Best Practices in Python and why Python is so popular

Python is a versatile language that has attracted a broad base of people in recent times. Python has become one of the most popular programming languages.  The popularity of Python grew exponentially during the last decade. According to an estimate, the previous five years saw more Python developers than the conventional Java/C++ programmers. Now the question is why is Python so popular? The primary reasons for this are its simplicity, speed, and performance.

Why does Python have an edge over the other programming languages? Let’s find out!

  • Everything is an object in Python
  • Support for Object-Oriented Programming – including multiple inheritances, instance methods, and class methods
  • Attribute access customization
  • List, dictionary, and set comprehensions
  • Generators expressions and generator functions (lazy iteration)
  • Standard library support of queues, fixed precisions decimals, rational numbers.
  • Wide-ranging standard library including OS access, Internet access, cryptography, and much more.
  • Strict nested scoping rules
  • Support for modules and packages
  • Python is used in the data science field
  • Python is used in machine learning and deep learning
  • Parallel Programming

As a Python developer, you must know some basic techniques and practices which could help you by providing a free-flowing work environment. Some of the best practices in Python are listed below.

Create Readable Documentation

In python, the best practice is readable documentation. You may find it a little burdensome, but it creates a clean code. For this purpose, you can use Markdown, reStructuredText, Sphinx, or docstrings. reStructuredText and Markdown are markup languages with plain text formatting syntax to make it easy to markup text and convert it into a format like HTML or PDF. Sphinx is a tool to create intelligent and beautiful documentation easily, while reStructuredText lets you create in-line documentation. It also enables you to export documentation in formats like HTML.

Follow Style Guidelines

Python follows a system of community-generated proposals known as Python Enhancement Proposals(abbreviated as PEPs) which attempt to provide the basic set of guidelines and standards for a wide variety of topics for proper Python Development. One of the most widely referenced PEPs ever created is PEP8, which is also termed as the “Python community Bible” for properly styling your code.

Immediately Correct your Code

When creating a python application, it is almost always more beneficial in the long-term to acknowledge quickly and repair broken code. (Join the Xaltius Academy to learn how!)

Give Preferences to PyPI over manual Coding

The above will help in obtaining a clean and elegant code. However, one of the best tools to improve your use of Python is the huge module repository namely The Python Package Index (short for PyPI). Not considering the level and experience of the Python Developer, this repository will be very beneficial for you. Most projects will initially begin by utilizing existing projects on PyPI. The PyPI has over 10,000 projects at the time of writing. There’s undoubtedly some code that will fulfill your project needs.

Watch out for Exceptions

The developer should watch out for exceptions. They creep in from anywhere and are difficult to debug.

Example: One of the most annoying is the KeyError exception. To handle this, a programmer must first check whether or not a key exists in the dictionary.

Write Modular and non-repetitive Code

A class/function should be defined if some operation is required to be performed multiple times. This will shorten your code, also increasing code readability and reducing debugging time.

Use the right data structures

The benefits of different data structures are very well known. This will result in higher working speed, storage space reduction, and higher code efficiency.

These are the good practices in Python that every Python developer must follow for a smooth experience in Python. Python is a growing language and its increased use in the field of Data Analytics and Machine Learning has proved to be very useful for the developers. Python for AI has also gained popularity in recent years. In the upcoming years, Python shall have a very bright future, and the programmers who are proficient in Python will have an advantage.

Data Visualization: 6 Best Practices

Our world is progressively filling up with data, all companies – significant multinationals to the minor young startups are stockpiling massive amounts of data and are looking for ways to analyse this data in the raw form and obtain processed information, that can make complete sense. Data Visualisations represent data in pictorial form for the marketing managers to understand complex data diggings.

According to a fact, 3.5 trillions of e-mails are sent every day for the promotion of many companies; companies prepare ads, stockpile enough resources to deliver them to as many users as they can. With a slight observation, a considerable portion of receivers can be cut-off, who have a meagre – conversion rate. Doing so will not only lower the wastage of their resources but will also help them concentrate more on the people belonging to a higher rate of conversion, thus increasing the chances of the product being sold. For doing this, the implementation of supreme data visualisation is necessary.

