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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 String Comparison Top Ideas

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Python String Comparison Top Ideas

In Python, you can compare two strings how you would do on numbers. Python is stricter on string comparison. It identifies any small differences in strings. So Python is case sensitive while comparing it checks for identical ones.

  
Python follow-suite other languages. It compares strings for exact equals. Else, treat them as different strings.

Python String Comparison 

  • Extra spaces
  • Uppercase or Lowercase

#1 Example

>>> a = "Virginia"
>>> b = "virginia"
>>> a == b False 

The strings a and b are looking similar. But there is case sensitive difference. Python checks for extra white spaces as well as Upper or Lowe case letters.

#2 Example

>>> greet1 = "Hello "
>>> greet2 = "Hello"
>>> greet1  == greet2 False

The greet1 has space at the end whereas greet2 does not. Python looks at whitespace when comparing strings, so the two aren’t considered equal.

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