Numpy manual contents

• Numpy User Guide
  • How to find documentation
  • Numpy basics
    • Data types
      • Array types and conversions between types
      • Array Scalars
    • Array creation
      • Introduction
      • Converting Python array_like Objects to Numpy Arrays
      • Intrinsic Numpy Array Creation
      • Reading Arrays From Disk
        • Standard Binary Formats
        • Common ASCII Formats
        • Custom Binary Formats
        • Use of Special Libraries
    • Indexing
      • Assignment vs referencing
      • Single element indexing
      • Other indexing options
      • Index arrays
      • Indexing Multi-dimensional arrays
      • Boolean or “mask” index arrays
      • Combining index arrays with slices
      • Structural indexing tools
      • Assigning values to indexed arrays
      • Dealing with variable numbers of indices within programs
    • Broadcasting
      • General Broadcasting Rules
    • Structured arrays (aka “Record arrays”)
      • Structured Arrays (aka Record Arrays)
        • Introduction
        • Defining Structured Arrays
        • Accessing and modifying field names
        • Accessing field titles
    • Subclassing ndarray
      • Credits
      • Introduction
        • ndarrays and object creation
        • __new__ and __init__
      • Simple example - adding an extra attribute to ndarray
      • Slightly more realistic example - attribute added to existing array
      • __array_wrap__ for ufuncs
      • Extra gotchas - custom __del__ methods and ndarray.base
  • Performance
  • Miscellaneous
    • IEEE 754 Floating Point Special Values:
    • Examples:
    • Interfacing to C:
    • Interfacing to Fortran:
    • Interfacing to C++:
    • Methods vs. Functions
  • Using Numpy C-API
    • How to extend NumPy
      • Writing an extension module
      • Required subroutine
      • Defining functions
        • Functions without keyword arguments
        • Functions with keyword arguments
        • Reference counting
      • Dealing with array objects
        • Converting an arbitrary sequence object
        • Creating a brand-new ndarray
        • Getting at ndarray memory and accessing elements of the ndarray
      • Example
    • Using Python as glue
      • Calling other compiled libraries from Python
      • Hand-generated wrappers
      • f2py
        • Creating source for a basic extension module
        • Creating a compiled extension module
        • Improving the basic interface
        • Inserting directives in Fortran source
        • A filtering example
        • Calling f2py from Python
        • Automatic extension module generation
        • Conclusion
      • weave
        • Speed up code involving arrays (also see scipy.numexpr)
        • Inline C-code
        • Simplify creation of an extension module
        • Conclusion
      • Pyrex
        • Pyrex-add
        • Pyrex-filter
        • Conclusion
      • ctypes
        • Having a shared library
        • Loading the shared library
        • Converting arguments
        • Calling the function
        • Complete example
        • Conclusion
      • Additional tools you may find useful
        • SWIG
        • SIP
        • Boost Python
        • Instant
        • PyInline
        • PyFort
    • Beyond the Basics
      • Iterating over elements in the array
        • Basic Iteration
        • Iterating over all but one axis
        • Iterating over multiple arrays
        • Broadcasting over multiple arrays
      • Creating a new universal function
      • User-defined data-types
        • Adding the new data-type
        • Registering a casting function
        • Registering coercion rules
        • Registering a ufunc loop
      • Subtyping the ndarray in C
        • Creating sub-types
        • Specific features of ndarray sub-typing
          • The __array_finalize__ method
          • The __array_priority__ attribute
          • The __array_wrap__ method

