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Let’s admit it: We all thought Python would be dead by now.
In the IT world, programming languages fall out of fashion from time to time. However, the rise of Python this year, thanks to its use in data science, proved that there are plenty of reasons to believe that it is as important today as it was several years ago.
Python offers versatility with over 100,000 different third-party Python libraries covering topics such as AI and text processing, machine learning and web development etc. Moreover, by featuring data-focused libraries that offer the highest levels of flexibility to those familiar with basic Python rules and syntax, Python is becoming everyone’s most-preferred language for data analysis. That’s why a lot of people who want to pivot into data science are taking Python online courses.
Learning the Basics of Python
Whether you are new to the world of programming or looking to explore the territories governed by Python, there are certain things that you need to know.
Many UNIX, Linux and Windows computers come with Python already installed. However, if you do need to install Python on your system, we recommend that you do so from python.org.
Getting Started With Python
You should be relieved to hear that learning Python doesn’t require as much effort as it does to code in languages such as C++. Given that you have some form on knowledge on the art of programming and coding, Python is easy in terms of readability and shorter syntaxes.
Before you begin your journey with Python, we recommend that you learn a little about some text editors and IDEs that can make editing Python easy.
Furthermore, a series of tutorials and training sessions such as a Python online course at QuickStart can offer Python beginners/programmers with the right resources they need to utilize their experience with Python to its maximum capacity. Start your 7-day free trial today!
How to Detect Errors in Python?
Coding, debugging and going through thousands and thousands of lines of code is no joke. However, in the world of coding and programming, no error should go unnoticed.
Keeping this in mind, it is important for Python programmers to understand how they can detect errors such as logical errors, runtime errors, and syntax errors.
Let’s start from the beginning.
Take the following example into consideration:
We design a program on Python that requires the user to input a number and tries to add five to the number entered.
If the program works successfully, we move on, repeating the process with the user. If the system fails, we send out a message that tells the user that they did not provide a valid input.
An error in Python will look something like this:
Image Source: http://www.confusedbycode.com
The message discussing the error provides us with a line number and the piece of text that requires our attention. Towards the bottom of the message, the system indicates the type of error that has occurred; in our case, a syntax error.
While these errors are pretty self-explanatory, handling these in the best possible way is crucial to the success of your programming and coding efforts.
To do this, we recommend that you first understand the three different types of errors and how each one of these can be tackled.
These errors are fairly common in the world of Python and are especially connected to new users. Occurring during the initial development phase and representing the incorrect use of a syntax, these errors can be caught when a program is compiled for the purpose of execution.
A dialog box usually appears on the screen, indicating the error.
You press OK on the dialog box. This will trigger Python to direct you to where the error has occurred in your source code. Using the location as a hint, we recommend that you first review the highlight chunk for errors and move your way to checking the entire line.
Logic (Semantic) Errors
Commonly described as error that results in the improper usage of logical implementation, logical errors can be quite difficult to track.
Unfortunately, an error message doesn’t pop up when you make a logic mistake. However, an incorrect input is the best indication of a logic error.
Being able to detect a logic error in your code is nothing short of a nightmare. While there is no way for you to quickly pick out the piece of code that is not working correctly, adding extra print statements can help.
We recommend that you modify your code to add a print statement to every statement that requires processing, respectively, and test to see if there are errors in that specific section of the code.
A runtime error can come up during the execution of your program. This usually happens when the flow of control reaches the line in your program featuring the error itself.
Comprehending runtime errors can seem a little complex. However, to break everything down for you:
- The first section of the error indicates the name of the file with the error.
- The next section indicates the line that has the error.
- The final section explains the nature of the error.
Using the hints provided within the actual error, runtime errors can be treated in no time. We recommend that you move your cursor around; until you are able to locate the line indicated by the Ln:indicator residing towards the bottom of the editor.
Once you’ve spotted the line that contains the error, your next step would be to review the code and check to see if any mistakes, such as naming a function or variable incorrectly, has occurred.
Acknowledging the importance of speed of development, offering valuable constructs, easy maintenance, and plenty of flexibility to the programmer, Python is a very important part of the software development industry. Therefore, seeking help from a Python online course and learning different ways to detect errors when developing a Python program can prove to be very beneficial to anyone’s career as a programmer.
Talk to our experts
for information and guidance on which Python course will be best for you, depending on your skills and your career level.