import numpy as np
import pandas as pd

def apply_transformation(data, transform):
    """
    Applies the specified transformation to the data.

    Args:
        data (pd.Series): Data to be transformed.
        transform (str): The transformation to apply.

    Returns:
        pd.Series: The transformed data.
    """
    data = np.array(data)

    if transform == 'identity':
        return data

    if transform.startswith('power_'):
        # Apply power transformations specified like 'power_2'.
        exponent = int(transform.split('_')[1])
        return np.power(data, exponent)

    elif transform == 'log':
        # Log transformation, checking for non-positive values.
        if (data <= 0).any():
            warnings.warn("Log transformation contains zero or negative values, which are problematic.")
        return np.log(data.clip(0.0001))

    elif transform == 'sqrt':
        # Square root transformation, checking for negative values.
        if (data < 0).any():
            warnings.warn("Square root of negative values is not defined.")
        return np.sqrt(data.clip(0))

    elif transform == 'inverse':
        # Inverse transformation, avoiding division by zero.
        if (data == 0).any():
            warnings.warn("Inverse transformation contains zero values, leading to infinity.")
        return 1 / data.clip(0.0001)

    return data
