Error about unmatched parenthesis running simple imputer - pandas

Question

dput(head("CustomTransformerData.csv"))
Here's what I'm trying to do:
Applies the SimpleImputer class to the data, where the strategy is set to mean. The name of this step should be "imputer".
Here's the code I'm using:
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
fileName = "CustomTransformerData.csv"
custom_transform = pd.read_csv("CustomTransformerData.csv")
data_num = custom_transform.drop(['x3'], axis = 1); #created the df for categorical data
data_cat = custom_transform.drop(['x1', 'x2', 'x4', 'x5'], axis = 1); #created the df for numerical data
#importing sklearn
from sklearn.base import BaseEstimator,TransformerMixin
##creating the transformer
class Assignment4Transformer(BaseEstimator, TransformerMixin):
def __init__(self, drop_x4 = True, y = None):
self.drop_x4 = drop_x4 #flag to drop the x4 column
def fit_transform(self, data, y=None):
return self
from sklearn.pipeline import Pipeline #importing the pipeline
from sklearn.impute import SimpleImputer #importing the SimpleImputer
from sklearn.preprocessing import StandardScaler #importint the preprocessor
def transform(self, data): #starting the function to determine x4
#not adding the x3 categorical data
if self.drop_x4: #a flag to catch and drop x4, giving a new index
data = np.delete(data, 2, axis=1)
return np.c_[data, new_col]
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')]) # this is where I encounter the below error
File "/var/folders/5v/f6glw1515sqbvblc482qs47c0000gn/T/ipykernel_42484/2823414947.py", line 1
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')])
^
SyntaxError: closing parenthesis ']' does not match opening parenthesis '('
Alternatively, I tried this as well, which did not error, but then the next code errored:
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')),
('attribs_adder', Assignment4Transformer()),])
std_scaler= StandardScaler(num_pipeline)
# Splitting the independent and dependent variables
std_scaler = data_num.data
response = data_num.target
# standardization
scale = object.fit_transform(data_num)
TypeError Traceback (most recent call last)
/var/folders/5v/f6glw1515sqbvblc482qs47c0000gn/T/ipykernel_42484/1423714864.py in <module>
----> 1 std_scaler= StandardScaler(num_pipeline)
2
3 # Splitting the independent and dependent variables
4 std_scaler = data_num.data
5 response = data_num.target
TypeError: __init__() takes 1 positional argument but 2 were given
So I'm not sure if going the second route was truly correct, and I just need help with this portion:
Applies the custom Assignment4Transformer class to the data. Make sure that your custom transformer uses the default argument where you drop the 𝑥4
x
4
column. The name of this step should be "custom_trans".
Applies the StandardScaler class to the data. The name of this step should be "std_scaler?
Data: (since it doesn't appear to be carrying through
x1 x2 x3 x4 x5
1 1.5 2.354152979 COLD 593 0.75
2 2.5 3.31404772 WARM 340 2.083333333
3 3.5 4.021604459 COLD 551 4.083333333
4 4.5 COLD 2368 6.75
5 5.5 5.847601001 WARM 2636 10.08333333
6 6.5 7.229910044 WARM 2779 14.08333333
7 7.5 7.997255234 HOT 1057 18.75
8 8.5 9.203946542 COLD 819 24.08333333
9 9.5 10.33534766 WARM 3349
10 10.5 11.11214192 HOT 3235 36.75
11 11.5 11.75961084 WARM 216 44.08333333
12 12.5 12.62909577 WARM 2529 52.08333333
13 13.5 14.08258887 COLD 1735 60.75
14 14.5 14.65767801 HOT 1254 70.08333333
15 15.5 HOT 1245 80.08333333
16 16.6 17.18411403 WARM 310 90.75
17 17.5 17.80077555 HOT 201 102.0833333
18 18.5 18.57886101 HOT 1767 114.0833333

Answer 1

In your first code block...
This
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')])
Should be this
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean'))])
I added a closing parenthesis after SimpleImputer(strategy='mean')
In your second code block...
StandardScaler is a class and needs to be instantiated before it can be used.
In your code here:
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')),
('attribs_adder', Assignment4Transformer()),])
std_scaler= StandardScaler(num_pipeline)
You give your num_pipeline to the class, but should instead define the std_scaler, and then use .fit or .fit_transform on data, or add it to the pipeline like so
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='mean')),
('attribs_adder', Assignment4Transformer()),])
std_scaler= StandardScaler()
# applying to data
std_scaler.fit_transform(some_data)
# adding to pipeline at 0th step
num_pipeline.steps.insert(0, ("scale", std_scaler))
# last step
num_pipeline.steps.extend([("scale", std_scaler)])
# some other step???
num_pipeline.steps.insert(1, ("scale", std_scaler))