How to Scale Machine Learning Data From Scratch With Python

Many machine learning algorithms expect data to be scaled consistently.

There are two popular methods that you should consider when scaling your data for machine learning.

In this tutorial, you will discover how you can rescale your data for machine learning. After reading this tutorial you will know:

  • How to normalize your data from scratch.
  • How to standardize your data from scratch.
  • When to normalize as opposed to standardize data.

Let’s get started.

  • Update Feb/2018: Fixed minor typo in min/max code example.
  • Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.
  • Update Aug/2018: Tested and updated to work with Python 3.6.
How To Prepare Machine Learning Data From Scratch With Python

How To Prepare Machine Learning Data From Scratch With Python
Photo by Ondra Chotovinsky, some rights reserved.


Many machine learning algorithms expect the scale of the input and even the output data to be equivalent.

It can help in methods that weight inputs in order to make a prediction, such as in linear regression and logistic regression.

It is practically required in methods that combine weighted inputs in complex ways such as in artificial neural networks and deep learning.

In this tutorial, we are going to practice rescaling one standard machine learning dataset in CSV format.

Specifically, the Pima Indians dataset. It contains 768 rows and 9 columns. All of the values in the file are numeric, specifically floating point values. We will learn how to load the file first, then later how to convert the loaded strings to numeric values.

You can learn more about this dataset on the UCI Machine Learning Repository (update: download from here).


This tutorial is divided into 3 parts:

  1. Normalize Data.
  2. Standardize Data.
  3. When to Normalize and Standardize.

These steps will provide the foundations you need to handle scaling your own data.

1. Normalize Data

Normalization can refer to different techniques depending on context.

Here, we use normalization to refer to rescaling an input variable to the range between 0 and 1.

Normalization requires that you know the minimum and maximum values for each attribute.

This can be estimated from training data or specified directly if you have deep knowledge of the problem domain.

You can easily estimate the minimum and maximum values for each attribute in a dataset by enumerating through the values.

The snippet of code below defines the dataset_minmax() function that calculates the min and max value for each attribute in a dataset, then returns an array of these minimum and maximum values.

We can contrive a small dataset for testing as follows:

With this contrived dataset, we can test our function for calculating the min and max for each column.

Running the example produces the following output.

First, the dataset is printed in a list of lists format, then the min and max for each column is printed in the format column1: min,max and column2: min,max.

For example:

Once we have estimates of the maximum and minimum allowed values for each column, we can now normalize the raw data to the range 0 and 1.

The calculation to normalize a single value for a column is:

Below is an implementation of this in a function called normalize_dataset() that normalizes values in each column of a provided dataset.

We can tie this function together with the dataset_minmax() function and normalize the contrived dataset.

Running this example prints the output below, including the normalized dataset.

We can combine this code with code for loading a CSV dataset and load and normalize the Pima Indians diabetes dataset.

Download the Pima Indians dataset from the UCI Machine Learning Repository and place it in your current directory with the name pima-indians-diabetes.csv (update: download from here). Open the file and delete any empty lines at the bottom.

The example first loads the dataset and converts the values for each column from string to floating point values. The minimum and maximum values for each column are estimated from the dataset, and finally, the values in the dataset are normalized.

Running the example produces the output below.

The first record from the dataset is printed before and after normalization, showing the effect of the scaling.

2. Standardize Data

Standardization is a rescaling technique that refers to centering the distribution of the data on the value 0 and the standard deviation to the value 1.

Together, the mean and the standard deviation can be used to summarize a normal distribution, also called the Gaussian distribution or bell curve.

It requires that the mean and standard deviation of the values for each column be known prior to scaling. As with normalizing above, we can estimate these values from training data, or use domain knowledge to specify their values.

Let’s start with creating functions to estimate the mean and standard deviation statistics for each column from a dataset.

The mean describes the middle or central tendency for a collection of numbers. The mean for a column is calculated as the sum of all values for a column divided by the total number of values.

The function below named column_means() calculates the mean values for each column in the dataset.

The standard deviation describes the average spread of values from the mean. It can be calculated as the square root of the sum of the squared difference between each value and the mean and dividing by the number of values minus 1.

The function below named column_stdevs() calculates the standard deviation of values for each column in the dataset and assumes the means have already been calculated.

Again, we can contrive a small dataset to demonstrate the estimate of the mean and standard deviation from a dataset.

Using an excel spreadsheet, we can estimate the mean and standard deviation for each column as follows:

Using the contrived dataset, we can estimate the summary statistics.

Executing the example provides the following output, matching the numbers calculated in the spreadsheet.

Once the summary statistics are calculated, we can easily standardize the values in each column.

The calculation to standardize a given value is as follows:

Below is a function named standardize_dataset() that implements this equation

Combining this with the functions to estimate the mean and standard deviation summary statistics, we can standardize our contrived dataset.

Executing this example produces the following output, showing standardized values for the contrived dataset.

Again, we can demonstrate the standardization of a machine learning dataset.

The example below demonstrate how to load and standardize the Pima Indians diabetes dataset, assumed to be in the current working directory as in the previous normalization example.

