دانلود کتاب Python Tools for Scientists: An Introduction to Using Anaconda, JupyterLab, and Python's Scientific Libraries

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Title Page
Copyright Page
Dedication
About the Author
About the Technical Reviewer
BRIEF CONTENTS
CONTENTS IN DETAIL
ACKNOWLEDGMENTS
INTRODUCTION
Why Python?
Navigating This Book
Part I: Setting Up Your Scientific Coding Environment
Part II: A Python Primer
Part III: The Anaconda Ecosystem
Part IV: The Essential Libraries
Appendix
Updates and Errata
Leaving Reviews
PART I: SETTING UP YOUR SCIENTIFIC CODING ENVIRONMENT
1 INSTALLING AND LAUNCHING ANACONDA
About Anaconda
Installing Anaconda on Windows
Installing Anaconda on macOS
Installing Anaconda on Linux
Getting to Know Anaconda Navigator
Launching Navigator
The Home Tab
The Environments Tab
The Learning Tab
The Community Tab
File Menu
Summary
2 KEEPING ORGANIZED WITH CONDA ENVIRONMENTS
Understanding Conda Environments
Working with Conda Environments Using Navigator
Launching Navigator
Creating a New Environment
Managing Packages
Duplicating Environments
Backing Up Environments
Removing Environments
Working with Conda Environments Using the Command Line Interface
Launching the Command Line Interface
Creating a New Environment
Specifying an Environment’s Location
Managing Packages
Duplicating and Sharing Environments
Restoring Environments
Removing Environments
Cleaning the Package Cache
Summary
3 SIMPLE SCRIPTING IN THE JUPYTER QT CONSOLE
Installing seaborn
Installing and Launching the Jupyter Qt Console Using Navigator
Installing and Launching the Jupyter Qt Console Using the CLI
The Qt Console Controls
Choosing a Syntax Style
Using Keyboard Shortcuts
Using Tabs and Kernels
Printing and Saving
Multiline Editing
Summary
4 SERIOUS SCRIPTING WITH SPYDER
Installing and Launching Spyder with Anaconda Navigator
Installing and Launching Spyder Using the CLI
Launching Spyder from the Start Menu
Configuring the Spyder Interface
Using Spyder with Environments and Packages
The Naive Approach
The Modular Approach
Using Project Files and Folders
Creating a Project in a New Directory
Creating a Project in an Existing Directory
Using the Project Pane
The Help Pane
The IPython Console
Using the Console for Output and Plotting
Using Kernels with the Console
Clearing the Namespace
The History Pane
Special Consoles
The Editor Pane
Writing a Program Using the Editor
Defining Code Cells
Setting the Run Configuration
Autocompleting Text
The Code Analysis Pane
The Variable Explorer Pane
The Profiler Pane
The Debugger Pane
Summary
5 JUPYTER NOTEBOOK: AN INTERACTIVE JOURNAL FOR COMPUTATIONAL RESEARCH
Installing Jupyter Notebook
The Naive Approach
The Modular Approach
Your First Jupyter Notebook
Creating Dedicated Project Folders
Navigating the Notebook Dashboard and User Interface
Naming a Notebook
Adding Text with a Markdown Cell
Adding Code and Making Plots with a Code Cell
Working with Output Cells
Adding an Image with a Markdown Cell
Saving the Notebook
Closing the Notebook
Getting Help
Keyboard Shortcuts
The Command Palette
Using Notebook Extensions
Installing Extensions
Enabling Extensions
Working with Widgets
Installing ipywidgets
Creating Widgets with Interact
Creating Widgets with Interactive
Manually Creating Widgets
Handling Events
Customizing Widgets
Embedding Widgets in Other Formats
Sharing Notebooks
Checking and Running Notebooks with the Kernel Menu
Downloading Notebooks
Sharing Notebooks via GitHub and Gist
Sharing Notebooks via Jupyter Notebook Viewer
Sharing Notebooks via Binder
Other Sharing Options
Trusting Notebooks
Turning Notebooks into Slideshows
Installing the RISE Extension
Creating a Slideshow
Using Speaker Notes
Summary
6 JUPYTERLAB: YOUR CENTER FOR SCIENCE
When to Use JupyterLab Instead of Notebook?
