numpy.mean() in Python | np.mean() in Python
Numpy mean() function is used to calculate arithmetic mean of the values along the specified axis.
Syntax of Numpy mean()
np.mean(a, axis=None, dtype=None)
Here, a parameter is used to pass Numpy array to mean() function.
axis parameter is optional and is used to specify the axis along which we want to perform arithmetic mean. When no axis value is passed then arithmetic mean of entire value is computed and a single value is returned.
dtype parameter controls the data type of the resultant mean.
Python program to calculate mean using numpy.mean() on 1-Dimensional Numpy Array
import numpy as np
a = np.array([32, 4, 8, 12, 20])
print('Original array is ', a)
print('Mean is ', np.mean(a))
Output of the above program
Original array is [32 4 8 12 20] Mean is 15.2
As you can see in the above output, np.mean() has computed mean for this array. In 1-dimensional array, it is optional to provide axis parameter to mean() function.
Python program to calculate mean using numpy.mean() on 2-Dimensional Numpy Array
import numpy as np
a = np.array([[32, 4, 8, 12, 20], [35, 5, 15, 10, 30]])
print('Mean of arr is ', np.mean(a))
print('Mean of arr along axis0 is ', np.mean(a, axis=0))
print('Mean of arr along axis1 is ', np.mean(a, axis=1))
Output of the above program
Mean of arr is 17.1 Mean of arr along axis0 is [33.5 4.5 11.5 11. 25. ] Mean of arr along axis1 is [15.2 19. ]
When axis value is not provided, then mean() function works like this-
When axis=0 is passed to mean() function, then it works like this-
When axis=1 is passed to mean() function, then it works like this-
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