Everyone says Python is great, but what is the magic of Python that makes it so popular?

In the field of data science, for example, Python has many complete toolkits to assist you with important data science tasks. This article will analyze the reasons why Python is so popular among scientists.

Data science needs Python:

Data science is the study that helps us extract information from a range of structured and unstructured data. It uses statistics, mathematics, and scientific calculations to analyze data.

Because Python syntax is so simple that even people without an engineering background can easily master it, Python has become one of the most important skills to excel in data science, and it is considered the best choice for data science. Also, Python is one of the most friendly coding languages for kids to learn because it is simple and easy to understand. That's why so many parents teaching Python to kids.  

Python has a long history in data science:

In 2016 Python surpassed R on Kaggle, a well-known data science competition platform, source: Finextra

In 2017, Python surpassed R in KDNuggets' annual data Scientist survey, source: KDNuggets

In 2018, about 66% of data scientists said they used Python every day, which is a huge number and makes it the language of choice for professional analysts, source: KDnuggets

According to experts, this trend will continue as the Python language evolves. Also, data scientists earn an average base salary of about $109,596 per year, according to Indeed's report. And the number of data scientist jobs on the market has increased dramatically in recent years.

Why Python is used for data science:

Python is a general-purpose, easy-to-use language, and is considered the best language in data science. Python has an advantage over other programming languages like R in terms of extensibility. It gives data scientists flexibility and offers different ways to solve problems. In terms of speed, Python once again stands out from its peers, such as Matlab and Stata.

Here are some important features of the Python language:

Python syntax is fairly simple to use, and anyone can learn Python in a relatively short time;

Many robust third-party libraries are used for data science applications. A library is a collection of modules that can be applied repeatedly to different programs.

A strong community of over 10 million people helps keep libraries and frameworks up to date. Source: the developer - tech

Libraries and frameworks are free to download and use, and the total number of Python libraries and frameworks is estimated at around 137,000;

Python is an interpreted programming language. Unlike C or C + +, Python code is first converted to bytecode for low-level instructions, which are then executed by the Python interpreter;

Python is cross-platform, which means that once code is written in Python, it can run on any operating system: Windows, Mac, Linux, and so on. Note that the Python interpreter is platform dependent;

Python can be automated, so we can automate certain time-consuming tasks in our daily lives. For example, a head teacher wants to create an electronic transcript for a student based on the scores in an Excel sheet. Given a class of 100 students, doing report cards one after another may not seem like a good option. To solve this problem, we can create a Python script based on an Excel worksheet that creates electronic transcripts for all students.

How do I use Python for data science?

Python provides libraries such as NumPy, Pandas, SciPy, and Matplotlib that make it easy to perform everyday tasks in data science. Some of these libraries are discussed below:

Numpy: Numpy is an acronym for Numerical Python. It is a Python library that provides support for mathematical functions that programmers can use to manipulate arrays of larger dimensions. It contains some useful features for manipulating arrays and matrices.

Pandas: Pandas is one of the most popular libraries among Python developers. The main goal of the library is to analyze and manipulate data through its built-in functions, and the library can easily handle large amounts of structured data. Pandas supports two types of data structures:

Series: one-dimensional data;

DataFrame: two-dimensional data.

SciPy: SciPy is another popular Python library for performing data science tasks, which is also useful in scientific computing. It provides the ability to solve scientific computing problems and computer programming tasks. It consists of the following submodules:

Signal and image processing

Optimization algorithm

integral

The interpolation

Matplotlib: Matplotlib is a very special Python library for data visualization. Visualization of data is important for any organization. It provides a way to visualize data. The library is not limited to drawing pie charts, bar charts, and histograms, but also advanced graphics. Another feature of this library is the support for customization, where any part of the graph can be effectively customized.

Matplotlib gives us the ability to zoom in and out of charts and save charts in image format.

When we enter an organization in a data science role, generally speaking, the organization follows the following workflow.

Get data from the company database using Python and SQL;

Use the PANDAS library to insert data into a data frame for later analysis;

The Pandas and Matplotlib libraries help analyze and visualize the data.

The Scikit library is responsible for preparing the prediction model by deeply analyzing and mining the organization's data and predicting future outcomes based on the given data.

What role does Python play?

Let's take a look at the steps in the data science process to understand Python's role.

1. Data collection and cleaning

With Python, you can load data in a variety of formats, such as CSV (comma-separated values), TSV (tab-separated values), or JSON from the network.

Whether you want to load SQL tables directly into your program or need to crawl web sites, Python makes it easy for you to do these tasks: the PyMySQL package for the first task, and the BeautifulSoup package for the second. PyMySQL makes it easy to connect to MySQL databases, perform queries, extract data, and more. BeautifulSoup helps you read XML and HTML data. After extracting and replacing values, you may also have to deal with missing and meaningless values during the data cleaning phase.

Also, if you're having trouble with a particular dataset, you can search the name of the dataset and add "Python" to it, and you might be able to find a solution.

2. Data exploration

Now that you've collected and standardized your data, it's time to explore the data. In this process, you need to untangle the problems you find in your business logic and turn that into a standardized data science problem.

In order to achieve this, it is necessary to analyze data types at a deeper level and separate them into different data types, such as numerical, ordinal, nominal and categorical, so as to provide the processing methods they need.

Once you have sorted out the categories that the data belongs to, you can explore the data using The Python libraries for data analysis, NumPy and Pandas. In addition, Python provides a lot of tools for data exploration, and you can search in search engines to get more information.

When these steps are complete, you can start the AI and data modeling machine learning steps.

3. Data modeling

This step is a critical part of the data science process, and in the feature selection phase before modeling, you may need to reduce the dimensions of existing data sets. The Python language is very handy for this task, and it has many advanced libraries of tools to help you solve problems.

If you want to perform a numerical model analysis of data, you just need to use Numpy in Python. SciPy makes it easy to use scientific counting and calculations. The SciKit-Learn code base on Python provides many intuitive interfaces to help you apply machine learning algorithms to data without noticing any difficulty.

Once the data is modeled, you may want to visualize it and interpret the valuable intelligence in the data.

4. Data visualization and interpretation

Python comes with a number of data visualization packages. Matplotlib is the most common library for generating basic graphs and charts. If you need a beautifully designed, advanced chart, you can also try Plotly, another Python package.

There is also a Python package, IPython, for interactive data visualization and support for leveraging the GUI toolkit. If you want to embed survey results into an interactive web page, the NBConvert function helps you convert IPython or put The Notebooks of Jupyter into HTML snippets.

After data visualization is complete, how you present your data is extremely important, and it must be in order to respond to the business logic of the project.

You can now use this valuable information to find answers to previous business logic questions, keeping in mind that your explanations will be very helpful to your company's project stakeholders.

Ready to embrace your data science goals with Python?

This article has given you plenty of reasons why you should program in Python while embarking on your data science journey. Here's a new reason: The top tech giants also use Python.

Google, Youtube, Instagram, NASA, IBM, Netflix, Spotify, Uber, Pinterest, Reddit and others are among the top companies using Python for data science research.

Python, which is best suited for data analysis, is best for handling large amounts of data. Its flexibility, learnability and library advantages make it the best language for working in big data, machine learning and other fields.