![]() ![]() To protect email data from a security breach, the dynamic data masking feature offers the Email. Sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) From the T-SQL statement for Random type of dynamic data masking, it can be noticed that the values from the Montlybill column are masked with values ranging from 3 to 9.When the Test user fetches data from the Customer table, the table will be as follows. Print(pd.DataFrame(pca.components_,columns=data_lumns,index = )) Here each column is Principal Component and each row is country. It will be of the same dimension as our data used for PCA. The principal components of interest are stored in x object. And (2D)2PCA calculates the correlation of both columns and rows in the same time. names(pcares) 1 'sdev' 'rotation' 'center' 'scale' 'x' We see that the resulting object has 5 variables. # Dump components relations with features: PCA analyzes the observed data which is usually described by several. ![]() It outputs an array of, so to get how components are linearly related to the different features and each coefficient represents the correlation between a particular pair of components and features.ĭf = pd.DataFrame(iris.data, columns=iris.feature_names)ĭata_scaled = pd.DataFrame(preprocessing.scale(df),columns = df.columns) In PCA documentation, The output you need is the task of components_ attribute.
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