Program a function `sumAverage(arr)` where `arr` is an array containing arrays full of numbers, for example:

```python
sum_average([[1, 2, 2, 1], [2, 2, 2, 1]])
```

First, determine the average of each array. Then, return the sum of all the averages.

- All numbers will be less than 100 and greater than -100.
- `arr` will contain a maximum of 50 arrays.
- After calculating all the averages, add them **all** together, **then** round down, as shown in the example below:

The example given: `sumAverage([[3, 4, 1, 3, 5, 1, 4], [21, 54, 33, 21, 77]])`, the answer being 44.
1. Calculate the average of each individual array:
```
[3, 4, 1, 3, 5, 1, 4] = (3 + 4 + 1 + 3 + 5 + 1 + 4) / 7 = 3
[21, 54, 33, 21, 77] = (21 + 54 + 33 + 21 + 77) / 5 = 41.2
```
2. Add the average of each array together:
```
3 + 41.2 = 44.2
```
3. Round the final average down:

~~~if:julia
In Julia, the `Statistics` package is preloaded.
~~~
```python
import math
math.floor(44.2) = 44
```

Source: codewars | Difficulty: EASY
Tags: Data structures

Mathematics

Starter code: def sum_average(arr):
	

Solutions:
from statistics import mean
from math import floor

def sum_average(arr):
	return floor(sum(map(mean, arr)))


from statistics import mean
from math import floor

def sum_average(arr):
	return floor(sum((mean(lst) for lst in arr)))


import math

def sum_average(arr):
	return math.floor(sum([sum(array) / len(array) for array in arr]))


from math import floor

def sum_average(arr):
	return floor(sum((sum_average(x) / len(x) if type(x) is list else x for x in arr)))


import math

def sum_average(arr):
	total = 0
	for x in arr:
		total += sum(x) / len(x)
	return math.floor(total)


import numpy as np

def sum_average(arr):
	nm = int(sum([np.mean(arr[i]) for i in range(len(arr))]))
	return nm - 1 if nm < 0 else nm


import numpy as np
from math import floor

def sum_average(arr):
	return floor(sum(map(np.average, arr)))


import numpy
import math

def sum_average(arr):
	print(arr)
	total = 0
	for item in arr:
		print(numpy.mean(item))
		total += numpy.mean(item)
	return math.floor(total)


import math

def sum_average(arr):
	x = 0
	for i in range(0, len(arr)):
		l = len(arr[i])
		s = sum(arr[i])
		v = s / l
		x += v
	w = math.floor(x)
	return w


def avg(seq):
	sum = 0
	count = 0
	for i in seq:
		sum += i
		count += 1
	return sum / count if count != 0 else 0
sum_average = lambda arr: __import__('math').floor(sum((avg(i) for i in arr)))