5 PYTHON MODULES AND THEIR USES

• OPERATOR: provides a set of pre-defined functions in python
• DECIMAL: Is used to print the complete decimal values where one number is divided by another one.
• RANDOM: This module is used to generate random numbers. Some of its pre-defined functions are randint (), choice () etc.
• STRING: String module provides asset of functions that are used to perform certain operation on characters. It has pre-defined functions like ascii_letters.
• MATH: Math modules are used to perform mathematical operations i.e. sqrt, factorial etc.

5 PYTHON EXTERNAL MODULES AND THEIR USES

TensorFlow: It is an open-source library used for high-level computations. It is also used in machine learning and deep learning algorithms. It contains a large number of tensor operations. Researchers also use this Python library to solve complex computations in Mathematics and Physics.

Matplotlib: This library is responsible for plotting numerical data for instance it plots high-defined figures like pie charts, histograms, scatterplots, graphs, etc.

Pandas: It is an open-source machine learning library that provides flexible high-level data structures and a variety of analysis tools. It eases data analysis, data manipulation, and cleaning of data. Pandas support operations like Sorting, Re-indexing, Iteration, Concatenation, Conversion of data, Visualizations, Aggregations, etc.

Numpy: The name “Numpy” stands for “Numerical Python”. It is the commonly used library. It is a popular machine learning library that supports large matrices and multi-dimensional data. It consists of in-built mathematical functions for easy computations. Even libraries like TensorFlow use Numpy internally to perform several operations on tensors. Array Interface is one of the key features of this library.

SciPy: The name “SciPy” stands for “Scientific Python”. It is an open-source library used for high-level scientific computations. This library is built over an extension of Numpy. It works with Numpy to handle complex computations. While Numpy allows sorting and indexing of array data, the numerical data code is stored in SciPy. It is also widely used by application developers and engineers.