How to Scale Data Using Standard Scaler But Keep Column Names

Python

In this code snippet we demonstrate how to scale data using Sklearn StandardScaler and then convert the transformed data back into a DataFrame with the column names of the original DataFrame.

 1|  from sklearn.preprocessing import StandardScaler
 2|  
 3|  scaler = StandardScaler()
 4|  scaler.fit(X_train)
 5|  X_train = pd.DataFrame(scaler.transform(X_train), columns = X_train.columns)
 6|  X_test = pd.DataFrame(scaler.transform(X_test), columns = X_test.columns)
detro
Scaler | Standard
Did you find this snippet useful?

Sign up for free to to add this to your code library

Remove Stop Words from Text in DataFrame Column
Python

Pandas | Nltk | Nlp

149
LightGBM Custom Loss Function
Python

LightGBM | Loss function

132
Tuning XGBoost Hyperparameters with Grid Search
Python

XGBoost | Tuning

127
How to Convert DataFrame Values Into Percentages
Python

Lambda | Percentages | Pipe

119
LightGBM Hyperparameter Tuning with GridSearch
Python

Python | LightGBM | Hyperparameter tuning | Gridsearch

91
Calculating Root Mean Squared Error (RMSE) with Sklearn and Python
Python

Rmse

86
Dynamically Create Columns in Pandas Dataframe
Python

Pandas | Columns

68
How to Train a Catboost Classifier with GridSearch Hyperparameter Tuning
Python

Catboost | Hyperparameter tuning

68
Overwriting a Python Logging File Instead of Appending
Python

Logging

65
Reading CSV Files from Amazon S3 into Pandas Dataframes
Python

Pandas | Python | S3

61