Principles of Data Science with Python

Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization

Order at Amazon: https://www.amazon.com/dp/1735241008

In this book, readers learn about:

• Programming with the Python language
• Data science, analysis, and visualization with the Python language
• Data structure in Python
• NumPy library and NumPy arrays
• Statistical functions
• Pandas library and Pandas DataFrames
• Time-series in Python
• Matplotlib library and data visualization
• SciPy library
• Interpolation, curve fitting, root finding, and numerical integration
• Signal processing and digital filtering
• Reading and writing data files

Contents

Chapter 1 Set Up Python 1

1.1 Introduction to Python Language 2

1.2 Install Python Directly 2

1.3 Install Python Using Python Distribution 3

1.4 Python IDE 4

1.5 IPython and Jupyter Notebook 5

1.6 Python Libraries and Packages 5

1.7 Run Python Script 6

Chapter 2 Introduction to Python Programming 11

2.1 Python Syntax Style 12

2.2 Python Built-in Functions, Standard Libraries, and Third-Party Libraries 13

2.3 Import Library 14

2.4 Mathematical Operators 16

2.5 Comparison Operators 18

2.6 Boolean Operators 19

2.7 Bitwise Operators 19

2.8 Integer and Floating Point 20

2.9 Complex Numbers 22

2.10 Strings 23

2.11 The range() Function 32

2.12 The if Statement 34

2.13 The for Statement 40

2.14 The while Statement 44

2.15 Define Function 46

2.16 The args and kwargs 53

2.17 Define Anonymous Function by Lambda Expression 55

2.18 Underscore ( _ ) 57

2.19 Work with File and Directory 59

Chapter 3 Introduction to Python List, Tuple, and Dictionary 61

3.1 Python Data Structures 62

3.2 List 63

3.3 Nested List 64

3.4 Tuple 65

3.5 Nested Tuple 66

3.6 Dictionary 67

3.7 List Indexing 69

3.8 List Slicing 73

3.9 Change Item Contents in List 75

Chapter 4 Working with Python List 77

4.1 Copy List 78

4.2 Append, Insert, and Delete List Items 79

4.3 Concatenate Lists 82

4.4 The len() Function 83

4.5 Sort List 84

4.6 The zip() Function 86

4.7 The enumerate() Function 88

4.8 List Comprehension 89

4.9 Generator Expression 92

4.10 The map() Function 94

4.11 List Initialization 97

4.12 Element-Wise Operation with for Statement 99

4.13 Element-Wise Operation with List Comprehension 101

4.14 Element-Wise Operation with map() Function 102

Chapter 5 Introduction to NumPy Library 105

5.1 NumPy Library 106

5.2 Install NumPy Library 106

5.3 Import NumPy Library 107

5.4 Vector, Matrix, Array, and Tensor 108

5.5 Create NumPy Array 109

5.6 Array Data Type 111

5.7 Array Attributes and Methods 113

5.8 Array Dimension 116

5.9 Array Indexing 119

5.10 Array Slicing 121

5.11 Indexing by Index List and Index Array 126

5.12 Boolean Indexing (Mask) 128

5.13 Change Element Contents in Array 131

5.14 NumPy Structured Array 132

Chapter 6 Working with NumPy Array 137

6.1 Import NumPy Library 138

6.2 NumPy Functions, Array Attributes, and Array Methods 138

6.3 Copy Array 140

6.4 Append, Insert, and Delete Array Elements 142

6.5 Obtain Array Shape and Size 145

6.6 Reshape Array 149

6.7 Flip Array 151

6.8 Add New Dimension to Array 153

6.9 Concatenate and Stack Arrays 156

6.10 Array Initialization 161

6.11 Element-Wise Operation and Comparison 163

6.12 Find Indexes 165

6.13 NaN and Inf 169

6.14 Generate Sequence of Numbers 170

Chapter 7 Basic Statistics with NumPy Library 175

7.1 Import NumPy Library 176

7.2 NumPy Array Axis 176

7.3 Statistical Functions 177

7.4 Sum and Mean of Array 178

7.5 Minimum and Maximum of Array 180

7.6 Sort Array 183

7.7 Random Number 187

7.8 Generate Reproducible Random Number 190

7.9 Random Number (Legacy Random Generator) 191

7.10 Generate Reproducible Random Number (Legacy Random Generator) 193

7.11 Histogram and Probability Density Function of Dataset 195

Chapter 8 Introduction to Pandas Library 199

8.1 Pandas Library 200

8.2 Install Pandas Library 200

8.3 Import Pandas Library 201

8.4 Create Pandas Series 202

8.5 Create Pandas DataFrame 204

8.6 Series and DataFrame Attributes and Methods 207

8.7 Series and DataFrame Indexing and Slicing 210

8.8 Multi Level Indexing 215

8.9 Change Item Contents in Series and DataFrame 219

Chapter 9 Working with Pandas Series and DataFrame 223

9.1 Import Pandas Library 224

9.2 Pandas Functions, Attributes, and Methods 224

9.3 Copy Series and DataFrame 226