• NumPy Reference
  • Array objects
    • The N-dimensional array (ndarray)
      • Constructing arrays
      • Indexing arrays
      • Internal memory layout of an ndarray
      • Array attributes
        • Memory layout
        • Data type
        • Other attributes
        • Array interface
        • ctypes foreign function interface
      • Array methods
        • Array conversion
        • Shape manipulation
        • Item selection and manipulation
        • Calculation
      • Arithmetic and comparison operations
      • Special methods
    • Scalars
      • Built-in scalar types
      • Attributes
      • Indexing
      • Methods
      • Defining new types
    • Data type objects (dtype)
      • Specifying and constructing data types
      • dtype
        • Attributes
        • Methods
    • Indexing
      • Basic Slicing
      • Advanced indexing
        • Integer
        • Boolean
      • Record Access
      • Flat Iterator indexing
    • Standard array subclasses
      • Special attributes and methods
      • Matrix objects
      • Memory-mapped file arrays
      • Character arrays (numpy.char)
      • Record arrays (numpy.rec)
      • Masked arrays (numpy.ma)
      • Standard container class
      • Array Iterators
        • Default iteration
        • Flat iteration
        • N-dimensional enumeration
        • Iterator for broadcasting
    • Masked arrays
      • The numpy.ma module
        • Rationale
        • What is a masked array?
        • The numpy.ma module
      • Using numpy.ma
        • Constructing masked arrays
        • Accessing the data
        • Accessing the mask
        • Accessing only the valid entries
        • Modifying the mask
          • Masking an entry
          • Unmasking an entry
        • Indexing and slicing
        • Operations on masked arrays
      • Examples
        • Data with a given value representing missing data
        • Filling in the missing data
        • Numerical operations
        • Ignoring extreme values
      • Constants of the numpy.ma module
      • The MaskedArray class
        • Attributes and properties of masked arrays
      • MaskedArray methods
        • Conversion
        • Shape manipulation
        • Item selection and manipulation
        • Pickling and copy
        • Calculations
        • Arithmetic and comparison operations
          • Comparison operators:
          • Truth value of an array (bool):
          • Arithmetic:
          • Arithmetic, in-place:
        • Representation
        • Special methods
        • Specific methods
          • Handling the mask
          • Handling the fill_value
          • Counting the missing elements
      • Masked array operations
        • Constants
        • Creation
          • From existing data
          • Ones and zeros
        • Inspecting the array
        • Manipulating a MaskedArray
          • Changing the shape
          • Modifying axes
          • Changing the number of dimensions
          • Joining arrays
        • Operations on masks
          • Creating a mask
          • Accessing a mask
          • Finding masked data
          • Modifying a mask
        • Conversion operations
          • > to a masked array
          • > to a ndarray
          • > to another object
          • Pickling and unpickling
          • Filling a masked array
        • Masked arrays arithmetics
          • Arithmetics
          • Minimum/maximum
          • Sorting
          • Algebra
          • Polynomial fit
          • Clipping and rounding
          • Miscellanea
    • The Array Interface
      • Python side
      • C-struct access
  • Universal functions (ufunc)
    • Broadcasting
    • Output type determination
    • Use of internal buffers
    • Error handling
    • Casting Rules
    • ufunc
      • Optional keyword arguments
      • Attributes
      • Methods
    • Available ufuncs
      • Math operations
      • Trigonometric functions
      • Bit-twiddling functions
      • Comparison functions
      • Floating functions
  • Routines
    • Array creation routines
      • Ones and zeros
      • From existing data
      • Creating record arrays (numpy.rec)
      • Creating character arrays (numpy.char)
      • Numerical ranges
      • Building matrices
      • The Matrix class
    • Array manipulation routines
      • Changing array shape
      • Transpose-like operations
      • Changing number of dimensions
      • Changing kind of array
      • Joining arrays
      • Splitting arrays
      • Tiling arrays
      • Adding and removing elements
      • Rearranging elements
    • Indexing routines
      • Generating index arrays
      • Indexing-like operations
      • Inserting data into arrays
      • Iterating over arrays
    • Data type routines
      • Creating data types
      • Data type information
      • Data type testing
      • Miscellaneous
    • Input and output
      • NPZ files
      • Text files
      • String formatting
      • Memory mapping files
      • Text formatting options
      • Base-n representations
      • Data sources
    • Fourier transforms (numpy.fft)
      • 1-dimensional
      • 2-dimensional
      • N-dimensional
      • Hermite symmetric
      • Real-valued
      • Helper routines
    • Linear algebra (numpy.linalg)
      • Matrix and vector products
      • Decompositions
      • Matrix eigenvalues
      • Norms and other numbers
      • Solving equations and inverting matrices
      • Exceptions
    • Random sampling (numpy.random)
      • Simple random data
      • Permutations
      • Distributions
      • Random generator
    • Sorting and searching
      • Sorting
      • Searching
    • Logic functions
      • Truth value testing
      • Array contents
      • Array type testing
      • Logical operations
      • Comparison
    • Binary operations
      • Elementwise bit operations
      • Bit packing
      • Output formatting
    • Statistics
      • Extremal values
      • Averages and variances
      • Correlating
      • Histograms
    • Mathematical functions
      • Trigonometric functions
      • Hyperbolic functions
      • Rounding
      • Sums, products, differences
      • Exponents and logarithms
