Dylan North

Dylan North


Text Similarity : Python-sklearn on MongoDB Collection

Check out some Python code that can calculate the similarity of an indexed field between all the documents of a MongoDB collection.


In this article, I set up a Python script that allows us to calculate the similarity of an indexed field between all the documents of a MongoDB collection. In the process I parallelized the executions on four threads to improve performance.

The script is detailed below, I hope it will be useful.

Python Script

import multiprocessing
import threading
import json, sys
import pymongo
import nltk, string

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import euclidean_distances
class SimilarityThread (threading.Thread):
   def __init__(self, threadID, data_array, totalSize, similarity_collection,startIndex):
   self.threadID = threadID
   self.data_array = data_array
   self.totalSize = totalSize
   self.similarity_collection = similarity_collection
   self.startIndex = startIndex
   def run(self):
      clacluateSimilarity( self.data_array, self.totalSize, self.similarity_collection,self.startIndex)

def clacluateDistance(txt1,txt2):
return euclidean_distances(txt1,txt2)[0][0]

def clacluateSimilarity( data_array, totalSize, similarity_collection, startIndex):
vectorizer = CountVectorizer()
for idx in range(startIndex,totalSize):
h = data_array[idx]
for idx1 in range((idx+1),totalSize):
h1 = data_array[idx1]
hSimilarity = {}
corpus = []
features = vectorizer.fit_transform(corpus).todense()
distance = clacluateDistance(features[0],features[1])
hSimilarity['distance'] = distance
if distance < 4:
print("Distance ====> %d " % distance)

def processTextSimilarity(totalSize, data_array,similarity_collection):

num_cores = multiprocessing.cpu_count()
print(":::num cores ==> %d " % num_cores)
threadList = ["Thread-1", "Thread-2", "Thread-3", "Thread-4"]
threadID = 1;
rootIndex = round(totalSize/4)
startIndex = 0
for tName in threadList:
thread = SimilarityThread(threadID, data_array, startIndex+rootIndex, similarity_collection,startIndex)
threadID += 1

# Wait for all threads to complete
for t in threads:

def main():
print('****** Text Similarity::start ******')
connection = pymongo.MongoClient("mongodb://localhost")
db = connection.kalamokomnoor
article = db.article
article_similarity = db.article_similarity

data_array = article.find({}).sort("id",pymongo.ASCENDING)
totalSize =  article.count_documents({}) 

print('###### :: totalSize : %d ' % totalSize)


print('****** Text Similarity::Ending ******')

if __name__ == '__main__':

#python #mongodb #data-science

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Text Similarity : Python-sklearn on MongoDB Collection
Ray  Patel

Ray Patel


top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Ruth  Nabimanya

Ruth Nabimanya


Query multiple mongoDB database collections easily with python


Perform queries across multiple MongoDB databases and collections, where the field names and the field content structure in each database may vary.

The Problem

Suppose you’ve got two database collections, “leak1” and “leak2”

In leak1, the schema looks like this:


and in leak2, the schema looks like this:

FName: "John"
LName: "Doe"

A simple program to iterate through all your collections and perform queries wouldn’t work, because:

  • the field names are different. Notice that in leak1, the first name field is FIRST_NAME, while in leak2, the first name field is named FName.
  • the field values might be structured differently. In leak1, everything is captialized. In leak2, it’s all title-case.

This program lets you write a configuration for each collection, specifying, in JSON, how to query each field.

It’s a work in progress, but so far, it works pretty well. It’ll probably be easier to understand if you take a look at the config files under ./collections/. Each JSON file under ./collections/ should be an array of objects. The program automatically processes all JSON files under that directory.

Some more info for how the configurations work can be found in the wiki.

#database #query multiple mongodb database collections easily with python #collections #multiple mongodb databases #mongodb #python

Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Navigating Between DOM Nodes in JavaScript

In the previous chapters you've learnt how to select individual elements on a web page. But there are many occasions where you need to access a child, parent or ancestor element. See the JavaScript DOM nodes chapter to understand the logical relationships between the nodes in a DOM tree.

DOM node provides several properties and methods that allow you to navigate or traverse through the tree structure of the DOM and make changes very easily. In the following section we will learn how to navigate up, down, and sideways in the DOM tree using JavaScript.

Accessing the Child Nodes

You can use the firstChild and lastChild properties of the DOM node to access the first and last direct child node of a node, respectively. If the node doesn't have any child element, it returns null.


<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>

var main = document.getElementById("main");
console.log(main.firstChild.nodeName); // Prints: #text

var hint = document.getElementById("hint");
console.log(hint.firstChild.nodeName); // Prints: SPAN

Note: The nodeName is a read-only property that returns the name of the current node as a string. For example, it returns the tag name for element node, #text for text node, #comment for comment node, #document for document node, and so on.

If you notice the above example, the nodeName of the first-child node of the main DIV element returns #text instead of H1. Because, whitespace such as spaces, tabs, newlines, etc. are valid characters and they form #text nodes and become a part of the DOM tree. Therefore, since the <div> tag contains a newline before the <h1> tag, so it will create a #text node.

To avoid the issue with firstChild and lastChild returning #text or #comment nodes, you could alternatively use the firstElementChild and lastElementChild properties to return only the first and last element node, respectively. But, it will not work in IE 9 and earlier.


<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>

var main = document.getElementById("main");
alert(main.firstElementChild.nodeName); // Outputs: H1
main.firstElementChild.style.color = "red";

var hint = document.getElementById("hint");
alert(hint.firstElementChild.nodeName); // Outputs: SPAN
hint.firstElementChild.style.color = "blue";

Similarly, you can use the childNodes property to access all child nodes of a given element, where the first child node is assigned index 0. Here's an example:


<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>

var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.childNodes;
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {

The childNodes returns all child nodes, including non-element nodes like text and comment nodes. To get a collection of only elements, use children property instead.


<div id="main">
    <h1 id="title">My Heading</h1>
    <p id="hint"><span>This is some text.</span></p>

var main = document.getElementById("main");

// First check that the element has child nodes 
if(main.hasChildNodes()) {
    var nodes = main.children;
    // Loop through node list and display node name
    for(var i = 0; i < nodes.length; i++) {