NumPy ndarray.mean() Method: Arithmetic Mean of Elements

NumPy ndarray.mean()

The numpy.ndarray.mean() method calculates the arithmetic mean of elements in a NumPy array. It can compute the mean over the entire array or along a specified axis.

Syntax

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ndarray.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Parameters

Parameter	Type	Description
`axis`	None, int, or tuple of ints, optional	Axis or axes along which the mean is computed. If `None`, computes the mean of the entire array.
`dtype`	data-type, optional	Type of the returned array and accumulator for the calculation. Defaults to the input array type.
`out`	ndarray, optional	Alternative output array for storing the result. Must have the same shape as expected output.
`keepdims`	bool, optional	If `True`, retains reduced dimensions with size one, allowing proper broadcasting.
`where`	array_like of bool, optional	Specifies elements to include in the mean calculation.

Return Value

Returns a scalar value if axis=None, or an array of mean values if an axis is specified. The mean is computed by summing all selected elements and dividing by their count.

Examples

1. Computing Mean of an Entire ndarray

In this example, we compute the mean of all elements in a NumPy array.

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import numpy as np  

# Creating a 2D NumPy array  
arr = np.array([[1, 2, 3],  
                [4, 5, 6]])  

# Computing the mean of all elements in the array  
result = arr.mean()  

# Printing the result  
print("Mean of entire array:", result)

Output:

Mean of entire array: 3.5

The mean is calculated as \( \frac{1+2+3+4+5+6}{6} = 3.5 \).

2. Computing Mean Along a Specific Axis

Here, we compute the mean along different axes of a 2D array.

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import numpy as np  

# Creating a 2D NumPy array  
arr = np.array([[10, 20, 30],  
                [40, 50, 60]])  

# Computing mean along axis 0 (column-wise mean)  
result_axis0 = arr.mean(axis=0)  

# Computing mean along axis 1 (row-wise mean)  
result_axis1 = arr.mean(axis=1)  

# Printing results  
print("Mean along axis 0 (column-wise):", result_axis0)  
print("Mean along axis 1 (row-wise):", result_axis1)

Output:

Mean along axis 0 (column-wise): [25. 35. 45.]
Mean along axis 1 (row-wise): [20. 50.]

Each column mean is computed independently, e.g., \( \frac{10+40}{2} = 25 \). Each row mean is computed similarly, e.g., \( \frac{10+20+30}{3} = 20 \).

3. Using `dtype` to Control Precision

The dtype parameter allows specifying a higher precision for the mean computation.

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import numpy as np  

# Creating an integer array  
arr = np.array([1, 2, 3, 4], dtype=np.int8)  

# Computing mean with default precision  
result_default = arr.mean()  

# Computing mean with higher precision using dtype=np.float64  
result_high_precision = arr.mean(dtype=np.float64)  

# Printing results  
print("Mean with default precision:", result_default)  
print("Mean with higher precision (float64):", result_high_precision)

Output:

Mean with default precision: 2.5
Mean with higher precision (float64): 2.5

Using dtype=np.float64 ensures higher numerical stability for calculations.

4. Using `where` Parameter in ndarray.mean()

The where parameter allows computing the mean only for selected elements.

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import numpy as np  

# Creating a NumPy array  
arr = np.array([10, 20, 30, 40, 50])  

# Creating a boolean mask to include only elements greater than 20  
mask = arr > 20  

# Computing mean only for selected elements  
result = arr.mean(where=mask)  

# Printing result  
print("Mean considering only elements > 20:", result)

Output:

Mean considering only elements > 20: 40.0

Only elements 30, 40, and 50 are included in the calculation, leading to a mean of (30+40+50)/3 = 40.0.

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NumPy ndarray.mean() Method