Index

A

• affinity propagation
  • clustering, performing with / Clustering with affinity propagation
• Amdahl's law
  • URL / Creating a process pool with multiprocessing
  • about / Creating a process pool with multiprocessing
• Anderson-Darling test
  • about / Preprocessing
• annotate() function / Legends and annotations
• annotations
  • about / Legends and annotations
• Apache Cassandra
  • about / Apache Cassandra
• application
  • writing, with NumPy arrays / A simple application
• ARMA models
  • about / ARMA models
  • reference link / ARMA models
• array() function
  • about / Creating a multidimensional array
• array shapes
  • manipulating / Manipulating array shapes
• array shapes, manipulating
  • about / Manipulating array shapes
  • arrays, stacking / Stacking arrays
  • arrays, splitting / Splitting NumPy arrays
  • NumPy array attributes / NumPy array attributes
  • arrays, converting / Converting arrays
• Artificial Neural Networks (ANN)
  • about / Neural networks
• astype function / Converting arrays
• Atom feeds
  • parsing / Parsing RSS and Atom feeds
• Augmented Dickey-Fuller (ADF)
  • about / Defining cointegration
  • reference link / Defining cointegration
• autocorrelation
  • about / Autocorrelation
• autocorrelation plots
  • about / Autocorrelation plots
• autocorrelations
  • about / Autocorrelation plots
• autoregressive model
  • about / Autoregressive models

B

• bag-of-words model
  • about / The bag-of-words model
• Bartlett window
  • about / Window functions
• basic matplotlib plots
  • about / Basic matplotlib plots
• BeautifulSoup
  • HTML, parsing with / Parsing HTML with Beautiful Soup
• bigrams() function / Analyzing word frequencies
• binarize() function / Preprocessing
• binary installers
  • using / Installing and exploring pandas
  • URL, for downloading / Installing and exploring pandas
• binomial distribution
  • gambling / Gambling with the binomial distribution
• binomial function / Gambling with the binomial distribution
• Blackman window
  • about / Window functions
• boolean indexing, NumPy arrays
  • about / Indexing NumPy arrays with Booleans
• Boost
  • about / Integrating Boost and Python
  • download link / Integrating Boost and Python
  • Python, integrating with / Integrating Boost and Python
• Bottleneck
  • about / Comparing Bottleneck to NumPy functions
  • comparing, to NumPy functions / Comparing Bottleneck to NumPy functions
  • references / Comparing Bottleneck to NumPy functions
• boxcar window
  • about / Window functions
• box plot
  • about / Plot.ly
• broadcasting
  • about / Broadcasting NumPy arrays
• bubble chart
  • about / Scatter plots

C

• C
  • about / Integrating SWIG and NumPy
• C++ / Integrating SWIG and NumPy
• Cardinal Number (CD) tag / Filtering out stopwords, names, and numbers
• Cassandra
  • references / Apache Cassandra
  • traditional relational databases / Apache Cassandra
  • key-value / Apache Cassandra
• Cassandra Query Language (CQL)
  • about / Apache Cassandra
• C code
  • calling / Calling C code
• character codes
  • about / Character codes
• classification
  • performing, with logistic regression / Classification with logistic regression
  • performing, with support vector machines (SVM) / Classification with support vector machines
• clustering
  • about / Clustering with affinity propagation
  • performing, with affinity propagation / Clustering with affinity propagation
• clusters / Clustering with affinity propagation
• code
  • profiling / Profiling the code
• coefficient of determination
  • URL / Regression with ElasticNetCV
• cointegration
  • about / Defining cointegration
  • defining / Defining cointegration
• column families
  • about / Apache Cassandra
• column stacking
  • about / Stacking arrays
• column_stack function
  • about / Stacking arrays
• Comma-separated Value (CSV) file / Basic descriptive statistics with NumPy
• Command Line Interface (CLI) / Using IPython as a shell
• Comprehensive R Archive Network (CRAN) / Installing rpy2
• concatenate function
  • about / Stacking arrays
• concat function / Concatenating and appending DataFrames
• correlate() function
  • about / Autocorrelation
• crossover operator / Genetic algorithms
• CSS
  • about / Parsing HTML with Beautiful Soup
• CSS selectors
  • about / Parsing HTML with Beautiful Soup
  • URL, for documentation / Parsing HTML with Beautiful Soup
• CSV files
  • writing, with NumPy / Writing CSV files with NumPy and pandas
  • writing, with pandas / Writing CSV files with NumPy and pandas
• Cython
  • installing / Installing Cython
• cytoolz package
  • about / Installing Cython

