input: Write a python function to perform PCA on a given DataFrame and return the explained variance ratio.
  assert perform_pca(pd.DataFrame({'Score1':[62,89,85,72,74],'Score2':[64,87,84,78,76]}))==[0.99779898, 0.00220102]
  assert perform_pca(pd.DataFrame({'Score1':[50,60,70,80,90],'Score2':[52,62,72,82,92]}))==[1.0, 0.0]
  assert perform_pca(pd.DataFrame({'Score1':[30,40,50,60,70],'Score2':[35,45,55,65,75]}))==[1.0, 0.0]
output: import pandas as pd
  from sklearn.decomposition import PCA
  
  def perform_pca(df):
      pca = PCA()
      pca.fit(df)
      return pca.explained_variance_ratio_.tolist()