دانلود Pluralsight Python Data Playbook Tutorial Series آموزش کار با داده ها در پایتون

Table of Contents:
Course Overview
Understanding Your Data
Removing and Fixing Columns with pandas
Indexing and Filtering Datasets
Handling Bad, Missing, and Duplicate Data

Importing Data: Python Data Playbook
Python is one of the most powerful and widely used languages to work with data. In this course, Importing Data: Python Data Playbook, you will learn foundational knowledge and gain the ability to import data from multiple different file formats, including: text, tabular data, binary formats as well as from databases. First, you will learn how to import text and CSV files. Next, you will discover how to import data from JSON, XML, SAS, Stata, HDF5, Matlab, Pickle files, and more. Finally, you will explore how to import relational data from databases, including: SQLite, MySQL, and PostgreSQL. When you're finished with this course, you will have the skills and knowledge of importing data into Python needed to analyze, visualize, and in general work with data.

Table of Contents:
Course Overview
Importing Text Data into Python Using NumPy
Importing CSV Data into Python Using csv and pandas
Import Data into Python from JSON and XML Files
Import Data into Python from Excel Files
Import Data into Python from Common Binary Data File Formats
Import Data into Python from Relational Databases

Pygal: Python Data Playbook
Vector image formats such as SVG possess many important advantages over scalar formats such as PNG and JPEG. Using SVG, you can build high-quality, compact visualizations that render on low-resolution devices and that can be scaled, zoomed, and moved without distortion.

In this course, Pygal: Python Data Playbook, you will gain the ability to construct an array of visualizations and render them to SVG format using Pygal.

First, you will learn the advantages of working with Pygal for building SVGs and understand the niche that Pygal occupies relative to other visualization packages such as Matplotlib, Seaborn, Bokeh, and Plotly. Next, you will discover how to build an array of visualizations in Pygal, from in-memory as well as file data. You will then construct a visualization including simple charts such as Line, Tree, and Bar graphs, as well as specialized types like TreeMaps and Sparklines. You will understand the different types of Styles and Configurations that can be used to govern chart appearance. You will work with built-in, parametric, and custom styles, as well as Chart, Serie, and Value configurations.

Finally, you will explore how to render Pygal visualizations to a range of image and non-image formats, including XML element trees and base64 encoded formats for online transfer. You will round out the course by building a web application using the Flask microframework in order to render and serve Pygal charts.

When you are finished with this course, you will have the skills and knowledge of building and rendering visualizations in Pygal needed to effectively harness the many advantages of the Scalable Vector Graphics format.

Table of Contents:
Course Overview
Getting Data into Pygal
Plotting Basic Pygal Charts
Visualizing Complex Data with Advanced Charts
Rendering Out Charts

Understanding Databases with SQLAlchemy: Python Data Playbook
Databases are an integral part of data science, and every programmer that interacts with data needs to be able to work with a database. In this course, Understanding Databases with SQLAlchemy: Python Data Playbook, you will learn foundational knowledge to work with databases using SQLAlchemy. First, you will see how to perform queries. Next, you will discover how to create databases and tables and populate them with data. Finally, you will explore how to manipulate the data you inserted and queried. When you are finished with this course, you will have the skills and knowledge of interacting with databases needed to successfully work with your database using Python with SQLAlchemy.

Table of Contents:
Course Overview
Up and Running with SQLAlchemy
Querying with SQLAlchemy
Creating Your Database
Manipulating Your Database
Final Takeaway

Web Scraping: Python Data Playbook
Learn how to tell a compelling graphical data story in a Jupyter Notebook with Seaborn having scraped information from a static web page with BeautifulSoup4 when no API is available.

Table of Contents:
Course Overview
Setting Up BeautifulSoup
Understanding Your Scraped Data
Making Scraped Data Usable

Advanced Web Scraping Tactics: Python Data Playbook
Web Scraping is a technique to extract the data from the web pages but in an automated way. A web scraping script can load and extract the data from multiple pages based on the requirements. Obtaining data manually from the browser is a tedious task, so it's better to do it in an automated manner.

This guide will walk you through how to handle unconventional issues and complexities that you may come across while doing Advanced Web Scraping. This guide will cover the complexities of web scraping, along with how to tackle them.

Table of Contents:
Introduction
Choose the Right Tool
Dynamic Pages or Client-Side Rendering
Honeypot Traps
Authentication
Captcha
IP Blocking
Frequent Changes in the Structure
Conclusion