D

• data
  • querying, in pandas / Querying data in pandas
  • storing, with PyTables / Storing data with PyTables
  • reading, to Excel with pandas / Reading and writing to Excel with pandas
  • writing, to Excel with pandas / Reading and writing to Excel with pandas
  • storing, in Redis / Storing data in Redis
• data aggregation
  • about / Data aggregation with pandas DataFrames
• database
  • populating, with SQLAlchemy / Populating a database with SQLAlchemy
  • querying, with SQLAlchemy / Querying the database with SQLAlchemy
• database cursor
  • about / Lightweight access with sqlite3
• databases
  • accessing, from pandas / Accessing databases from pandas
• DataFrame
  • about / pandas DataFrames
  • URL / pandas DataFrames
  • creating / pandas DataFrames
  • statistical methods / Statistics with pandas DataFrames
  • data aggregation / Data aggregation with pandas DataFrames
  • concatenating / Concatenating and appending DataFrames
  • appending / Concatenating and appending DataFrames
  • joining / Joining DataFrames
  • pickling / Comparing the NumPy .npy binary format and pickling pandas DataFrames
  • reading, to HDF5 stores / Reading and writing pandas DataFrames to HDF5 stores
  • writing, to HDF5 stores / Reading and writing pandas DataFrames to HDF5 stores
• dataset
  • about / Dataset – databases for lazy people
• datasets package / Interfacing with R
• data type objects
  • about / Data type objects
• dates
  • dealing with / Dealing with dates
• Debian
  • NumPy, installing on / On Linux
• decision tree
  • about / Decision trees
• decision tree learning
  • about / Decision trees
• DELETE method / Using REST web services and JSON
• depth-wise splitting
  • about / Splitting NumPy arrays
• depth stacking
  • about / Stacking arrays
• descriptive statistics
  • with NumPy / Basic descriptive statistics with NumPy
• detrend() function
  • about / Filtering
• detrend filter
  • about / Filtering
• dill
  • about / Comparing the NumPy .npy binary format and pickling pandas DataFrames
• doc_features() function / Installing Cython
• dsplit function
  • about / Splitting NumPy arrays
• dstack function
  • about / Stacking arrays
• dtype attributes
  • about / The dtype attributes
• dtype constructors
  • about / The dtype constructors

E

• eigenvalues
  • obtaining / Finding eigenvalues and eigenvectors with NumPy
  • about / Finding eigenvalues and eigenvectors with NumPy
• eigenvectors
  • obtaining / Finding eigenvalues and eigenvectors with NumPy
  • about / Finding eigenvalues and eigenvectors with NumPy
• ElasticNetCV
  • regression, performing with / Regression with ElasticNetCV
• ElasticNetCV class / Regression with ElasticNetCV
  • about / Support vector regression
• elastic net regularization
  • about / Regression with ElasticNetCV
  • URL / Regression with ElasticNetCV
• euclidean_distances() function
  • about / Clustering with affinity propagation
• Excel
  • data, reading to / Reading and writing to Excel with pandas
  • data, writing to / Reading and writing to Excel with pandas
• execute() call / Accessing databases from pandas
• executemany() method / Accessing databases from pandas
• exponential moving average
  • about / Moving averages

