Blog Category: Python

Hiring a Python developer can be a game-changer for your app development project, but finding the right fit is essential. With Python’s versatility powering everything from web development to Artificial Intelligence, and Data Science, the demand for skilled Python developers is through the roof.

In most of the situations, search feels like a maze, especially with all those ads for ‘Top talents at affordable rates’. Choosing poorly, though, can lead to costly reworks, missed deadlines, and plenty of complications. In this blog, I’ll walk you through some common hiring mistakes to avoid, so you can find a Python developer who’s truly right for your project.

7 Mistakes to Avoid While Hiring Python Developers

1. Highly Depending On Resumes

Resumes don’t always reflect the full story. A developer might list down multiple tools and frameworks, but do they actually use those tools in real world projects? Many businesses end up hiring Python developers based on flashy resumes only to discover later that the developer doesn’t have the skills needed for the project.

The Fix – Don’t just consider the CVs at face value. Put candidates through a pre-defined coding assessment or ask them to solve real world assignments relevant to your project. It’s easy to say you can code, but difficult to prove it.

Tip – Running short of resources and time to test every profile and give them assignments, prefer partnering with a software development company. They’ll have already done the rigorous vetting process to select valid profiles fit for your project.

2. Ignoring Cultural Fit

Even when you source the best Python developer, if they won’t blend well with your team, it might lead to unnecessary delays, frustration, and miscommunication. Soft skills like team collaboration and communication are just as critical as technical expertise.

The Fix – Take time to understand how a developer behaves in a team setting. Let them meet the team, question the way they handle the feedback, and check their adaptability to your company’s workflow.

Tip – The best thing to sidestep this challenge is to hire from the in-house team of a software development company that prioritizes cultural fit profiles for your project.

3. Importance of Quality Assurance

Hiring dedicated Python developers who don’t consider testing or quality assurance is a disaster waiting to happen. An experienced talent will not only write clean code but also ensure that it’s thoroughly optimized and tested. Missing out this step leads to unstable applications that eventually cost more time and money to fix.

The Fix- Hire Python developers who prioritize and implement quality assurance at every phase of app development.

Tip – The Python developer you hire should have a good grasp of code reviews, testing methodologies, and agile development practices.

4. Defining Your Specific Python Needs

Being a versatile language, Python is used for everything from web development to AI. However, the expertise required to build a Django-based app or a Flask-based app is different from what’s needed for data analysis and machine learning.

The Fix – Be aware of your project needs upfront. If you need a Python developer with specific experience – like using Flask for application development or Pandas for data science, ensure you are targeting developers with that expertise.

Tip – Many mobile app development companies specialize in matching the perfect Python developer with your specific project needs. They have in-house Python experts who have also worked in ML and AI projects, data scraping projects, RAG app development, LangChain projects, and beyond.

5. Overlooking Remote Teams

In a remote-first trend, why limit the profile sourcing process to local candidates? Chances are that the right Python developers might not be in your city or country. But hiring remote talents comes with its own set of problems like language barriers, time zones, and different work styles.

The Fix – Hire remote Python developers but ensure you have the right process and tools in place. Set clear communication protocols, be flexible with time zones, and use project management tools.

Tip – Working with a web app development company can be the best solution here. They know the complexities of remote work and have a team of vetted talent to select from, saving you a lot of stress.

6. Highly Emphasizing Cost

It’s easy to get attracted to the concept of hiring a Python developer at a lower price, but more affordable doesn’t always mean great. A less experienced Python developer might take twice as long to finish the same task, costing you more in the long run.

The Fix – Emphasize the value a Python developer brings and not just the price. It’s worth paying slightly more for developers who can deliver high-quality code efficiently.

Tip – Partnering with a trusted app development company can help you find a sweet spot between cost and quality. They provide a flexible pricing model to give you access to top Python developers without majorly affecting your budget.

