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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 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
Python Ideas to Remove HTML tags


How I Removed Using BeautifulSoup

  1. Import BeautifulSoup
  2. Python Logic to Remove HTML tags
  3. 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.


You need to import BeautifulSoup for Text analytics
Before Executing the code


After Run the tags are parsed. The means in the output tags removed.

I have shared Python sample logic on how to remove HTML tags. Also, given the package name you need. It is a useful example for text analytics.
Result after executing the code

Bottom-line of Result

Below are the steps you need for HTML tags parsing:
  1. Reads input HTML data
  2. Removes HTML tags
  3. Prints only text data

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