Data Science is something that has taken over our world with a flash. Take a look around and you'll notice that Data Science has taken over every field, be it technology or future predictions. Hence, it becomes important that you have a good understanding of Data Science. It will lead to better opportunities and development in the future.
Since Data Science is a vast subject, it becomes important to understand every basic. The very basis for understanding Data Science is Python, a programming language. Now, a new question arises- How to learn Python and what are its libraries that play an important role in Data Science?
In this article, we are gonna mention important libraries that would help you greatly.
What is Python
Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library.
Important Libraries for Python
Now, let's see what are the important Python libraries.
TensorFlow
The first in the list of python libraries for data science is TensorFlow. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors. It’s used across various scientific fields. TensorFlow is basically a framework for defining and running computations that involve tensors, which are partially defined computational objects that eventually produce a value.
SciPy
SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s extensively used for scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.
NumPy
NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
Matplotlib
Matplotlib has powerful yet beautiful visualizations. It’s a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it’s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications.
Conclusion
Well, that was it for the important Python libraries that would be very helpful in learning Data Science. I hope you found some help from this.
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