PCA Implemented using Python 3

pca.py

import numpy as np
class PCA: 
    def __init__(self,n_component=None):

        """Principal component analysis (PCA) implementation.
        Transforms a dataset of possibly correlated values into n linearly
        uncorrelated components. The components are ordered such that the first
        has the largest possible variance and each following component as the
        largest possible variance given the previous components. This causes
        the early components to contain most of the variability in the dataset.
        Parameters
        ----------
        n_components : int
        
        """

        self.n_component =n_component
        self.components_ =None 
        self.explained_variance_ = None
        self.mean = None

    def fit(self,data):
        global values,vectors
        #mean
        meandata= np.mean(data.T,axis=1)
        self.mean = meandata.round(2)
        C = data - self.mean
        # calculate covariance matrix of centered matrix
        V = np.cov(C.T)
        # eigendecomposition of covariance matrix
        values, vectors = np.linalg.eig(V)
        key = np.argsort(values)[::-1][:self.n_component]
        values, vectors = values[key], vectors[:, key]

        self.components_ = vectors[0:self.n_component]
        self.explained_variance_ = values

    @property
    def variance_ratio(self):
        if len(vectors)!=0:
            s_squared = values** 2
            variance_ratio = s_squared / (s_squared).sum()
            return variance_ratio[0:self.n_component]


    def transform(self, data):
        meandata= np.mean(data.T,axis=1)
        self.mean = meandata.round(2)
        C = data - self.mean
        return np.dot(C, self.components_.T)

pca implemented only using python