Python New Features
Python Training in Vizag | Best Python Training Institutes
python training in vizag
As of my knowledge cutoff in September 2021, the latest stable version of Python is Python 3.10, which was released in October 2021. Here are some of the new features introduced in Python 3.10:
- Parenthesized context managers: A new syntax is introduced to allow multiple context managers to be grouped together in a single with statement, making the code more readable.
- Structural Pattern Matching: A new syntax is introduced to simplify the code that matches against the shape of data structures like tuples, lists, and dicts.
- Improved error messages: Python 3.10 includes improved error messages to help developers diagnose and fix issues more easily.
- Improved performance: Python 3.10 includes a number of performance improvements, including faster dictionary lookups and improved string concatenation performance.
- Parenthesized expressions as statement: Python 3.10 introduces a new syntax to allow the use of expressions as standalone statements.
- Custom string interpolation: Python 3.10 introduces a new syntax for string interpolation that allows developers to define their own custom string formatting methods.
- Structural Pattern Matching for dicts: The structural pattern matching syntax introduced in Python 3.10 now also supports matching against the structure of dictionaries. One of the best Python institute in vizag JNNC Technologies Python course is excellent in JNNC.
These are just a few of the new features introduced in Python 3.10, and there are many more improvements and additions that you can learn about in the Python 3.10 release notes.
Data science using Python
Python is largely used for data analysis. There are mainly five libraries available to perform data analysis. Numpy, Pandas, SciPy, Matplotlib and Scikit-learn.
- Numpy is used for scientific computing. It facilitates with multi-dimensional arrays and matrices. It also helps us to perform high-level mathematical functions to operate on those arrays.
- Pandas offer data structures and used to perform manipulating Numerical tables and time series.
- Scipy is used for numerical integration and optimization.
- Matplotlib is for 2d Plotting. It is used to generate data visualizations, bar charts, scatter plots.
- Scikit-learn is used for machine learning. It supports vector machines, naïve Bayes, logistic regression and gradient boosting.
NumPy
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Numpy stands for Numeric python, it is an open source. It is a package in python. Numpy library is mainly used to process multi-dimensional arrays objects.python training in vizag
- The main operations can be performed by using are Mathematical and logical operations on arrays.
- Operations related to linear algebra. It has a built-in function for linear algebra and for generating random numbers.
- It is used along with the packages SciPy and Mat-plotlib. All these combinations are an alternative to MatLab.
Pandas
- Pandas is a library for python software and Pandas stands for Python Data Analysis.
- Its name is derived from Word Panel Data.
- Its main priority is data munging. By using it we can load, prepare, manipulate, model and analyze data.
- In Windows, if we Install Anaconda Package, Pandas will be installed.
Scipy
- It is an Open-Source Library mainly used for Scientific computing.
- It supports special functions, ODE, Integration, and Parallel Programming tools.
- It is very easy to use and can operate on an array of NumPy library.
Matplotlib
- It is a library in Python for plotting 2D graphs by using python scripts. It supports bar charts, error charts, histogram, and power spectra.
- It can also be used with wxPython and PyQt. Using Matplotlib with Numpy is an alternative MatLab.
scikit-learn
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It is an Open source library provides many supervised and unsupervised algorithms.
- It includes Regression, Classification, Clustering, Model Selection, and Preprocessing.
Learn about python course in Vizag
The course structure for a Python programming course can vary depending on the level of the course and the intended audience, but here is a general outline of what you might expect to cover in a comprehensive Python course:
- Introduction to Python: This would cover the basics of Python, including data types, variables, operators, and expressions.
- Control Structures: This would cover decision-making and loop structures, such as if-else statements and for and while loops.
- Functions and Modules: This would cover how to define and call functions, how to pass arguments to functions, and how to use modules to organize code.
- Lists and Dictionaries: This would cover the use of lists and dictionaries in Python, including how to create, access, and manipulate them.
- File Input/Output: This would cover how to open, read, and write to files in Python.
- Exception Handling: This would cover how to handle errors and exceptions in Python, including try-except blocks and raising exceptions.
- Object-Oriented Programming: This would cover the basics of object-oriented programming (OOP) in Python, including classes, objects, inheritance, and polymorphism.
- Regular Expressions: This would cover how to use regular expressions in Python to search, replace, and match patterns in strings.
- Web Development: This would cover how to use Python for web development, including frameworks like Django and Flask.
- Data Science and Machine Learning: This would cover how to use Python for data science and machine learning, including libraries like NumPy, Pandas, and Scikit-learn.
- Advanced Topics: This would cover advanced topics in Python, such as decorators, generators, and context managers.
Of course, the specific course structure may vary depending on the instructor or institution offering the course, but this should give you a general idea of what you might expect to learn in a Python programming course.
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