F

• f2py
  • Fortran code, using through / Using Fortran code through f2py
• fancy indexing
  • about / Fancy indexing
• Fast Fourier Transform (FFT)
  • about / Fourier analysis
• fft() function
  • about / Fourier analysis
• fftshift() function
  • about / Fourier analysis
• filtering
  • about / Filtering
• fit() method / Classification with logistic regression
• fitness function
  • about / Genetic algorithms
• flatten function
  • about / Manipulating array shapes
• folds
  • about / Classification with logistic regression
• format parameter
  • URL, for documentation / Writing CSV files with NumPy and pandas
• Formula Translation System
  • about / Using Fortran code through f2py
• Fortran
  • about / Using Fortran code through f2py
  • reference link / Using Fortran code through f2py
• Fortran code
  • using, through f2py / Using Fortran code through f2py
• Fourier analysis
  • about / Fourier analysis
  • examples / Fourier analysis
• Fourier series
  • about / Fourier analysis
• FreqDist class / Analyzing word frequencies
• frequency-inverse document frequency (tf-idf) / Creating word clouds
• functions, matplotlib / matplotlib
• functions, NumPy / NumPy
• functions, pandas / pandas
• functions, scikit-learn / Scikit-learn
• functions, scipy.fftpack / scipy.fftpack
• functions, scipy.signal / scipy.signal
• functions, scipy.stats / scipy.stats

G

• generations
  • about / Genetic algorithms
• Genetic algorithms
  • overview / Genetic algorithms
  • URL / Genetic algorithms
• genetic operators
  • about / Genetic algorithms
  • crossover / Genetic algorithms
  • mutation / Genetic algorithms
  • evaluate / Genetic algorithms
  • mate / Genetic algorithms
  • mutate / Genetic algorithms
  • select / Genetic algorithms
• Gentoo
  • NumPy, installing on / On Linux
• GET method / Using REST web services and JSON
• gfortran compiler
  • about / Using Fortran code through f2py
  • download link / Using Fortran code through f2py
• Global Interpreter Lock (GIL)
  • about / Creating a process pool with multiprocessing
• Google App Engine (GAE)
  • setting up / Setting up Google App Engine
  • download link / Setting up Google App Engine
• Graphical Processor Units (GPUs)
  • URL / Scatter plots
  • about / Scatter plots
• Graphviz
  • URL / Profiling the code
• grid search
  • about / Classification with support vector machines
• GridSearchCV class / Classification with support vector machines
• GridSearchCV object / Classification with support vector machines
• Gutenberg corpus
  • about / Filtering out stopwords, names, and numbers
• Gutenberg project
  • URL / Filtering out stopwords, names, and numbers

H

• Hanning window
  • about / Window functions
• HDF
  • about / Storing data with PyTables
• HDF5
  • about / Storing data with PyTables
  • URL, for installing / Storing data with PyTables
• HDF5 stores
  • DataFrame, writing to / Reading and writing pandas DataFrames to HDF5 stores
  • DataFrame, reading to / Reading and writing pandas DataFrames to HDF5 stores
• headers
  • ncalls / Profiling the code
  • tottime / Profiling the code
  • percall / Profiling the code
  • cumtime / Profiling the code
• Hilbert-Huang transform
  • about / Generating periodic signals
• history, IPython shell
  • displaying / Using IPython as a shell
• horizontal splitting
  • about / Splitting NumPy arrays
• horizontal stacking
  • about / Stacking arrays
• hsplit function
  • about / Splitting NumPy arrays
• hstack function
  • about / Stacking arrays
• HTML
  • about / Parsing HTML with Beautiful Soup
  • parsing, with BeautifulSoup / Parsing HTML with Beautiful Soup

I

• if a* else b statement / Decision trees
• information
  • exchanging, with MATLAB/Octave / Exchanging information with MATLAB/Octave
• installation, Cython / Installing Cython
• installation, NLTK / Installing NLTK
• installation, pandas
  • about / Installing and exploring pandas
• installation, rpy2 / Installing rpy2
• Internet Engineering Task Force (IETF)
  • about / Parsing HTML with Beautiful Soup
• IPython
  • installing, on Windows / On Windows
  • URL / On Windows
  • installing, on Linux / On Linux
  • installing, on Mac OS X / On Mac OS X
  • building, from source / Building NumPy, SciPy, matplotlib, and IPython from source
  • git commands / Building NumPy, SciPy, matplotlib, and IPython from source
  • installing, with setup tools / Installing with setuptools
  • using, as shell / Using IPython as a shell
  • notebooks / IPython notebooks
• IPython notebooks
  • references / IPython notebooks
  • about / IPython notebooks
• IPython Parallel
  • about / IPython Parallel
• IPython shell
  • features / Using IPython as a shell
• IPython shell, using
  • pylab switch / Using IPython as a shell
  • session, saving / Using IPython as a shell
  • system shell command, executing / Using IPython as a shell
• IPython source code
  • URL, for downloading / Building NumPy, SciPy, matplotlib, and IPython from source
• IRC channel
  • URL / Where to find help and references
• isalpha() method / Creating word clouds
• isStopword() function / Installing Cython
• isStopWord3() function / Installing Cython

