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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 par

Python Function Argument: How to Pass it to Decorator

A decorator is a wrapper and provides additional functionality to a function. Also, it may modify the behavior, such as changing the return type/adding new abilities.

Python Decorators

Python Decorators

Precisely, it is another form of function pointers. Also, it accepts function argument, then either wraps the function or returns a new one. Moreover, it modifies the inputs/outputs supplied to it. It helps you add behavior to functions (objects) dynamically (without changing the function behavior).

Function Argument

Below, you will find an example of passing a function argument to a decorator. The below function modifies inputs and returns output.

def to_upper(func):
    if isinstance(text,str):
        return text.upper()

def say():
    return "welcome"

def hello():
    return "hello"
a = to_upper(say)

b = to_upper(hello)



** Process exited - Return Code: 0 **
Press Enter to exit terminal



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