Python standardscaler example
Python standardscaler example. preprocessing import StandardScaler sc = StandardScaler() # get numeric data num_d = d. sqrt(scaler. This process helps in improving the convergence of gradient-based optimization algorithms and makes the model training process more efficient. Step 1: Import Necessary Modules First, we’ll import all of the modules that we will need to perform k-means clustering: Sep 13, 2023 · The StandardScaler stands out as a widely used tool for implementing data standardization. Visualize Scikit-Learn Preprocessing StandardScaler with Python. Since it uses median and quartiles instead of mean and variance, it can handle features with a large number of outliers without being influenced by them. normalize() function to normalize an array-like dataset. each column of X, INDIVIDUALLY, so that each column/feature/variable will have μ = 0 and σ = 1. 0 and divide by the standard deviation to give the standard deviation of 1. fit_transform(training), columns = X. Oct 5, 2021 · You can rewrite your code with Pipeline() as follows:. fit(X_validate) X_standard = scaler. To visualize the effects of using the StandardScaler on a dataset, we can create a simple scatter plot before and after scaling the features. issparse(X): if self. loc[:,numerical]) Output Jul 10, 2014 · In this post you will discover two simple data transformation methods you can apply to your data in Python using scikit-learn. Mar 14, 2018 · You have to define a class extending StandardScaler that only performs the transformations on the columns passed as arguments, keeping the others intact. load_iris() X = iris. df:. Jun 10, 2020 · StandardScaler. data y = iris. Feb 24, 2021 · The StandardScaler in sklearn essentially computes the z score of each feature which ensures each feature in the dataset has a mean of 0 and variance of 1. fit_transform - 27 examples found. with_mean: raise ValueError( "Cannot center sparse matrices: pass `with_mean=False` " "instead. class sklearn. array(query) - scaler. Examples Jun 20, 2024 · Data normalization is a crucial preprocessing step in machine learning. StandardScaler() is a class supporting the Transformer API; I would always use the latter, even if i would not need inverse_transform and co. linear_model import Ridge from sklearn. Feb 12, 2022 · preprocessing. inverse_transform - 13 examples found. However, the outliers have an influence when computing the empirical mean and standard deviation. Therefore, it makes mean = 0 and scales the data to unit variance. copy bool Python StandardScaler. 067 0. ) Normalizer() rescales each sample. First, a little convenience wrapper: import typing import pandas as pd class SklearnWrapper: def __init__(self, transform: typing. Aug 31, 2016 · import pandas as pd import numpy as np from sklearn. StandardScaler() function with example in python. Packages. datasets import make_regression from sklearn. My limited understanding is that it needs to work with 2-D arrays instead of a 1-D array. MinMaxScaler scales all the data features in the range [0, 1] or else in the range [-1, 1] if there are negative values in the dataset. select_dtypes(include='float64'). Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. columns) normed_test_data = pd. transform(new_data) Dec 14, 2021 · It seems that your DF doesn't have the columns in the axis. datasets. 13 and scikit-learn version 1. preprocessing import StandardScaler import numpy as np from sklearn import datasets iris = datasets. (unit variance: Unit variance means that the standard deviation of a sample as well as the variance will tend towards 1 as the sample size tends towards infinity. This tutorial uses: pandas; statsmodels; statsmodels. target scal = StandardScaler() X_t = scal. shape train_data = np. fit_transform(temp. 9. fit_transform(X) pca May 16, 2022 · Feature Scaling StandardScaler performs the task of Standardization. StandardScaler extraídos de proyectos de código abierto. js devs to use Python's powerful scikit-learn machine learning library – without having to know any Python. 