Index
A
- Anaconda
- Python, installing with / Installing Python with Anaconda
- downloading / Downloading Anaconda
- installing / Installing Anaconda
- terminal, opening / Opening a terminal
- home directory, finding / Finding your home directory
- system's PATH, manipulating / Manipulating your system path
- installation, testing / Testing your installation
- environments, managing / Managing environments
- conda commands / Common conda commands
- references / References
- notebooks, downloading / Downloading the notebooks
- arguments / Functions
- array manipulation routines
- references / Basic array manipulations
- arrays
- creating / Creating arrays
- references / Creating arrays
- loading, from files / Loading arrays from files
- basic array manipulations / Basic array manipulations
- computing / Computing with NumPy arrays
- selection / Selection and indexing
- indexing / Selection and indexing
- boolean operations / Boolean operations on arrays
- mathematical operations / Mathematical operations on arrays
- density map, with NumPy / A density map with NumPy
B
- Basemap
- about / The matplotlib Basemap toolkit
- references / The matplotlib Basemap toolkit
- Bokeh
- boolean operations
- on arrays / Boolean operations on arrays
- brew
- broadcasting
- about / Basic array manipulations
- brownian motion / Random walk
C
- C
- writing in Python, Cython used / Writing C in Python with Cython
- C++
- URL / C/C++ with Python
- C/C++, with Python
- about / C/C++ with Python
- Cython / C/C++ with Python
- SWIG / C/C++ with Python
- weave / C/C++ with Python
- ctypes / C/C++ with Python
- cffi / C/C++ with Python
- C compiler
- installing / Installing Cython and a C compiler for Python
- chaining syntax / Joins
- code cell, Notebook / Structure of a notebook cell, Code cells
- column-major order (Fortran-order)
- computing, techniques
- about / Further high-performance computing techniques
- Message Passing Interace (MPI) / MPI
- distributed computing / Distributed computing
- C/C++, with Python / C/C++ with Python
- Graphics Processing Units (GPUs) / GPU computing
- PyPy / PyPy
- Julia / Julia
- conda / Installing Python with Anaconda
- commands / Common conda commands
- conditional branches / Conditional branches
- ctypes / C/C++ with Python
- Cython
- used, for writing C in Python / Writing C in Python with Cython
- installing / Installing Cython and a C compiler for Python
- Eratosthenes Sieve, implementing / Implementing the Eratosthenes Sieve in Python and Cython
- URL / Implementing the Eratosthenes Sieve in Python and Cython, C/C++ with Python
- user guide, URL / Implementing the Eratosthenes Sieve in Python and Cython
- tutorials, URL / Implementing the Eratosthenes Sieve in Python and Cython
D
- 3D visualization libraries
- about / 3D visualization
- Mayavi / Mayavi
- VisPy / VisPy
- data
- manipulating / Manipulating data
- selecting / Selecting data
- columns, selecting / Selecting columns
- rows, selecting / Selecting rows
- boolean indexing, filtering with / Filtering with boolean indexing
- numbers, computing with / Computing with numbers
- text, working with / Working with text
- dates and times, working with / Working with dates and times
- missing data, handling / Handling missing data
- Data-Driven Documents (D3)
- about / Displaying rich HTML elements in the Notebook
- references / JavaScript and D3 in the Notebook
- dataset, in Notebook
- exploring / Exploring a dataset in the Notebook
- data subset / Provenance of the data
- URL / Provenance of the data
- references / Provenance of the data
- public datasets / Provenance of the data
- downloading / Downloading and loading a dataset
- loading / Downloading and loading a dataset
- plots creating, matplotlib used / Making plots with matplotlib
- descriptive statistics, with pandas and seaborn / Descriptive statistics with pandas and seaborn
- decorators
- about / Functional programming
- URL / Functional programming
- density map
- computing / A density map with NumPy
- distributed computing
- Apache Spark / Distributed computing
- Dask / Distributed computing
- xray / Distributed computing
- Bolt / Distributed computing
E
- Eratosthenes Sieve
- implementing, in Python / Implementing the Eratosthenes Sieve in Python and Cython
- implementing, in Cython / Implementing the Eratosthenes Sieve in Python and Cython
- expit function
- about / A density map with NumPy
F
- functional programming / Functional programming
- functions / Functions
G
- General-Purpose GPU computing (GPUGPU) / GPU computing
- GeoPandas
- about / GeoPandas
- Git Distributed Version Control System (DVCS) / Downloading the notebooks
- GitHub / Downloading the notebooks
- GNU C Compiler (gcc) / Installing Cython and a C compiler for Python
- Graphics Processing Units (GPUs)
- about / GPU computing
- group-by operation
- about / Group-by
- GUI event loop support
