The Complete Python Programming Course: Beginner to Advanced | Data Science | Machine Learning скачать в хорошем качестве

The Complete Python Programming Course: Beginner to Advanced | Data Science | Machine Learning

Download all programs .py, .ipy, .csv files and examples used in this course: https://drive.google.com/file/d/1Tqhd... I am using Spyder IDE in this course. You can download it from this link: https://www.spyder-ide.org Learn the basics of Python. This tutorial is also for Python beginners. So even if you have no prior knowledge of Python, you won’t face any difficulty understanding this tutorial. Data types specify the different sizes and values that can be stored in the variable. For example, Python stores numbers, strings, and a list of values using different data types. Learn different types of Python data types along with their respective in-built functions and methods. Python is a dynamically typed language; therefore, we do not need to specify the variable’s type while declaring it. Whatever value we assign to the variable based on that data type will be automatically assigned. For example, name = 'Kristina' here Python will store the name variable as a str data type. No matter what value is stored in a variable (object), a variable can be any type like int, float, str, list, set, tuple, dict, bool, etc. The function is a block of code defined with a name. We use functions whenever we need to perform the same task multiple times without writing the same code again. It can take arguments and returns the value. Functions are an essential part of the Python programming language. A function is a block of code defined with a name. We use functions whenever we need to perform the same task multiple times without writing the same code again. It can take arguments and returns the value. Python has a DRY principle like other programming languages. DRY stands for Don’t Repeat Yourself. Consider a scenario where we need to do some action/task many times. We can define that action only once using a function and call that function whenever required to do the same activity. Function improves efficiency and reduces errors because of the reusability of a code. Once we create a function, we can call it anywhere and anytime. The benefit of using a function is reusability and modularity. Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data. NumPy provides various powerful data structures, implementing multi-dimensional arrays and matrices. These data structures are used for the optimal computations regarding arrays and matrices. Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables. is the most famous python library providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Seaborn is one of the go-to tools for statistical data visualization in python. Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.  Python Iterators: They are objects that can be iterated upon. In this tutorial, you will learn how iterator functions and how you can build your own iterator using iter and next methods. Python Generators: It make iterations easily using Python generators, how it is not quite the same as iterators and normal functions, and why you should use it. Python Decorators: A decorator takes in a function, includes some functionality, and brings it back. In this tutorial, you will learn how you can make a decorator and why you should use it.