More Useful Array Operations.
Table Of Contents:
- Maximum,
- Minimum,
- Sum,
- Mean,
- Product,
- Standard Deviation
(1) data.max()
- data.max( ) will give you the ‘max’ value present in the array.
Example:
import numpy as np
data = np.array([3,1,5,9,2,-3,9,10])
data.max()Output:
10(2) data.min()
- data.min( ) will give you the ‘minimum’ value present in the array.
Example:
import numpy as np
data = np.array([3,1,5,9,2,-3,9,10])
data.min()Output:
-3(3) data.sum()
- data.sum( ) will give you the ‘sum’ of values present in the array.
Example:
import numpy as np
data = np.array([3,1,5,9,2,-3,9,10])
data.sum()Output:
36(4) np.prod()
- np.prod( ) will give you the ‘product’ of values present in the array.
Example:
import numpy as np
data = np.array([3,1,5,9,2,-3,9,10])
np.prod(data)Output:
-72900(5) np.std()
- np.std( ) will give you the ‘standard deviation’ of values present in the array.
Example:
import numpy as np
data = np.array([3,1,5,9,2,-3,9,10])
np.std(data)Output:
4.301162633521313