7. Consider Long-Term Planning

Many businesses make the mistake of hiring a developer who is apt to meet their instant project requirements but does not anticipate future challenges. It’s vital to hire experts who can not only build for current project needs but also ensure the code is scalable and future-proof.

The Fix – During the interview, question about how to tackle long-term scalability and maintenance. Their goal is just to complete the tasks allocated or do they think ahead?

Tip – Software development service providers often hire Python developers who are professionals in taking a long-term view of projects assigned, ensuring your code evolves as your project scales.

What Affects A Python Developer’s Cost?

Several factors influence the cost of hiring Python developers.

• Skill Level and Experience – Highly experienced developers command hire salaries due to their capability to handle complicated projects with efficiency.
• Geographic Location – Python coders from regions with a high cost of living typically quote higher salaries than those in countries with a lower cost of living.
• Training and Recruitment Costs – If developers need orientation or other training, this will affect the overall cost of hiring Python developers.

Why Partner With TOPS To Hire Python Developers?

Our team understands the problem businesses face while hiring Python developers. With our rigorous vetting process and exhaustive experience, we ensure that only the right experts make it to your project. Here’s how we differentiate from the rest-

• Proven Track Record – A decade of successful projects delivered across various industries.
• Stringent Vetting Process – With a 4-step rigorous screening process based on the candidate’s technical expertise and soft skills, we shortlist the valid profiles for your vproject.
• Full-Stack Expertise – From AI/ML, Cloud, to DevOps, we provide end-to-end solutions.
• Continuous Improvement – Our Python developers are constantly learning and using the latest technologies.
• Cultural Alignment – We select Python developers who best fit your company’s culture and goal.

The Wrap

To set yourself up for a winning hire, recognize the mistakes we have outlined and use the tips to avoid them. Be it testing real-world skills, thinking long-term, or emphasizing communication, these strategies can make an impact on your overall project success.

And if you are still confused about hiring the right Python developer for your project? You don’t have to do it alone. Hiring the project-fit developers from the in-house team of a reputable Python development company can make the entire hiring process quicker and far less stressful.

Which one proves best- the homegrown tech capabilities or the software development outsourcing services? Scroll on to the most curious data solving your project puzzles on what creates value from the often overlooked business opportunities.

For many companies, outsourcing IT services is a source of untapped value and savings. Some companies consider it as a ‘noncore’ spend. The case is reversed for companies with the technology innovation capacities and skill sets to address potential opportunities and leverage full value from their in-house development team.

Most importantly, global companies in the dominant industries acquire a great portion of software services from external digital transformation service providers. Annually, the global spending on IT outsourcing services is about $900 billion.

The IT outsourcing market has spiked from $122.9 billion in 2023 to $129.13 billion by the end of 2024 states The Business Research Company.

To scale your software development with the right decision and grit, let’s analyze the win-win mechanisms offered by in-house v/s outsourcing Python development services.

We have jotted down all possible advantages and drawbacks of opting for in-house Python development capacity v/s Python development outsourcing services to make a wise business decision on what value it brings to your project.

Comparison of In-house v/s Outsourcing Python App Development Services

Gauge with an in-depth analysis of which option matches your Python app development project needs- a managed IT services provider or a housed staff. Let’s explore which type of app development sourcing positives outweigh the negatives.

What’s the Cost of In-house Python App Development?

The cost of Python app development by the in-house team includes multiple parameters to come up with an accurate cost figure. It involves budget calculations for developer’s salaries, HR costs, cost of the development tools, hardware cost for developing a workstation for each developer, and testing devices. Managing an internal software developer and supervising the team takes time and resources.

What’s the Cost of Outsourcing Python App Development?

The cost of Python development services outsourcing depends on the app complexities, unique features, and the location of your mobile app development company. The right formula to calculate the outsourcing cost involves external teams cost and time taken on the development phases.

Which One To Choose Out of the Two- Inhouse or Outsource?