Running the example prints the first row of the dataset, first in a raw format as loaded, and then standardized which allows us to see the difference for comparison.

3. When to Normalize and Standardize

Standardization is a scaling technique that assumes your data conforms to a normal distribution.

If a given data attribute is normal or close to normal, this is probably the scaling method to use.

It is good practice to record the summary statistics used in the standardization process, so that you can apply them when standardizing data in the future that you may want to use with your model.

Normalization is a scaling technique that does not assume any specific distribution.

If your data is not normally distributed, consider normalizing it prior to applying your machine learning algorithm.

It is good practice to record the minimum and maximum values for each column used in the normalization process, again, in case you need to normalize new data in the future to be used with your model.


There are many other data transforms you could apply.

The idea of data transforms is to best expose the structure of your problem in your data to the learning algorithm.

It may not be clear what transforms are required upfront. A combination of trial and error and exploratory data analysis (plots and stats) can help tease out what may work.

Below are some additional transforms you may want to consider researching and implementing:

  • Normalization that permits a configurable range, such as -1 to 1 and more.
  • Standardization that permits a configurable spread, such as 1, 2 or more standard deviations from the mean.
  • Exponential transforms such as logarithm, square root and exponents.
  • Power transforms such as box-cox for fixing the skew in normally distributed data.


In this tutorial, you discovered how to rescale your data for machine learning from scratch.

Specifically, you learned:

  • How to normalize data from scratch.
  • How to standardize data from scratch.
  • When to use normalization or standardization on your data.

Do you have any questions about scaling your data or about this post?
Ask your question in the comments below and I will do my best to answer.

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13 Responses to How to Scale Machine Learning Data From Scratch With Python

  1. Daniel Armstrong October 14, 2016 at 1:18 pm #

    I am wanting to use transaction totals by month as a feature. I plan to use Xgboost and from what I have read it is better to use binary variables. Will normalizing work or should bin the totals into deciles so I can make them binary?

    • Jason Brownlee October 15, 2016 at 10:15 am #

      Hi Daniel,

      I think you could try a model with transaction totals as-is, binned values, and with binary values (above a threshold or something).

      I don’t know the problem you’re working on, but generally it is good practice to try a few different framings of a problem and see which works best.

      • Daniel Armstrong October 17, 2016 at 9:47 pm #

        Thank you for your time

  2. Logan April 6, 2017 at 11:14 pm #

    Hi, it’s a very good tutorial. But I still have a doubt. If I wanted to use scikit function to normalize my data and then print it in order to verufy if it really worked, how should I proceed?
    I’ve tried something like

    scaler = MinMaxScaler(feature_range=(0, 1))
    dataset = scaler.fit_transform(dataset)

    but it doesn’t work. It doesn’t effect the data at all

    • Jason Brownlee April 9, 2017 at 2:41 pm #

      The code looks good.

      Try printing the transformed array rather than plotting.

  3. Jeremy April 3, 2018 at 1:09 am #

    Hi Jason,

    If I have two model (classification and regression), and each of them output log loss and absolute error.

    So, if I want to combine the output errors, do I have to normalize both errors first before performing the addition? If yes, can normalization formula above be used to perform normalization on both errors?

    • Jason Brownlee April 3, 2018 at 6:37 am #

      Log loss on a regression problem does not make sense.

      • Jeremy April 3, 2018 at 3:27 pm #

        Sorry, my previous post might confused you. Actually, in my case classification problem outputs log loss error function while regression problem outputs absolute error function (MSE, MAE, R2, etc).

        So I want to sum up both errors (from classification and regression problem), and need to normalize them first. scikit-learn provides Normalized parameter in log loss function which it will return the mean loss per sample. However regression loss function such as RMSE does not have Normalization parameter which the mean loss output needs to be normalized manually.

        Can you suggest how can I normalized the final output of loss error in regression problem?

        Your opinion on this matter is highly appreciated.

        • Jason Brownlee April 4, 2018 at 6:06 am #

          Why would you want to normalize the error?

          It might be better to interpet the error score than to transform it. For example RMSE is in the units of the output variable directly.

  4. KS October 12, 2018 at 9:28 am #

    What is the scale formula for -1 -> 1?

    • Jason Brownlee October 12, 2018 at 11:28 am #

      Good question, you can use:

      Here’s a snippet to demo the function:

  5. Michael January 12, 2019 at 2:05 am #

    Hello Jason,

    In your post you recommend using standardization when the data is normally distributed and normalization when the data is not normally distributed. Do you know why that is? I think I’ve seen standardization described as a z-score elsewhere, but I don’t understand why computing this value for non-normally distributed data isn’t recommended as inputs to machine learning algorithms. Couldn’t a machine learning algorithm still derive value from these inputs even with non-normally distributed data?

    • Jason Brownlee January 12, 2019 at 5:42 am #

      Standardization shifts data to have a zero mean and unit standard deviation. For data that is not Gaussian, this transform would not make sense – the data would not be centered and there is no standard deviation for non Gaussian data.

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