Numpy manual contents

• NumPy User Guide
  • Introduction
    • What is NumPy?
    • Building and installing NumPy
      • Binary installers
        • Windows
        • Linux
        • Mac OS X
      • Building from source
        • Prerequisites
        • FORTRAN ABI mismatch
          • Choosing the fortran compiler
          • How to check the ABI of blas/lapack/atlas
        • Disabling ATLAS and other accelerated libraries
        • Supplying additional compiler flags
        • Building with ATLAS support
          • Ubuntu 8.10 (Intrepid) and 9.04 (Jaunty)
          • Ubuntu 8.04 and lower
    • 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
    • I/O with Numpy
      • Importing data with genfromtxt
        • Defining the input
        • Splitting the lines into columns
          • The delimiter argument
          • The autostrip argument
          • The comments argument
        • Skipping lines and choosing columns
          • The skip_header and skip_footer arguments
          • The usecols argument
        • Choosing the data type
        • Setting the names
          • The names argument
          • The defaultfmt argument
          • Validating names
        • Tweaking the conversion
          • The converters argument
          • Using missing and filling values
          • missing_values
          • filling_values
          • usemask
        • Shortcut functions
    • 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
    • Byte-swapping
      • Introduction to byte ordering and ndarrays
      • Changing byte ordering
        • Data and dtype endianness don’t match, change dtype to match data
        • Data and type endianness don’t match, change data to match dtype
        • Data and dtype endianness match, swap data and dtype
    • Structured arrays (aka “Record arrays”)
      • Structured Arrays (and Record Arrays)
        • Introduction
        • Defining Structured Arrays
        • Accessing and modifying field names
        • Accessing field titles
        • Accessing multiple fields at once
        • Filling structured arrays
        • More information
    • Subclassing ndarray
      • Credits
      • Introduction
        • ndarrays and object creation
      • View casting
      • Creating new from template
      • Relationship of view casting and new-from-template
      • Implications for subclassing
        • A brief Python primer on __new__ and __init__
        • The role of __array_finalize__
      • 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:
    • How numpy handles numerical exceptions:
    • 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
        • numpy.ndarray
          • numpy.dtype
          • numpy.imag
          • numpy.real
          • numpy.all
          • numpy.any
          • numpy.argmax
          • numpy.argmin
          • numpy.argsort
          • numpy.choose
          • numpy.clip
          • numpy.compress
          • numpy.conj
          • numpy.copy
          • numpy.cumprod
          • numpy.cumsum
          • numpy.diagonal
          • numpy.dot
          • numpy.mean
          • numpy.nonzero
          • numpy.prod
          • numpy.ptp
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.squeeze
          • numpy.std
          • numpy.sum
          • numpy.swapaxes
          • numpy.take
          • numpy.trace
          • numpy.transpose
          • numpy.var
      • Indexing arrays
      • Internal memory layout of an ndarray
      • Array attributes
        • Memory layout
          • numpy.ndarray.flags
          • numpy.ndarray.shape
          • numpy.ndarray.strides
          • numpy.ndarray.ndim
          • numpy.ndarray.data
          • numpy.ndarray.size
          • numpy.ndarray.itemsize
          • numpy.ndarray.nbytes
          • numpy.ndarray.base
        • Data type
          • numpy.ndarray.dtype
        • Other attributes
          • numpy.ndarray.T
          • numpy.ndarray.real
          • numpy.ndarray.imag
          • numpy.ndarray.flat
          • numpy.ndarray.ctypes
        • Array interface
        • ctypes foreign function interface
          • numpy.ndarray.ctypes
      • Array methods
        • Array conversion
          • numpy.ndarray.item
          • numpy.ndarray.tolist
          • numpy.ndarray.itemset
          • numpy.ndarray.setasflat
          • numpy.ndarray.tostring
          • numpy.ndarray.tofile
          • numpy.ndarray.dump
