How to Download Python Packages for AWS Lambda Layers Easily

AWS Cloud services, like AWS Lambda, are a flexible way to quickly deploy data solutions. AWS uses their own flavor of the Linux operating system. Which means we can’t just pip install Python packages locally and then deploy them to AWS Lambda directly. Usually, we’d need to use Docker or an EC2 to prep our Python package layers prior to upload to Lambda.

In this tutorial, we'll learn How to Download Python Packages for AWS Lambda Layers Easily

#python #aws #lambda 

What is GEEK

Buddha Community

How to Download Python Packages for AWS Lambda Layers Easily
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

Ray  Patel

Ray Patel


Top 20 Most Useful Python Modules or Packages

 March 25, 2021  Deepak@321  0 Comments

Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.

Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:

  1. Web Development
  2. Data Science
  3. Machine Learning
  4. AI and graphical user interfaces.

Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.

#python #packages or libraries #python 20 modules #python 20 most usefull modules #python intersting modules #top 20 python libraries #top 20 python modules #top 20 python packages

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

How to Download Python Packages for AWS Lambda Layers Easily

AWS Cloud services, like AWS Lambda, are a flexible way to quickly deploy data solutions. AWS uses their own flavor of the Linux operating system. Which means we can’t just pip install Python packages locally and then deploy them to AWS Lambda directly. Usually, we’d need to use Docker or an EC2 to prep our Python package layers prior to upload to Lambda.

In this tutorial, we'll learn How to Download Python Packages for AWS Lambda Layers Easily

#python #aws #lambda 

What Are Python Lambda Functions ?

Learning Python Takes Time

Traveling the road from rookie to Pythonista can take a while and varies from person to person. Most people start learning Python by reviewing the basic data structures for numbers, Booleans and strings, and then move to complex data structures like lists and dictionaries. From there you learn loops and if/else control logic, and eventually learn to write reusable code via functions.

Once you start exploring functions, you’ll come across lambda functions which can seem pretty intimidating at first. Similarly to list comprehensions, lambda functions allow you to write succinct code. Often something that would take several lines as a defined function can be done in one line using a lambda function!

Reviewing Python Functions

Typical Python functions are simply a self-contained set of instructions designed to perform a specific task. They are important to master and understand because they allow us to keep code organized by breaking it into smaller, reusable chunks. If we’re writing a large program, utilizing functions can make the code easier to read and debug too.

In Python, we define a function using the def keyword, then give the function a name along with any necessary arguments that impact the function body’s execution. Often times, the return keyword is used to terminate the function and return the desired output to whatever called the function. Here is an example function:

#python example function
def my_function(string):
     return print(string) 

testing my_function

The example my_function takes a string as an argument and prints the string as the returned output.

What Is A Lambda Function?

To take your Python skills to the next level, you need to master both normal functions and lambda functions. Lambda functions are great for making code shorter and more concise, which is the way of the Pythonista! We’ll take a look at a few simple examples to review the syntax, and then look at some actual use cases for lambda functions that will make you look like a pro.

As we see in the previous example, normal functions are defined using def and given a name. Lambda functions are defined using the lambda keyword and are nameless. They are anonymous functions that are not defined in any namespace, and they are intended for single use.

When I first learned about lambda functions, it seemed a bit tricky to wrap my head around. The syntax can feel confusing since a lambda function can take any number of arguments, but can only have one expression. Here are a couple simple examples:

#lambda syntax
#lambda <arguments> : <return expression>#simple example
lambda_example = lambda x: x + x#multiple arguments example
multiple_arguments = lambda x, y, z: (x + y) * z

Testing lambda function examples

Notice the multiple arguments are separated by commas. When calling the function, the caller provides the arguments. The return expression is defined after the colon (:). There is a single return expression, but the return expression can be simple or complex. It could even be another function, which makes lambda functions a powerful tool in your Python programming toolbox.

Using Lambda Functions

Let’s take a look at how we can use lambda to write functions the Pythonic way. Below we have a function that checks a list of strings for the word “two.” It returns the list of strings along with True or False depending on whether the word was in the strings:

sentences = ['Sentence one.', 'Sentence two.', 'Sentence three.']def contains_two(text):
    answers = []
    for s in txt:
        if 'two' in s:
    return zip(answers, text)

Testing contains_two function

Instead of defining a function and using a for loop to go through the list, we can rewrite this succinctly using Python’s map() function along with a lambda function.

contains_two_lambda = map(lambda x: (True, x) if 'two' in x else (False, x), sentences)

Testing contains_two_lambda function

The map() function takes a function and a sequence as arguments, running the function on the given sequence. The function we pass to map() is a lambda function, allowing us to perform the same thing our defined function does in only one line of code!

Lambda functions work well with functions like map(), reduce(), and filter(), three built-in Python functions that take a function as arguments. Lambda functions are also popular in data munging as they can work well with pandas dataframes. They can make applying logical operations to columns and rows concise one-liners.

Using Lambda Functions With Pandas DataFrames

As an analyst, I find myself using Pandas DataFrames all the time. Since DataFames are a popular data structure, let’s take a look at how easy it is to use lambda functions for transforming data in dataframes. Let’s create a simple pandas dataframe with some mock data:

#import dependencies
import pandas as pd#create dataframe
df = pd.DataFrame({
  'Occupation': ['Data Tech','Sales','Analyst','Engineer'],
  'Name': ['John Smith', 'Jodie Whales', 'Eric Kleppen', 'Richard Heart'],
  'Salary': [50000, 75000, 80000, 100000],
  'YearsAtCompany': [2, 3, 1, 3],})

Example dataframe

Next, let's create a new column called newSalary using a lambda function. If I’m giving out raises based on the column YearsAtCompany and want to see what a person’s new salary will be, I can use the apply() function similar to how we used map() in the previous example:

df['newSalary'] = 
df['Salary'].apply(lambda x: x * (1 + df['YearsAtCompany']/10))

Verifying newSalary column

Notice that the apply() function takes the lambda function as an argument and applies the function to each row in the Salary column. The expression in the lambda function uses YearsAtCompany to determine the size of the raise.

Wrapping Up

Writing Python like a pro takes practice, so don’t worry if you feel intimidated by new concepts like lambda functions. The syntax looks strange, but lambda functions allow you to write concise, one-time-use functions that are great for applying logical operations.

Although they can only perform one expression, the expression can be complex and even include another function. Lambda functions can work well with series of data, as even data structures like Pandas DataFrames. 

This story was originally published at

#python #lambda #programmers