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The aforementioned quote by Joseph Santarcangelo can be supported by the data from Stack Overflow, which finds that Python is the most rapidly expanding significant programming language globally. This concludes that Python is the first big step toward Data Science.
Before diving into why use Python for Data Science or libraries of Python, let's first go through the fundamentals.
What is Python?
Python is an interpreted, high-level, and general-purpose programming language. It emphasizes code readability and simplicity, with a design philosophy that emphasizes clear and concise syntax. Python supports multiple programming paradigms, including object-oriented, functional, and procedural, and provides extensive standard libraries for diverse tasks.
If you are unfamiliar with the technical terms, here’s the breakdown:
- Interpreted: It means that Python code is executed line by line, translating and executing each instruction at runtime.
- High-level & general-purpose: High-level means Python provides abstraction from low-level details, making it easier to write and understand code. General purpose implies it can be used for a wide range of applications, from web development to data analysis and more.
- Object-oriented: It means that Python supports the object-oriented programming paradigm, where code is organized around objects that have attributes (data) and methods (functions) associated with them. This approach allows for modular and reusable code design.
- Functional: The code is structured around the evaluation of mathematical functions. It emphasizes immutability, pure functions, avoiding side effects, promoting code clarity, and facilitating parallel programming.
- Procedural: The code is organized into procedures or functions. It focuses on the step-by-step execution of instructions, utilizing control structures such as loops and conditionals. Procedural programming emphasizes the sequence of actions to solve a problem.
Python's beginner-friendly nature stems from its user-friendly design, concise syntax, versatility, and open-source nature. Its widespread adoption across various platforms and industries further solidifies its appeal.
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Python 2 Vs Python 3
Both are different versions of the Python programming language. Python 3 introduced significant changes and improvements over Python 2, including syntax enhancements, better Unicode support, and improvements in performance and library support.
They have similarities but significant differences. Developers, especially beginners, must consider trade-offs like code compatibility, third-party library support, and language features when choosing between them.
The main similarities between them are:
- Basic Syntax: Both versions have similar fundamental syntax structures and keywords.
- Core Programming Concepts: They share core programming concepts like variables, loops, conditionals, functions, and exception handling.
- Programming Paradigms: Python 2 and Python 3 support procedural, object-oriented, and functional programming paradigms.
- Third-Party Libraries: They have a large ecosystem of third-party libraries and frameworks that can be used interchangeably between the two versions.
The primary differences between them are:
| Feature | Python 2 | Python 3 |
| Print Syntax | print "Hello" | print("Hello") |
| Unicode Handling | Uses ASCII by default | Uses Unicode by default |
| xrange() Function | Available | Replaced with range() function |
| Exception Handling | Uses except Exception, e | Uses except Exception as e |
| Syntax | Less consistent and more verbose syntax | More consistent and streamlined syntax |
| Library Compatibility | Some libraries are not compatible with Python 3 | Improved library support for Python 3 |
| Unicode Support | Limited | Improved and enhanced |
| String Handling | ASCII by default | Unicode (UTF-8) by default |
| Source Code Encoding | No default source code encoding | Source code encoded in UTF-8 by default |
When choosing between Python 2 and Python 3, it's important to consider that Python 3 is generally easier to learn. While Python 3 is favored for new projects, some companies still rely on Python 2 due to migration challenges. It's worth noting that Python 2 is no longer actively developed or maintained, lacking bug fixes, security updates, and new features.
Why Use Python For Data Science
Using Python for data science and data analytics is one of the greatest chances for any data scientist, whether they are aspiring or experienced. This all-purpose programming language can aid in creating desktop and online apps. Additionally, it supports the creation of sophisticated scientific and mathematical applications.
Python is very well-liked in the programming community for two reasons:
- first, it can handle a huge variety of jobs, and
- second, it is very user-friendly for beginners.
English terms are utilized in the grammar of Python code, making it user-friendly for beginners because anybody can grasp it and get started.
5 Most Important Python Skills To Become A Data Scientist
- Programming Fundamentals: As a data scientist, your main role involves utilizing data to derive actionable insights. This requires strong Python programming skills for efficient code writing and code comprehension. Some basic Python programming fundamentals to master are Data Types, Variables, Operators, Lists, Dictionaries, Functions, Modules, Packages, & etc.
- Data Storage and Retrieval: Data scientists primarily handle data by retrieving, storing, and processing it. So, proficiency in data storage and retrieval is crucial for efficient data management. Some common approaches that you should learn are flat files, CSV files, JSON files, Relational databases, NoSQL databases, cloud storage, etc.
- Data Manipulation & Analysis: As a data scientist, data preparation and manipulation are significant tasks for analysis and modeling. Python skills are essential for cleaning and preparing data, and handling diverse types and sizes of datasets. Proficiency in NumPy, Pandas, PySpark, and specialized libraries is valuable for efficient analysis of structured, image, text, and audio data.
- Data Visualization: Data visualization is vital in data science for exploring, understanding, and communicating insights. Data scientists require solid skills in visualization tools to identify patterns, & trends, and effectively convey findings. Some of the popular libraries & tools in Python to master are Matplotlib, Seaborn, Plotly, etc.
- Applied Machine Learning: Mastering applied machine learning in Python is crucial for data scientists. Machine learning utilizes algorithms and models to enhance computer performance without explicit programming. Some important concepts to learn in machine learning are Decision Trees, Ensemble Technique, and Area of Regression. Univariate & Multi-variate Linear Regression, etc.
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Top 5 Python Libraries For Data Science
- NumPy: NumPy, also known as Numerical Python, is a powerful library for scientific computing and array operations. It simplifies working with arrays and matrices, enabling efficient mathematical operations and improved performance through vectorization.
- Pandas: Pandas is a valuable library designed for the intuitive handling of labeled and relational data. It utilizes data structures like Series and DataFrames, enabling tasks such as conversion, handling missing data, adding/deleting columns, imputing missing values, and generating plots. Pandas is essential for data wrangling, manipulation, and visualization.
- Matplotlib: Matplotlib is a widely-used data science library that facilitates the creation of various visualizations, including histograms, scatterplots, and non-Cartesian graphs. It empowers Python to rival scientific tools like MatLab and Mathematica. While Matplotlib requires more code for advanced visualizations, it offers an object-oriented API for embedding plots into applications and seamlessly integrates with other popular plotting libraries.
- Seaborn: Seaborn, built on Matplotlib, is a valuable Python machine-learning tool for visualizing statistical models. It offers a wide range of visualizations, such as heatmaps, time series, joint plots, and violin diagrams, that effectively summarize and depict data distributions. Its extensive gallery of visualizations is a major advantage for data exploration and analysis.
- Plotly: Plotly is a web-based data visualization tool that provides a wide range of pre-built graphics accessible through the Plotly website. It excels in interactive web applications and continuously expands its library with new graphics, features, and support for linked views, animation, and crosstalk integration.
While not exhaustive, the Python ecosystem provides numerous tools that aid in machine learning tasks and algorithm development. Data scientists and software engineers working on Python-based data science projects rely on these essential tools to construct high-performance ML models.
The Bottom Line
The Data Science job market is experiencing significant growth, with companies of all sizes seeking data science professionals. Python is favored by these companies due to its capabilities in modeling, analyzing datasets, and preparing data for machine learning projects.
According to a report from Statista, Python was the third most in-demand language by recruiters in 2022. It indicates that many companies are actively seeking professionals with Python skills for data science positions.
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