Keep getting 'numpy.ndarray' object has no attribute 'fit' despite combing over code over and over

Question

Afternoon all, I am trying to run my pipeline but I keep running into the error 'numpy.ndarray' object has no attribute 'fit' and I cannot figure out why. Below is the code for the transformer, the pipeline, and the dataframes. Any tip is appreciated:

    import pandas as pd
    import numpy as np
    data = pd.read_csv("CustomTransformerData.csv")
    data

    from sklearn.base import BaseEstimator, TransformerMixin

    #column index
    x1_ix, x2_ix, x3_ix, x4_ix, x5_ix = 0,1,2,3,4

    class Assignment4Transformer(BaseEstimator, TransformerMixin):
        def __init__(self, add_x6 = True, y = None):
          self.add_x6 = add_x6
        def fit(self, data, y=None):
          return self
        def transform(self, data):
          if self.add_x6:
            y=[]
            x4 = (x1_ix**3) / (x5_ix)
            y.append(x4)
            x1 = (x1_ix**3) / (x5_ix)
            y.append(x1)
            x2 = (x1_ix**3) / (x5_ix)
            y.append(x2)
            x3 = (x1_ix**3) / (x5_ix)
            y.append(x3)
            x5 = (x1_ix**3) / (x5_ix)
            y.append(x5)
            x6 = (x1_ix**3) / (x5_ix)
            y.append(x6)
        
             return y


    attr_adder = Assignment4Transformer(add_x6 = True)
    assignment4_extra_attribs = attr_adder.transform(data)

    from sklearn.pipeline import Pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.preprocessing import StandardScaler
    num_pipeline=Pipeline([
                       ('imputer',SimpleImputer(strategy="mean")), 
                       ('attribs_adder', Assignment4Transformer), 
                       ('std_scaler', StandardScaler())
    ])

    data_num = data.drop("x3", axis = 1)
    data_cat = data.drop(["x1", "x2", "x4", "x5"], axis = 1)

    data_num_transformed = num_pipeline.fit_transform(data_num)
    
    Output of last line:
     error 'numpy.ndarray' object has no attribute 'fit

Answer 1

In the 4th line an additional data variable is present. You remove it and check. Also the return statement for transform function should match the indentation of the previous line

y.append(x6)

Check these two points and share the results.