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fatso
answered May 14 '23 00:00
In Python, numpy.mean() is a function provided by the NumPy library that calculates the arithmetic mean of a given set of numbers or elements in an array.
Here's how you can use numpy.mean() to calculate the mean of a list of numbers:
import numpy as np
today_list = [1, 2, 3, 4, 5]
mean = np.mean(today_list)
print(mean)
In this example, np.mean(today_list) calculates the mean of the elements in today_list , which is (1+2+3+4+5)/5 = 3. The calculated mean is stored in the variable mean and then printed to the console.
You can also use numpy.mean() to calculate the mean of elements in a NumPy array:
import numpy as np
today_array = np.array([[1, 2], [3, 4]])
mean = np.mean(today_array)
print(mean)
In this example, np.mean(today_array) calculates the mean of all elements in today_array, which is (1+2+3+4)/4 = 2.5. The calculated mean is stored in the variable mean and then printed to the console.
Note that numpy.mean() provides several optional parameters, such as axis, dtype, and keepdims, which can be used to control how the mean is calculated .