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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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Text Vs. Binary Vs. UTF-8 Top differences

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Here are the differences between Text files, Binary files, and UTF-8. These would help understanding files correctly for beginners. Text File It contains plain text characters. When you open a text file in a text editor, it displays human-readable content.  The text may not be in a language you know or understand, but you will see mostly normal characters that you can type at any keyboard. Binary File It stores information in bytes that aren’t quite so human readable.  If you open the binary file in a text editor, it will not be readable. UTF-8 UTF-8 is short for Unicode Transformation Format, 8-bit, and is a standardized way to represent letters and numbers on computers. The original ASCII set of characters, which contains mostly uppercase and lowercase letters, numbers, and punctuation marks, worked okay in the early days of computing. But when other languages were brought into the mix, these characters were just not enough. Many standards for dealing with other languages have been p
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Python Web data - How to Extract HTML Tags Easily

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With BeautifulSoup you can extract HTML and XML tags easily that present in Web data. Here is the best example of how to remove these. The prime step of text analytics is cleaning . You can remove HTML tags using BeautifulSoup parser. Check out Python Logic and removing HTML tags. When analyzing web data, consider the below examples for your projects. Python Ideas to Remove HTML tags How I Removed Using BeautifulSoup Import BeautifulSoup Python Logic to Remove HTML tags Before and after executing the code 1. Import BeautifulSoup import  BeautifulSoup  from  bs4 2. Python BeautifulSoup: How to Remove HTML Tags from  bs4  import  BeautifulSoup soup =  BeautifulSoup ("<!DOCTYPE html><html><body><h1>My First Heading</h1><p>My first paragraph.</p></body></html>") text = soup. get_text() print (text) 3. Before and After Run Before the run see the below code. Before Executing the code After Run the tags are parsed. The means in
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