This article aims to compare and contrast the functionalities of the np.mean() and np.average() functions in Python NumPy.

Both functions are utilized to compute averages, with np.mean() returning the arithmetic mean and np.average() enabling the calculation of weighted averages if weights are provided.

While the two functions are equivalent when weights are not supplied, they differ in their ability to handle masks and the presence of the dtype keyword.

The source code for np.mean() includes a try-except block, whereas np.average() contains additional code for computing the weighted mean and returns the scaling factor if weights are provided.

Notably, np.mean() is recommended for large arrays, particularly when the dtype=float64 is specified, while np.average() should be explicitly used for weighted averages.

By examining the similarities and distinctions between np.mean() and np.average(), this article seeks to provide a comprehensive understanding of their respective applications in Python NumPy.

Comparing Functions

The comparison between np.mean() and np.average() in Python NumPy includes evaluating their differences in functionality and behavior.

np.mean() computes the arithmetic mean along the specified axis, while np.average() can compute a weighted average if the weights parameter is supplied. The two functions are equivalent if weights are not provided.

np.mean() returns the average of the array elements, while np.average() returns the average and scaling factor if weights are supplied. Both functions take into account masks and compute the mean only over unmasked values.

However, np.mean() has additional options for input and output, allowing the dtype keyword, which is essential for getting correct results in some cases. On the other hand, np.average() does not allow the dtype keyword and can give incorrect results if not specified.

Overall, np.mean() is preferred for general use, while np.average() should be used explicitly for weighted averages.

Mean vs. Average

When comparing the two functions, it is important to understand the distinction between the concepts of mean and average.

The mean is a measure of central tendency that represents the arithmetic average of a set of values. It is calculated by summing all the values and dividing by the number of values.

On the other hand, average is a more general term that can refer to different types of averages, including the mean.

In the context of numpy, np.mean() calculates the arithmetic mean along the specified axis, while np.average() can compute a weighted average if the weights parameter is supplied.

Therefore, np.mean() is more commonly used when calculating simple averages, while np.average() is preferred when dealing with weighted averages.

Differences and Usage

Differences between np.mean() and np.average() can be observed in terms of their computational methods and usage in statistical calculations.

While both functions can compute the average of array elements, np.mean() computes the arithmetic mean along the specified axis, while np.average() can compute a weighted average if the weights parameter is supplied. If weights are not provided, the two functions are equivalent.

Additionally, np.average() takes an optional weight parameter and returns the average along with the scaling factor if weights are supplied. On the other hand, np.mean() always computes an arithmetic mean and has additional options for input and output. It also takes into account masks and computes the mean only over unmasked values. In contrast, np.average() does not consider masks.

Furthermore, np.average() does not allow the dtype keyword, which is necessary for obtaining correct results in some cases, while np.mean() allows the dtype keyword.

Overall, np.mean() is preferred for large arrays and np.average() should be explicitly used for weighted averages.