Python for Accounting and Finance A Mind-Mapping Approach.pdf
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2.3 Comments 22
2.3.1 Key Concepts 22
2.3.2 Follow‑Along Activity 23
2.3.3 Your Project 25
2.4 Arithmetic Operations 26
2.4.1 Key Concepts 26
2.4.2 Follow‑Along Activity 27
2.4.3 Your Project 30
2.5 Receiving User Input 31
2.5.1 Key Concepts 32
2.5.2 Follow‑Along Activity 32
2.5.3 Your Project 36
3 Control Structures 41
3.1 Conditional Statements 41
3.1.1 Key Concepts 41
3.1.2 Follow‑Along Activity 45
3.1.3 Your Project 48
3.2 For Loop 49
3.2.1 Key Concepts 49
3.2.2 Follow‑Along Activity 50
3.2.3 Your Project 52
3.3 While Loop 53
3.3.1 Key Concepts 53
3.3.2 Follow‑Along Activity 54
3.3.3 Your Project 56
3.4 Exceptions 58
3.4.1 Key Concepts 59
3.4.2 Follow‑Along Activity 61
3.4.3 Your Project 63
4 Data Structures and Python Tools 67
4.1 Lists 67
4.1.1 Key Concepts 67
4.1.2 Follow‑Along Activity 68
4.1.3 Your Project 71
4.2 Dictionaries 72
4.2.1 Key Concepts 73
4.2.2 Follow‑Along Activity 74
4.2.3 Your Project 76
4.3 Functions 77
4.3.1 Key Concepts 78
4.3.2 Follow‑Along Activity 78
4.3.3 Your Project 80
Contents ix
4.4 Modules and Libraries 82
4.4.1 Key Concepts 82
4.4.2 Follow‑Along Activity 83
4.4.3 Your Project 86
5 Working with DataFrames 91
5.1 DataFrames 91
5.1.1 Key Concepts 92
5.1.2 Follow‑Along Activity 93
5.1.3 Your Project 98
5.2 Data Cleaning and Handling Missing Data 100
5.2.1 Key Concepts 100
5.2.2 Follow‑Along Activity 101
5.2.3 Your Project 104
6 Exploratory Data Analysis 109
6.1 Importing and Preparing Financial Datasets 109
6.1.1 Key Concepts 109
6.1.2 Follow‑Along Activity 111
6.1.3 Your Project 114
6.2 Summary Statistics 116
6.2.1 Key Concepts 116
6.2.2 Follow‑Along Activity 117
6.2.3 Your Project 120
6.3 Correlation Analysis 121
6.3.1 Key Concepts 121
6.3.2 Follow‑Along Activity 122
6.3.3 Your Project 125
7 Time Series and Panel Data Analysis 129
7.1 Time Series Analysis 129
7.1.1 Key Concepts 130
7.1.2 Follow‑Along Activity 131
7.1.3 Your Project 138
7.2 Forecasting with OLS Regression in Time Series 141
7.2.1 Key Concepts 141
7.2.2 Follow‑Along Activity 142
7.2.3 Your Project 148
7.3 Panel Data Analysis 150
7.3.1 Key Concepts 150
7.3.2 Follow‑Along Activity 151
7.3.3 Your Project 157
x Contents
8 Data Visualisation Techniques 163
8.1 Line, Bar, and Pie Charts 163
8.1.1 Key Concepts 163
8.1.2 Follow‑Along Activity 165
8.1.3 Your Project 170
8.2 Violin Plot 172
8.2.1 Key Concepts 173
8.2.2 Follow‑Along Activity 173
8.2.3 Your Project 176
8.3 Surface Plot 177
8.3.1 Key Concepts 177
8.3.2 Follow‑Along Activity 178
8.3.3 Your Project 181
8.4 Matplotlib and Seaborn 183
8.4.1 Key Concepts 183
8.4.2 Guided Tour of Matplotlib 183
8.4.3 Guided Tour of Seaborn 184
9 Automating Tasks 187
9.1 Automating PDF Tasks Using pypdf 187
9.1.1 Key Concepts 187
9.1.2 Follow‑Along Activity 188
9.1.3 Your Project 194
9.2 Automating Word File Management Using os and
python‑docx 196
9.2.1 Key Concepts 196
9.2.2 Follow‑Along Activity 197
9.2.3 Your Project 200
9.3 Automating Excel Tasks Using openpyxl and pandas 201
9.3.1 Key Concepts 202
9.3.2 Follow‑Along Activity 203
9.3.3 Your Project 206
10 Web Scraping and Accessing Cryptocurrency Data 211
10.1 Ethical Web Scraping 212
10.1.1 What Is Ethical Web Scraping? 212
10.1.2 Legal and Regulatory Considerations 213
10.1.3 Responsible Technical Practices 214
10.1.4 Ethical and Legal Checklist for Web Scraping 214
10.2 Scraping Tables from Websites 215
10.2.1 Key Concepts 215
10.2.2 Follow‑Along Activity 217
10.3 Scraping Files (Word, Excel, and PDFs) 224
10.3.1 Key Concepts 224
10.3.2 Follow‑Along Activity 225
Contents xi
10.4 Accessing Cryptocurrency Data Using CoinGecko API 231
10.4.1 Key Concepts 231
10.4.2 Follow‑Along Activity 233
10.4.3 Your Project 238
11 Introduction to Machine Learning 245
11.1 Machine Learning 245
11.1.1 What Is Machine Learning? 245
11.1.2 Machine Learning vs. Traditional Statistical
Methods 246
11.1.3 Categories of Machine Learning 247
11.2 Machine Learning Workflow 251
11.2.1 Problem Definition 252
11.2.2 Data Collection and Preparation 252
11.2.3 Model Selection and Training 253
11.2.4 Model Evaluation and Tuning 253
11.2.5 Deployment 254
11.3 Limitations and Considerations of Machine Learning 254
11.3.1 Overfitting 254
11.3.2 Bias and Fairness 255
11.3.3 Ethical and Regulatory Considerations in Accounting
and Finance 256
11.3.4 Model Interpretability and Human Expertise 256
12 Supervised Learning 261
12.1 Decision Trees 261
12.1.1 Key Concepts 261
12.1.2 Follow‑Along Activity 263
12.1.3 Your Project 269
12.2 Random Forest 271
12.2.1 Key Concepts 272
12.2.2 Follow‑Along Activity 273
12.2.3 Your Project 279
12.3 Logistic Regression 281
12.3.1 Key Concepts 281
12.3.2 Follow‑Along Activity 282
12.3.3 Your Project 288
13 Unsupervised Learning 293
13.1 k‑means Clustering 293
13.1.1 Key Concepts 293
13.1.2 Follow‑Along Activity A 295
13.1.3 Follow‑Along Activity B 303
ontents
13.2 Hierarchical Clustering 309
13.2.1 Key Concepts 309
13.2.2 Follow‑Along Activity 311
13.2.3 Your Project 316
13.3 Isolation Forest 317
13.3.1 Key Concepts 318
13.3.2 Follow‑Along Activity 318
13.3.3 Your Project 324
14 Advanced Machine Learning and Financial Modelling 329
14.1 Neural Networks 329
14.1.1 Key Concepts 329
14.1.2 Follow‑Along Activity 331
14.1.3 Your Project 340
14.2 Basic Algorithmic Trading 343
14.2.1 Key Concepts 344
14.2.2 Follow‑Along Activity 345
14.2.3 Your Project 350
14.3 Financial Modelling: Monte Carlo Simulation 353
14.3.1 Key Concepts 354
14.3.2 Follow‑Along Activity A 354
14.3.3 Follow‑Along Activity B 359