The Usage of Python for the same reason of ease of use, adaptability and robust library
ecosystem has gained a lot of popularity in the data science field. But, like all other tools, it has
its pros and cons. Now, let’s take a look at a complete list of the pros and cons of Python in data
science.
Python is very easy (and readable) to use, which makes it accessible for both beginners and
experts. Python is easy to learn and has a straightforward syntax that lets people with a
background in business, social sciences or engineering quickly get to grips with it and start
analyzing data.• Analysis and data manipulation by pandas• For numerical computation we use
NumPy.• Data visualization using Matplotlib and Seaborn• But for scientific computing:
SciPyMachine learning with scikit-learnDeep learning using PyTorch and TensorFlow Science
Projects.
Python’s ease of use, adaptability, and robust library ecosystem have made it one of the most
popular programming languages in the data science field. However, like any other tool, it has its
advantages and disadvantages. Here’s a comprehensive look at the pros and cons of Python in
data science.
Python’s readability and ease of use are huge advantages, making it accessible to beginners
and experts alike. It’s clear and straightforward syntax allows people from various fields,
business, social sciences, engineering—to quickly learn Python and start analyzing data.
Python offers a wide range of frameworks and packages specifically designed for data science,
including:
Pandas for analysis and data manipulation
NumPy for numerical computation
Matplotlib and Seaborn for data visualization
SciPy for scientific computing
Scikit-learn for machine learning
PyTorch and TensorFlow for deep learning
The Python community is very alive and resourceful. Frequently, there are forums and tutorials
to read through and documentation to look over, with lots of troubleshooting and best practice
information waiting on Stack Overflow and GitHub.
No matter what you are dealing with (databases – SQL, web applications, big data tools –
Hadoop and Spark), Python integrations with other languages and technologies is a seamless
process.
Python is a cross platform, working on Windows, Mac OS, and Linux with minimal fuss, and
facilitating collaborative development on different platforms.
Data visualization is very important in data science, and Python is good at it. There are many
tools you can use for plotting and visualizing data, from just using Matplotlib, Seaborn, and
Plotly.
With data volume growing, data scientists can process large datasets using Python by
leveraging big data frameworks like Apache Spark and Dask.
Python is a general-purpose language that isn’t just limited to data science, it can also be used
to script, automate and to develop web applications.
Interactive coding, visualization and documentation in one place, and with this Jupyter
Notebooks are a means to collaborate and share insights within the data science community.
As Python is popular in data science, there are plenty of jobs for data scientists who can
understand Python, that is good for employment.
Python is an interpreted language, so it struggles with high-speed calculations more than
compiled languages such as C and Java. That can be a limitation in high performance projects.
Python GIL enables only one thread to execute at once, which would negatively impact
performance especially for the multi-threaded applications in data intensive processes, although
we can get around this using multiprocessing.
Python is not memory efficient, and a data scientist working on a project that has large amounts
of data, may result in higher hardware costs and less performance.
Python is not that popular for mobile applications, languages like Swift and Kotlin are more
popular. There are frameworks like Kivy and BeeWare, but they aren’t as developed.
Python’s static typing allows for dynamic preparation of the execution of the code, leading to its
flexibility for quick prototyping, but it exposes us to runtime errors which are hard to find and
debug specifically when we work with large projects.
However, because Python does not provide low level programming and close control over
system resources is necessary, it is not the ideal choice for low level programming. The
preferred languages for such tasks are C or Rust.
Python is easy to learn for beginners, but advanced features like decorators, context manager,
and Meta class can discourage new users to learn Python faster.
One major bottleneck when working with Python is having to handle its magnificently huge
library ecosystem. There are problems when libraries are not compatible with each other, and
the problem as it is often called — dependency hell can arise which is a pain for a developer.
I conclude that Python is a strong tool in data science, particularly for those that know its
strengths and weaknesses. This gives you knowledge of the pros and cons of Python so you
can make your choice as to how best you can leverage the power of Python.