1 and Theano 0. e height, weight)by removing the mean and scaling to unit variance. var_)と Mar 4, 2019 · StandardScaler does not meet the strict definition of scale I introduced earlier. columns] = sc. values) -- Edit May 2019 (Tested for pandas 0. Aug 3, 2022 · Python sklearn library offers us with StandardScaler() function to standardize the data values into a standard format. StandardScaler results in a distribution with a standard deviation equal to 1. Jul 8, 2019 · from sklearn. Python StandardScaler. transform = transform def __call__(self, df): transformed = self. Our dataset contains variable values that are different in scale. For all the above methods you need to import sklearn. Method 1: Using StandardScaler and Normalizer. import numpy as np from sklearn import decomposition from sklearn import datasets from sklearn. An open source TS package which enables Node. New in version 1. Mar 8, 2019 · In this tutorial, you will learn about sklearn. It's focused on making scikit-learn easier to use with pandas. select_dtypes(exclude=['object']) # update the cols with their normalized values d[num_d. preprocessing import StandardScaler # I'm selecting only numericals to scale numerical = temp. From lines 796 to 807 you'll see. Estos son los ejemplos en Python del mundo real mejor valorados de sklearn. The continuous variables need to be scaled, but at the same time, a couple of categorical variables are also of integer type. By default, 25 percent of samples are assigned to the test set. Following the series of publications on data preprocessing, in this tutorial, I deal with Data Normalization in Python scikit-learn. loc[:,numerical] = StandardScaler(). Here, you can do practice also. Scikit Learn’s StandardScaler combined with Normalizer offers a two-step process for applying L2 normalization. The standard score of a sample x is calculated as: z = (x - u) / s. fit(X_train) StandardScaler. partial_fit extracted from open source projects. transform(x) print(x) returns Wanna apply a specific scaler, say StandardScaler, on a specific feature, keeping other features intact. var_ = var_array new_data = scaler. How to deal with outliers Dec 19, 2021 · In this library, a preprocessing method called standardscaler() is used for standardizing the data. If you’re interested in learning how and when to implement k-means clustering in Python, then this is the right place. mean_) - np. fit(train_df['t']) train_df['t']= scaler. 137 The latter is demoed on the first part of the present example. 497 0. Nov 29, 2019 · import numpy as np from sklearn. api; numpy; scikit-learn; sklearn StandardScaler and other scalers that work featurewise are preferred in case meaningful information is located in the relation between feature values from one sample to another sample, wherease Normalizer and other scalers that work stamplewise are preferred in case meaningful information is located in the relation between feature values from Nov 11, 2023 · Dive into data standardization with Python! This tutorial explores Z-Score and Standard Scaler methods, providing step-by-step guidance on transforming data for optimal analysis. 🤯 StandardScaler - sklearn Python docs ↗ Python docs ↗ (opens in a new tab) Contact ↗ Contact ↗ (opens in a new tab) Log-likelihood of each sample under the current model. fit_transform(X_train), columns=cols) X_test_sc = pd. In this tutorial, you’ll learn: What k-means clustering is The Supervised Learning with scikit-learn course is the entry point to DataCamp's machine learning in Python curriculum and covers k-nearest neighbors. preprocessing. Feb 3, 2022 · Python's Sklearn library provides a great sample dataset generator which will help you to create your own custom dataset. We can apply the StandardScaler to the Sonar dataset directly to standardize the input variables. transform(train_df['t']) After this, I would like to: run regression model; check the Nov 12, 2019 · Generally you would want to use Option 1 code. samples_generator. I don't want to use pandas. fit_transform ( data ) Aug 28, 2020 · StandardScaler Transform. This tutorial will data for flights in and out of NYC in 2013. If True, center the data before scaling. the dataset format is something like: [ [1, 0. StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature. Sklearn Standardscaler on One Column. transform(X_validate) But here it comes the problem using the saved the model I want to restore the model with a single sample as an input: X_scaler = preprocessing. Feature scaling มีหลายสูตร แต่สูตรที่ใช้งานได้ดีและเป็นที่นิยม คือสูตร StandardScaler ใน preprocessing module ของ scikit-learn โดย StandardScaler จะมีสูตรดังนี้: คือ Input x Nov 29, 2021 · 4/5 – Analyze a Balance Sheet with Python; 3/5 – Financial Ratio Analysis Using Python; 2/5 – Comparing Financial Performance of Companies with Python – P&L Statement; 1/5 – Fundamental Financial Analysis: Using Python for Efficient Stock Evaluation; Favorite Sites Axis used to compute the means and standard deviations along. reshape(train_data, shape=(-1, num_features)) train_data = scaler. I'm using the boston housing dataset, and prepping it this way: Aug 31, 2022 · The following step-by-step example shows how to perform k-means clustering in Python by using the KMeans function from the sklearn module. If the names of the columns were correct, you would lose the DataFrame and get an array by running this code. . preprocessing import StandardScaler sc = StandardScaler() X_train_std = sc. StandardScaler (*, copy = True, with_mean = True, with_std = True) [source] ¶ Standardize features by removing the mean and scaling to unit variance. 