The Quick and Easy Way to Analyze Numpy Arrays

The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array.

Sum

You can find the sum of Numpy arrays using the np.sum() function. 

For example: 

import numpy as np 

a = np.array([1,2,3,4,5]) 

b = np.array([6,7,8,9,10]) 

result = np.sum([a,b]) 

print(result) 

# Output will be 55

Mean

You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array. 

For example, the mean of a Numpy array of [1,2,3,4,5] would be 

result = np.mean([1,2,3,4,5]) 

print(result) 

#Output: 3.0

Standard Deviation

To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a parameter and returns the standard deviation of that given array. 

For example: 

import numpy as np 

arr = np.array([1, 2, 3, 4, 5]) 

std_dev = np.std(arr) 

print(std_dev) 

# Output: 1.5811388300841898

Max

To find the max Numpy Array, you can use the max() function from the Numpy library. 

For example, to find the max value in an array of numbers: 

import numpy as np 

arr = np.array([1, 3, 4, 6, 10]) 

print(np.max(arr)) 

This would output 10, which is the max value of the array.

Min

The easiest way to find the minimum value of a Numpy array is with the np.min() function. This function takes in a Numpy array and returns the minimum value in the array. 

Example: 

import numpy as np 

a = np.array([1, 5, 10, 100, 200]) 

min_val = np.min(a)

 print(min_val) 

# Output: 1