J

• jackknife() function / IPython Parallel
• jackknife resampling
  • about / IPython Parallel
• Java
  • URL, for downloading / Apache Cassandra
  • URL, for installation instructions / Apache Cassandra
  • NumPy arrays, sending to / Sending NumPy arrays to Java
• Java Development Kit (JDK) / Sending NumPy arrays to Java
• Java Runtime Environment (JRE) / Sending NumPy arrays to Java
• Java Virtual Machine (JVM)
  • about / Sending NumPy arrays to Java
• Joblib
  • about / Speeding up embarrassingly parallel for loops with Joblib
  • used, for improving performance of long-running Python function / Speeding up embarrassingly parallel for loops with Joblib
• join() method / Joining DataFrames
• JSON
  • about / Using REST web services and JSON
  • URL / Using REST web services and JSON
  • using / Using REST web services and JSON
  • reading, with pandas / Reading and writing JSON with pandas
  • writing, with pandas / Reading and writing JSON with pandas
• Jug
  • about / Performing MapReduce with Jug
  • MapReduce, performing with / Performing MapReduce with Jug
• Jython
  • about / Sending NumPy arrays to Java

K

• k-fold cross-validation
  • about / Classification with logistic regression
• kernel function
  • about / Classification with support vector machines
• keyspace
  • about / Apache Cassandra
• Kruskal-Wallis one-way analysis of variance / Interfacing with R

L

• LabelBinarizer class / Preprocessing
• lag plot
  • about / Lag plots
  • example / Lag plots
• learning curve
  • about / Support vector regression
• learning_curve() function
  • about / Support vector regression
• least absolute shrinkage and selection operator (LASSO) / Regression with ElasticNetCV
• leaves
  • about / Decision trees
• legend() function / Legends and annotations
• legends
  • about / Legends and annotations
• len() function / Calling C code
• lightweight access, sqlite3 / Lightweight access with sqlite3
• linear algebra
  • with NumPy / Linear algebra with NumPy
  • matrices, inverting with NumPy / Inverting matrices with NumPy
  • linear systems, solving with NumPy / Solving linear systems with NumPy
• linear systems
  • solving, with NumPy / Solving linear systems with NumPy
• Linux
  • IPython, installing on / On Linux
• Linux distributions
  • overview / On Linux
• list of locations indexing, NumPy arrays
  • about / Indexing with a list of locations
• loads() function / Using REST web services and JSON
• loadtxt function / Basic descriptive statistics with NumPy
• logarithmic plots
  • about / Logarithmic plots
• logistic function
  • about / Classification with logistic regression
• logistic regression
  • about / Classification with logistic regression
  • URL / Classification with logistic regression
  • classification, performing with / Classification with logistic regression
• logspace() function
  • about / Classification with support vector machines
• loremIpsum.html file
  • URL / Parsing HTML with Beautiful Soup

