English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 16 Hours | 5.42 GB

Integrate Excel and Python and get the best of two Worlds! Python Beginners welcome. Use Pandas, Seaborn & co. in Excel Excel vs. Python – what is the best tool for Data Science, Business and Finance? The answer is: Use Excel and Python together and integrate both tools with xlwings. Get the best of two worlds! With xlwings, you can use Python Data Science libraries like Numpy, Pandas, Scipy, Matplotlib, Seaborn and Scikit-learn directly in Excel! You can run Python code in Excel and boost your Excel projects! More and more Professionals and Developers use- Excel as Frontend
- Python as analytical Backend.

- Experienced Python Coders: Use Excel as Graphical User Interphase (GUI) | Run your Python scripts with
- Excel | Present your results with Excel Dashboards
- Excel Users and complete Python Beginners: Boost your Excel projects with clean and powerful Python code!
- Mixed Groups: Non-Coders can run and use Python code simply by clicking on buttons in Excel.

- You will learn and master the xlwings library from scratch
- For Excel Users and complete Python Beginners: This course includes a Python Crash Course that is tailor-made for you!
- It´s the most comprehensive and practical (hands-on) xlwings course on the web
- It covers three comprehensive real-world projects.
- Project 1: You will learn how to boost your financial model in Excel by adding a Python Monte Carlo Simulation – Run your Excel calculation 10,000 times with different sets of inputs and analyze the results!
- Project 2: You will learn how to create Dashboard Apps with Excel (Graphical User Interface) and Python (analytical Backend).
- Project 3: You will learn how to use Pandas methods and functions on your datasets directly in Excel.

- widely spread (750 million users)
- standardized
- intuitive to use
- most users are well-trained
- it requires low/zero set up
- it requires low/zero maintenance
- and it´s still the best choice for financial models & spreadsheet calculations

- Automate Excel from Python e.g. to produce reports or to interact with Jupyter Notebooks.
- Write macros in Python that you can run from buttons in Excel, e.g. to load in data from a database or an external API.
- Write UDFs (user-defined functions) and leverage the power from NumPy, Pandas and machine learning libraries.
- Leverage Python’s scientific stack for interactive data analysis using Jupyter Notebooks, NumPy, Pandas, scikit-learn, etc.
- Use xlwings to automate Excel reports with Python.
- Write Excel tools with Python instead of VBA and call your code directly from within Excel, e.g. via a button on the sheet.
- This also works great for prototyping web apps.
- Write (array) UDFs in a breeze by taking advantage of all the functionality already available in libraries like NumPy and Pandas.
- Dynamic array formulas are supported.

- Automate Excel with clean and powerful Python Code
- Learn and master the xlwings library from 0 to 100
- Use Excel as Graphical User Interface (GUI) and run your Python code with Excel
- Create powerful Dashboard Apps with Excel (frontend) and Python (backend)
- Use powerful Data Visualization Tools (Matplotlib, Seaborn) in Excel
- Learn Python from scratch with a taylor-made Crash Course (For Python beginners)
- Write UDFs (user defined functions) and use Numpy, Pandas and Machine Learning Libraries directly in Excel
- Write Excel tools with Python instead of VBA and call your code directly from within Excel
- Use xlwings to automate Excel reports with Python
- Prototype Web apps
- Write and use Dynamic Arrays with xlwings
- Run your financial model 10,000 times & more with a Python Monte Carlo Simulation
- Load (financial) data from Web APIs directly into Excel
- Run Python Scripts from within Excel with Run main and RunPython
- Replace VBA macros with clean and powerful Python code

