# Numpy mean()

numpy.mean(a,axis=None,dtype=None,out=None,keepdims=False)
Return arithmetic mean of elements across given axis.
 a array, elements to get the mean value axis Int (optional ), or tuple, default is None. If axis given then across the axis is returned. dtype data-type( Optional ), Data Type of returned array or value. out Optional. If given then output to be stored. Must be of same time as of the output keepdims Bool ( Optional ), output matches to the input array dimension.
We will use these parameters in our examples.

## Sample array

You can use randint() to create an array for our examples. Or can use fixed elements to create the array.
import numpy as np
# my_data=np.random.randint(2,high=7,size=(3,3),dtype='int16')
my_data=np.array([[6, 3, 2], [2, 6, 2], [6, 2, 3]])
print(my_data)
Output
[[6 3 2]
[2 6 2]
[6 2 3]]

## Axis

Sum of the elements across the axis.
print("mean()      : ", my_data.mean())
print("mean(axis=0):", my_data.mean(axis=0))
print("mean(axis=1):", my_data.mean(axis=1))
Output
mean()      :  3.5555555555555554
mean(axis=0): [4.66666667 3.66666667 2.33333333]
mean(axis=1): [3.66666667 3.33333333 3.66666667]

## dtype

The data type of the output. By default the output will have the dtype of input array.
print("mean(axis=1,dtype=np.int8) : ", my_data.mean(axis=1,dtype=np.int8))
print("mean(axis=1,dtype=np.int32) : ", my_data.mean(axis=1,dtype=np.int32))
print("mean(axis=1,dtype=np.float64) : ", my_data.mean(axis=1,dtype=np.float64))
print("mean(axis=1,dtype=np.complex128) : ", my_data.mean(axis=1,dtype=np.complex128))
Output
mean(axis=1,dtype=np.int8) :  [3 3 3]
mean(axis=1,dtype=np.int32) :  [3 3 3]
mean(axis=1,dtype=np.float64) :  [3.66666667 3.33333333 3.66666667]
mean(axis=1,dtype=np.complex128) :  [3.66666667+0.j 3.33333333+0.j 3.66666667+0.j]

## keepdims

If it is set to True ( keepdims=True ) then it will take the dimension of input array.
print("mean(keepdims=True) : ", my_data.mean(keepdims=True))
print("mean(keepdims=False) : ", my_data.mean(keepdims=False))
Output
mean(keepdims=True) :  [[3.55555556]]
mean(keepdims=False) :  3.5555555555555554

## out

Alternative output array, must be of same shape as expected output. Let us first check with axis.
x = np.zeros(3,dtype=int)
print(my_data.mean(axis=0,out=x))
print(x)
Output
[4 3 2]
[4 3 2]
Without using axis
y = np.array(1)
print(my_data.mean(out=y))
print(y)
Output
3
3

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