Installing JupyterLab
The Naive Approach
The Modular Approach
Building a 3D Astronomical Simulation
Using Dedicated Project Folders
The JupyterLab Interface
The Menu Bar
The Left Sidebar
Creating a New Notebook
Naming the Notebook
Using Markdown Cells
Adding Code and Making Plots
Adding a Console
Displaying an Image File
Exploring the Simulation
Opening Multiple Notebooks
Saving the Workspace
Clearing the Workspace
Closing the Workspace
Taking Advantage of the JupyterLab Interface
Creating Synchronized Views
Copying Cells Between Notebooks
Staying Focused by Using Single Document Mode
Using the Text Editor
Running a Script in a Terminal
Running a Script in a Notebook
Simultaneously Writing and Documenting Code
Using JupyterLab Extensions
Installing and Managing Extensions with the Extension Manager
Installing and Managing Extensions Using the CLI
Installing ipywidgets for JupyterLab
Creating Custom Extensions
Sharing
Summary
PART II: A PYTHON PRIMER
7 INTEGERS, FLOATS, AND STRINGS
Mathematical Expressions
Mathematical Operators
The Assignment Operator
Augmented Assignment Operators
Precedence
The math Module
Error Messages
Data Types
Accessing the Data Type
Integers
Floats
Strings
Summary
8 VARIABLES
Variables Have Identities
Assigning Variables
Using Expressions
Operator Overloading
Using Functions
Chained Assignment and Internment
Using f-Strings
Naming Variables
Reserved Keywords
Variables Are Case Sensitive
Best Practices for Naming Variables
Managing Dynamic Typing Issues
Handling Insignificant Variables
Getting User Input
Using Comparison Operators
Summary
9 THE CONTAINER DATA TYPES
Tuples
Creating Tuples
Converting Other Types to Tuples
Working with Tuples
Lists
Creating Lists
Working with Lists
Sets
Creating Sets
Working with Sets
Creating Frozensets
Dictionaries
Creating Dictionaries
Combining Two Sequences into a Dictionary
Creating Empty Dictionaries and Values
Working with Dictionaries
Summary
10 FLOW CONTROL
The if Statement
Working with Code Blocks
Using the else and elif Clauses
Using Ternary Expressions
Using Boolean Operators
Loops
The while Statement
The for Statement
Loop Control Statements
Replacing Loops with Comprehensions
Handling Exceptions
Using try and except
Forcing Exceptions with the raise Keyword
Ignoring Errors
Tracing Execution with Logging
Summary
11 FUNCTIONS AND MODULES
Defining Functions
Using Parameters and Arguments
Positional and Keyword Arguments
Using Default Values
Returning Values
Naming Functions
Built-in Functions
Functions and the Flow of Execution
Using Namespaces and Scopes
Using Global Variables
Using a main() Function
Advanced Function Topics
Recursion
Designing Functions
Lambda Functions
Generators
Modules
Importing Modules
Inspecting Modules
Writing Your Own Modules
Naming Modules
Writing Modules That Work in Stand-Alone Mode
Built-in Modules
Summary
12 FILES AND FOLDERS
Creating a New Spyder Project
Working with Directory Paths
The Operating System Module
Absolute vs. Relative Paths
The pathlib Module
The Shell Utilities Module
Working with Text Files
Reading a Text File
Closing Files Using the with Statement
Writing to a Text File
Reading and Writing Text Files Using pathlib
Working with Complex Data
Pickling Data
Shelving Pickled Data
Storing Data with JSON
Catching Exceptions When Opening Files
Other Storage Solutions
Summary
13 OBJECT-ORIENTED PROGRAMMING
When to Use OOP
Creating a New Spyder Project
Defining the Frigate Class
Defining Instance Methods
Instantiating Objects and Calling Instance Methods
Defining a Guided-Missile Frigate Class Using Inheritance
