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Introduction to Python for Data Science
Python is a general-purpose, object-oriented, interpreted, high-level language and is very popular in the market. Python has a very rich library that contains pre-defined code for almost every purpose and to use python for a task using only needs the logic, as most of the coding part is handled by python itself.
Python has a large community of developers which provides an extra benefit to newcomers and the experienced python user that there is no issue with any bugs.
Before moving to the introduction of python for data science let’s see some basics of data science.
What is Data Science?
Data science is the process of extracting information and insights from massive amounts of data by organizing, processing, and analyzing the data. It entails several distinct disciplines, such as mathematical and statistical modeling, data extraction from its source, and data visualization approaches. It frequently entails working with big data technology to collect both structured and unstructured data. We'll look at several cases where data science is employed in the following sections and how python will be helpful for that.
What is Python?
As we have seen in the opening section Python is a general-purpose, object-oriented, interpreted, high-level language and very popular in the market. Python was created in the late 1980s but it was first under come used when its first code was written in December 1989. As we have already seen how the libraries of python are making it one step forward as compared to the other programming languages in almost every field.
Python is utilized as a data science programming language since it provides expensive mathematical or statistical capabilities. It is one of the primary reasons that data scientists all around the world choose Python. If you look at recent trends, you will see that Python has emerged as the computer language of choice, particularly for data science.
Python in Data Science
Data science programming requires a very versatile but flexible language that is simple to write code in yet can handle very sophisticated mathematical processing. Python is best suited for such needs since it has previously proved itself as a language for both general and scientific computing. Furthermore, it is always being enhanced in the form of new additions to its variety of libraries geared at various programming requirements. The next sections will go through the properties of Python that make it the ideal language for data research.
Python is a basic and easy-to-learn language with fewer lines of code than other related languages such as R. Its simplicity also makes it resilient enough to handle complicated circumstances with minimum code and far less uncertainty about the program's overall flow.
Because python is cross-platform, the same code may be used in numerous contexts without modification. As a result, it is ideal for usage in a multi-environment arrangement.
It runs quicker than other data analysis languages, such as R and MATLAB which is the most required thing in most tasks.
Its outstanding memory management capacity, particularly garbage collection, allows it to manage very huge volumes of data transformation, slicing, dicing, and visualization smoothly.
Most notably, Python includes a vast library of libraries that serve as specific analytical tools. The NumPy library, for example, deals with scientific computing, and its array requires far less memory than the standard Python list for maintaining numeric data. And the number of such bundles is constantly increasing.
Python offers packages that may directly utilize code written in other languages such as Java or C. This aids in improving code performance by reusing existing code from other languages if a better result is obtained.
Python Libraries for Data Science
The thing which makes python in front of each task is python libraries, none of the other languages can match the level of the libraries provided by python. Libraries contain pre-defined code for a particular task and users don’t have to write that bunch of code again to create a project. There are some libraries of python which are helpful for data science, let’s look into them
NumPy is the most powerful when we want to work on n-dimensional arrays. NumPy contains the basic algebra function such as the linear algebra function and it provides advanced random number capabilities. Also, it provides integration with other programming languages or other tools.
To perform the structured data manipulations and operations we can use the Pandas library of python. Pandas library is not very old in python and was added very recently and it boots the Python use in data science.
Matplotlib library is used to plot graphs of various kinds for data science. By using the matplotlib library we can plot any kind of graph.
The scikit-learn library of python is a combination of NumPy and matplotlib and is mostly used to plot graphs. In data science many times we need to visualize the data for such operations we need these libraries.
In this article, we have seen python programming language for data science. Python is a general-purpose, object-oriented, interpreted, high-level language and is very popular in the market. Python has a very rich library that contains pre-defined code for almost every purpose and to use python for a task using only needs the logic, as most of the coding part is handled by python itself. There are some libraries of python which are helpful for data science like NumPy, Pandas, Matplotlib, etc. and this list is so long.
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