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Python for Excel: A Modern Environment for Automation and Data Analysis [Pehme köide]

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  • Formaat: Paperback / softback, 275 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 16-Mar-2021
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492081000
  • ISBN-13: 9781492081005
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Python for Excel: A Modern Environment for Automation and Data AnalysisSuurem pilt
  • Formaat: Paperback / softback, 275 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 16-Mar-2021
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492081000
  • ISBN-13: 9781492081005
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781492081005.html
Märksõnad:

While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started.

  • Use Python without extensive programming knowledge
  • Get started with modern tools, including Jupyter notebooks and Visual Studio code
  • Use pandas to acquire, clean, and analyze data and replace typical Excel calculations
  • Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports
  • Use xlwings to build interactive Excel tools that use Python as a calculation engine
  • Connect Excel to databases and CSV files and fetch data from the internet using Python code
  • Use Python as a single tool to replace VBA, Power Query, and Power Pivot
Preface xi
Part I Introduction to Python
1 Why Python for Excel?
3(16)
Excel Is a Programming Language
4(8)
Excel in the News
5(1)
Programming Best Practices
6(5)
Modern Excel
11(1)
Python for Excel
12(5)
Readability and Maintainability
13(1)
Standard Library and Package Manager
14(1)
Scientific Computing
15(1)
Modern Language Features
16(1)
Cross-Platform Compatibility
17(1)
Conclusion
17(2)
2 Development Environment
19(24)
The Anaconda Python Distribution
20(7)
Installation
20(1)
Anaconda Prompt
21(3)
Python REPL: An Interactive Python Session
24(1)
Package Managers: Conda and pip
25(2)
Conda Environments
27(1)
Jupyter Notebooks
27(7)
Running Jupyter Notebooks
28(1)
Notebook Cells
29(2)
Edit vs. Command Mode
31(1)
Run Order Matters
32(1)
Shutting Down Jupyter Notebooks
33(1)
Visual Studio Code
34(7)
Installation and Configuration
36(1)
Running a Python Script
37(4)
Conclusion
41(2)
3 Getting Started with Python
43(34)
Data Types
43(7)
Objects
44(1)
Numeric Types
45(2)
Booleans
47(2)
Strings
49(1)
Indexing and Slicing
50(2)
Indexing
51(1)
Slicing
52(1)
Data Structures
52(6)
Lists
53(2)
Dictionaries
55(2)
Tuples
57(1)
Sets
57(1)
Control Flow
58(6)
Code Blocks and the pass Statement
58(1)
The if Statement and Conditional Expressions
59(1)
The for and while Loops
60(3)
List, Dictionary, and Set Comprehensions
63(1)
Code Organization
64(6)
Functions
65(1)
Modules and the import Statement
66(3)
The datetime Class
69(1)
PEP 8: Style Guide for Python Code
70(4)
PEP 8 and VS Code
73(1)
Type Hints
73(1)
Conclusion
74(3)
Part II Introduction to pandas
4 NumPy Foundations
77(8)
Getting Started with NumPy
77(4)
NumPy Array
77(2)
Vectorization and Broadcasting
79(1)
Universal Functions (ufunc)
80(1)
Creating and Manipulating Arrays
81(3)
Getting and Setting Array Elements
82(1)
Useful Array Constructors
83(1)
View vs. Copy
83(1)
Conclusion
84(1)
5 Data Analysis with pandas
85(40)
DataFrame and Series
85(6)
Index
88(2)
Columns
90(1)
Data Manipulation
91(16)
Selecting Data
92(5)
Setting Data
97(3)
Missing Data
100(2)
Duplicate Data
102(1)
Arithmetic Operations
103(2)
Working with Text Columns
105(1)
Applying a Function
105(2)
View vs. Copy
107(1)
Combining DataFrames
107(4)
Concatenating
108(1)
