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The Quick and Easy Way to Analyze Numpy Arrays

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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
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Python Abstract Classes to Learn Quickly

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Here's the best example of how to understand the abstract classes of python quickly. An abstract class means it is a skeleton. You cannot instantiate abstract classes. For Example: The abstract class name is 'dog' o = dog() It gives an error. Since the ' dog' is an abstract class. I am sharing with you a clear understanding of abstract class. So that you can tell confidently about these classes. The syntax for Abstract Class from abc , import ABC , abstractmethod  class <class name>(ABC): @abstractmethod  def <method name>():  #abstract class definition Python Abstract Class Explanation from abc import ABC, abstractmethod class AbsBaseClass(ABC): def __init__(self): print("Abstract class") @abstractmethod def abMeth(self): pass def conMeth(self): print("I'm a concrete method") class Derived(AbsBaseClass): def abMeth(self): print("I'm redefined") o=Derived() o.abMeth() o.conMeth() References Star Python Certification
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