numpy.mean#

numpy.mean(a,axis=None,dtype=None,out=None,keepdims=<novalue>,*,where=<novalue>)[source]#

Compute the arithmetic mean along the specified axis.

Returns the average of the array elements. The average is taken overthe flattened array by default, otherwise over the specified axis.float64 intermediate and return values are used for integer inputs.

Parameters:
aarray_like

Array containing numbers whose mean is desired. Ifa is not anarray, a conversion is attempted.

axisNone or int or tuple of ints, optional

Axis or axes along which the means are computed. The default is tocompute the mean of the flattened array.

If this is a tuple of ints, a mean is performed over multiple axes,instead of a single axis or all the axes as before.

dtypedata-type, optional

Type to use in computing the mean. For integer inputs, the defaultisfloat64; for floating point inputs, it is the same as theinput dtype.

outndarray, optional

Alternate output array in which to place the result. The defaultisNone; if provided, it must have the same shape as theexpected output, but the type will be cast if necessary.SeeOutput type determination for more details.SeeOutput type determination for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the input array.

If the default value is passed, thenkeepdims will not bepassed through to themean method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised.

wherearray_like of bool, optional

Elements to include in the mean. Seereduce for details.

New in version 1.20.0.

Returns:
mndarray, see dtype parameter above

Ifout=None, returns a new array containing the mean values,otherwise a reference to the output array is returned.

See also

average

Weighted average

std,var,nanmean,nanstd,nanvar

Notes

The arithmetic mean is the sum of the elements along the axis dividedby the number of elements.

Note that for floating-point input, the mean is computed using thesame precision the input has. Depending on the input data, this cancause the results to be inaccurate, especially forfloat32 (seeexample below). Specifying a higher-precision accumulator using thedtype keyword can alleviate this issue.

By default,float16 results are computed usingfloat32 intermediatesfor extra precision.

Examples

>>>importnumpyasnp>>>a=np.array([[1,2],[3,4]])>>>np.mean(a)2.5>>>np.mean(a,axis=0)array([2., 3.])>>>np.mean(a,axis=1)array([1.5, 3.5])

In single precision,mean can be inaccurate:

>>>a=np.zeros((2,512*512),dtype=np.float32)>>>a[0,:]=1.0>>>a[1,:]=0.1>>>np.mean(a)np.float32(0.54999924)

Computing the mean in float64 is more accurate:

>>>np.mean(a,dtype=np.float64)0.55000000074505806 # may vary

Computing the mean in timedelta64 is available:

>>>b=np.array([1,3],dtype="timedelta64[D]")>>>np.mean(b)np.timedelta64(2,'D')

Specifying a where argument:

>>>a=np.array([[5,9,13],[14,10,12],[11,15,19]])>>>np.mean(a)12.0>>>np.mean(a,where=[[True],[False],[False]])9.0
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