The benefits of both are substantial. However for most software development projects, the perks of IT outsourcing are far-reaching. From cost saving, time commitment, and access to global expertise to gain a competitive edge, it helps SMEs, established businesses, to even large-scale setups thrive with optimized digital transformation.

Explore the checklist to select the right IT outsourcing services provider and discover how TOPS can be your project-ready outsourcing partner.

Don’t Tackle It Alone, Bring A SideKick Along

Access experienced python developers from the comfort of your office. Python development outsourcing service offered by TOPS itself is an essential checklist to select the right outsourcing partner for your app development project.

• We rightsize your IT investments to fit your business needs.
• Ready-to-use software development infrastructure.
• Every project is backed by a project manager and a team of experienced quality analysts ensuring the code quality and minimal to zero errors in the development phases.
• We ensure no overrun of your project spendings.
• Full confidentiality and shared accountability.
• A client-centric approach helps us deliver complete transparency in communication along with end-to-end and prompt development assistance during and after the development phases.

Want to harness data from multiple external platforms to gain accuracy in decisions and operations across your business ecosystems?

Data Scraping involves employing a critical algorithm or a program to retrieve, structure, and process huge amounts of data from the web. On the flip side, at the heart of data scraping app development is Python, a programming language popular for its ease of use, extensive libraries, and faster development tools.

Whether you want to build eCommerce intelligence, generate leads, conduct market research, enhance social media strategies, or monitor brand presence, Python for data scraping solutions makes the app development journey more agile, faster, and above all easier to integrate trending technologies.

This blog is a quick ride through the ‘how’, ‘what’, and ‘why’ to use Python for developing data scraping apps. Let’s get started!

How Does Web Scraping Work?

A single Python script sends an HTTP request to a website. Which then receives a raw HTTP response that needs to be parsed using the Beautiful Soup- one of the Python libraries for data scraping. It helps turn the raw response into structured data. This structured data is then processed to use it in the script to identify the text content in the data.

Here, a combination of scrapers and crawlers is used to fetch data from websites. By browsing a website, the web crawler indexes the content. Parallely, the web scraper extracts the data you have requested. You can mention the file format in which you want the data results to be visualized along with the storage option that you want a web scraper to save the data.

Prerequisites to Build Data Scraping Apps Using Python

To develop a scalable Python web scraping app, explore the chief essentials, making it seamless to retrieve data from the web efficiently.

Selenium

It is one of the most popular open-source web development tools for automating the web browsing functions allowing users to extract data and interact with the website seamlessly.

Playwright

It is the latest cross-language and cross-platform web browser automation tool used to scrape web apps and dynamic web content. Using Playwright, it’s easy for web headless browsers like Chrome, Safari, and Microsoft Edge to navigate the web just like humans do.

Types of Data Scraping

Developers use varied web scraping techniques like HTTP programming, HTML parsing, Human copy-and-paste, DOM parsing, text pattern matching, computer vision web page analysis, or vertical aggregation based on the type and purpose to harness data from the web. Data scraping approaches vary based on the data sources and task complexities. Explore different types of data scraping that are high-in-demand among the innovators.

Web Scraping

It encompasses text, prices, images, or any other data on the web pages which are used to gather market intelligence, monitor competitors, or track product prices.

Screen Scraping

It includes data extraction from the display output of other programs when the data is not accessible directly through databases or APIs.

Social Media Scraping

Data extraction from social media platforms to leverage data related to user profiles, comments, posts, or other relevant data. It is used in market research, sentiment analysis, and understanding customer preferences.

Email Scraping

Extract email addresses from the websites to build mailing lists along with implementing legal and ethical implications.

How To Scrape Critical Data From the Web?

1. Analyze Web Structure Using Development Tools

Understanding the website structure will help determine the exact location of the data you want to extract. Based on the type of website, it has a variety of categories, listings, pricing, ratings, or other data.