          • numpy.ndarray.dumps
          • numpy.ndarray.astype
          • numpy.ndarray.byteswap
          • numpy.ndarray.copy
          • numpy.ndarray.view
          • numpy.ndarray.getfield
          • numpy.ndarray.setflags
          • numpy.ndarray.fill
        • Shape manipulation
          • numpy.ndarray.reshape
          • numpy.ndarray.resize
          • numpy.ndarray.transpose
          • numpy.ndarray.swapaxes
          • numpy.ndarray.flatten
          • numpy.ndarray.ravel
          • numpy.ndarray.squeeze
        • Item selection and manipulation
          • numpy.ndarray.take
          • numpy.ndarray.put
          • numpy.ndarray.repeat
          • numpy.ndarray.choose
          • numpy.ndarray.sort
          • numpy.ndarray.argsort
          • numpy.ndarray.searchsorted
          • numpy.ndarray.nonzero
          • numpy.ndarray.compress
          • numpy.ndarray.diagonal
        • Calculation
          • numpy.ndarray.argmax
          • numpy.ndarray.min
          • numpy.ndarray.argmin
          • numpy.ndarray.ptp
          • numpy.ndarray.clip
          • numpy.ndarray.conj
          • numpy.ndarray.round
          • numpy.ndarray.trace
          • numpy.ndarray.sum
          • numpy.ndarray.cumsum
          • numpy.ndarray.mean
          • numpy.ndarray.var
          • numpy.ndarray.std
          • numpy.ndarray.prod
          • numpy.ndarray.cumprod
          • numpy.ndarray.all
          • numpy.ndarray.any
      • Arithmetic and comparison operations
        • numpy.ndarray.__lt__
        • numpy.ndarray.__le__
        • numpy.ndarray.__gt__
        • numpy.ndarray.__ge__
        • numpy.ndarray.__eq__
        • numpy.ndarray.__ne__
        • numpy.ndarray.__nonzero__
        • numpy.ndarray.__neg__
        • numpy.ndarray.__pos__
        • numpy.ndarray.__abs__
        • numpy.ndarray.__invert__
        • numpy.ndarray.__add__
        • numpy.ndarray.__sub__
        • numpy.ndarray.__mul__
        • numpy.ndarray.__div__
        • numpy.ndarray.__truediv__
        • numpy.ndarray.__floordiv__
        • numpy.ndarray.__mod__
        • numpy.ndarray.__divmod__
        • numpy.ndarray.__pow__
        • numpy.ndarray.__lshift__
        • numpy.ndarray.__rshift__
        • numpy.ndarray.__and__
        • numpy.ndarray.__or__
        • numpy.ndarray.__xor__
        • numpy.ndarray.__iadd__
        • numpy.ndarray.__isub__
        • numpy.ndarray.__imul__
        • numpy.ndarray.__idiv__
        • numpy.ndarray.__itruediv__
        • numpy.ndarray.__ifloordiv__
        • numpy.ndarray.__imod__
        • numpy.ndarray.__ipow__
        • numpy.ndarray.__ilshift__
        • numpy.ndarray.__irshift__
        • numpy.ndarray.__iand__
        • numpy.ndarray.__ior__
        • numpy.ndarray.__ixor__
      • Special methods
        • numpy.ndarray.__copy__
        • numpy.ndarray.__deepcopy__
        • numpy.ndarray.__reduce__
        • numpy.ndarray.__setstate__
        • numpy.ndarray.__new__
        • numpy.ndarray.__array__
        • numpy.ndarray.__array_wrap__
        • numpy.ndarray.__len__
        • numpy.ndarray.__getitem__
        • numpy.ndarray.__setitem__
        • numpy.ndarray.__getslice__
        • numpy.ndarray.__setslice__
        • numpy.ndarray.__contains__
        • numpy.ndarray.__int__
        • numpy.ndarray.__long__
        • numpy.ndarray.__float__
        • numpy.ndarray.__oct__
        • numpy.ndarray.__hex__
        • numpy.ndarray.__str__
        • numpy.ndarray.__repr__
    • Scalars
      • Built-in scalar types
      • Attributes
        • numpy.generic.flags
        • numpy.generic.shape
        • numpy.generic.strides
        • numpy.generic.ndim
        • numpy.generic.data
        • numpy.generic.size
        • numpy.generic.itemsize
        • numpy.generic.base
        • numpy.generic.dtype
        • numpy.generic.real
        • numpy.generic.imag
        • numpy.generic.flat
        • numpy.generic.T
        • numpy.generic.__array_interface__
        • numpy.generic.__array_struct__
        • numpy.generic.__array_priority__
        • numpy.generic.__array_wrap__
      • Indexing
      • Methods
        • numpy.generic
          • numpy.all
          • numpy.any
          • numpy.argmax
          • numpy.argmin
          • numpy.argsort
          • numpy.choose
          • numpy.clip
          • numpy.compress
          • numpy.conj
          • numpy.copy
          • numpy.cumprod
          • numpy.cumsum