M

• Mac OS X
  • IPython, installing on / On Mac OS X
• Mandriva
  • NumPy, installing on / On Linux
• manual pages
  • reading / Reading manual pages
  • help function, calling / Reading manual pages
  • question mark, quering / Reading manual pages
• map() method
  • about / Creating a process pool with multiprocessing
• Map phase
  • about / Performing MapReduce with Jug
• MapReduce
  • performing, with Jug / Performing MapReduce with Jug
  • URL / Performing MapReduce with Jug
• MATLAB
  • about / Exchanging information with MATLAB/Octave
  • information, exchanging with / Exchanging information with MATLAB/Octave
• matplotlib
  • URL / On Windows
  • building, from source / Building NumPy, SciPy, matplotlib, and IPython from source
  • git commands / Building NumPy, SciPy, matplotlib, and IPython from source
  • installing, with setup tools / Installing with setuptools
  • functions / matplotlib
• matplotlib.pyplot.loglog() function / Logarithmic plots
• matplotlib subpackages
  • about / matplotlib subpackages
• matrices
  • inverting, with NumPy / Inverting matrices with NumPy
• mean shift algorithm
  • about / Mean Shift
• medfilt() function
  • about / Filtering
• median() function / Comparing Bottleneck to NumPy functions
• median filter
  • about / Filtering
  • reference link / Filtering
• merge() function / Joining DataFrames
• meteorological data, Dutch KNMI institute
  • reference link / Preprocessing
• missing values
  • handling / Handling missing values
• MongoDB
  • about / PyMongo and MongoDB
• MongoDB distribution
  • URL, for downloading / PyMongo and MongoDB
• Moore's law
  • about / Logarithmic plots
• morley dataset / Interfacing with R
• moving averages
  • about / Moving averages
• MPI
  • references / Installing MPI for Python
  • installing, for Python / Installing MPI for Python
• multidimensional array
  • creating / Creating a multidimensional array
• multiprocessing
  • about / Creating a process pool with multiprocessing
  • process pool, creating with / Creating a process pool with multiprocessing
• mutation operator
  • about / Genetic algorithms

N

• naive
  • about / Naive Bayes classification
• Naive Bayes classification
  • about / Naive Bayes classification
• names
  • filtering out / Filtering out stopwords, names, and numbers
• ndarray
  • about / The NumPy array object
• neural network
  • overview / Neural networks
• NLTK
  • installing / Installing NLTK
• normal distribution
  • sampling / Sampling the normal distribution
• normality test
  • performing, with SciPy / Performing a normality test with SciPy
• numbers
  • filtering out / Filtering out stopwords, names, and numbers
• Numeric
  • about / Character codes
• NumPy
  • URL / On Windows, Where to find help and references
  • installing, on Red Hat / On Linux
  • installing, on Mandriva / On Linux
  • installing, on Gentoo / On Linux
  • installing, on Debian / On Linux
  • installing, on Ubuntu / On Linux
  • building, from source / Building NumPy, SciPy, matplotlib, and IPython from source
  • git commands / Building NumPy, SciPy, matplotlib, and IPython from source
  • installing, with setup tools / Installing with setuptools
  • references / Where to find help and references
  • descriptive statistics / Basic descriptive statistics with NumPy
  • linear algebra, performing / Linear algebra with NumPy
  • matrices, inverting with / Inverting matrices with NumPy
  • linear systems, solving with / Solving linear systems with NumPy
  • eigenvalues, obtaining / Finding eigenvalues and eigenvectors with NumPy
  • eigenvectors, obtaining / Finding eigenvalues and eigenvectors with NumPy
  • random numbers / NumPy random numbers
  • about / Installing and exploring pandas
  • CSV files, writing with / Writing CSV files with NumPy and pandas
  • SWIG, integrating with / Integrating SWIG and NumPy
  • functions / NumPy
• NumPy-masked array
  • creating / Creating a NumPy-masked array
  • extreme values, disregarding / Disregarding negative and extreme values
  • negative values, disregarding / Disregarding negative and extreme values
• numpy.i interface file
  • reference link / Integrating SWIG and NumPy
• numpy.linalg subpackage
  • using / Linear algebra with NumPy
  • eig function / Finding eigenvalues and eigenvectors with NumPy
  • eigvals function / Finding eigenvalues and eigenvectors with NumPy
• numpy.ma subpackage / Creating a NumPy-masked array
• numpy.median() function / Comparing Bottleneck to NumPy functions
• NumPy .npy binary format
  • comparing / Comparing the NumPy .npy binary format and pickling pandas DataFrames
• NumPy array attributes
  • about / NumPy array attributes
• NumPy array elements
  • selecting / Selecting NumPy array elements
• NumPy array object
  • about / The NumPy array object
• NumPy arrays
  • about / NumPy arrays
  • used, for writing application / A simple application
  • advantages / The advantages of NumPy arrays
  • one-dimensional slicing / One-dimensional slicing and indexing
  • indexing / One-dimensional slicing and indexing
  • stacking / Stacking arrays
  • splitting / Splitting NumPy arrays
  • converting / Converting arrays
  • views,creating / Creating array views and copies
  • copies, creating / Creating array views and copies
  • indexing, with list of locations / Indexing with a list of locations
  • indexing, with booleans / Indexing NumPy arrays with Booleans
  • broadcasting / Broadcasting NumPy arrays
  • sending, to Java / Sending NumPy arrays to Java
• NumPy functions
  • Bottleneck, comparing to / Comparing Bottleneck to NumPy functions
• NumPy modules
  • about / NumPy and SciPy modules
• NumPy numerical types
  • about / NumPy numerical types
  • bool / NumPy numerical types
  • inti / NumPy numerical types
  • int8 / NumPy numerical types
  • int16 / NumPy numerical types
  • int32 / NumPy numerical types
  • int64 / NumPy numerical types
  • uint8 / NumPy numerical types
  • uint16 / NumPy numerical types
  • uint32 / NumPy numerical types
  • uint64 / NumPy numerical types
  • float16 / NumPy numerical types
  • float32 / NumPy numerical types
  • float64 / NumPy numerical types
  • float / NumPy numerical types
  • complex64 / NumPy numerical types
  • complex128 / NumPy numerical types
  • complex / NumPy numerical types
  • data type objects / Data type objects
  • character codes / Character codes
  • dtype constructors / The dtype constructors
  • dtype attributes / The dtype attributes