Firstly, open the developer’s tools and try selecting the content element on the webpage, you will discover the tags and classes of the selected content elements. This data is critical as it helps compile all other elements with such types of details.

Now you know which class to target, you need to get the HTML from the website.

2. Get The HTML From The Website

After understanding the website structure, to get the HTML from the website, ‘Requests’- a Python library is used to send a GET request to the targeted website URL.

import requests

# Base URL
base_url = 'https://watch.toscrape.com/'

# Send GET request to the base URL
response = requests.get(base_url)

# Get the HTML content
html_content = response.text

# Print the HTML content
print(html_content)

This script will give a status code but you need to focus on the actual HTML content. Through response.text, you will get the HTML content of the website homepage that will serve as your initiation point to extract the data. This process varies based on the type of website you want to scrape data from.

Static websites don’t need any login credentials but dynamic websites do. Headless browsers like Selenium, Playwright, or Puppeteer are used in case of data extraction from dynamic websites.

Scrape Data From Dynamic Websites

For instance, to scrape data from a dynamic platform that majorly relies on JavaScript for content rendering.

First, install Playwright using pip, the Python package manager. It’s simple; just type pip install playwright in the command prompt and press ‘Enter’. Then, you need to install the necessary browser binaries by running playwright install.

Use Playwright in your script after importing it.

# Import Playwright
from playwright.sync_api import sync_playwright

# Use Playwright to open a browser, navigate to the website, and get the HTML source
with sync_playwright() as p:

    # Set up the browser. In this example, we're using Chromium.
    browser = p.chromium.launch()
    page = browser.new_page()

    # Navigate to the website
    page.goto("https://quotes.toscrape.com/js/")

    # Get the HTML source
    html_source = page.content()

    print("HTML source of the website:", html_source)

# Close the browser to free up resources
browser.close()

You have your HTML ready to process it using the Python library – playwright.

Scrape Data When the Website Structure Changes Unexpectedly

Website structure may change due to the dynamic elements that alter the CSS or HTML of the webpages, or if the website is redesigned, or updated. To ensure that the data scraping code doesn’t miss essential information-

• Choose the right selectors to navigate and extract the data you want, depending on how the website is structured and coded. The right approach is to use the selectors based on class names, unique identifiers, or semantic meaning as these attributes are most likely to remain consistent across varied website versions.
• To handle unexpected website structure changes, use logging and error handling in the web scraping code. It detects and rectifies the exceptions that occur during code execution. ‘Try-except blocks’ in Python are used for handling exceptions as well as catching that involve parsing errors, connection failures, or timeout errors. This exception handling process helps avoid crashing the code, discover and fix real causes and analyze and monitor the web scraping process.
• Fallback strategies involve using different selectors for a single data point and selecting the best one out of all. By using BeautifulSoup, Scrapy, or lxml, Python libraries for data scraping helps source varied formats of data from the same website such as XML, JSON, or RSS feeds and using cookies to bypass varied versions of the website or bypass the anti-scraping measures.
• Implement ethical data scraping principles to handle website changes. These principles respect the interests of both data providers and consumers. These principles include robots.text rules, respects the website’s privacy policies, avoids excessive web scraping and seeks data provider’s permission before scraping data from their website.

Exception Handling

Even the best laid plans go amiss which is also true with the web scraping process. Sometimes you might fetch incomplete data from the website. When the web scraping script encounters these loopholes, it throws errors that can be fixed through Python’s ‘try-except blocks’. It lets us dictate how the program must react to the errors, ensuring it doesn’t burn or crash.

Another way to handle the exceptions is to check the HTTP status codes or implement the retry mechanism to handle exceptions. Using timeouts in your network requests, log errors, and robots.text help handle the web scraping exceptions gracefully.

Proxies

A proxy server acts as an intermediary for requests received from clients needing resources from the servers that offer those resources. As web scraping involves multiple requests sent to the server from an IP address, the server might detect multiple requests and block the IP address to avoid further scraping of data. This is where proxies are used to continue the scraping as IP addresses change and create anonymity by hiding the IP address.

