Using NumPy Statistical Functions

NumPy’s statistical functions allow you to analyze the contents of an array. For example, you can find the maximum value of an entire array or the maximum value of an array along a given axis.

Let’s say you want to find the maximum value in the salary_bonus array you created in the previous post. You can do this with the NumPy array’s max() function:

print(salary_bonus.max())

The function returns the maximum amount paid in the past three months to any employee in the dataset:

3400

NumPy can also find the maximum value of an array along a given axis. If you want to determine the maximum amount paid to each employee in the past three months, you can use NumPy’s amax() function, as shown here:

print(np.amax(salary_bonus, axis = 1))

By specifying axis = 1, you instruct amax() to search horizontally across the columns for a maximum in the salary_bonus array, thus applying the function across each row. This calculates the maximum monthly amount paid to each employee in the past three months:

[3400 3200 3000]

Similarly, you can calculate the maximum amount paid each month to any employee by changing the axis parameter to 0: 

print(np.amax(salary_bonus, axis = 0))

The results are as follows:

[3200 3400 3400]