Writing a function that will normalize using either min max method or z-score method

I am fairly new to Python, so there may be a lot to improve upon, but in the following code I am trying to write a function that takes in the location of the data file, the attribute that has to be normalized and the type of normalization to be performed(‘min_max’ or ‘z_score’)

After this, based on the normalization type that is mentioned, I want it to apply the appropriate formula and return a dictionary where key = original value in the dataset, value = normalized value.

def normalization (fname, attr, normType):

result = {
    
}
 
df = pd.read_csv(fname)
targ = list(df[df.columns[attr]])
scaler = MinMaxScaler()
 
df["minmax"] = scaler.fit.transform(df[[df.columns[attr]]])

df["zscore”] = ((df[[df.columns[attr]]]) - (df[[df.columns[attr.mean()]]]))/ (df[[df.columns[attr.std(ddof=1)]]])

if normType == "min_max":
    
        
        result = dict(zip(targ, df.minmax.values.tolist())
else:
               
        result = dict(zip(targ, df.zscore.values.tolist())


return result

I continually get an error specifically on the line with the zscore calculation and have been struggling to troubleshoot it. I would appreciate any help that could point me in the right direction. Thanks

Edit: Error message shown is “SyntaxError: EOL while scanning string literal”

Answer

"zscore” alone causes that error. The problem is that the isn’t a proper double-quotes character so the string isn’t properly terminated. Not sure how it got there, maybe bad formatting in a document while pasting code around. The fix: "zscore"