtype: float64] 

can in pca on dataframe collection. thanks!

your dataframe collection dictionary (dict) of dataframe objects.

to perform analysis need have array of data work with. first step convert data single dataframe. pandas natively supports concatenating dictionary of dataframes, e.g.

import pandas pd  df = {     'currency1': pd.dataframe([[0.297453,0.5]]),     'currency2': pd.dataframe([[0.297453,0.5]]) }        x = pd.concat(df) 

you can perform pca on values dataframe, e.g.

pca = pca(n_components=5) pca.fit(x.values)