What is the difference between np.mean() vs np.average()?

What is the distinction between np.mean() and np.average()? 'Mean' & 'Average' are not the same thing. They are frequently used interchangeably, but they should not even be. The np.mean() method returns the arithmetic mean, but the np.average() function returns the algebraic mean if no additional parameters are specified, but it may also be used to compute a weighted average.

I assume it's a fairly straightforward response. Let us delve into the intricacies of the NumPy mean and average methods. After that, we'll go into the differences between numpy.mean() and numpy.average() in detail. And last, with a focus on the question: Why would I ever use np.mean()?

Since NumPy is primarily used to interact with data sets, it is critical to comprehend the mathematical notion that is causing this misunderstanding. Even though this is incorrect, average and mean are often used interchangeably in everyday speech and basic math. In statistics,

• The term "mean" often refers to the "arithmetic mean", which is the sum of a set of numbers divided by the total number of the numbers in the set.
• The term "average" refers to a variety of computations, the "arithmetic mean" is one of them. Other terms include 'median,' 'mode,' 'weighted mean,' 'interquartile mean,' and a variety of others.

You are now well acquainted with the statistical definition. Let's get started and go through syntax in detail.

numpy.mean() Function

Syntax:

Parameter:

In NumPy, np.mean() will compute the 'Arithmetic Mean' along a given axis. Here's how you'd utilize it:

Code:

Output:

Since we did not provide the value of the parameter axis in the preceding code, the mean of the flattened array is calculated by default.

numpy.average() Function

Syntax:

numpy.average(), on the contrary, allows you to compute a Weighted Mean, with each value in your array having a distinct weight. For instance, consider the following code example:

Code:

Output:

In the prior example, we saw that if we used a non-weighted average, the result would be 7.0. However, it turns out to be 4.666666666666667 because of the weights we added to it.

If you don't understand what a 'weighted mean' is. Let us attempt to make things simpler:

A mean is the summation of all elements divided by the total number of elements. This signifies that they all have the same weight or are only measured once. This means :

A weighted mean includes elements with varying weights. It can be hard to imagine, so we'll envision the weighting fits more comfortably over the numbers and looks something like this:

Although there is only a single occurrence of the number 1 in the actual data set, we are counting it here as 14 times its regular weight (i.e. 1). This may also be done in another way; for example, we might measure an element at 1/3 of its regular weight. If no weight argument is given to np.average(), it will simply return the equal-weighted average over the flattened axis, which is comparable to np.mean().

Returning to the actual topic. As NumPy is typically utilized in mathematical applications, the distinction between Average() and Mean() must be clarified. Let us now compare and contrast the functions np.average() and np.mean().

Difference between np.average() and np.mean()

• Many more arguments, such as dtype, out, where, and others, are available in the np.mean() method that is not accessible in the np.average() function.
• If the weights option is specified, the np.average() function may compute a weighted mean, but np.mean() cannot.
• Since np.average does not take into consideration boolean masks, it will compute the average over the entire collection of data. While the np.mean() method takes into account boolean masks, compute the mean solely across unmasked data.

You've now seen the key differences between the np.average() and np.mean() functions. You may have noticed that if the weights are not supplied for np.average(), it acts similarly to np.mean(); you may be asking why you would ever use np.mean(). Let us try to answer this question in the following part.

Why Would I Ever Use np.mean()?

np.mean() accepts a few arguments that np.average() does not. One of the most important is the dtype argument, which allows you to specify the data type that will be employed in the computation. Let me illustrate this with an example.

Code:

Output:

According to the computation above, our average is 0.5500002. However, if we change the dtype argument to float64, we obtain a different result:

Code:

Output:

According to the computation above, our average is 0.5499999999999998.

When both of them are rounded to two significant digits, the result is 0.55 in both examples. This precision becomes critical when performing repeated sets of operations on the number, particularly when working with very big (or very small) numbers that require great accuracy.

Consider the following scenario.

Even with simpler equations, you might be wrong by a few decimal places, which can be significant in:

• Scientific simulations require several phases and a considerable degree of precision due to long formulae.
• In medical research, for example, the discrepancy between a few percentage points of precision might be critical.
• Being incorrect by a few pennies in major financial models or when tracking significant sums of money can result in mistakes totaling hundreds of thousands of dollars at the end of the year.

Finally, when analyzing data, you may be put in a situation where you are requested to determine the average of a dataset. You might wish to use a different average approach to get the most precise representation of the dataset. If you're questioning what the "optimal" technique is in Python to average a NumPy array, my answer is "it depends."