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NumPy | mean method

schedule Aug 10, 2023
Last updated
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PythonNumPy
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NumPy's mean(~) method computes the mean value along the specified axis.

Parameters

1. a | array-like

The input array.

2. axislink | None or int or tuple of int | optional

The axis along which to compute the mean. For 2D arrays, the allowed values are as follows:

Axis

Meaning

0

Row-wise computation of mean

1

Column-wise computation of mean

None

All values used to compute the mean

By default, axis=None.

3. dtype | string or type | optional

The data-type to use during the computation of the mean. If the inputs are integers, then float64 is used. Otherwise, the same data-type will be used.

NOTE

As a best practice, you should specify float64 as dtype since if your input is of type float32, then float32 will be used during the computation of the mean, which would result in less accurate results.

4. out | NumPy array | optional

Instead of creating a new array, you can place the computed mean into the array specified by out.

Return value

If axis is unset, then a scalar is returned. Otherwise, a Numpy array is returned.

Examples

Computing the mean of 1D array

np.mean([1,2,3])
2.0

Computing the mean of 2D array

Consider the following array:

a = np.array([[1,2],[3,4]])
a
array([[1, 2],
       [3, 4]])

Mean of all values

np.mean(a)
2.5

Mean of each column

np.mean(a, axis=0)
array([2., 3.])

Mean of each row

np.mean(a, axis=1)
array([1.5, 3.5])
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Published by Isshin Inada
Edited by 0 others
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