**+ Table of Contents**

**Getting Started**

1 Introduction (don´t skip!)

2 Course Overview (don´t skip!)

3 Tips How to get the most out of this Course (don´t skip!)

4 FAQ Your Questions answered

5 How to download and install Anaconda for Python coding

6 Jupyter Notebooks – let´s get started

7 How to work with Jupyter Notebooks

**First Steps with xlwings (Reading and Writing Elements)**

8 Introduction and Downloads

9 How to install xlwings

10 How to use xlwings as a Data Viewer

11 How to connect to an Excel Workbook

12 How to read and write single Values

13 How to assign a name

14 How to write Excel Functions with Python

15 Range Shortcuts

16 Case Study – Bringing it all together

17 Homework

**Reading and writing many Values**

18 Section Downloads

19 One-dimensional Data Structures

20 How to write Values vertically

21 Rows and Columns (1dim vs. 2dim)

22 How to read two-dimensional Data Structures

23 Advanced Reading with expand

24 How to write two-dimensional Data Structures

25 Range Indexing and Slicing

26 Efficiency

27 Homework

**Project 1 Monte Carlo Simulations in Excel with Python (Part 1)**

28 Introduction

29 Section Downloads

30 The Excel Model explained (Part 1)

31 The Excel Model explained (Part 2)

32 Running a simple Monte Carlo Simulation

33 A more advanced and realistic Monte Carlo Simulation

34 Final Considerations

**Running Python Scripts in Excel – RunPython**

35 Introduction and Downloads

36 Installing the xlwings add-in and other preparations

37 Running Python Scripts with Run main

38 Troubleshooting (Part 1)

39 All you need to know about VBA Macros

40 Running Python Scripts with RunPython

41 Troubleshooting (Part 2)

42 Run main vs RunPython

43 Excursus Converting Jupyter Notebooks to .py

44 Homework

**Project 1 Monte Carlo Simulations in Excel with Python (Part 2)**

45 Introduction and Downloads

46 Monte Carlo Simulation with RunPython (Part 1)

47 Monte Carlo Simulation with RunPython (Part 2)

**Using Matplotlib and Seaborn in Excel with xlwings**

48 Introduction and Downloads

49 How to write a Matplotlib Plot into Excel

50 How to update the Plot

51 How to change Size and Position (Part 1)

52 How to change Size and Position (Part 2)

53 How write a Seaborn Plot into Excel

54 How to create Excel Charts with Python

55 Homework Adding a Plot to the Monte Carlo Simulation (Project 1)

**Project 2 Build Dashboard Apps with Excel (GUI) and Python (analytical backend)**

56 Introduction and Downloads

57 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 1)

58 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 2)

59 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 3)