Bypassing Captcha and 2FA

Some websites have highly complicated authentication methods such as Captcha, CSRF tokens, or even two-factor authentication (2FA). It’s important to adopt the web scraping script to handle these complexities.

You can parse the login page firsthand to extract the CSRF token and add it to the login request. Headless browsers like Selenium, Playwright, or Puppeteer are used in case of data extraction from websites with Captcha or 2FA authentication methods.

3. Parsing HTML Content and Data Extraction

After fetching the HTML content, it’s time to structure this data how you need using ‘BeautifulSoup’, one of the best Python libraries for data scraping apps. This Python library is mainly used for pulling data out of XML and HTML files.

Firstly, convert the HTML into a BeautifulSoup object.

from bs4 import BeautifulSoup

# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')

After converting the HTML, use find_all() method that helps return the list of all the data examples of the specific tag and its related attributes. To know which tags to consider, ID, Xpath, or Class are the few ways to locate the elements.

Now it’s time to fetch specific details out of the raw data sets, using find() to search within the elements. For example, to get the price, you need to look for the ‘p’ tag with class.

Next, to start filtering the data, you need to add an if statement informing Python to check for the exact data you are looking for.

# Extract watch information from the parsed HTML
for watch in soup.find_all('article', {'class': 'product_pod'}):
    if watch.find('p', {'class': 'star-rating Five'}):
        title = watch.find('h3').find('a')['title']
        price = watch.find('p', {'class': 'price_color'}).text[1:]
        watch_url = base_url + watch.find('h3').find('a')['href']
        image_url = base_url + watch.find('img')['src'][3:]

Review the whole script that navigates webpage data and filters it with the exact details you want.

Pagination in Web Scraping

Previous steps showed extracting data from the first page of the website. What if you want to scrape data from the whole website?

Here, the website URL structure gives us hints on how to initiate pagination in data scraping. By using Python’s ‘for-loop’, it reads through each page, and removes the page number from the URL along with the iteration index.

Now, merge that script into the existing script to scrape all the pages on the website.

4. Storing Scraped Data in CSV File

If you want to save this structured data for future use, you can store the data in CSV or other file formats.

Note, Python will save this data file in the existing working directory if you don’t specify another location to store the CSV file.

Firstly, import the CSV library and create a file. You are ready to start data scraping by looping all the webpages. After running the script, you will get a nice CSV of the structured data you want to fetch from the website.

Storing Scraped Data in a JSON File

If you need a JSON file, the process is similar to that of writing it to a CSV file. The only difference is that you need to add the details to a list and write that list to a JSON file.

Python makes it super easy to organize and save data scraped from the web.

It is recommended to partner with a trusted software development company critical to ensure the design and development choices are made in line with the best data scraping practices. This also involves leveraging sufficient skillsets and capabilities to develop essential data models for your unique software project.

Your Trusted Data Scraping App Development Company

Python development team at TOPS makes sure of the data quality, accurately articulates business needs, uses legit methods to scrape data without annoying the external platforms, and helps embed the core use of data scraping apps in your business processes and across the team.

Focusing on the larger to even smaller level of ‘high-value’ use cases for our client to leverage the scraped data has been our USP. Being the top Python development company for 9 years, we have exhaustively worked on the web scraping development environments and made it a forte.

The reason why Python is extensively used and considered one of the top-used programming languages in 2024 is its ease of use, clean and readable syntax, extensive 3rd party integration capabilities, and wide range of free libraries.

In fact, the design philosophy of the Python programming language focuses on code readability through exclusive use of whitespace. On the flip side, its object-oriented approach helps developers write clean and logical codes for small, medium, and large projects.

This has made the Python programming language a staple in data science and machine learning allowing developers to use the language to create data visualization, statistical calculations, manipulate and analyze data, build foundational models, and more.

