Top 10 Data Scientists’ Guide to Efficient Coding in Python

Python: A necessary skill for Data Scientists

As more organizations realize the value that data science can bring them, the methodologies to achieve this value are also improving. People look for the most efficient ways and coding in Python is one such. Its precise and effective syntax makes it possible for professionals to code far less compared to other programming languages. The example below, which is a code for the output "Hello Adam", highlights this.

print "Hello Adam";

In comparison, here’s a code for the same output written in Java.

class A {
  public static void main(String args[]){
System.out.println("Hello Adam");
}              
}

This example is just one of many that underlines why Python is a top choice among coding languages. However, despite its many positives, data scientists still need to know how to maximize it.

Here are 10 things that can help you code more efficiently with Python.

Strengthening core programming concepts

Coding is an art. It’s only with practice that you can write better and cleaner codes. There’s much more to it than memorizing syntax; you have to build a robust foundation of core programming concepts. Mastering the basics will enable you to easily configure solutions into codes for a computer.

For a newbie:

"Automate the Boring Stuff with Python" is an excellent resource. You can learn the basics of Python programming for tasks like filling online forms, online content search and download, PDF encryption, merge split, and much more. It will help you perform practical programming even if you have never written a line of code in your life.

For an experienced coder:

If you want to add Python to your list of skills, there are ample courses available online. Here are some of the concepts that you should be acquainted with for efficient coding in Python.

• Map function: Operates as an iterator and returns results when a function is applied to each item of the provided iterable.
• Lambda function: An anonymous function that only has one expression but can take up any number of arguments.
• Itertools: A module that offers functions that operate on iterators to eventually produce complex iterators.
• Exception handling: Enables exceptions in code to be handled smoothly.
• Decorators: Allows programmers to modify the behavior of a particular class or function.
• Collections: A module in Python that offers distinct container types to store and access objects.
• Magic methods: Special methods that are invocated internally from the class when a certain action is taken on the same.
• Generators: A normal function that is used to produce iterators and does not return a single value.
• Regular expressions: A chain of characters that forms a search pattern by helping you match or find strings or a set of strings.
• Threading: Used to run multiple threads at the same time.

To practice your learnings and get core programming concepts down pat, check these helpful resources:

• Practicepython.org
• The Python Challenge
• Code fights
• How to think like a computer scientist

Data Science libraries

Python has a significant number of libraries that come in very handy for data scientists. There are also different types of libraries curated for various jobs like data exploration, math, data mining, etc. Here are the top Python libraries that data scientists use.

• SciPy
• NumPy
• Scrappy
• Pandas
• BeautifulSoup
• Matplotlib
• Seaborn
• Keras
• PyTorch
• PyCaret

Tqdm for loop in a code

Writing codes for loops over a large dataset can be difficult. To reduce the hassle, tqdm comes to the rescue, displaying a progress bar in alignment with code. You can check the progress bar for loop execution, the time taken to complete the code, the speed of iteration per second, and more.

Image source

If you’re looking to pass an appropriate description to the loop, the “DESC” parameter will get it done.

Type hinting

When creating codes for big scripts, type hinting is a must. It is defined as explicitly stating all the types of arguments included in a Python function definition. It helps specify return types in a given Python function definition. Although it isn’t used frequently, it’s still considered an excellent standard for coding in Python.

Kwargs and Args

Kwargs and Args are useful for clearly defining the parameters in a function.

• Args specify the unknown number of positional arguments.
• Kwargs specify the unknown number of keyboard documents.

Let’s understand this with a practical example. Assume that while writing a function with input as unknown directed paths, a number of files are printed within each. However, you don’t know how many paths the user will input. Kwargs and Args will help you define the number of parameters in a function definition.

VS code extensions

Python editors have ample choices for idealizing their codes. However, the best is VScode. To make the most of it, install the extensions below.

• Path Intellisense: Automatically completes file names.
• Pylance: Helps you write code faster by performing court completion and parameter suggestions.
• Python Indent: Performs the indentation of code that runs on multiple lines.
• Python Docstring Generator: Generates docstrings for the functions in Python

Pre-commit hooks

The first draft of a code can be messy and improper in terms of formatting. But if you’re thinking of fixing them one at a time, it can be time- and energy-intensive. This is where free commit hooks come to the rescue. They save a great deal of time by performing auto-formatting of codes with just one line of command: “pre-commit run”.

Tip: Before performing a pre-commit run, ensure that the files are staged, i.e., git add, or save them from being skipped.

Interactive visualizations with basic statistics

Statistics is defined as the lifeblood of data science, which is why it’s important to know the theoretical and practical aspects. It will help you understand the problems that statistics can solve for you.

Some of the basic statistical concepts you should know are listed below. Once you’re familiar with them, you can start implementing them in Python.

• Probability basics
• Significant testing
• Mean
• Sampling
• Median
• Mode
• Standard deviation
• Frequency distributions
• Confidence intervals
• Hypothesis testing

Statsmodels is recommended for building statistical models in Python. The website statsmodels.org has useful tutorials on how to implement basic statistical concepts using Python.

Visualizing data with Matplotlib

Matplotlib is a complete package for producing basic visualizations like bar charts, histograms, line charts, scatter plots, and box plots. Another good plotting library is Seaborn. However, you don’t need to get deep into Matplotlib. Today, organizations also utilize tools like Qlik, Tableau, etc., for interactive visualization creations.

Practice

Once you’re properly familiar with Python programming concepts, it’s time to practice. Here are a few things that can help.

DIY projects

Pick a project that is related to a real-time data science project. You can get a clear idea of the dataset, engineer features, goals, etc., when working with it.

Benefit: It will provide a real-time experience of the proper data science workflow. You will get acquainted with the right steps to follow when handling projects.

Kaggle competitions
Participate in competitions hosted on Kaggle’s website. You can get insights on a project with the tutorials provided and start with the given dataset for a pre-defined goal.

Benefit: These competitions are a good platform for practice. You can start with basic projects and move on to more challenging ones. The competitions also offer attractive prizes to winners.

As a data scientist, you don’t need to burn the midnight oil trying to memorize every syntax. It will come gradually the more you write codes and read the documentation. There’s also no need to learn the A to Z of coding; writing logical and clean codes will do the job. Comparatively, there are fewer topics that data scientists need to learn in Python programming for their field, and subjects like memory leaks, big O notation, and cryptography in Python are of little use.