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Description of  Master Pandas For Data Handling

Published 2/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 4.75 GB | Duration: 13h 18m Learn to Master the worlds most powerful software for Advanced Data Handling What you'll learn Master the Pandas library for advanced Data Handling The fundamental concepts and language of the Pandas DataFrame object All aspects of changing, modifying and selecting Data from a Pandas DataFrame File handling with the Pandas library Use the .concat(), .join(), and .merge() functions/methods to combine Pandas DataFrame objects Scale and Standardize data Advanced Data Preparation with Pandas, including model-based imputation of missing data Make advanced Data Descriptions with Pandas, including cross-tabulations, groupings, and descriptive statistics Make Data Visualizations with Pandas, Matplotlib, and Seaborn Cloud Computing - use Anaconda Cloud Notebook (Jupyter Notebook). Learn to use Cloud Computing resources Optional: use Anaconda Distribution's Jupyter Notebook and Conda package management system Requirements Everyday experience using a computer with Windows, MacOS, Ios, Android, ChromeOS, or Linux is recommended Basic Python knowledge is recommended Access to a computer with an internet connection The course only uses costless software Walk-you-through installation and setup videos for Windows 10/11 is included Description Overview Section 1: Introduction Lecture 1 Introduction to Master Pandas for Data Handling Lecture 2 Setup of the Anaconda Cloud Notebook Lecture 4 The Conda Package Management System (optional) Section 2: Master Pandas for Data Handling Lecture 5 Master Pandas for Data Handling: Overview Lecture 6 Pandas theory and terminology Lecture 7 Creating a DataFrame from scratch Lecture 8 Pandas File Handling: Overview Lecture 9 Pandas File Handling: The .csv file format Lecture 10 Pandas File Handling: The .xlsx file format Lecture 11 Pandas File Handling: SQL-database files and Pandas DataFrame Lecture 12 Pandas Operations & Techniques: Overview Lecture 13 Pandas Operations & Techniques: Object Inspection Lecture 14 Pandas Operations & Techniques: DataFrame Inspection Lecture 15 Pandas Operations & Techniques: Column Selections Lecture 16 Pandas Operations & Techniques: Row Selections Lecture 17 Pandas Operations & Techniques: Conditional Selections Lecture 18 Pandas Operations & Techniques: Scalers and Standardization Lecture 19 Pandas Operations & Techniques: Concatenate DataFrames Lecture 20 Pandas Operations & Techniques: Joining DataFrames Lecture 21 Pandas Operations & Techniques: Merging DataFrames Lecture 22 Pandas Data Preparation I: Overview & workflow Lecture 23 Pandas Data Preparation II: Edit DataFrame labels Lecture 24 Pandas Data Preparation III: Duplicates Lecture 25 Pandas Data Preparation IV: Missing Data & Imputation Lecture 26 Pandas Data Description I: Overview Lecture 27 Pandas Data Description II: Sorting and Ranking Lecture 28 Pandas Data Description III: Descriptive Statistics Lecture 29 Pandas Data Description IV: Crosstabulations & Groupings Lecture 30 Pandas Data Visualization I: Overview Lecture 31 Pandas Data Visualization II: Histograms Lecture 32 Pandas Data Visualization III: Boxplots Lecture 33 Pandas Data Visualization IV: Scatterplots Lecture 34 Pandas Data Visualization V: Pie Charts Lecture 35 Pandas Data Visualization VI: Line plots Anyone who knows the basics of Python programming and want to learn the Pandas library,Anyone who is a new student at the University level and want to learn Data Handling skills that they will have use for in their entire data science, engineering or academic careers,Anyone who knows Python and wants to extend your knowledge of the Pandas library and Data Handling,Anyone who knows about Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know,Anyone who wants to learn advanced Data Handling and improve their capabilities and productivity