O

• object-relational mapping (ORM)
  • about / SQLAlchemy
• Octave
  • about / Exchanging information with MATLAB/Octave
  • download link / Exchanging information with MATLAB/Octave
  • information, exchanging with / Exchanging information with MATLAB/Octave
• one-point crossover
  • about / Genetic algorithms
• opinion mining
  • about / Sentiment analysis

P

• pandas
  • installing / Installing and exploring pandas
  • exploring / Installing and exploring pandas
  • data, querying / Querying data in pandas
  • CSV files, writing with / Writing CSV files with NumPy and pandas
  • data writing, to Excel / Reading and writing to Excel with pandas
  • data reading, to Excel / Reading and writing to Excel with pandas
  • JSON, writing with / Reading and writing JSON with pandas
  • JSON, reading with / Reading and writing JSON with pandas
  • databases, accessing from / Accessing databases from pandas
  • functions / pandas
• pandas, requisites
  • NumPy / Installing and exploring pandas
  • python-dateutil / Installing and exploring pandas
  • pytz / Installing and exploring pandas
• pandas plotting
  • about / Plotting in pandas
• parallel() function / Speeding up embarrassingly parallel for loops with Joblib
• parsing, Atom feeds
  • about / Parsing RSS and Atom feeds
• parsing, HTML
  • with BeautifulSoup / Parsing HTML with Beautiful Soup
• parsing, RSS
  • about / Parsing RSS and Atom feeds
• Part of Speech (POS) tags / Filtering out stopwords, names, and numbers
• PCRE
  • about / Integrating SWIG and NumPy
  • download link / Integrating SWIG and NumPy
• periodic signals
  • generating / Generating periodic signals
• phase spectrum
  • about / Spectral analysis
• pickling
  • about / Comparing the NumPy .npy binary format and pickling pandas DataFrames
  • URL / Comparing the NumPy .npy binary format and pickling pandas DataFrames
• pivot table
  • about / Pivot tables
• pkgutil module
  • about / NumPy and SciPy modules
• pkg_check.py file / matplotlib subpackages
• plot() method
  • about / Plotting in pandas
• Plot.ly
  • about / Plot.ly
  • using / Plot.ly
• Pony ORM
  • about / Pony ORM
• POST method / Using REST web services and JSON
• pos_tag() function
  • about / Filtering out stopwords, names, and numbers
• power spectrum
  • about / Spectral analysis
• predict() method
  • about / Regression with ElasticNetCV
• preinstalled Python libraries
  • reference link / Running programs on PythonAnywhere
• preprocessing
  • about / Preprocessing
• Principal Component Analysis (PCA)
  • about / Finding eigenvalues and eigenvectors with NumPy
• probability density functions (pdf) / Sampling the normal distribution
• process pool
  • creating, with multiprocessing / Creating a process pool with multiprocessing
• profiling
  • about / Profiling the code
• programs
  • running, on PythonAnywhere / Running programs on PythonAnywhere
• proper noun singular (NNP) tag / Filtering out stopwords, names, and numbers
• properties, ndarray
  • ndim / NumPy array attributes
  • size / NumPy array attributes
  • itemsize / NumPy array attributes
  • nbytes / NumPy array attributes
  • T / NumPy array attributes
  • j / NumPy array attributes
  • real / NumPy array attributes
  • imag / NumPy array attributes
  • flat / NumPy array attributes
• pseudo-random numbers
  • about / NumPy random numbers
• PUT method / Using REST web services and JSON
• pydoc module
  • about / NumPy and SciPy modules
• pylab switch, IPython shell / Using IPython as a shell
• PyMongo
  • about / PyMongo and MongoDB
• PyTables
  • data, storing with / Storing data with PyTables
• Python
  • software requisites / Software used in this book
  • URL / Software used in this book
  • URL, for documentation / Querying data in pandas
  • integrating, with Boost / Integrating Boost and Python
  • MPI, installing for / Installing MPI for Python
• python-bs4
  • URL, for downloading / Parsing HTML with Beautiful Soup
• python-dateutil
  • about / Installing and exploring pandas
• PythonAnywhere
  • about / Running programs on PythonAnywhere
  • programs, running on / Running programs on PythonAnywhere