Additionally, Gen AI led the rise in its adoption and scaling, which highly earned the business value for the takes, shapers, and makers who used the AI and Machine Learning models customized with proprietary data or built from scratch.

Even if you haven’t worked closely with ML models, this blog is a detailed guide that will save your time in exploring and selecting the best Python libraries for Machine Learning projects. It also covers answers to why decision makers like you choose Python as backend technology over others.

Top 5 Python Libraries For Machine Learning in 2024

Being one of the trusted Python development companies, TOPS has explicitly researched and has come up with the list of best Python libraries for Machine Learning after considering its usage, use cases, and popularity. Scroll down to explore the capabilities and implementation of these Python libraries for ML models.

1. TensorFlow Python

Python’s Machine Learning Library built by Google, TensorFlow Python is popular for what’s called ‘differentiable programming’. It automatically computes a specific function’s derivatives with the programming language. With TensorFlow, users can easily evaluate and develop Machine Learning and Deep Learning models through its flexible framework and architecture. It’s easy to leverage TensorFlow Python to visualize ML models on both mobile and desktop.

Reasons for TensorFlow’s Popularity

• Building, training, and deploying ML models.
• Makes simple data graphs out of complex computations.
• Flexibility with deep and recurrent neural networks.

Capabilities of TensorFlow

• Users can divide work into multiple GPUs or CPUs, allowing developers to benefit from parallel computing.
• Easy to build neural networks as it offers functions to activate, built layers, and loss functions.
• Interactive visualization of model graphs and training metrics helps in model visualization through the use of TensorBoard.
• Seamless deployment of TensorFlow models on environments like Cloud, web, and mobile for easy scalability.

Use Cases of TensorFlow

• Object recognition, facial recognition, medical imaging, and quality analysis in manufacturing.
• Language translation, text classification, and sentiment analysis.
• Speech and translation recognition.
• Unique recommendation systems.

Apps Built Using TensorFlow

• Google Photos
• Google Voice Search

import tensorflow as tf

# Initializing constants
x = tf.constant([2, 4, 6])
y = tf.constant([1, 3, 5])

# Addition
res = tf.add(x, y)

# Initializing Session
sess = tf.Session()

# Result
print(sess.run(res))

# Closing Session
sess.close()

2. PyTorch

PyTorch is highly popular for its use in large and dense data sets or graphs. It is an open-source ML Python library that is based on Torch, a C programming language framework. This Python library is used in Machine Learning applications, especially for natural language processing and Computer Vision. PyTorch is a deep-learning Python library built by the Facebook AI research team.

Capabilities of PyTorch

• Helps users execute multi-dimensional operations and supports broadcasting and slicing.
• Extended data sets are possible for scalable Deep Learning models. You can leverage efficient distributed computing that offers distributed communication, optimization, and data loading.
• It offers tools for seamless deployment and integration into development environments.

Use Cases of PyTorch

• Solve tasks related to gaming features, decision making, and robotics that are controlled through reinforcement learning libraries.
• Leverage pre-trained models like GPT, BERT, or ResNet to streamline particular tasks.

Apps Built Using PyTorch

• Samsung Electronics
• Apple

3. Keras

Designed specifically for building neural networks for Machine Learning models, Keras becomes fully functional to train neural networks with minimal coding. It builds and evaluates neural networks with ML and Deep Learning models.

Keras is Popular For

• Works across multiple environments with flexibility and portability.
• It is extensible in nature.
• Easy integration with activation functions, optimizers, layers, and objectives.
• Mostly used for agile prototyping.

Capabilities of Keras

• Offers easy functions and tools for data augmentation techniques.
• You can build your own neural network model through custom activation and loss functions.
• Seamless evaluation of trained models and offers required tools for calculating precision, F1 score, and recall.
• Helps users leverage the potential of GPU for boosting inference and training resulting in GPU acceleration as well as distributed computing.

