你如何为StandardScaler编写Python函数？

Question

I am trying to write some more concise code for a project. I have created new variables to rescale certain columns of a pandas dataframe. I would like to create a function that would this more efficiently. Does anyone have any ideas or resources on how I can accomplish this?

scaler = StandardScaler()


whole_scaled = scaler.fit_transform(df_milk_types['Whole'].values.reshape(-1, 1))
red_fat_scaled = scaler.fit_transform(df_milk_types['Two Percent Fat'].values.reshape(-1, 1))
low_fat_scaled = scaler.fit_transform(df_milk_types['One Percent Fat'].values.reshape(-1, 1))
skim_scaled = scaler.fit_transform(df_milk_types['Skim'].values.reshape(-1, 1))

Answer 1

Well the method that you are using is efficient enough already. But if you want to implement it yourself you can do something like:

def Standardize(x):
     x = np.asarray(x)
     return (x - np.mean(x)) / (np.std(x))

But keep in mind that written like this you will not be able to apply a reverse transform as you will loose that mean and the std of the original data.

Anyway ... applying the method is now trivial:

df.loc[:, 'column_name'] = Standardize(df.loc[:, 'column_name'])

Another thing to keep in mind is that when the number of rows in the dataframe is a big number np.std will return memory error ...