## numpy.mean()

Numpy.mean() is function in Python language which is responsible for calculating the arithmetic mean for the all the elements present in the array entered by the user. Simply put the functions takes the sum of all the individual elements present along the provided axis and divides the summation by the number of individual calculated elements. The axis along which the calculation is made has to be prespecified or else the default value for axes will be taken.

## Syntax :

The following is the syntax that displays how to implement numpy.mean().

numpy.mean (a, axis =None, dtype = None, out = None, keepdims =< novalue>)

• ## a *: *array *_ *like *

The array is being entered by the user or prompted to be entered. In case the array entered is not of an integer data type, then the conversion of the form is tried on the data entered.

• ## axis : None *, * *int *, * *tuple * (optional parameter)

The computation of the axis along the elements of the specified array entered by the user is done. By default, the mean of the pre-flattened array is computed. In case the array entered is a tuple, in such a case the mean is computed over various axes of the array.

• ## dtype * *: * *data *– *type *, (parameter is optional)

For the computation of the mean the parameter type is utilized. By default, the float 64data type is used for arrays with integer data sets. In case the data being input is floating it remains the same as the dtype entered.

• ## out : ndarray, (parameter is optional)

It is an alternative array which is made to record the resultant mean. By default, the parameters stay None. In case it is provided the array is needed to have the same ascertained shape as that of the output which is expected.

• ## keepdims: bool, (parameter is optional)

If the parameter specified is True, the axis or axes which are deduced are kept in the expected result as the dimensions having size one. The option enables the result to be broadcasted correctly in response to the array which has been entered. In case value by default, a parameter is passed then the keepdims parameter would not be passed on to the method-specific for mean with respect to the array and its sub-classes. However, it must be noted that for non-default values passed the keepdims parameter would be applicable to raising exceptions if any.

• ## m : ndarray

If the parameter out=None, then in such a case a new array is returned which contains the mean values. Else, in such cases, the reference values with respect to the elements if retuned.

### Example for Implementation of the NumPy.mean()

```import numpy as n1
a1 = n1.array([[10,20,30],[30,40,50],[40,50,60]])
print 'The new array entered by the user is:'
print a1
print 'Application of the Numpy.mean() function on the array entered:'
print n1.mean(a1)
print 'Application of the mean() function alongside the axis - 0:'
print n1.mean(a1, axis = 0)
print ' Application of the mean() function alongside the axis - 1:'
print n1.mean(a1, axis = 1)
```

Apply now for Advanced Python Training Course