What are some common myths about programming that are prevalent among non-programmers?

1. Non-programmers cannot learn to code.

2. Coding is challenging to read and understand.

3. There are a lot of things to remember.

4. You should be good at math to start coding.

5. Coding is boring.

Let’s review each of these myths through the Python lens.

Non-programmers cannot learn to code

Python is among the easiest programming languages for a beginner. It does not require prior coding experience, and even a non-programmer can quickly learn Python from scratch. In addition, Python is dynamically typed, which means one only needs to assign a value to a variable at runtime. While other programming languages have to mention the data type explicitly, the Python interpreter can detect the data type by itself.

Below are a few differences when declaring the data type between Java and Python.

Coding is challenging to read and understand

What non-programmers think coding will look like:

Python is a structured programming language; the indentation only indicates a code block. In other programming languages, the indentation is only for readability. Python will give you an error if you skip the indentation; each block in the Python code is identified using the indentation space. As a result, the Python code is easier to read and understand. Typically, the Python code will look like the figure below:

There are a lot of things to remember

Python is one of the most versatile programming languages. Experts often recommend Python as the first programming language to learn because it is easy to understand and use. However, memorizing the syntax, functions, and library can be confusing. It is better instead to understand the basic ideology of Python programming. In the current situation, Google is helpful when you need it.

Example of simple English syntax >> Print (“Hello Indium”) which will print Hello Indium.

You should be good at Math to start coding

Non-programmers often think strong math knowledge is essential for programming languages. However, Python only requires basic mathematical understanding. Advanced mathematical operations like logarithmic, exponential, and factorial are accessed through built-in Math modules. Some of the math modules built into library operations are:

a. math. factorial () the returns factorial of the given number

b. math.pow() returns the value of x to the power of y

c. math. sqrt() returns the square root of the number

Output

Coding is boring

Every person’s interest is different. Coding is not only used in building websites but in other fields as well—space research, airplanes, weather forecasting, the finance market, games, etc. For those interested in gaming, Python is a great choice to begin coding. Other exciting areas where Python is widely used:

  1. Web Development
  2. Game Development
  3. Machine Learning and Artificial Intelligence
  4. Data Science and Data Visualization
  5. Desktop GUI
  6. Web Scraping Applications
  7. Business Applications

Scope of Python in Digital Assurance

Python plays a vital role in testing applications. It is also involved in the automation testing of frameworks that run the test plan using scripts rather than manually. Python comes with built-in libraries and tools that support automation testing. Due to its versatility, the Python automation framework is a popular choice for application testing. Some popular Python testing frameworks:

  1. Unittest
  2. Pytest
  3. Nose2

Unittest

Unittest is the first Python-based automated unit test framework that works with the Python standard library. It supports code reusability, test automation, test collection, and setup codes. To import unittest into the program, use the keyword “import unittest.”

Pytest

The Pytest framework is general-purpose but excels at functional and API testing. It supports simple or complex text code to test APIs, databases, and UIs. Parallel test execution is also possible using Pytest, which promotes built-in assert statements.

Nose2

Nose2 is also a Python-based unit testing frameworkthat can run DocTests and Unittests. In addition, Nose2 supports multiple functions for writing tests and exceptions. This testing framework builds on top of Unittest, supports plugins, and has more features than Unittest.

Final Thoughts

A Python testing framework increases the code’s test coverage, reusability, speed, and productivity in the software development life cycle. Maintaining the test script and generating the reports will be easy. In addition, automation testing reduces human errors and allows the early detection of defects. On the business front, automation testing reduces business expenses in resource hiring and significantly reduces the application’s launch time in the market.

Author

Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.