Q

• Quandl
  • URL / Querying data in pandas

R

• R
  • download link / Installing rpy2
  • interfacing with / Interfacing with R
• random numbers, NumPy
  • real random numbers / NumPy random numbers
  • pseudo-random numbers / NumPy random numbers
  • binomial distribution, gambling / Gambling with the binomial distribution
  • normal distribution, sampling / Sampling the normal distribution
  • normality test, performing with SciPy / Performing a normality test with SciPy
• random_state parameter / Classification with logistic regression
• rankdata() function / Comparing Bottleneck to NumPy functions
• ravel function
  • about / Manipulating array shapes
• read_sql() method / Querying the database with SQLAlchemy
• real random numbers
  • about / NumPy random numbers
• Red Hat
  • NumPy, installing on / On Linux
• Redis
  • about / Storing data in Redis
  • data, storing in / Storing data in Redis
  • URL / Storing data in Redis
• Reduce phase
  • about / Performing MapReduce with Jug
• regression
  • performing, with ElasticNetCV / Regression with ElasticNetCV
• remote data access
  • about / Remote data access
• reshape function
  • about / Manipulating array shapes
• resize method
  • about / Manipulating array shapes
• REST
  • URL / Using REST web services and JSON
  • about / Using REST web services and JSON
• REST web services
  • using / Using REST web services and JSON
• rfft() function
  • about / Fourier analysis
• ridge method / Regression with ElasticNetCV
• rolling_mean() function
  • about / Moving averages
• row stacking
  • about / Stacking arrays
• row_stack function
  • about / Stacking arrays
• rpy2
  • installing / Installing rpy2
  • reference link, for upgrading / Installing rpy2
• R squared
  • about / Regression with ElasticNetCV
• RSS
  • about / Parsing RSS and Atom feeds
  • parsing / Parsing RSS and Atom feeds
  • URL / Parsing RSS and Atom feeds

