array([1500, 1520, 1540, 1590, 1590, 1600, 1600, 1560, 1560, 1560, 1580,
1520, 1460, 1510, 1520, 1320, 1320, 1300, 1300, 1320, 1320, 1320,
1320, 1300, 1300, 1340, 1300, 1300, 1300, 1400, 1480, 1360, 1420,
1480, 1580, 1530, 1500, 1480, 1480, 1480, 1460, 1540, 1490, 1480,
1480, 1520, 1500, 1460, 1480, 1480, 1500, 1500, 1600, 1540, 1480,
1460, 1560, 1600, 1560, 1600, 1600, 1600, 1620, 1600, 1580, 1600,
1700, 1620, 1620, 1620, 1700, 1700, 1680, 1640, 1620, 1670, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1670,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1600, 1680, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0], dtype=int64)
>Solution :
You can filter the array
arr = np.array([41, 42, 43, 44, 0, 0,0])
arr.mean() # 24.2857
arr[arr != 0].mean() # 42.5
edit: (!= to include negative numbers thanks to @tdelaney)