Data Visualisation can take everyone by surprise. It is here that a meaningless looking pile of data starts making sense and delivers a specific result as per the likes of the end user or developer. It takes shape with the combined effort of ones creativity, attention, knowledge, and thinking. Data Visualisation can be useful, as well as harmful. (Read: 5 common mistakes that lead to Bad Data Visualization)To help your cause by not misleading your visualisation, here are some of the best practices for making your visualisation clear, useful and productive.

A. Plan your Resources
Create a sequence of steps by obtaining your requirements, your raw data, and other factors that might affect the result. This requires knowledge and experience for a data scientist to choose which method to use to for visualising your data. Planning the resources can be very helpful, as it will lead to greater efficiency with the efficient workload.

B. Know your Audience
The most essential and unavoidable step in creating great visualisations is knowing what to deliver. Focus on the likes of the audience, their mindsets, their queries, their perceptions and beliefs; and then plan effectively. It is not necessary that all the viewers will receive the information in the same way. For example, a probability density graph has a different meaning for an HR manager and a chief sales executive. So, it’s very vital that you know your target audience and prepare visualisations according to their perspective.

C: Predict after-effects
Predicting what might be the effect on the end users can add up to your cause. There can be a no-action session where everything is going positive in your way, while a downfall in a particular field may require some immediate action.

D: Classify your Dashboard
There are three main types of dashboards – strategic, analytical and operational. Following the below steps would let you know which dashboard suits best.

  • Strategic Dashboard: It represents a top notch level view of the inquiry line answered in a daily specific routine and presents KPIs in a minimally interactive way.
  • Analytical Dashboard: It provides a range of investigative approaches to a central specific topic.
  • Operational dashboard: It provides a regularly updated answer to a line of enquiry based on response to events.

E: Identify the Type of data

  • Data is of three types: categorical, ordinal and quantitative. Different types of visualisation work better with different kinds of data. A single relation of a data works best with line plot, two pieces of data work better with a scatter plot. A brief description of the type of data is given below:
    • Quantitative: Defines the number of data
    • Ordinal: Data that belongs to the same sequence. Ex: Medals – Gold, Silver and Bronze.
    • Categorical: Data that is of one type. Ex: Gender – Male, female and Other.

F: Use of Visual Features

  • Having done the above, a perfect choice of colour, hue, saturation can glorify your visualisation. It is just a matter of the presence of mind that draws attention.
  • Using the wrong hue and saturation configurations can bring ruin to all your efforts. A good set of visual features gives a final touch up to your data visualisation.

Create some stunning reports and real time dashboards with Xaltius’ BI and Analytics Services.

Concluding, modern technologies like machine learning and AI by itself will find no use for business corporates, if not for data visualisation. Data Visualisation has itself found its field of study and interests and finds its importance in every walk of analysing data.

{DATA SCIENCE AND WEB DEVELOPMENT}

Data Science and Python, though not a lot of people may realize it, go hand in hand with each other. Businesses today, especially the higher level management, require to see accurate and efficient depictions of various data science solutions and projects. Knowing both bridges that gap considerably.

Xaltius took to imparting the fundamentals of both these areas to over 150 students at NUS Business School over an 8 hour, hands-on intensive seminar and workshop.

Through the workshop the keys takeaways for the students were:

  • Understand the fundamentals of Python and working with small datasets.
  • How to create basic data visualizations through seaborn in python.
  • How to tell a story about your data, which is one of the most important lessons.
  • Basics of HTML, CSS and JavaScript which would help users learn about how to create basic web pages.

The end of the workshop ended by doing a small hack with the students where they were given data and had to tell a story around it. They were given an opportunity to present their findings and many of did amazingly well in such a short period!

If you are interested conduct such workshops and talks for your institution or be part of one, please get in touch with us.

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