60 Building a Stock Performance Dashboard App (Part 1)

61 Building a Stock Performance Dashboard App (Part 2)

62 Improving the Source Code and Errors

**Reading and Writing Data Structures (Numpy, Pandas) & Converters**

63 Section Downloads

64 (Default) Converters

65 The Numpy Converter

66 The Dictionary Converter

67 The DataFrame Converter (Part 1)

68 The DataFrame Converter (Part 2)

69 Data Science Application Inspecting and Manipulating DataFrames in Excel

70 The Pandas Series Converter

71 Excursus How to load Data from Excel into Pandas with pd.read excel()

72 Excursus Advanced import with pd.read excel()

73 Excursus How to load Financial Data Time Series with pd.read excel()

**User-defined Functions (UDF) and Dynamic Arrays with xlwings (Windows only)**

74 Introduction and Downloads

75 Preparations and your first UDF

76 How to change the Name and Location of the Python Module

77 Troubleshooting (UDF)

78 UDFs – Behind the Scenes

79 More complex UDFs and the @xw.arg Decorator

80 How to create Numpy UDFs

81 UDFs and Array Formulas

82 How to create Dynamic Arrays with xlwings UDFs

83 How to create Pandas UDFs

84 How to add Docstrings

85 Homework

**Project 3 Use Pandas UDFs in Excel for Data Science and Finance (Windows only)**

86 Introduction and Downloads

87 How to load Financial Data from the Web into Excel with the DataReader UDF

88 How to resample Time Series in Excel with the resample UDF

89 How to calculate Financial Returns with a PandasNumpy UDF

90 How to get Summary Statistics of a Dataset with the describe UDF

91 How to create a Dataset´s Correlation Matrix with the corr UDF

92 Taken all together – the Super UDF

93 How to perform innerouterleftright joins with the merge UDF

**APPENDIX 1 Python Crash Course for Excel Users**

94 Introduction and Overview

95 Section Downloads

96 Intro to the Time Value of Money (TVM) Concept (Theory)

97 Calculate Future Values (FV) with Python Compounding

98 Calculate Present Values (FV) with Python Discounting

99 Interest Rates and Returns (Theory)

100 Calculate Interest Rates and Returns with Python

101 Introduction to Variables

102 Excursus How to add inline comments

103 Variables and Memory (Theory)

104 More on Variables and Memory

105 Variables – Dos, Don´ts and Conventions

106 The print() Function

107 Coding Exercise 1

108 TVM Problems with many Cashflows

109 Intro to Python Lists

110 Zero-based Indexing and negative Indexing in Python (Theory)

111 Indexing Lists

112 For Loops – Iterating over Lists

113 The range Object – another Iterable

114 Calculate FV and PV for many Cashflows

115 The Net Present Value – NPV (Theory)

116 Calculate an Investment Project´s NPV

117 Coding Exercise 2

118 Data Types in Action

119 The Data Type Hierarchy (Theory)

120 Excursus Dynamic Typing in Python

121 Build-in Functions

122 Integers

123 Floats

124 How to round Floats (and Integers) with round()

125 More on Lists

126 Lists and Element-wise Operations

127 Slicing Lists

128 Slicing Cheat Sheet

129 Changing Elements in Lists

130 Sorting and Reversing Lists

131 Adding and removing Elements fromto Lists

132 Mutable vs. immutable Objects (Part 1)

133 Mutable vs. immutable Objects (Part 2)

134 Coding Exercise 3

135 Tuples

136 Dictionaries

137 Intro to Strings

138 String Replacement

139 Booleans

140 Operators (Theory)

141 Comparison, Logical and Membership Operators in Action

142 Coding Exercise 4

143 Conditional Statements

144 Keywords pass, continue and break

145 Calculate a Project´s Payback Period

146 Defining your first user-defined Function

147 What´s the difference between Positional Arguments vs. Keyword Arguments

148 How to work with Default Arguments

149 Coding Exercise 5

**APPENDIX 2 Matplotlib, Numpy, Pandas and Seaborn Crash Course**

150 Downloads for this Section

151 Matplotlib Introduction

152 Line Plots

153 Scatter Plots

154 Customizing Plots (Part 1)

155 Customizing Plots (Part 2)

156 Coding Exercise 6

157 Modules, Packages and Libraries – No need to reinvent the Wheel

158 Numpy Arrays

159 Indexing and Slicing Numpy Arrays

160 Vectorized Operations with Numpy Arrays

161 Changing Elements in Numpy Arrays & Mutability

162 View vs. copy – potential Pitfalls when slicing Numpy Arrays

163 Numpy Array Methods and Attributes

164 Numpy Universal Functions

165 Boolean Arrays and Conditional Filtering

166 Coding Exercise 7

167 How to work with nested Lists

168 dimensional Numpy Arrays

169 How to slice 2-dim Numpy Arrays (Part 1)

170 How to slice 2-dim Numpy Arrays (Part 2)

171 Recap Changing Elements in a Numpy Array slice

172 How to perform row-wise and column-wise Operations

173 Coding Exercise 8

174 Intro to Tabular Data Pandas

175 Create your very first Pandas DataFrame (from csv)

176 Pandas Display Options and the methods head() & tail()

177 First Data Inspection

178 Coding Exercise 9

179 Selecting Columns

180 Selecting one Column with the dot notation

181 Zero-based Indexing and Negative Indexing

182 Selecting Rows with iloc (position-based indexing)

183 Slicing Rows and Columns with iloc (position-based indexing)

184 Position-based Indexing Cheat Sheets

185 Selecting Rows with loc (label-based indexing)

186 Slicing Rows and Columns with loc (label-based indexing)

187 Label-based Indexing Cheat Sheets

188 Summary, Best Practices and Outlook

189 Coding Exercise 10

190 First Steps with Pandas Series

191 First Steps with Pandas Index Objects

192 Importing Time Series Data from csv-files

193 Initial Analysis Visualization of Time Series

194 Seaborn Introduction

**What´s next**

195 Get your special BONUS here!

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