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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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10 Excusive Steps You need for Web Scrapping

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Here're ten Python technics to clean the scraped data. The scraped Text has unwanted hidden data. So, as part of cleaning it try to remove these ten in your data.

10 Steps for Web scrapping

Data is prime input for text analytics projects. After cleaning, you can feed to Machine/Deep Learning systems.
  1. Removing HTML tags
  2. Tokenization
  3. Removing unnecessary tokens and stop-words
  4. Handling contractions
  5. Correcting spelling errors
  6. Stemming
  7. Lemmatization
  8. Tagging
  9. Chunking
  10. Parsing

10 Technics to Clean Text in Python
10 Technics to Clean Text in Python


1. Removing HTML tags

The unstructured text contains a lot of noise ( data from web pages, blogs, and online repositories.)when you use web/screen scraping. 

The HTML tags, JavaScript, and Iframe tags typically don't add much value to understanding and analyzing text. Our purpose is to remove HTML tags, and other noise.


2. Tokenization

  • Tokens are independent and minimal textual components. And have a definite syntax and semantics. A paragraph of text or a text document has several elements. Those you can further break down into clauses, phrases, and words. 
  • The popular tokenization techniques include sentence and word tokenization. These, you can use to break down a text document (or corpus) into sentences. And each sentence into words. 
  • Thus, tokenization is the process of breaking down or splitting textual data into smaller and more meaningful components called tokens.


Python is popular in Text analytics. Here, you will find various cleaning techniques used in text analytics.
Text Analytics in Python 


3. Removing Unnecessary Stop Words

Stopwords have little or no significance and are usually removed from a text when processing it. These usually occur most frequently if you aggregate a corpus of text based on the particular tokens and their frequencies. Words like "a," "the," "and," and so on are stopwords.


4. Handling contractions

The best examples of contractions are you'll, it's, etc.

5. Correcting spelling errors

Auto correcting spelling errors. While doing a Google search, you will find it corrects your spelling automatically.

6. Stemming

Here you can reduce words to the root level. The best example is Snowball, this you stem it to root level as Snow and Ball.


7. Lemmatization

Based on the context, bring the words to the root level, and make them meaningful.


8. Tagging

This is the concept of group particular words under a Tag.


9. Chunking

It is of constructing from various words of Verbs, Nouns, Adjectives, etc. Check out here on Data Chunking.


10. Parsing

The data will pass through some syntax rules. The output will then feed to Machine Learning systems. The syntax rules vary from project to project.

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