⌨️ () Dictionaries（01:11:14 - 01:21:50）
Data Analysis with Python for Excel Users - Full Course

Learn how to use Python and Pandas for data analysis. This course will be especially helpful if you have experience with Excel, but that is not required. Learn how to create pivot tables, work with data, and make visualizations using Python, Pandas, and Jupyter Notebook.

💻 Source Code & Datasets: https://github.com/ifrankandrade/python-course-for-excel-users
🔗 Datasets: https://drive.google.com/drive/folders/12hFh6RPlX5bWzSqpoMvAeT94RCjCvHw5?usp=sharing

✏️ Course developed by Frank Andrade.
🔗 YouTube Channel: https://www.youtube.com/c/FrankAndrade5
🔗 PDF Python Cheat Sheet for this Course: https://artificialcorner.com/p/redeem-my-udemy-courses-for-free
🔗 My Complete Python Courses for Data Analysis & Data Science: https://www.udemy.com/user/frank-andrade-13/

⭐️ Course Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:01:48) Install Python and Jupyter Notebook with Anaconda
⌨️ (0:03:53) Jupyter Notebook Interface
⌨️ (0:13:56) Cell Types and Cell Mode
⌨️ (0:21:34) Jupyter Notebook Shortcuts
⌨️ (0:26:39) Module 1 - Hello World
⌨️ (0:30:30) Data Type
⌨️ (0:39:10) Variables
⌨️ (0:46:53) Lists
⌨️ (1:11:14) Dictionaries
⌨️ (1:21:50) IF Statement
⌨️ (1:28:04) FOR Loop
⌨️ (1:33:49) Functions
⌨️ (1:40:59) Modules
⌨️ (1:44:41) Module 2 -Introduction to Pandas
⌨️ (1:51:08) How to create a dataframe
⌨️ (2:07:34) How to show a dataframe
⌨️ (2:14:24) Basic Attributes, Functions and Methods
⌨️ (2:26:17) Selecting One Column from a Dataframe
⌨️ (2:32:13) Selecting Two or More Columns from a Dataframe
⌨️ (2:37:50) Add New Column to a Dataframe (Simple Assignment)
⌨️ (2:47:51) Operations in dataframes
⌨️ (2:56:04) The value_counts() method
⌨️ (3:00:16) Sort a DataFrame with the sort_values() method
⌨️ (3:09:56) Module 3: Introduction to Pivot Tables in Pandas
⌨️ (3:14:42) The pivot() method
⌨️ (3:20:49) The pivot_table() method
⌨️ (3:29:00) Data Visualization with Pandas (New Dataset + Pivot Table)
⌨️ (3:38:38) Lineplot
⌨️ (3:43:05) Barplot
⌨️ (3:50:52) Piechart
⌨️ (3:54:36) Save Plot and Export Pivot Table

🎉 Thanks to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster

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⌨️ () Dictionaries（01:11:14 - 01:21:50）Data Analysis with Python for Excel Users - Full Course

⌨️ () Intro

⌨️ () Install Python and Jupyter Notebook with Anaconda

⌨️ () Jupyter Notebook Interface

⌨️ () Cell Types and Cell Mode

⌨️ () Jupyter Notebook Shortcuts

⌨️ () Module 1 - Hello World

⌨️ () Data Type

Like the way you have explained the concepts - thanks Frank. Not sure if I missed out, but I could not find anything related to tuples between the timestamp to 1:21:50. Is it explained later?

⌨️ () Variables

⌨️ () Lists

⌨️ () Dictionaries

⌨️ () IF Statement

⌨️ () FOR Loop

⌨️ () Functions

⌨️ () Modules

⌨️ () Module 2 -Introduction to Pandas

⌨️ () How to create a dataframe

⌨️ () How to show a dataframe

⌨️ () Basic Attributes, Functions and Methods

⌨️ () Selecting One Column from a Dataframe

⌨️ () Selecting Two or More Columns from a Dataframe

⌨️ () Add New Column to a Dataframe (Simple Assignment)

⌨️ () Operations in dataframes

Excuse me, off topic question, I just want to know how did you add the bracket symbols and the single quote symbols to both sides of the selected string, like in ? Btw, thank you so much. The course is helpful and easy to follow for such a new data learner like me.

⌨️ () The value_counts() method

⌨️ () Sort a DataFrame with the sort_values() method

@ how did you open that ... not sure what to call it ... dialog box? drop down?

I didn't understand the use of the lambda function in the ordering section (), as you are trying to lower() the values but none of those are becoming lower. Could you explain deeper what failed there?

⌨️ () Module 3: Introduction to Pivot Tables in Pandas

⌨️ () The pivot() method

⌨️ () The pivot_table() method

hahahahahahahahahahah Men spend MORE money on beauty (and health) than women :P . This can not be true :D :D :D

⌨️ () Data Visualization with Pandas (New Dataset + Pivot Table)

⌨️ () Lineplot

⌨️ () Barplot

⌨️ () Piechart

⌨️ () Save Plot and Export Pivot Table

freeCodeCamp.org

Timetable

よく話題になっている単語

⌨️ () Overview

⌨️ () Indexing

⌨️ () Retrieval

⌨️ () Generation

⌨️ () Query Translation (Multi-Query)

⌨️ () Query Translation (RAG Fusion)

⌨️ () Query Translation (Decomposition)

⌨️ () Query Translation (Step Back)

⌨️ () Query Translation (HyDE)

⌨️ () Routing

⌨️ () Query Construction

⌨️ () Indexing (Multi Representation)

⌨️ () Indexing (RAPTOR)

⌨️ () Indexing (ColBERT)

⌨️ () CRAG

⌨️ () Adaptive RAG

⌨️ () Is RAG Really Dead?

⌨️ () Video Introduction

⌨️ () Introduction to Data Science and A/B Testing

⌨️ () Basics of A/B Testing in Data Science

⌨️ () Key Parameters of A/B Testing for Data Scientists

⌨️ () Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing

⌨️ () Designing an A/B Test: Data Science Approach

⌨️ () Resources for A/B Testing in Data Science

⌨️ () Analyzing A/B Test Results in Python: Data Science Techniques

⌨️ () Data Science Portfolio Project: Case Study with AB Testing

⌨️ () Reintroduction to A/B Testing in the Data Science Process

⌨️ () Data Science Techniques: Loading Data with Pandas for A/B Testing

⌨️ () Data Science Visualization: Using Matplotlib and Seaborn for A/B Test Click Data

⌨️ () Data Science Power Analysis: Understanding A/B Test Model Parameters

⌨️ () Data Science Calculations: Pooled Estimates and Variance for A/B Testing

⌨️ () Computing A/B Test P-Values: Data Science Methods for Statistical Significance

⌨️ () Practical Significance in A/B Testing: A Data Science Perspective

⌨️ () Conclusion: Wrapping Up A/B Testing in Data Science

⌨️ () Introduction

⌨️ () Create account

⌨️ () Create server

⌨️ () Install software

⌨️ () Configure Postgresql

⌨️ () Dictionaries（01:11:14 - 01:21:50）
Data Analysis with Python for Excel Users - Full Course