- URL / GUI toolkits
H
- high-level plotting libraries
- about / High-level plotting
- Bokeh / Bokeh
- Vincent and Vega / Vincent and Vega
- Plotly / Plotly
- HTML elements
- displaying, in Notebook / Displaying rich HTML elements in the Notebook
I
- IJulia kernel
- URL / Julia
- image processing
- about / Image processing
- indentation / Indentation
- InteractiveShell instance
- IPython
- about / Jupyter and IPython
- references / References, Other topics
- features / Ten Jupyter/IPython essentials
- display system, URL / Displaying SVG in the Notebook
- IPython, features
- IPython, using as extended shell / Using IPython as an extended shell
- magic commands / Learning magic commands
- tab completion / Mastering tab completion
- Markdown cell, in Notebook / Writing interactive documents in the Notebook with Markdown
- interactive widgets, creating in Notebook / Creating interactive widgets in the Notebook
- Python scripts, running from IPython / Running Python scripts from IPython
- Python objects, introspecting / Introspecting Python objects
- Python code, debugging / Debugging Python code
- Python code, benchmarking / Benchmarking Python code
- Python code, profiling / Profiling Python code
- IPython.parallel
- about / Distributing tasks on several cores with IPython.parallel
- direct interface / Direct interface
- load-balanced interface / Load-balanced interface
- documentation, URL / Load-balanced interface
- IPython 4.0
- URL / Jupyter and IPython
- IPython Cookbook
- IPython extension
- custom magic command, creating / Creating a custom magic command in an IPython extension
- about / Creating a custom magic command in an IPython extension
J
- JavaScript
- used, for customizing Notebook interface / Customizing the Notebook interface with JavaScript
- JavaScript extensions
- joins
- about / Joins
- Julia
- about / Julia
- Jupyter
- about / Jupyter and IPython
- Notebook, URL / Jupyter and IPython
- URL / Jupyter and IPython
- features / Ten Jupyter/IPython essentials
- Jupyter kernel
- writing / Writing a new Jupyter kernel
- references / Writing a new Jupyter kernel
- Jupyter Notebook
- launching / Launching the Jupyter Notebook
- Just-In-Compiler (JIT) / Accelerating Python code with Numba
K
- kernel / The Notebook dashboard
- keyword arguments / Positional and keyword arguments
L
- Leaflet
- about / Leaflet wrappers: folium and mplleaflet
- folium / Leaflet wrappers: folium and mplleaflet
- mplleaflet / Leaflet wrappers: folium and mplleaflet
- references / Leaflet wrappers: folium and mplleaflet
- libdynd
- URL / GPU computing
- list comprehension / Loops
- loops / Loops
M
- magic commands
- about / Using IPython as an extended shell, Learning magic commands
- creating, in IPython extension / Creating a custom magic command in an IPython extension
- manipulation functions
- reference link / A density map with NumPy
- maps
- creating / Maps and geometry
- matplotlib Basemap toolkit / The matplotlib Basemap toolkit
- GeoPandas / GeoPandas
- Leaflet / Leaflet wrappers: folium and mplleaflet
- Markdown cell, Notebook / Markdown cells
- mathematical functions, NumPy
- mathematical operations
- on arrays / Mathematical operations on arrays
- Math Kernel Library (MKL)
- matplotlib
- about / matplotlib and seaborn essentials
- plots with / Common plots with matplotlib
- figures, customizing / Customizing matplotlib figures
- gallery, URL / Customizing matplotlib figures
- figures, in Notebook / Interacting with matplotlib figures in the Notebook
- references / Interacting with matplotlib figures in the Notebook
- high-level plotting, with seaborn / High-level plotting with seaborn
- Mayavi / Mayavi
- Message Passing Interace (MPI)
- Microsoft Visual C++ Compiler for Python 2.7
- MinGW
- Miniconda
- modal interface, Notebook
- about / The Notebook modal interface
- keyboard shortcuts, in both modes / Keyboard shortcuts available in both modes
- keyboard shortcuts, in edit mode / Keyboard shortcuts available in the edit mode
- keyboard shortcuts, in command mode / Keyboard shortcuts available in the command mode
- multidimensional array
- about / Multidimensional arrays
N
- ndarray
- about / Multidimensional arrays, The ndarray
- dimensions / The ndarray
- shape / The ndarray
- strides / The ndarray
- data type (dtype) / The ndarray
- vector operations / Vector operations on ndarrays
- storing, in memory / How an ndarray is stored in memory
- nopython mode / Random walk
- URL / Random walk
- Notebook
- about / Jupyter and IPython, Introducing the Notebook, The Notebook dashboard
- references / References, References
- IPython console, launching / Launching the IPython console
- Jupyter Notebook launching / Launching the Jupyter Notebook
- dashboard / The Notebook dashboard
- user interface / The Notebook user interface
- cell, structure / Structure of a notebook cell
