Let’s Talk About Python in Data Science

According to  Predictive Analytics Lab’s Jobs Site, the demand for data scientists continues to grow, day after day, as businesses across different industries increasingly depend on data driven insights. There are numerous learning paths to this most modern profession, and selecting the correct one widely depends on where one is in their career. Apart from statistical and mathematical skills, programming proficiency is one of the must-have skills for all aspiring data scientist.

The battle between programming languages has always been of interest to many as Data Scientists keep looking for the best language that gets their tasks done with good performance incurring the least cost. Python stands out as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science. To add on that, Python has emerged as the default language for AI and ML, and data science has an intersection with artificial intelligence. Therefore, it is evident that this multipurpose language is the most used programming language among data scientists.

Like any other language or tool, Python has some best practices to follow before, during, and after the process of writing your code. These make the code readable and create a standard across the industry. Other developers working on the project should be able to read and understand your code.

To help with your data science work, here are some of the best python practices. Mastering these capabilities will — dare I say it — make you an even sexier data scientist.

10 Best Practices of using Python

1.      Use virtual environments

Consider the following scenario where you have two projects: ProjectA and ProjectB, both of which have a dependency on the same library, LibraryC. The problem becomes apparent when we start requiring different versions of LibrayC. Maybe ProjectA needs v1.0.0, while ProjectB requires the newer v2.0.0, for example.

This is a real problem for Python since libraries are installed and stored according to just their name, there is no differentiation between versions. Thus, both projects, ProjectA and ProjectB, would be required to use the same version, which is unacceptable in many cases. Here come virtual environments to the rescue, at its core, the main purpose of Python virtual environments is to create an isolated environment for python projects. This allows you to have different dependencies for different projects.

Whether you’re working solo or with collaborators, having a virtual environment is helpful for the following reasons:

1. Avoiding package conflicts
2. Providing clear line of sight on where packages are being installed
3. Ensuring consistency in package version utilized by the project

You can learn how to set up virtual environments here.

2. Create a Code Repository and Implement Version Control

A code repository is a place where codes are stored! Now that’s not special.  You can simply create a folder on your computer called Source Code and put it all in there.  What’s the point in giving this storage place a fancy name?

Here is why a code repository is created in it with versioning systems. The code repository holds the source code while the version system software archives that code. You can archive all your files in a repository, keeping any other versions or files, even if you aren’t using them at the moment. Code repositories also give you a way to name or tag the different versions, keeping records of changes within the same project. Some of the widely used repositories include Kaggle and GitHub.

3. Find good utility code

If you can reuse existing code that works, is reasonably well written, and performs well enough, why waste the time writing it over again?

Python is an exceedingly well-resourced language. You can speed up your data science discoveries by recognizing you don’t have to go it alone — you can and should reuse the utility code of the programmers who’ve come before you. Code reuse has many benefits. Most obviously, you don’t have to write so much code and reduced debugging and testing effort.

You can keep a directory of code you once wrote for future reference or use web resources such as kaggle or Chris Albon’s blog to look at previous works of other programmers.

4. Write Readable Code

Ensure your code meets the PEP8 standard. PEP 8 is a document that provides guidelines and best practices on how to write Python code. It was written in 2001 by Guido van Rossum, Barry Warsaw, and Nick Coghlan. The primary focus of PEP 8 is to improve the readability and consistency of Python code.

Writing readable code here is crucial. Other people, who may have never met you or seen your coding style before, will have to read and understand your code. Here are some of the guidelines can follow to make it easier for others to read your code:

• Use naming conventions for identifiers (variables, functions, or classes names)- this makes it easier to understand the code.
• You should use line breaks and indent your code.
• Use comments, and whitespaces around operators and assignments.
• Keep the maximum line length 79 characters.
• You should stay consistent.

5. Run your code every time you make a small change

Do not start with a blank file, sit down and code for an hour and then run your code for the first time. You’ll be endlessly confused with all of the little errors you may have created that are now stacked on top of each other. It’ll take you forever to peel back all the layers and figure out what is going on.