S

• scale() function / Preprocessing
• scatter plot
  • about / Scatter plots
  • creating / Plotting in pandas
• scikit-learn
  • about / A tour of scikit-learn
  • functions / Scikit-learn
• SciPy
  • URL / On Windows
  • building, from source / Building NumPy, SciPy, matplotlib, and IPython from source
  • git commands / Building NumPy, SciPy, matplotlib, and IPython from source
  • installing, with setup tools / Installing with setuptools
  • references / Where to find help and references
  • normality test, performing with / Performing a normality test with SciPy
• scipy.constants module / Interfacing with R
• scipy.fftpack
  • functions / scipy.fftpack
• scipy.io.savemat() function
  • about / Exchanging information with MATLAB/Octave
• scipy.optimize.leastsq() function / Autoregressive models
• scipy.signal
  • functions / scipy.signal
• scipy.signal package
  • about / Filtering
• scipy.stats
  • functions / scipy.stats
• scipy.stats.kruskal() function / Interfacing with R
• scipy.stats.rankdata() function / Comparing Bottleneck to NumPy functions
• SciPy modules
  • about / NumPy and SciPy modules
• SciPy Superpack
  • URL / On Mac OS X
• score() method / Classification with logistic regression
• semilogx() function / Logarithmic plots
• semilogy() function / Logarithmic plots
• sentiment analysis
  • about / Sentiment analysis
• Series
  • about / pandas Series
  • creating / pandas Series
• session, IPython shell
  • saving / Using IPython as a shell
• setup tools
  • used, for installing IPython / Installing with setuptools
  • used, for installing SciPy / Installing with setuptools
  • used, for installing matplotlib / Installing with setuptools
  • used, for installing NumPy / Installing with setuptools
• sklearn.preprocessing module
  • about / Preprocessing
• social network analysis
  • about / Social network analysis
• soft margin
  • about / Classification with support vector machines
• software requisites, Python
  • NumPy / Installing software and setup
  • SciPy / Installing software and setup
  • matplotlib / Installing software and setup
  • IPython / Installing software and setup
• SourceForge website
  • URL / On Mac OS X
• spectral analysis
  • about / Spectral analysis
• SQL
  • about / Lightweight access with sqlite3
• SQLAlchemy
  • about / SQLAlchemy
  • installing / Installing and setting up SQLAlchemy
  • setting up / Installing and setting up SQLAlchemy
  • URL, for support page / Installing and setting up SQLAlchemy
  • database, populating with / Populating a database with SQLAlchemy
  • database, querying with / Querying the database with SQLAlchemy
• SQLite
  • about / Lightweight access with sqlite3
• Stack Overflow software
  • URL / Where to find help and references
• Standard Development Kit (SDK) / Setting up Google App Engine
• statistical methods
  • about / Statistics with pandas DataFrames
  • describe / Statistics with pandas DataFrames
  • count / Statistics with pandas DataFrames
  • mad / Statistics with pandas DataFrames
  • median / Statistics with pandas DataFrames
  • min / Statistics with pandas DataFrames
  • max / Statistics with pandas DataFrames
  • mode / Statistics with pandas DataFrames
  • std / Statistics with pandas DataFrames
  • var / Statistics with pandas DataFrames
  • skew / Statistics with pandas DataFrames
  • kurt / Statistics with pandas DataFrames
• statsmodels subpackages
  • about / statsmodels subpackages
• stopwords
  • about / Installing NLTK
  • filtering out / Filtering out stopwords, names, and numbers
• str attribute
  • about / The dtype attributes
• strlen() function
  • about / Calling C code
• support vector machines (SVM)
  • about / Classification with support vector machines
  • classification, performing with / Classification with support vector machines
• support vector regression (SVR)
  • about / Classification with support vector machines, Support vector regression
  • URL / Classification with support vector machines
• SWIG
  • intergating, with NumPy / Integrating SWIG and NumPy
  • about / Integrating SWIG and NumPy
  • download link / Integrating SWIG and NumPy
  • reference link, for user mailing lists / Integrating SWIG and NumPy
• system shell command, IPython shell
  • executing / Using IPython as a shell

T

• tagging
  • about / Filtering out stopwords, names, and numbers
• TfidfVectorizer class / Creating word clouds
• three-dimensional plots
  • about / Three-dimensional plots
• timeit module / Installing Cython
• tolist function / Converting arrays
• transpose function
  • about / Manipulating array shapes
• triangular window
  • about / Window functions
• trigrams() function / Analyzing word frequencies

U

• Ubuntu
  • NumPy, installing on / On Linux
• unpickling
  • about / Comparing the NumPy .npy binary format and pickling pandas DataFrames

V

• vertical splitting
  • about / Splitting NumPy arrays
• vertical stacking
  • about / Stacking arrays
• vsplit function
  • about / Splitting NumPy arrays
• vstack function
  • about / Stacking arrays

W

• Wakari
  • URL / Working with Wakari
  • working with / Working with Wakari
• wiener() function
  • about / Filtering
• Wiener filter
  • about / Filtering
  • reference link / Filtering
• window function
  • about / Window functions
  • reference link / Window functions
• Windows
  • IPython, installing on / On Windows
• word clouds
  • creating / Creating word clouds
• word frequencies
  • analyzing / Analyzing word frequencies
• Wordle
  • about / Creating word clouds
  • URL / Creating word clouds