scaler = StandardScaler() scaler.fit(pc_data[feature]) data_ori_nor = scaler.transform(pc_data[feature])

These lines of code are using the StandardScaler function from the scikit-learn library to standardize or normalize the data in the pc_data dataframe.

The scaler is first initialized with the StandardScaler() function.

Then, the fit() method of the scaler object is called with the feature data from the pc_data dataframe as the input. This calculates the mean and standard deviation of the feature data, which are used to standardize the data later.

Finally, the transform() method of the scaler object is called with the same feature data as input. This applies the standardization transformation to the data and returns the standardized data as a new dataframe called data_ori_nor.

The standardized data is useful for machine learning algorithms that perform better with normalized data, such as logistic regression or neural networks.