Last updated 11/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 336.73 MB | Duration: 1h 3m
Take First Step Toward Data Analysis With Pandas - Learn about DataFrames, Jupyter Notebook, iPython and Pandas Commands
What you'll learn
Fundamentals of Data Analysis.
Working with Pandas, iPython, Jupyter Notebook.
Important Jupyter Notebook Commands.
Working with CSV, Excel, TXT, JSON Files and API Responses.
Working with DataFrames (Indexing, Slicing, Adding and Deleting).
Requirements
Basics of Python
Description
Welcome to Data Analysis Basics with Pandas and Python - For Beginners,This course will help you to understand the fundamentals of Data Analysis with Python and Pandas library. You will learn,1. Fundamentals of Data Analysis.2. Working with Pandas, iPython, Jupyter Notebook.3. Important Jupyter Notebook Commands.4. Working with CSV, Excel, TXT, JSON Files and API Responses. 5. Working with DataFrames (Indexing, Slicing, Adding and Deleting).Pandas is an open-source library providing high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modelling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.After completing this course you will have a good understanding of Pandas and will be ready to explore Data Analysis in-depth in future.
Overview
Section 1: Course Introduction
Lecture 1 Course Introduction
Lecture 2 Welcome - Lets Get Started!
Section 2: What is Pandas?
Lecture 3 What is Pandas?
Lecture 4 Starting With Pandas And iPython
Section 3: Jupyter Notebooks
Lecture 5 Working with Jupyter Notebooks
Lecture 6 Important Jupyter Notebook Commands
Section 4: Working on Data
Lecture 7 Working with CSV, Excel, TXT and JSON Files
Lecture 8 Working with API Response
Lecture 9 Indexing and Slicing Dataframe Tables[Part 1]
Lecture 10 Indexing and Slicing Dataframe Tables[Part 2]
Lecture 11 Deleting Columns and Rows
Lecture 12 Adding and Updating new Columns and Rows
Section 5: Thank You For Being Here!
Lecture 13 Thank You For Being Here!
Python Programmers and Developers,Student interested in learning Pandas
Homepage
Code:
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