Instead, you should be running any script changes or web page updates every few minutes – it’s really not possible to test and run your code too often. The more code that you change or write between times that you run your code, the more places you have to go back and search if you hit an error.

Plus, every time you run your code, you’re getting feedback on your work. Is it getting closer to what you want, or is it suddenly failing?

6. Read the error message

It’s really easy to throw your hands up and say “my code has an error” and feel lost when you see a stack trace. But in my experience, about 2/3rds of error messages you’ll see are fairly accurate and descriptive. The language runtime tried to execute your program, but ran into a problem. Maybe something was missing, or there was a typo, or perhaps you skipped a step and now it’s not sure what you want it to do.

The error message does its best to tell you what went wrong. At the very least, it will tell you what line number it got to in your program before crashing, which gives you a great clue for places to start hunting for bugs.

7.  Correct Broken Code Immediately

Make sure to correct your broken code immediately before proceeding. If you let it be while you work on something else, it can lead to worse problems later. If you can’t seem to figure out what your error message is trying to tell you, your best bet is to copy and paste the last line of the error message into Google. Chances are, you’ll get a few stackoverflow.com results, where people have asked similar questions and gotten explanations and answers.

8. Using pandas-profiling for automated EDA

Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that involves performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

It is a good practice to understand the data first and try to gather as many insights from it. EDA is all about making sense of data in hand, before getting them dirty with it. However, sometimes it is a hectic task and takes a lot of time, according to a study EDA takes around 30% effort of the project but it cannot be eliminated. Python provides certain open-source modules like: Pandas Profiling that can automate the whole process of EDA and save a lot of time.

Pandas Profiling is a python library that not only automates the EDA process but also creates a detailed EDA report in just a few lines of code. Pandas Profiling can be used easily for large datasets as it is blazingly fast and creates reports in a few seconds.

Here is a link to Pandas Profiling. here or here .

9. Adding visualizations to feature analysis

A feature is a measurable property of the object you’re trying to analyze. In datasets, features appear as columns. Features are the basic building blocks of datasets. The quality of the features in your dataset has a major impact on the quality of the insights you will gain when you use that dataset for machine learning.

You can improve the quality of your dataset’s features with processes like feature selection and feature engineering, which are notoriously difficult and tedious. This is where data visualization comes in.

Data visualizations make big and small data easier for the human brain to understand, detect patterns, trends, and outliers in groups of data, this results to easier feature analysis.

10. Measuring and optimizing runtime

As the data science field is increasingly converging to software engineering, the demand for concise, highly performant code has increased. The performance of a program should be assessed in terms of time, space, and disk use — keys to scalable performance.

Python offers some profiling utilities to showcase where your code is spending time. To support the monitoring of a function’s runtime, Python offers the timeit function. Some optimization principles for improving your code while working with python and pandas:

1. Use pandas the way it’s meant to be used: do not loop through data frame rows, use the apply method instead.
2. Leverage NumPy arrays for more even efficient coding
3. Proper Data Types Usage in Python.
4. Replace list comprehension with generator expressions.
5. Replace global variables with local variables.
6. Avoid dot operation.
7. Avoid Unnecessary Abstraction.
8. Avoid Data Duplication.

Final Words

Data science involves extrapolating useful information from massive stores of registers and data which are usually uncategorized and hard to correlate with any meaningful accuracy. Machine learning can make connections between disparate datasets but requires thoughtful computational sophism. Python fills this need by being a general-purpose programming language that allows a data scientist to create CSV output for simpler data reading in a spreadsheet.

Again, Python is still under development, meaning it receives regular updates and releases. So, you can be rest assured that learning python for data science is the best investment you can make. As big data and machine learning become more common in business and other organizations, the demand for more python skilled personnel  will keep on rising. It is for this reason that  Predictive Analytics Lab offers training in big data and analytics using python programming language. .

What are you waiting for? Enroll for Business Analytics and Data Science Courses – With Python or follow our Facebook, Twitter, Linked In and Instagram pages for constant updates. Another  great  way of growing and improving your python programming skills is to frequently attend our data science meetups, lab guests, bootcamps and conferences.

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