Overview:
- The mean() function of numpy.ndarray calculates and returns the mean value along a given axis.
- The mean value is calculated based on the axis specified.
- If no axis is specified, all the values of the n-dimensional array is considered while calculating the mean value.
- For details of axis of n-dimensional arrays refer to the cumsum() and cumprod() section.
- The numpy.ndarray also provides methods var(), std() methods that calculates the variance and standard deviation along any given axis of a ndarray object.
Example:
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import numpy as np
# Create a 3 dimensional ndarray nd_array = np.array([[[5,5,5,5], [6,6,6,6], [7,7,7,7]],
[[8,8,8,8], [9,9,9,9], [9,9,9,9]],
[[10,10,10,10], [11,11,11,11], [12,12,12,12]]] ) print("Input Array:") print(nd_array)
print("Shape of the array:") print(nd_array.shape)
print("Dimensions of the array:") print(nd_array.ndim)
print("Mean of a numpy.ndarray object - No axis specified:") print(nd_array.mean())
print("Mean of a numpy.ndarray object - Along axis 0:") print(nd_array.mean(axis=0))
print("Mean of a numpy.ndarray object - Along axis 1:") print(nd_array.mean(axis=1))
print("Mean of a numpy.ndarray object - Along axis 2:") print(nd_array.mean(axis=2))
print("Variance of a numpy.ndarray object - No axis specified:") print(nd_array.var())
print("Variance of a numpy.ndarray object - Along axis 0:") print(nd_array.var(axis=0))
print("Variance of a numpy.ndarray object - Along axis 1:") print(nd_array.var(axis=1))
print("Variance of a numpy.ndarray object - Along axis 2:") print(nd_array.var(axis=2))
print("Standard deviation of a numpy.ndarray object - No axis specified:") print(nd_array.std())
print("Standard deviation of a numpy.ndarray object - Along axis 0:") print(nd_array.std(axis=0))
print("Standard deviation of a numpy.ndarray object - Along axis 1:") print(nd_array.std(axis=1))
print("Standard deviation of a numpy.ndarray object - Along axis 2:") print(nd_array.std(axis=2)) |
Output:
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Input Array: [[[ 5 5 5 5] [ 6 6 6 6] [ 7 7 7 7]]
[[ 8 8 8 8] [ 9 9 9 9] [ 9 9 9 9]]
[[10 10 10 10] [11 11 11 11] [12 12 12 12]]] Shape of the array: (3, 3, 4) Dimensions of the array: 3 Mean of a numpy.ndarray object - No axis specified: 8.55555555556 Mean of a numpy.ndarray object - Along axis 0: [[ 7.66666667 7.66666667 7.66666667 7.66666667] [ 8.66666667 8.66666667 8.66666667 8.66666667] [ 9.33333333 9.33333333 9.33333333 9.33333333]] Mean of a numpy.ndarray object - Along axis 1: [[ 6. 6. 6. 6. ] [ 8.66666667 8.66666667 8.66666667 8.66666667] [ 11. 11. 11. 11. ]] Mean of a numpy.ndarray object - Along axis 2: [[ 5. 6. 7.] [ 8. 9. 9.] [ 10. 11. 12.]] Variance of a numpy.ndarray object - No axis specified: 4.69135802469 Variance of a numpy.ndarray object - Along axis 0: [[ 4.22222222 4.22222222 4.22222222 4.22222222] [ 4.22222222 4.22222222 4.22222222 4.22222222] [ 4.22222222 4.22222222 4.22222222 4.22222222]] Variance of a numpy.ndarray object - Along axis 1: [[ 0.66666667 0.66666667 0.66666667 0.66666667] [ 0.22222222 0.22222222 0.22222222 0.22222222] [ 0.66666667 0.66666667 0.66666667 0.66666667]] Variance of a numpy.ndarray object - Along axis 2: [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] Standard deviation of a numpy.ndarray object - No axis specified: 2.16595429885 Standard deviation of a numpy.ndarray object - Along axis 0: [[ 2.05480467 2.05480467 2.05480467 2.05480467] [ 2.05480467 2.05480467 2.05480467 2.05480467] [ 2.05480467 2.05480467 2.05480467 2.05480467]] Standard deviation of a numpy.ndarray object - Along axis 1: [[ 0.81649658 0.81649658 0.81649658 0.81649658] [ 0.47140452 0.47140452 0.47140452 0.47140452] [ 0.81649658 0.81649658 0.81649658 0.81649658]] Standard deviation of a numpy.ndarray object - Along axis 2: [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] |