# np.mean() vs np.average() in Python NumPy?

I notice that

```
In [30]: np.mean([1, 2, 3])
Out[30]: 2.0
In [31]: np.average([1, 2, 3])
Out[31]: 2.0
```

However, there should be some differences, since after all they are two different functions.

**What are the differences between them?**

np.average takes an optional weight parameter. If it is not supplied they are equivalent. Take a look at the source code: Mean, Average

np.mean:

```
try:
mean = a.mean
except AttributeError:
return _wrapit(a, 'mean', axis, dtype, out)
return mean(axis, dtype, out)
```

np.average:

```
...
if weights is None :
avg = a.mean(axis)
scl = avg.dtype.type(a.size/avg.size)
else:
#code that does weighted mean here
if returned: #returned is another optional argument
scl = np.multiply(avg, 0) + scl
return avg, scl
else:
return avg
...
```

From: stackoverflow.com/q/20054243

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