![]() In cell number : We import the library pandas and sklearn. In this, we are going to discuss about that. We can also normalize the particular dataset column. ![]() Method 2: Normalize a particular column in a dataset using sklearn In cell number : We can see from the results, our all integer data are now normalized between 0 and 1. In cell number : We called the normalize method from the preprocessing and passed the numpy_array, which we just created as a parameter. In cell number : We created a NumPy array with some integer value that is not the same. That’s why this is the sklearn normalization method. You can see that we import the preprocessing from the sklearn itself. In cell number : We import all the required libraries, NumPy and sklearn. The sklearn method is a very famous method to normalize the data. So, in this article, we are focusing on numeric data. So, every data type has a different method to normalize. The normalization also depends upon the data type like images, text, numeric, etc. So, if you have chosen the wrong method to normalize your data, you might get something different from your expectations. Normalization is not an easy task because all your results depend upon the choice of your normalize method. Normalization of data is a technique that helps to get the result faster as the machine has to process a smaller range of data.
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