140 0. またmean_、var_などの標準化の際に算出した値を保持しているため、機械学習モデルの学習時にfitしておけば、モデルの推論時に利用するデータ 1 に対してもprocessed_query = (np. 2. cluster import KMeans from sklearn. Jul 22, 2018 · In the example below, pipe = Pipeline([ ('scale', StandardScaler()), ('reduce_dims', PCA(n_components=4)), ('clf', SVC(kernel = 'linear', C = 1))]) param_grid = dict Feb 9, 2024 · This Data Science Tutorial with Python tutorial will help you learn the basics of Data Science along with the basics of Python according to the need in 2024 such as data preprocessing, data visualization, statistics, making machine learning models, and much more with the help of detailed and well-explained examples. mean_ = mean_array scaler. StandardScaler class sklearn. preprocessing import StandardScaler import numpy as np df. preprocessing import StandardScaler scaler = StandardScaler() scaler. It destroys the purpose of train-test split. On the second part of the example we show how Principal Component Analysis (PCA) is impacted by normalization of features. # Normalize the data sc = StandardScaler() normed_train_data = pd. You are feeding to the SVM a target vector with dimension (1,10) which means one row and ten columns, this is wrong and it's caused by you're using of reshaping in Jul 15, 2024 · In the previous example, you used a dataset with twelve rows, or observations, and got a training sample with nine rows and a test sample with three rows. data. What is StandardScaler? The StandardScaler class provided by Scikit Learn applies the standardization on the input (features) variable, making sure they have a mean of approximately 0 and a standard deviation of approximately 1. DataFrame(transformed, columns=df. pipeline import Pipeline # generate the data X, y = make_regression(n_samples=1000, n_features=100, noise=10, bias=1 Jul 5, 2021 · The correct way of scaling both the features and the target in Python with Scikit-Learn for a regression problem would be wit pipelines as follow: 18 hours ago · Key reasons to use map(): Simplify code: Abstracts away looping boilerplate code in a declarative style; Faster and more efficient: Significant performance gains under the hood by leveraging optimizations in the Python runtime related to function caching and reused objects that allow map() to operate faster than traditional loops in most cases Saved searches Use saved searches to filter your results more quickly Jun 8, 2016 · Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples. sklearn. >>> import numpy >>> from sklearn. Standardize features by removing the mean and scaling to unit variance. fit_transform(testing Oct 1, 2020 · As you mentioned, applying the scaling results in a numpy array, to get a dataframe you can initialize a new one: import pandas as pd cols = X_train. 0. If you use transformations like MinMaxScaler or StandardScaler, Jul 14, 2020 · Pour normaliser les données on peut utiliser le module scikit-learn preprocessing avec StandardScaler: scaler = preprocessing. set_output (*, transform = None) [source] # Set output container. Be sure that the names are correct. DataFrame(data_array,columns = data_norm. fit_transform extracted from open source projects. Often, you will want to convert an existing Python function into a transformer to assist in data cleaning or processing. StandardScaler() will normalize the features i. " Updated example: Jul 13, 2024 · Practical Example : Data Normalization with Python . preprocessing import StandardScaler, PolynomialFeatures from sklearn. 2)--As joelostblom mentions in the comments, "Since 0. StandardScalerとは機械学習によく使われるライブラ… May 26, 2022 · I'm trying to normalize a pandas dataframe while grouping it based on the dates. fit(X) x = sc. 