      • Other special functions
      • Floating point routines
      • Arithmetic operations
      • Handling complex numbers
      • Miscellaneous
    • Functional programming
    • Polynomials
      • Basics
      • Fitting
      • Calculus
      • Arithmetic
      • Warnings
    • Financial functions
      • Simple financial functions
    • Set routines
      • Making proper sets
      • Boolean operations
    • Window functions
      • Various windows
    • Floating point error handling
      • Setting and getting error handling
      • Internal functions
    • Masked array operations
      • Constants
      • Creation
        • From existing data
        • Ones and zeros
      • Inspecting the array
      • Manipulating a MaskedArray
        • Changing the shape
        • Modifying axes
        • Changing the number of dimensions
        • Joining arrays
      • Operations on masks
        • Creating a mask
        • Accessing a mask
        • Finding masked data
        • Modifying a mask
      • Conversion operations
        • > to a masked array
        • > to a ndarray
        • > to another object
        • Pickling and unpickling
        • Filling a masked array
      • Masked arrays arithmetics
        • Arithmetics
        • Minimum/maximum
        • Sorting
        • Algebra
        • Polynomial fit
        • Clipping and rounding
        • Miscellanea
    • Numpy-specific help functions
      • Finding help
      • Reading help
    • Miscellaneous routines
      • Buffer objects
      • Performance tuning
    • Mathematical functions with automatic domain (numpy.emath)
    • Matrix library (numpy.matlib)
    • Optionally Scipy-accelerated routines (numpy.dual)
      • Linear algebra
      • FFT
      • Other
    • Numarray compatibility (numpy.numarray)
    • Old Numeric compatibility (numpy.oldnumeric)
    • C-Types Foreign Function Interface (numpy.ctypeslib)
  • Packaging (numpy.distutils)
    • Modules in numpy.distutils
      • misc_util
      • Other modules
    • Conversion of .src files
      • Fortran files
        • Named repeat rule
        • Short repeat rule
        • Pre-defined names
      • Other files
  • Numpy C-API
    • Python Types and C-Structures
      • New Python Types Defined
        • PyArray_Type
        • PyArrayDescr_Type
        • PyUFunc_Type
        • PyArrayIter_Type
        • PyArrayMultiIter_Type
        • PyArrayFlags_Type
        • ScalarArrayTypes
      • Other C-Structures
        • PyArray_Dims
        • PyArray_Chunk
        • PyArrayInterface
        • Internally used structures
    • Configuration defines
      • Guaranteed to be defined
    • Data Type API
      • Enumerated Types
      • Defines
        • Max and min values for integers
        • Number of bits in data types
        • Bit-width references to enumerated typenums
        • Integer that can hold a pointer
      • C-type names
        • Boolean
        • (Un)Signed Integer
        • (Complex) Floating point
        • Bit-width names
      • Printf Formatting
    • Array API
      • Array structure and data access
        • Data access
      • Creating arrays
        • From scratch
        • From other objects
      • Dealing with types
        • General check of Python Type
        • Data-type checking
        • Converting data types
        • New data types
        • Special functions for PyArray_OBJECT
      • Array flags
        • Basic Array Flags
        • Combinations of array flags
        • Flag-like constants
        • Flag checking
      • Array method alternative API
        • Conversion
        • Shape Manipulation
        • Item selection and manipulation
        • Calculation
      • Functions
        • Array Functions
        • Other functions
      • Array Iterators
      • Broadcasting (multi-iterators)
      • Array Scalars
      • Data-type descriptors
      • Conversion Utilities
        • For use with PyArg_ParseTuple
        • Other conversions
      • Miscellaneous
        • Importing the API
        • Internal Flexibility
        • Memory management
        • Threading support
          • Group 1
          • Group 2
        • Priority
        • Default buffers
        • Other constants
        • Miscellaneous Macros
        • Enumerated Types
    • UFunc API
      • Constants
      • Macros
      • Functions
      • Generic functions
      • Importing the API
    • Numpy core libraries
      • Numpy core math library
        • Floating point classification
        • Useful math constants
  • Numpy internals
    • Numpy C Code Explanations
      • Memory model
      • Data-type encapsulation
      • N-D Iterators
      • Broadcasting
      • Array Scalars
      • Advanced (“Fancy”) Indexing
        • Fancy-indexing check
        • Fancy-indexing implementation
          • Creating the mapping object
          • Binding the mapping object
          • Getting (or Setting)
      • Universal Functions
        • Setup
        • Function call
          • One Loop
          • Strided Loop
          • Buffered Loop
        • Final output manipulation
        • Methods
          • Setup
          • Reduce
          • Accumulate
          • Reduceat
    • Internal organization of numpy arrays
    • Multidimensional Array Indexing Order Issues
  • Acknowledgements

• Release Notes
  • NumPy 1.3.0 Release Notes
    • Highlights
      • Python 2.6 support
      • Generalized ufuncs
      • Experimental Windows 64 bits support
    • New features
      • Formatting issues
      • Nan handling in max/min
      • Nan handling in sign
      • New ufuncs
      • Masked arrays
      • gfortran support on windows
      • Arch option for windows binary
    • Deprecated features
      • Histogram
    • Documentation changes
    • New C API
      • Multiarray API
      • Ufunc API
      • New defines
      • Portable NAN, INFINITY, etc...
    • Internal changes
      • numpy.core math configuration revamp
      • umath refactor
      • Improvements to build warnings
      • Separate core math library
      • CPU arch detection

• About NumPy
  • NumPy community
• About this documentation
  • Conventions

• Reporting bugs

• Numpy License

• Glossary
  • Jargon