9.4 Append, Insert, and Delete Single Row or Single Column 227

9.5 Append, Insert, and Delete Multiple Rows or Multiple Columns 231

9.6 Concatenate Series and DataFrames 235

9.7 Merge and Join Series and DataFrames 238

9.8 Reindex Data 245

9.9 Shift Data 246

9.10 Arithmetic and Element-Wise Operation 248

9.11 Apply Function 250

9.12 Group Data 253

9.13 Clean and Fill Missing Data 260

9.14 Rolling Window 265

Chapter 10 Date, Time, and Time-Series 273

10.1 Import Libraries 274

10.2 Date and Time in Python 274

10.3 Date and Time in NumPy 279

10.4 Date and Time in Pandas 282

10.5 Generate Time-Series with Python and NumPy 284

10.6 Generate Date and Time Indexes in Pandas 288

10.7 Generate Time-Series with Pandas 290

10.8 Indexing and Slicing Pandas Time-Series 293

10.9 Shift Data in Pandas Time-Series 296

10.10 Clean and Fill Missing Data in Pandas Time-Series 299

10.11 Resampling Pandas Time-Series 303

Chapter 11 Introduction to Data Visualization with Matplotlib Library 313

11.1 Matplotlib Library 314

11.2 Install Matplotlib Library 314

11.3 Import Matplotlib Library 315

11.4 The Pyplot Module 316

11.5 Line Plot 318

11.6 Set Color 321

11.7 Set Line Style and Line Width 324

11.8 Add Marker 327

11.9 Add Labels 329

11.10 Set Axis Limits, Ticks, and Scale 331

11.11 Add Grid Lines 334

11.12 Add Text and Annotation 336

11.13 Add Mathematical Text 339

11.14 Plot Multiple Lines and Add Legend 342

11.15 Create Multiple Figures 346

11.16 Customize Matplotlib Style 347

11.17 Seaborn Library 351

Chapter 12 Advanced Data Visualization with Matplotlib Library 355

12.1 Import Matplotlib Library 356

12.2 Colormaps 356

12.3 Extract Colors from Colormap 359

12.4 Create Colormap 361

12.5 Scatter Plot 363

12.6 Contour and Image Plot 367

12.7 Bar Plot 370

12.8 Histogram Plot 372

12.9 Axes 375

12.10 Create Subplots 379

12.11 Create Unequal Subplots 383

12.12 Procedural and Object-Oriented Interfaces 386

12.13 Time-Series Plot 390

12.14 The 3-Dimensional Plot 393

12.15 Map Plot 396

12.16 Data Visualization with Pandas 401

Chapter 13 Interpolation, Curve Fitting, Root Finding, and Numerical Integration with SciPy Library 405

13.1 SciPy Library 406

13.2 Install SciPy Library 406

13.3 Import SciPy Library 407

13.4 Generate 1-Dimensional Grid Coordinates 408

13.5 Generate 2-Dimensional Grid Coordinates 409

13.6 The 1-Dimensional Interpolation 412

13.7 The 2-Dimensional Interpolation 415

13.8 Curve Fitting 420

13.9 Curve Fitting by Optimization 423

13.10 Root Finding 426

13.11 Solve System of Linear Equations 429

13.12 Numerical Integration 431

Chapter 14 Introduction to Signal Processing 433

14.1 Import SciPy Library 434

14.2 Wave Function 434

14.3 Sampling Frequency 436

14.4 Control Data Quality 439

14.5 Detrend Data 443

14.6 Time and Frequency Domains 445

14.7 Fourier Analysis 447

14.8 Fast Fourier Transform 448

14.9 Frequency Ordering of Fast Fourier Transform 449

14.10 Double-Sided FFT and Single-Sided FFT 452

14.11 Wave Amplitudes from FFT 456

14.12 Estimate Power Spectral Density from FFT 459

14.13 Estimate Power Spectral Density from Periodogram and Welch Method 463

Chapter 15 Basics of Window Function and Digital Filter 469

15.1 Import SciPy Library 470

15.2 Convolution 470

15.3 Window Function 471

15.4 Digital Filter 475

15.5 Digital Filter Band-Forms 478

15.6 Basic Low-Pass FIR Filter 479

15.7 Basic High-Pass, Band-Pass and Band-Stop FIR Filters 483

15.8 Design Basic FIR Filters with SciPy Library 485

15.9 Smooth Data by Moving Average 488

15.10 Smooth Data by Savitzky-Golay Filter 493

15.11 Smooth Data by Butterworth Filter 496

15.12 Filter Out Frequency Range from Data 499

Chapter 16 Read and Write Data Files 507

16.1 Import Libraries 508

16.2 Read Text and ASCII Files with Python 508

16.3 Read CSV Files with Python 512

16.4 Read Text, ASCII, and CSV Files with NumPy 514

16.5 Read Text, ASCII, and CSV Files with Pandas 515

16.6 Save and Load Data Files 517

References 521

Index 523

License

CC BY-NC-SA 4.0 License

Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization

Copyright (c) 2022 Arash Karimpour

All rights reserved

Principles of Data Science with Python: Introduction to Scientific Computing, Data Analysis, and Data Visualization © 2020 by Arash Karimpour is licensed under CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)