🤯 StandardScaler - sklearn Python docs ↗ Python docs ↗ (opens in a new tab) Contact ↗ Contact ↗ (opens in a new tab) 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. DataFrame(sc. If 0, independently standardize each feature, otherwise (if 1) standardize each sample. Jun/2016: First published; Update Mar/2017: Updated for Keras 2. For example, to build a transformer that applies a log transformation in a pipeline, do: Can t be scaled with StandardScaler, so I instead predict t' and then inverse the StandardScaler to get back the real time? For example: from sklearn. transform(X_test) Fitting the entire dataset to the standard scaler object causes the model to learn about test set. Aug 21, 2023 · This example demonstrates how to use StandardScaler in a machine learning pipeline to scale features before training a model. Jul 9, 2014 · from sklearn. This tutorial explains how to use the standard scaler encoding from scikit-learn. StandardScaler¶ class sklearn. Syntax: object = StandardScaler ( ) object . Oct 14, 2023 · In this guide, we've taken a look at what Feature Scaling is and how to perform it in Python with Scikit-Learn, using StandardScaler to perform standardization and MinMaxScaler to perform normalization. See Introducing the set_output API for an example on how to use the API. Excerpt from the docs: The function scale provides a quick and easy way to perform this operation on a single array-like dataset Mar 9, 2024 · An example input could be a raw data vector, while the desired output is the same vector with L2 normalization applied. Apr 14, 2024 · In this short article, we will learn how we can use sklearn standardscaler to convert data into standard scale. to_numpy() instead of . Nov 23, 2016 · The main idea is to normalize/standardize i. Using the scikit-learn preprocessing. To illustrate this, we compare the principal components found using PCA on unscaled data with those obatined when using a StandardScaler to scale data first. Sep 16, 2017 · preprocessing. columns). Whether you're a beginner or aiming to enhance your data preprocessing skills, this guide equips you with the knowledge to standardize data effectively. transform(X_test), columns=cols) Python StandardScaler - 60 ejemplos encontrados. Here’s an example of how to use RobustScaler in Python: Dec 30, 2019 · from sklearn. preprocessing import StandardScaler scaler = StandardScaler() num_instances, num_time_steps, num_features = train_data. here's an example. The scaling shrinks the range of the feature values as shown in the left figure below. StandardScaler()の引数】 ・引数copy:Trueの場合は元のデータは変換されず、Falseの場合は変換元のデータを使って変換する(デフォルトはTrue) ・引数with_mean: Trueの場合は平均値を0とする。 Jun 23, 2020 · Explanation: If you set with_mean and with_std to False, then the mean μ is set to 0 and the std to 1, assuming that the columns/features are coming from the normal gaussian distribution (which has 0 mean and 1 std). Two techniques that you can use to consistently rescale your time series data are normalization and standardization. StandardScaler in Python (with Examples) Jan 18, 2021 · Image by Lorenzo Cafaro from Pixabay. transform - 60 examples found. Let’s get started. Jun 9, 2017 · Above answer is OK when you have use train data and test data in single run But what if you want to test or infer after training. array(([34, 56, 234])) y = pd. fit_transform(dfTest[['A','B']]. This will surely help. It ensures that features contribute equally to the model by scaling them to a common range. 4. You can implement a transformer from an arbitrary function with FunctionTransformer. My dataset looks like this: date permno ret cumret mom1m mom3m mom6m 2004-01-30 80000 0. Callable): self. normalize() Function to Normalize Data You can use the scikit-learn preprocessing. In general, this issue is called data This tutorial was an excellent and comprehensive introduction to PCA in Python, which covered both the theoretical, as well as, the practical concepts of PCA. In this tutorial, you will discover how you can apply normalization and standardization rescaling to your time series data […] Oct 31, 2019 · このようにStandardScalerを利用することで少ない分量で標準化を実現できます。. See the code in this example, you would have to program something similar to ItemSelector. preprocessing import StandardScaler >>>; input_scaler = StandardSca Jan 7, 2019 · I'm working through some examples of Linear Regression under different scenarios, comparing the results from using Normalizer and StandardScaler, and the results are puzzling. pyplot as plt import numpy as np # linear alg Python StandardScaler. 0, it is recommended to use . You can rate examples to help us improve the quality of examples. StandardScaler() X_test_test = X_scaler. values) return pd. import pandas as pd import numpy as np X = pd. The reason for using fit and then transform with train data is a) Fit would calculate mean,var etc of train set and then try to fit the model to data b) post which transform is going to convert data as per the fitted model. On the other hand, StandardScaler rescales the data to have a mean of 0 and a standard deviation of 1. Aug 24, 2016 · StandardScaler() standardizes features (such as the features of the person data i. inverse_transform. Aug 7, 2019 · Try converting the array into a dataframe. 