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Python for Excel Users : A Beginner's Guide / Chi-Chun Chou and David Wang
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Boca Raton, FL : CRC Press, [2026]Beschreibung: 1 Online-Ressource (361 p.)ISBN:- 9781040399545
- 1040399541
- 9781003567103
- 100356710X
- 005.13/3 23/eng/20250910
Inhalte:
Zusammenfassung: In today's data-driven world, the ability to efficiently analyze and interpret information is more crucial than ever, especially in the business sector. ""Python for Excel Users: A Beginner's Guide"" is tailored for business students and professionals proficient in Microsoft Excel but are ready to embark on their Python journeyPPN: PPN: 1945078561Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- I.1 Features of the Book -- I.2 The "Throughout-Book" Case: Campus Bookstore -- I.3 The File Folders -- I.4 Excel Demonstration -- I.5 Python Demonstration -- I.6 Python Preparation -- I.6.1 What Is Python? -- I.6.2 What Can Python Do? -- I.6.3 Why Python Is So Popular? -- I.6.4 Advantages of Learning and Using Python -- I.6.5 How to Start? -- I.6.6 Introduction to Anaconda Navigator -- I.6.7 How to Install Anaconda Navigator? -- I.6.8 Jupyter Notebook -- I.6.9 Jupyter's Markdown
Chapter 1 Data Exploration and Cleaning -- 1.1 Basic Concepts of Data Exploration and Cleaning -- 1.2 Understanding the "Invoice_raw" Worksheet -- 1.3 Exploration and Cleaning Tasks -- 1.4 Excel -- 1.5 Python -- 1.5.1 Introduction to Pandas -- 1.5.2 Install and Import Pandas -- 1.5.3 Data Import -- 1.5.4 Data Exploration and Cleaning -- 1.6 Discussions -- 1.7 Exercises -- Chapter 2 Basic Computation -- 2.1 Basic Concepts of Computation -- 2.2 Computation -- From Basic to Complex -- 2.3 Basic Computations With Bookstore Dataset -- 2.4 Excel -- 2.5 Python -- 2.5.1 Basic Concepts of Programming
2.5.2 Why Learn Python When We Already Have Excel? -- 2.5.3 Using Variables in Python -- 2.5.4 Basic Data Types -- 2.5.5 Compound Assignment Operators -- 2.5.6 Data Import -- 2.5.7 Basic Computation Exercises -- 2.6 Discussions -- 2.7 Exercises -- Chapter 3 Aggregating Data Using Group-By and Pivot Tables -- 3.1 General Guidelines for Group-By Aggregations -- 3.2 Aggregations With Bookstore Dataset -- 3.3 Excel -- 3.3.1 Excel Tables -- 3.3.2 Converting Bookstore Dataset to Excel Table -- 3.4 Python -- 3.4.1 Data Aggregations in Pandas -- 3.4.2 Pandas' Aggregation Methods
3.4.3 Create a Test DataFrame -- Create a Dictionary Using {} -- Using Dict() to Create Dictionaries -- Access Members in a Dictionary -- Create a List Using [] -- Using List() to Create a List -- Access Members in a List -- 3.4.4 Aggregations in a DataFrame -- Aggregation for Columns -- Aggregation for Specific Column(s) -- Aggregations for Rows -- Aggregation for Specific Row(s) -- Summarizing Pandas' Axis Settings -- 3.4.5 Aggregation Exercises With Pandas -- Groupby() -- Loop Through a List -- The For Band in Bands Approach -- The Indentation Prompt ":" -- The Indentation Length
Compare With the Conventional Approach -- A Pythonic Choice -- Loop Through a String -- Non-Index Loop -- Combine Groupby() With Aggregation Methods -- 3.5 Discussions -- 3.6 Exercises -- Chapter 4 Data Visualization and Ranking -- 4.1 General Guidelines for Data Visualization -- 4.2 General Guidelines for Data Ranking -- 4.3 Data Ranking and Visualization With Bookstore Dataset -- 4.4 Excel -- 4.5 Python -- 4.5.1 Pandas Plotting -- 4.5.2 Matplotlib -- 4.5.3 Plotting Exercises Using Pandas and Matplotlib¶ -- Direct [] Accessor -- The Loc[] Indexer -- Fancy Indexing -- The Iloc[] Indexer
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