Libraries for Data Science in Python

Libraries for Data Science in Python

In the world of programming, a library consists of a number of precompiled codes which can be used to perform a specific task in the program. Generally, a library may also contain configuration data, a huge amount of documentation, templates and classes etc. Libraries in Python make programming convenient and simple for programmers to perform their task in less time. In the field of Data Science, Machine Learning and Data Visualization libraries play a very important role. All the library files have DLL (Dynamic Load Libraries) extension and when a program is linked to a particular library then the linker will automatically reach to that library and finally the program is executed. The syntax, semantics and tokens are contained by the Python Standard Libraries. The libraries of Python are basically written in C language. The standard library in Python contains about 200 core modules. To make Python a high programming language all the core modules work together.

Python has a number of factors and some of them are listed below;

  • Python has a wide range of libraries which can be used when necessary.
  • Python is very easy to learn and it is also called the “Beginners Programming Language”.
  • Python is portable which allows the developers to be more productive in the sense of software development and deployment.

Some of the common libraries used in Python are as described below;

  • Pandas: it is one of the most important libraries which are widely used by Data Scientist and Software developers. The essential steps such as analyzing the data, data manipulation and data cleaning are performed in Pandas. The operations like Iteration, Concatenation, Re-indexing, Data Conversion, Data Visualization, Aggregations etc are supported by the Pandas library.
  • Numpy: Numerical Python (Numpy) is a library which supports multi – dimensional data and large matrices. For easy computation purposes it has built-in – built mathematical functions.
  • TensorFlow: it was developed by “Google” in collaboration with “Brain Team”. It is used for high level computations and it is an Open Source Library. It is widely used in Deep Learning algorithms and Machine Learning. To solve the high computations from Physics and Mathematics, this library is widely used by researchers.
  • Matplotlib: this library is used in Data Analytics where we can plot the numerical data effectively. High-defined plots such as bar plots, Pie charts, Histogram, Scatter Plot, Density Plot, and Box Plot etc can be plotted to visualize the data.
  • SciPy: this library is used to compute high level scientific operations. This library works with Numpy where the data code is stored in SciPy.
  • Scrapy: this library is used to extract meaningful data from vast chunks of data sources. This library is more suitable in Data Mining and Automatic Data Testing.
  • PyGame: this library is used in video game development which provides an easy interface to Standard Direct Media Library (SDL).
  • PyTorch: it is one of the largest machine learning libraries which enables to perform tensor computations along with the GPU acceleration. Complex applications related to the neural network can also be solved with the help of PyTorch.
  • PyBrain: it is one of the modular libraries in machine learning for Python. The main goal of this library is to provide flexible and easy to use algorithms to perform different tasks in machine learning.
  • Scikit – Learn: this library is used to work on the complex data. The supervised and unsupervised algorithms (like Classification, Clustering, Linear Regression) etc are supported by Scikit – Learn.
  • Sympy: this library is used in all the symbolic mathematics (computer algebra system). The package source code is found in GitHub.

We can use a library which is suitable for our purpose. Hence we can say that the Python libraries play a very pivotal role for the software developers and data scientists.

Importance of libraries in Python:

In Python, the library consists of bundles of codes that can be used number of times. For a programmer, the presence of library makes Python simple in programming and convenient to use. Developers don’t need to write the code again and again as the libraries are inbuilt.

Importance of Module Libraries in Python:

Module libraries support to organize the Python code in a logical manner. It is basically a file which contains a set of Python code. Module libraries may contain variables, classes, functions, runnable codes etc.

Real world applications can be developed with the help of Python. Python has been proficiently used in the areas like Error Debugging due to its high level and dynamic capacity. Many global applications such as YouTube, Dropbox make use of Python for different purposes. As the Python libraries are readily available, users can perform a variety of tasks and build their models. Python is truly said to be the “Language of Future ” as it has a number of applications in Data Science, Software Engineering, Machine Learning etc.


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