Instantiating a New Guided-Missile Frigate Object
Using the super() Function for Inheritance
Objects Within Objects: Defining the Fleet Class
Reducing Code Redundancy with Dataclasses
Using Decorators
Defining the Ship Class
Identifying Friend or Foe with Fields and Post-Init Processing
Optimizing Dataclasses with __slots__
Making a Class Module
Summary
14 DOCUMENTING YOUR WORK
Comments
Single-Line Comments
Multiline Comments
Inline Comments
Commenting-Out Code
Docstrings
Documenting Modules
Documenting Classes
Documenting Functions and Methods
Keeping Docstrings Up to Date with doctest
Checking Docstrings in the Spyder Code Analysis Pane
Summary
PART III: THE ANACONDA ECOSYSTEM
15 THE SCIENTIFIC LIBRARIES
The SciPy Stack
NumPy
SciPy
SymPy
pandas
A General Machine Learning Library: scikit-learn
The Deep Learning Frameworks
TensorFlow
Keras
PyTorch
The Computer Vision Libraries
OpenCV
scikit-image
PIL/Pillow
The Natural Language Processing Libraries
NLTK
spaCy
The Helper Libraries
Requests
Beautiful Soup
Regex
Dask
Summary
16 THE INFOVIS, SCIVIS, AND DASHBOARDING LIBRARIES
InfoVis and SciVis Libraries
Matplotlib
seaborn
The pandas Plotting API
Altair
Bokeh
Plotly
HoloViews
Datashader
Mayavi and ParaView
Dashboards
Dash
Streamlit
Voilà
Panel
Choosing a Plotting Library
Size of Dataset
Types of Plots
Format
Versatility
Maturity
Making the Final Choice
Summary
17 THE GEOVIS LIBRARIES
The Geospatial Libraries
GeoPandas
Cartopy
Geoplot
Plotly
folium
ipyleaflet
GeoViews: The HoloViz Approach
KeplerGL
pydeck
Bokeh
Choosing a GeoVis Library
Summary
PART IV: THE ESSENTIAL LIBRARIES
18 NUMPY: NUMERICAL PYTHON
Introducing the Array
Describing Arrays Using Dimension and Shape
Creating Arrays
Accessing Array Attributes
Indexing and Slicing Arrays
Manipulating Arrays
Shaping and Transposing
Joining Arrays
Splitting Arrays
Doing Math Using Arrays
Vectorization
Broadcasting
The Matrix Dot Product
Incrementing and Decrementing Arrays
Using NumPy Functions
Reading and Writing Array Data
Summary
19 DEMYSTIFYING MATPLOTLIB
Anatomy of a Plot
The pyplot and Object-Oriented Approaches
Using the pyplot Approach
Creating and Manipulating Plots with pyplot Methods
Working with Subplots
Building Multipanel Displays Using GridSpec
Using the Object-Oriented Style
Creating and Manipulating Plots with the Object-Oriented Style
Working with Subplots
Building Multipanel Displays Using GridSpec
Insetting Plots
Plotting in 3D
Animating Plots
Styling Plots
Changing Runtime Configuration Parameters
Creating and Using a Style File
Applying Style Sheets
Summary
20 PANDAS, SEABORN, AND SCIKIT-LEARN
Introducing the pandas Series and DataFrame
The Series Data Structure
The DataFrame Data Structure
The Palmer Penguins Project
The Project Outline
Setting Up the Project
Importing Packages and Setting Up the Display
Loading the Dataset
Displaying the DataFrame and Renaming Columns
Checking for Duplicates
Handling Missing Values
Exploring the Dataset
Predicting Penguin Species Using K-Nearest Neighbors
Summary
21 MANAGING DATES AND TIMES WITH PYTHON AND PANDAS
Python datetime Module
Getting the Current Date and Time
Assigning Timestamps and Calculating Time Delta
Formatting Dates and Times
Converting Strings to Dates and Times
Plotting with datetime Objects
Creating Naive vs. Aware Objects
Time Series and Date Functionality with pandas
Parsing Time Series Information
Creating Date Ranges
Creating Periods
Creating Time Deltas
Shifting Dates with Offsets
Indexing and Slicing Time Series
Resampling Time Series
Summary
APPENDIX ANSWERS TO THE “TEST YOUR KNOWLEDGE” CHALLENGES
INDEX