Use Cases of Keras

• Streamlines generating creative content, data augmentation, and image synthesis tasks.
• Simplified speech recognition.
• Speedy object detection.
• Smooth image classification such as identification of objects from images.

Apps Built Using Keras

• Netflix
• Uber

4. Scikit-Learn

Scikit-Learn’s simplified design makes it a user-friendly library for those new to ML models. It ensures engaging UI Machine Learning tasks. It is unique among the popular Machine Learning libraries of Python. It makes it easy for classification, clustering, dimensionality reduction, regression, and model selection.

Capabilities of Scikit-Learn

• Smooth functionalities for handling data, managing missing values, feature extraction techniques, and encoding categorical variables.
• Allows searching structures, relationships, and patterns with unlabeled data.
• To make predictions, it helps models learn from variable data sets.

Use Cases of Scikit-Learn

• Allows predicting class labels through algorithms such as random forests or gradient boosting methods.
• Implementation of decision tree regression, linear regression, and polynomial regression.
• Helps leverage clustering algorithms to discover patterns and for seamless classification of identical data points without class labels.

Apps Built Using Scikit-Learn

• Inria
• Spotify

5. Matplotlib

It is a Python library specifically developed to visualize data through tools that build static, interactive, and animated visualizations. This involves creating beautiful graphs, bar charts, plots, histograms, and more. This is popularly known for data visualization patterns. It has modules that make plotting easier through tools for controlling font properties, line styles, and more.

Capabilities of Matplotlib

• Helps showcase different bar sizes for data comparison.
• Swift visualization of the relationship between two attributes to explore the trends, changes, and patterns.
• Seamlessly add text, labels, or annotations.
• Create pie charts to represent % as a whole with effective personalization in labels and colors.
• Data visualization in a three-dimensional landscape.

Use Cases of Matplotlib

• Histograms
• Geographic maps
• Heatmaps

# Program to form a linear plot

# Import packages & modules
import matplotlib.pyplot as plt
import numpy as np

# Initializing data
x = np.linspace(0, 4, 10)

# Plotting data
plt.plot(x, x, label='linear')

# Adding legend
plt.legend()

# Showing plot
plt.show()

In order to select the right Python libraries for your Machine Learning project, you need an expert consultation that helps drive your software development project in the right direction using the latest tools and agile methodologies. And TOPS has rich experience working on complex Python projects, offering new-age Python development services.

We have solved some of the unique business challenges by building next-gen Python apps

• Our Python team has built an Infrastructure as a Code (IaaC) platform where the users can register on the platform, choose the plan, and provide Amazon marketplace details. Once users add the details, the platform generates AWS infrastructure in the backend for the user, based on their requirements. We have used Cloud Formation, RDS, Lambda, EC2, SQS, S3 from AWS and Boto3 to access AWS via Python code.
• An app that controls product reviews with automated discovery of negative /positive reviews through the use of Python libraries like SKLearn, TF-IDF, Pandas, NumPy, Django, and Django Rest Framework (DRF).
• By using the Azure AI, Django, and Django Rest Framework, we generated an OCR model that fetched data from the parking fines receipts.
• Built a solution where users can upload their documents like PDF, Doc file, and other formats. The ML models fine-tune the text inside the document and users can ask questions that the model answers based on the fine-tuned text. We have used FastAPI, Transformers, Watchdog, Cryptography, LlamaIndex, Gradio, PyTorch, and Sentence-Transformers for this project.

Explore how TOPS Infosolutions can be your extended tech team to handle any technology complexities for your software development needs.

Why TOPS as Your Trusted Software Development Partner?

We have a rock solid project management process in place to meet our client’s needs, minimize project risks, and avoid budget overruns. What’s most significant is our consistency in staying abreast with the latest technologies and trends. Hire Python developers who have gained tech excellence and worked on time-tested development processes for optimum outputs.