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Principles of Data Science with Python

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

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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/)