85 Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. array([11, 598, 1])) from sklearn. fit - 60 examples found. fit_transform(df) In this example, we are going to transform the whole data into a standardized form. Oct 2, 2020 · Logistic Regression for Machine Learning: complete Tutorial; FREE Python crash course (Python Basics) Learn Python Pandas for Data Science: Quick Tutorial (Python Pandas) Python NumPy Tutorial: Practical Basics for Data Science (Python NumPy) Once you are ready, try following the steps below and practice on your Python environment! The “unit std” is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance. DataFrame(np. StandardScaler(copy=True, with_mean=True, with_std=True) [source] Standardize features by removing the mean and scaling to unit variance. StandardScaler(*, copy=True, with_mean=True, with_std=True) [source] #. You’ll walk through an end-to-end example of k-means clustering using Python, from preprocessing the data to evaluating results. StandardScaler(). Following are the types of samples it provides. Example with Boston House Price Dataset. And 1 squared = 1. transform(X_train) StandardScaler. As already said in my previous tutorial, Data Normalization involves adjusting values measured on different scales to a common scale. import numpy as np from sklearn. Nov 5, 2018 · +1 Although avoiding the use of internal variables is a valid concern and warnings that this approach is dangerous are important (for example, mean_ and scale_ are not defined until you call fit_transform), this solution lets you recover the state of the StandardScaler without using external libraries or pickled files. transform extracted from open source projects. fit_transform Mar 12, 2024 · 1 介绍StandardScaler 是一种常用的数据标准化方法,用于将数据转换为均值为 0,标准差为 1 的标准正态分布。 标准化过程如下: 计算原始数据的均值 mean 和标准差 std。对原始数据进行标准化处理,即对每个数据点… Python StandardScaler. This tutorial will help beginner May 8, 2021 · I have typed in the following python commands along with sample data. Sep 11, 2020 · If you click on [source] on the right side you can see the source code. preprocessing import StandardScaler # After preparing and splitting the training and testing dataset, we got X_train # from only the targeted user X_test # from other "n-1" anomaly users # features selection using VarianceThreshold on training set sel Apr 20, 2023 · The advantage of using RobustScaler over StandardScaler and MinMaxScaler is that it is more robust to outliers. Aug 17, 2022 · I'm having trouble to find the correct code standardize my data among the 3 options below: # Option 1 from sklearn. from sklearn. fit_transform(num_d) # convert string variable to One Hot Encoding d = pd. import pandas as pd from sklearn. Puedes valorar ejemplos para ayudarnos a mejorar la calidad de los ejemplos. Configure output of transform and fit_transform. col1 col2 col3 1 0 A 1 10 C 2 1 A 3 20 B This is how I did it: サクッと標準化したいけど、ごちゃごちゃした解説はいらないよ〜という人向けに、お手軽で実装できる標準化の方法を書いてみました。#1. transform. Parameters: transform {“default”, “pandas”, “polars”}, default=None. columns, index=df. 9, 7620] ] I need to transform only one column, the third in this example. 24. Apply LabelEncoder and OnehotEncoder to categorical variables. fit extracted from open source projects. StandardScaler() 【preprocessing. preprocessing import StandardScaler iris = datasets. inverse_transform extracted from open source projects. Mar 9, 2013 · This tutorial was tested using Python version 3. Python serves as the backbone for implementing algorithms Mar 3, 2022 · I need to apply StandardScaler of sklearn to a single column col1 of a DataFrame:. To do that we first need to create a standardscaler() object and then fit and transform the data. fit Python StandardScaler. 2, 1000], [2, 0. copy() #Has training + test data frames combined to form single data frame normalizer = StandardScaler() data_array = normalizer. However, I need to know if I can generate the StandardScaler() object again from the saved arrays of mean and variance, something like: scaler = StandardScaler() scaler. This operation is performed feature-wise in an independent way. with_mean bool, default=True. columns # This will transform the selected columns and merge to the original data frame temp. if sparse. StandardScaler. data sc = StandardScaler() sc. set_index Aug 21, 2023 · Python code Examples Example 1: Scaling a Dataset using Scikit-Learn scale Scikit-Learn’s preprocessing. partial_fit - 10 examples found. values. StandardScaler makes the mean of the distribution approximately 0. How this is possible as we know that StandardScaler doesn't work with NaN values? If there is any other solution (Which is not dependent on Scikit Learn) also please mention that. transform(X) #On new data, though data count is one but An open source TS package which enables Node. model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0. MinMaxScaler scales the data to a fixed range, typically between 0 and 1. preprocessing import StandardScaler data_norm = data_x_filled. Moreover, we will also learn why it is important to scale the data before training the model. 