- modal interface / The Notebook modal interface
- dataset, exploring / Exploring a dataset in the Notebook
- HTML elements, displaying / Displaying rich HTML elements in the Notebook
- Scalable Vector Graphics (SVG), displaying / Displaying SVG in the Notebook
- JavaScript / JavaScript and D3 in the Notebook
- D3 / JavaScript and D3 in the Notebook
- Notebook interface
- customizing, JavaScript used / Customizing the Notebook interface with JavaScript
- Numba
- Python code, accelerating with / Accelerating Python code with Numba
- URL / Random walk
- documentation, URL / Random walk
- numexpr
- URL / Universal functions
- NumPy
- arrays / Creating and loading arrays
- references / Loading arrays from files
- density map, computing / A density map with NumPy
- versus pandas / A density map with NumPy
- NumPy universal functions (ufuncs)
- URL / Universal functions
O
- Object-oriented programming (OOP) / Object-oriented programming
- operations
- complex operations / Complex operations
- group-by operation / Group-by
- joins / Joins
P
- pandas
- versus NumPy / A density map with NumPy
- Partial Differential Equation (PDE)
- about / Multidimensional arrays
- passage by assignment / Passage by assignment
- Plotly
- about / Plotly
- plots
- about / Choosing a plotting backend
- inline plots / Inline plots
- exported figures / Exported figures
- plt.savefig(), URL / Exported figures
- GUI toolkits / GUI toolkits
- dynamic inline plots / Dynamic inline plots
- web-based visualization / Web-based visualization
- D3.js, URL / Web-based visualization
- mpld3, URL / Web-based visualization
- customization options, URL / Common plots with matplotlib
- positional arguments / Positional and keyword arguments
- Powershell
- URL / Opening a terminal
- pure function / Passage by assignment
- PyCuda
- URL / GPU computing
- pylab mode
- PyOpenCL
- URL / GPU computing
- PyPy
- Python
- about / What are Python, IPython, and Jupyter?
- competitors / What are Python, IPython, and Jupyter?
- installing, with Anaconda / Installing Python with Anaconda
- special characters, URL / String escaping
- C compiler, installing / Installing Cython and a C compiler for Python
- Cython, installing / Installing Cython and a C compiler for Python
- Eratosthenes Sieve, implementing / Implementing the Eratosthenes Sieve in Python and Cython
- Python, code
- debugging / Debugging Python code
- benchmarking / Benchmarking Python code
- profiling / Profiling Python code
- Python, fundamentals
- about / A crash course on Python
- Hello world / Hello world
- variables / Variables
- string escaping / String escaping
- lists / Lists
- loops / Loops
- indentation / Indentation
- conditional branches / Conditional branches
- functions / Functions
- keyword arguments / Positional and keyword arguments
- positional arguments / Positional and keyword arguments
- passage by assignment / Passage by assignment
- errors / Errors
- Object-oriented programming (OOP) / Object-oriented programming
- functional programming / Functional programming
- Python 2 and 3 / Python 2 and 3
- references / Going beyond the basics
- Python 2 and 3 / Python 2 and 3
- Python code
- accelerating, with Numba / Accelerating Python code with Numba, Random walk
- random walk / Random walk
- Python Package Index (PyPI)
- about / Common conda commands
- references / Common conda commands
Q
- Qt console
R
- record arrays
- about / The ndarray
- relational database management systems (RDBMS)
- about / Complex operations
- row-major order (C-order)
S
- Scalable Vector Graphics (SVG)
- about / Displaying SVG in the Notebook
- displaying, in Notebook / Displaying SVG in the Notebook
- scikit-image
- about / Image processing
- references / Image processing
- seaborn
- about / matplotlib and seaborn essentials
- high-level plotting with / High-level plotting with seaborn
- sequential locality
- statistical functions, NumPy
- strides
- structured arrays
- about / The ndarray
- reference link / The ndarray
- Structured Query Language (SQL)
- about / Complex operations
- SWIG / C/C++ with Python
U
- universal functions
- about / Universal functions
- references / Universal functions
V
- variables / Variables
- vector (or vectorized) operations
- on ndarrays / Vector operations on ndarrays
- comparing / Why operations on ndarrays are fast
- vector computing
- about / A primer to vector computing
- multidimensional array / Multidimensional arrays
- ndarray / The ndarray
- vector operations, on ndarray / Vector operations on ndarrays
- in NumPy / How fast are vector computations in NumPy?
- vectorization / Computing with numbers
- Vega
- about / Vincent and Vega
- references / Vincent and Vega
- Vincent
- about / Vincent and Vega
- references / Vincent and Vega
- VisPy
W
- Wakari
- weave / C/C++ with Python
- web technologies
- references / JavaScript and D3 in the Notebook