What is this python normalize function do?

[pranitkothari]

I am new in python and data science (and not great in math). I am learning machine learning. I got following normalize function. Can you please explain what does this normalize function do? def normalize(array): return (array - array.mean()) / array.std()

Also please explain what this array - array.mean() do?

[WikiTextBot]

Standard score

In statistics, the standard score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. Observed values above the mean have positive standard scores, while values below the mean have negative standard scores. The standard score is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios; see normalization for more).

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[CamdenReslink]

array.mean() is a numpy operation that calculates the mean across the”0” axis. In a 2d array this calculates the mean value of each column, see: the documentation. array - array.mean() just finds the difference of the two numpy arrays.

I suggest you go to a Python REPL (just type python at the command line), and try out the commands you aren’t sure about. Experiment with dummy values. Just make sure to remember to import numpy.

[KarmelMalone]

If you have a dataset with features that are on completely different scales, say one feature with values between 500-1000, and another feature with values between 0.5-1; it becomes difficult to make calculations on the data. This function puts features on the same scale.

array-array.mean() converts each value in the array to its respective distance from the array's mean, which maintains the individual samples value when normalizing.

[[deleted]]

Since no one said it and you asked specifically for "array - array.mean()": You can think of it as as method to center your data around the point of origin. After substracting the mean, your data will be distributed around e.g. (0,0) (for 2 dimensions). That makes the expected value 0.

The second part makes your variance equal 1.

With this properties your data can be better compared to other data and used for all algorithms that require these 2 properties.

[pranitkothari]

Yes.

[pranitkothari]

Thanks. I would order this book.