Joining and Merging
109(2)
Descriptive Statistics and Data Aggregation
111(4)
Descriptive Statistics
111(1)
Grouping
112(1)
Pivoting and Melting
113(2)
Plotting
115(4)
Matplotlib
115(2)
Plotly
117(2)
Importing and Exporting DataFrames
119(4)
Exporting CSV Files
120(1)
Importing CSV Files
121(2)
Conclusion
123(2)
6 Time Series Analysis with pandas
125(18)
Datetime Index
126(5)
Creating a Datetime Index
126(2)
Filtering a Datetime Index
128(1)
Working with Time Zones
129(2)
Common Time Series Manipulations
131(7)
Shifting and Percentage Changes
131(2)
Rebasing and Correlation
133(3)
Resampling
136(1)
Rolling Windows
137(1)
Limitations with pandas
138(1)
Conclusion
139(4)
Part III Reading and Writing Excel Files Without Excel
7 Excel File Manipulation with pandas
143(12)
Case Study: Excel Reporting
143(4)
Reading and Writing Excel Files with pandas
147(7)
The read_excel Function and ExcelFile Class
147(5)
The to_excel Method and ExcelWriter Class
152(2)
Limitations When Using pandas with Excel Files
154(1)
Conclusion
154(1)
8 Excel File Manipulation with Reader and Writer Packages
155(28)
The Reader and Writer Packages
155(14)
When to Use Which Package
156(1)
The excel.py Module
157(2)
OpenPyXL
159(4)
Xlsx Writer
163(2)
Pyxlsb
165(1)
Xlrd, xlwt, and xlutils
166(3)
Advanced Reader and Writer Topics
169(10)
Working with Big Excel Files
169(4)
Formatting DataFrames in Excel
173(5)
Case Study (Revisited): Excel Reporting
178(1)
Conclusion
179(4)
Part IV Programming the Excel Application with xlwings
9 Excel Automation
183(26)
Getting Started with xlwings
184(10)
Using Excel as Data Viewer
184(2)
The Excel Object Model
186(7)
Running VBA Code
193(1)
Converters, Options, and Collections
194(10)
Working with DataFrames
195(1)
Converters and Options
196(2)
Charts, Pictures, and Defined Names
198(4)
Case Study (Re-Revisited): Excel Reporting
202(2)
Advanced xlwings Topics
204(4)
Xlwings Foundations
204(2)
Improving Performance
206(1)
How to Work Around Missing Functionality
207(1)
Conclusion
208(1)
10 Python-Powered Excel Tools
209(14)
Using Excel as Frontend with xlwings
209(9)
Excel Add-in
210(2)
Quickstart Command
212(1)
Run Main
212(1)
RunPython Function
213(5)
Deployment
218(4)
Python Dependency
218(1)
Standalone Workbooks: Getting Rid of the xlwings Add-in
219(1)
Configuration Hierarchy
220(1)
Settings
221(1)
Conclusion
222(1)
11 The Python Package Tracker
223(28)
What We Will Build
223(3)
Core Functionality
226(14)
Web APIs
226(3)
Databases
229(9)
Exceptions
238(2)
Application Structure
240(10)
Frontend
241(4)
Backend
245(3)
Debugging
248(2)
Conclusion
250(1)
12 User-Defined Functions (UDFs)
251(30)
Getting Started with UDFs
252(5)
UDF Quickstart
252(5)
Case Study: Google Trends
257(14)
Introduction to Google Trends
257(1)
Working with DataFrames and Dynamic Arrays
258(5)
Fetching Data from Google Trends
263(4)
Plotting with UDFs
267(2)
Debugging UDFs
269(2)
Advanced UDF Topics
271(6)
Basic Performance Optimization
272(2)
Caching
274(2)
The Sub Decorator
276(1)
Conclusion
277(4)
A Conda Environments 281(4)
B Advanced VS Code Functionality 285(6)
C Advanced Python Concepts 291(8)
Index 299
Felix Zumstein is creator and maintainer of xlwings, a popular open-source package that allows the automation of Excel with Python on Windows and macOS. He also organizes the xlwings meetups in London and NYC to promote a broad range of innovative solutions for Excel.

As CEO of xltrail, a version control system for Excel files, he has talked to hundreds of users who use Excel for business critical tasks and has therefore gained deep insight into the typical uses cases and issues with Excel across various industries.