Python is a general-purpose and high-level dynamic programming language that focuses on code readability. After being founded in the year 1991 by Guido Van Rossum, Python has only soared in popularity.
Its syntax allows programmers to write codes in fewer steps as compared to Java or C++. Some of the other reasons behind Python’s popularity include its versatility, effectiveness, ease of understanding, and robust libraries.
Python’s high-level data structures and dynamic binding make it a popular choice for rapid application development. Data scientists usually prefer Python over other programming languages.
But what exactly makes Python suitable for data science? Why do data scientists prefer working with Python? Let’s find out –
The Benefits of Python
A big reason why Python is widely preferred is because of the benefits it offers. Some of the major benefits of Python are –
- Ease of learning: Python has always been known as a simple programming language in terms of syntax. It focuses on readability and offers uncluttered simple-to-learn syntax. Moreover, the style guide for Python, PEP 8, provides a set of rules to facilitate code formatting.
- Availability of support libraries: Python offers extensive support for libraries including those for web development, game development, or machine learning. It also provides a large standard library that includes areas like web services tools, internet protocols, and string operations. Moreover, many high-use programming tasks are pre-scripted into the standard library. This significantly reduces the length of the code that needs to be written.
- Free and open–source: Python can be downloaded for free and one can then start writing code in a matter of minutes. It has an OSI-approved open-source license. This makes Python free to use and distribute. Being open-source, Python can also be used for commercial purposes.
- A vibrant community: Another benefit of being an open-source language is the availability of a vibrant community that keeps actively working on making the language more user-friendly and stable. Its community is one of the best in the world and contributes extensively to the support forums.
- Productivity – The object-oriented design of Python provides improved process control capabilities. This, along with strong integration and text processing capabilities, contribute to increased productivity and speed. Python can be a great option for developing complex multi-protocol network applications.
- Easy integration – Python makes it easy to develop web services by invoking COM or COBRA components, thanks to enterprise application integration. It possesses XML and other markup languages that make Python capable of running on all modern operating systems through the same byte code. The presence of third-party modules also makes Python capable of interacting with other languages and platforms.
- Characteristic features – Python has created a mark for itself because of some characteristic features. It is interactive, interpretable, modular, dynamic, object-oriented, portable, high-level, and extensible in C++ & C.
Why Python is Ideal for Data Science Functions
Data science is about extrapolating useful information from large datasets. These large datasets are unsorted and difficult to correlate unless one uses machine learning to make connections between different data points. The process requires serious computation and power to make sense of this data.
Python can very well fulfill this need. Being a general programming language, it allows one to create CSV output for easy data interpretation in a spreadsheet. Python is not only multi-functional but also lightweight and efficient at executing code. It can support object-oriented, structural, and functional programming styles and thus can be used anywhere.
Python also offers many libraries specific to data science, for example, the pandas library.
So, irrespective of the application, data scientists can use Python for a variety of powerful functions including casual analytics and predictive analytics.
Popular Data Science Libraries in Python
As discussed above, a key reason for using Python for data science is because Python offers access to numerous data science libraries.
Some popular data science libraries are –
- Pandas – It is one of the most popular Python libraries and is ideal for data manipulation and analysis. It provides useful functions for manipulating large volumes of structured data. Pandas is also a perfect tool for data wrangling. Series and DataFrame are two data structures in the Pandas library.
- NumPy – Numpy or Numerical Python is a Python library that offers mathematical functions to handle large dimension arrays. NumPy offers vectorization of mathematical operations on the NumPy array type. This makes it ideal for working with large multi-dimensional arrays and matrices.
- SciPy – It is also a popular Python library for data science and scientific computing. It provides great functionality to scientific mathematics and computing programming. It contains submodules for integration, linear algebra, optimization, special functions, etc.
- Matplotlib – Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is a useful Python library for data visualization. Matplotib provides various ways of visualizing data effectively and enables quickly making line graphs, pie charts, and histograms, etc.
- scikit-learn – It is a Python library focused on machine learning. scikit-learn provides easy tools for data mining and data analysis. It provides common machine learning algorithms and helps to quickly implement popular algorithms on datasets and solve real-world problems.
A python is an important tool for data analysts. The reason for its huge popularity among data scientists is the slew of features it offers along with the wide range of data–science–specific libraries that it has.
Moreover, Python is tailor-made for carrying out repetitive tasks and data manipulation. Anyone who has worked with a large amount of data would be aware of how often repetition happens. Python can be thus be used to quickly and easily automate the grunt work while freeing up the data scientists to work on more interesting parts of the job.
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