0 Apr 9, 2019 · Sure, you can use any sklearn operation and apply it to a groupby object. May 26, 2020 · StandardScaler removes the mean and scales each feature/variable to unit variance. 25, random Aug 7, 2024 · StandardScaler follows Standard Normal Distribution (SND) . That’s because you didn’t specify the desired size of the training and test sets. The Anomaly Detection in Python, Dealing with Missing Data in Python, and Machine Learning for Finance in Python courses all show examples of using k-nearest neighbors. It's fast and very easy to use. Python Examples Python Examples , the Python sklearn module has a method called StandardScaler() which returns a Scaler object with methods for transforming data StandardScaler# StandardScaler removes the mean and scales the data to unit variance. If True, scale the data to unit variance (or equivalently, unit standard deviation). Dec 7, 2023 · Both MinMaxScaler and StandardScaler scale the data (features), but they use different methods to achieve this. Sklearn Standardscaler on a Simple Dataset. model_selection import train_test_split from sklearn. Syntax: scaler = StandardScaler() df = scaler. columns sc = StandardScaler() X_train_sc = pd. Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples. fit(x) x = scaler. fit_transform(df. We will use the default configuration and scale values to subtract the mean to center them on 0. If you want to dive deeper into dimensionality reduction techniques then consider reading about t-distributed Stochastic Neighbor Embedding commonly known as tSNE , which is a non-linear Nov 3, 2016 · I have the following code. The variance is equal to 1 also, because variance = standard deviation squared. Dec 11, 2019 · when I validate I do (my data is multiple samples in a csv file): scaler = StandardScaler(). These are the top rated real world Python examples of sklearn. fit_tra Mar 17, 2022 · Edited from a tutorial in Kaggle, I try to run the code below and data (available to download from here): Code: import seaborn as sns import matplotlib. Dec 20, 2018 · Assuming that your data is shaped [num_instances, num_time_steps, num_features] what I would do is first reshape the data and then normalize the data. We've also taken a look at how outliers affect these processes and the difference between a scale-sensitive model being trained with and Mar 1, 2016 · Edit 2: Came across the sklearn-pandas package. get_dummies(d) subject_id pH urinecolor_red urinecolor_yellow 0 -0. 2, TensorFlow 1. However, models are not supposed to learn anything about test set. The standard score of a sample x is calculated as: sklearn. index) StandardScaler in Sklearn with Python with Python with python, tutorial, tkinter, button, overview, canvas, frame, environment set-up, first python program, operators Apr 18, 2019 · I know that I can pickle the whole StandardScaler() object and use it later to transform new data. 1, 2400], [3, 0. Applying StandardScaler would result in undesired effects. supported by StandardScaler(). with_std bool, default=True. e. Apr 22, 2017 · Apply StandardScaler to continuous variables. columns=['sepal_len', 'sepal_wid', 'petal_len Aug 28, 2019 · Some machine learning algorithms will achieve better performance if your time series data has a consistent scale or distribution. Apr 19, 2018 · you can get cluster_centers on a kmeans, and just push that into your pca. sklearn-pandas is especially useful when you need to apply more than one type of transformation to column subsets of the DataFrame, a more common scenario. μ = 0 and σ = 1 your features/variables/columns of X, individually, before applying any machine learning model. fit_transform(data_norm) data_norm = pd. Jun 17, 2018 · I want to StandardScaler (Through SK learn) certain DataFrame, which contains a lot of NaN values and after performing this scaler shift I want to assign all NaN to -1. This scaler normalizes the data by subtracting the mean and dividing by the standard deviation. 053 1. kkw zndjx txxjr vqzp yjrqj szgf blu ranhx ilazsy akbs