Code Less, Benefit More: 10 Tips from Python Data Science
Code Less, Benefit More: 10 Tips from Python Data Science
Python data science is a bag full of tricks you can use to get the work done faster and more effectively. Here are 10 incredibly useful tips from Python data science.
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You don’t need to be an experienced developer to know that Python is a straightforward way to kickstart your IT career. Python tops the list of the most popular programming languages, so it’s always a good idea to learn more about it and find ways to improve your coding skills.
The learning part is particularly important if you run data science projects because you can drastically reduce the work while generating the same (or even better) coding outcomes. In this post, we will show you 10 incredibly useful tips from Python data science.
Things to Know Before You Begin
Before you start focusing on Python data science tricks, you need to make sure that you have the ground knowledge needed for this stage of learning.
First of all, you should understand what data science really means. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. The process usually works in a few basic steps:
- Research question
- Data collection
- Data cleaning
- Data analysis and visualization
- Creating the appropriate machine learning model
- Showcase results
A few practical steps coders should take before moving on to data science hacks:
- Get acquainted with Python and its data structures, functions, comprehensions, etc.
- Use Pandas to practice data manipulation and learn how to visualize insights
- Scikit-learn will help you figure out the basics of machine learning
- Dive deeper into the subject through advanced machine learning resources
- Be a sturdy data science learner and keep working on real-life projects
Useful Python Data Science Hacks You Should Try
With everything we’ve stated so far, the only thing left is to discuss the most useful tips in Python data science. We analyzed a lot of solutions and selected these 10 suggestions for you:
1. Improve Readability With Single-Function Tasks
Many coders will choose to compile multiple functions into one task only. Although it may seem like the simplest way to code in Python, it actually deteriorates the overall readability of the code. This is why we suggest ordering only one function per task.
The number of functions is pretty much irrelevant, so it’s better to divide the code into smaller units and make them all neat and clean. In other words, each function should be limited to one level of abstraction only.
2. Take Advantage of Type Hints
Another interesting technique readability-wise is to make use of type hints. How does it work? You already know that Python data science is all about data manipulation, which is why programmers add multiple data types to their functions. Different data types act as arguments such as Numpy Array or lists.
If you want to simplify the procedure, you can use type hints to determine the type of arguments and the corresponding returned objects.
3. Limit the Number of Arguments Per Function
Arguments are what functions are made off, but do you know that you should limit the number of arguments per function? Let us explain why.
Python functions can contain anything from zero to multiple arguments, but the ones with 3+ arguments are considered to be intricate and difficult to figure out. At the same time, unit tests are getting much more difficult because the number of argument combinations is also getting higher.
4. Try to Create One-Line Functions
Although it’s not always possible to follow this tip, the general rule of thumb is to try and create one-line functions. This is the only way to make functions fully readable and avoid problems with huge piles of lines.
After all, the point is to simplify the code and reduce time-waste when checking for errors in your functions. If one-liners are not possible, you could at least try to write a two-line function instead.
5. Beware of Error Management
Every Python programmer is prone to making mistakes because it’s simply impossible to remain fully focused 100% of your time. Although it’s not what most coders love doing, you need to be aware of error management and simplify decision-making when you bump into bugs.
A very useful tip is to begin top-level scripts with a statement that limits the level of activity. That includes statements such as finally, except, try, and so on. In this case, errors are easier to spot and you will be able to react based on the error type.
6. Go For Loggers Instead of Prints
Perhaps it looks like a major dilemma, but it’s actually easy to make a decision on whether to use a logger or a print. It’s like thinking about hiring assignment help – you know it’s a much better option than doing the work on your own.
Prints make the finest solution if you want to test and/or debug. However, loggers perform better if you want to activate the code. Loggers rely on direct formatting and contain multiple information layers, which makes them much more convenient for data science projects.
Docstring is an essential element in Python. It must appear for any statement declaration (function, class, or method definition). They make the code more explicit and serve as documentation for the object.
7. Choose the Right Type of Docstrings
As one of the vital segments of Python coding, docstrings demand special attention. They follow statement declarations to clarify the code and document individual objects. Your job is to choose the right type of docstrings:
- Single-line docstrings have a brief description and support the function by giving it additional layers of information.
- Multi-line docstrings are reserved for complex functions followed by a more comprehensive elaboration.
What matters the most is to check Python documentation because there are exceptions to the general rules on how to use single- and multi-line docstrings.
8. Unit Testing Is Fundamental
You don’t need to be a Python data scientist to appreciate the significance of unit testing. Every programmer should approach testing with the three laws of TDD on their mind:
- Write production code only to pass a failing unit test.
- Write no more of a unit test than sufficient to fail (compilation failures are failures).
- Write no more production code than necessary to pass the one failing unit test.
9. Use Comments When Appropriate
Comments can help you upgrade the code, but you have to be careful not to overuse the feature. Here’s when you should rely on comments:
- To add noted about legal issues such as licensing and copyrights
- To add missing or new information
- To explain complex constructs very briefly
- To warn about the impact of possible changes in code lines
10. Make Use of Libraries for Code Formatting
How you format the code is also a major issue in Python data science, so we encourage you to utilize libraries such as Black and Yapf for automated formatting.
The Bottom Line
Python data science is a bag full of tricks you can use to get the work done faster and more effectively. In this post, we showed you 10 tips from Python data science to help you code less and benefit more. Which tip did you like the most? Do you know other Python data science hacks? Make sure to write a comment – we would love to see it!
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