StandardScaler

A custom implementation of a StandardScaler class for scaling numerical data in a pandas DataFrame or NumPy array. The class scales the features to have zero mean and unit variance.

Features

• fit: Calculate the mean and standard deviation of the data.
• transform: Scale the data to have zero mean and unit variance.
• fit_transform: Fit the scaler and transform the data in one step.
• get_params: Retrieve the mean and standard deviation calculated during fitting.

Methods

1. __init__(self)
   • Initializes the StandardScaling class.
   • No parameters are required.
2. fit(self, data)
   • Calculates the mean and standard deviation of the data.
   • Parameters:
     • data (pandas.DataFrame or numpy.ndarray): The data to fit.
   • Raises:
     • TypeError: If the input data is not a pandas DataFrame or NumPy array.
3. transform(self, data)
   • Transforms the data to have zero mean and unit variance.
   • Parameters:
     • data (pandas.DataFrame or numpy.ndarray): The data to transform.
   • Returns:
     • numpy.ndarray: The scaled data.
   • Raises:
     • Error: If transform is called before fit or fit_transform.
4. fit_transform(self, data)
   • Fits the scaler to the data and transforms the data in one step.
   • Parameters:
     • data (pandas.DataFrame or numpy.ndarray): The data to fit and transform.
   • Returns:
     • numpy.ndarray: The scaled data.
5. get_params(self)
   • Retrieves the mean and standard deviation calculated during fitting.
   • Returns:
     • dict: Dictionary containing the mean and standard deviation.
   • Raises:
     • Error: If get_params is called before fit.

Error Handling

• Raises a TypeError if the input data is not a pandas DataFrame or NumPy array in the fit method.
• Raises an error if transform is called before fit or fit_transform.
• Raises an error in get_params if called before fit.

Use Case

Output

Installation

No special installation is required. Just ensure you have pandas and numpy installed in your Python environment.