numpy functions

Consider the case where you have a numpy.array and want to find the sums of the values. One way to do this is to write a loop over the array since arrays support iteration and it also supports the len function. For example, you could write this solution in two ways:

import numpy as np

x = np.arange(10)

# Option 1: Indexing with range
result = 0
for i in range(len(x)):
    result += x[i]
print(result)

# Option 2: Iterating over array
result = 0
for val in x:
    result += val
print(result)

While this works, remember that whenever possible you want to avoid using loops since Python is slow. Conveniently, numpy provides a sum function to take the sum for us. Like with pandas , this numpy code is written in that fast language C, so calling out to that is incredibly fast. Kind of confusingly, there are two common ways to call sum that are essentially equivalent. We show both since you will probably run into both.

import numpy as np

x = np.arange(10)

# Option 1: Call it on the array
result = x.sum()
print(result)

# Option 2: Call it on numpy
result = np.sum(x)
print(result)

Just like sum , numpy also provides functions for min , max , and mean which are also commonly used.