          • numpy.diagonal
          • numpy.mean
          • numpy.nonzero
          • numpy.prod
          • numpy.ptp
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.squeeze
          • numpy.std
          • numpy.sum
          • numpy.swapaxes
          • numpy.take
          • numpy.trace
          • numpy.transpose
          • numpy.var
        • numpy.generic.__array__
        • numpy.generic.__array_wrap__
        • numpy.generic.squeeze
        • numpy.generic.byteswap
        • numpy.generic.__reduce__
        • numpy.generic.__setstate__
        • numpy.generic.setflags
      • Defining new types
    • Data type objects (dtype)
      • Specifying and constructing data types
        • numpy.dtype
      • dtype
        • Attributes
          • numpy.dtype.type
          • numpy.dtype.kind
          • numpy.dtype.char
          • numpy.dtype.num
          • numpy.dtype.str
          • numpy.dtype.name
          • numpy.dtype.itemsize
          • numpy.dtype.byteorder
          • numpy.dtype.fields
          • numpy.dtype.names
          • numpy.dtype.subdtype
          • numpy.dtype.shape
          • numpy.dtype.hasobject
          • numpy.dtype.flags
          • numpy.dtype.isbuiltin
          • numpy.dtype.isnative
          • numpy.dtype.descr
          • numpy.dtype.alignment
        • Methods
          • numpy.dtype.newbyteorder
          • numpy.dtype.__reduce__
          • numpy.dtype.__setstate__
    • Indexing
      • Basic Slicing
      • Advanced indexing
        • Integer
        • Boolean
      • Record Access
      • Flat Iterator indexing
    • Standard array subclasses
      • Special attributes and methods
      • Matrix objects
        • numpy.matrix.T
        • numpy.matrix.H
        • numpy.matrix.I
        • numpy.matrix.A
        • numpy.matrix
          • numpy.all
          • numpy.any
          • numpy.argmax
          • numpy.argmin
          • numpy.argsort
          • numpy.choose
          • numpy.clip
          • numpy.compress
          • numpy.conj
          • numpy.copy
          • numpy.cumprod
          • numpy.cumsum
          • numpy.diagonal
          • numpy.dot
          • numpy.mean
          • numpy.nonzero
          • numpy.prod
          • numpy.ptp
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.squeeze
          • numpy.std
          • numpy.sum
          • numpy.swapaxes
          • numpy.take
          • numpy.trace
          • numpy.transpose
          • numpy.var
        • numpy.asmatrix
        • numpy.bmat
      • Memory-mapped file arrays
        • numpy.memmap
        • numpy.memmap.flush
      • Character arrays (numpy.char)
        • numpy.chararray
          • numpy.argsort
          • numpy.copy
          • numpy.nonzero
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.split
          • numpy.squeeze
          • numpy.swapaxes
          • numpy.take
          • numpy.transpose
        • numpy.core.defchararray.array
      • Record arrays (numpy.rec)
        • numpy.recarray
          • numpy.all
          • numpy.any
          • numpy.argmax
          • numpy.argmin
          • numpy.argsort
          • numpy.choose
          • numpy.clip
          • numpy.compress
          • numpy.conj
          • numpy.copy
          • numpy.cumprod
          • numpy.cumsum
          • numpy.diagonal
          • numpy.dot
          • numpy.mean
          • numpy.nonzero
          • numpy.prod
          • numpy.ptp
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.squeeze
          • numpy.std
          • numpy.sum
          • numpy.swapaxes
          • numpy.take
          • numpy.trace
          • numpy.transpose
          • numpy.var
        • numpy.record
          • numpy.all
          • numpy.any
          • numpy.argmax
          • numpy.argmin
          • numpy.argsort
          • numpy.choose
          • numpy.clip
          • numpy.compress
          • numpy.conj
          • numpy.copy
          • numpy.cumprod
          • numpy.cumsum
          • numpy.diagonal
          • numpy.mean
          • numpy.nonzero
          • numpy.prod
          • numpy.ptp
          • numpy.put
          • numpy.ravel
          • numpy.repeat
          • numpy.reshape
          • numpy.resize
          • numpy.searchsorted
          • numpy.sort
          • numpy.squeeze
          • numpy.std
          • numpy.sum
          • numpy.swapaxes
          • numpy.take
          • numpy.trace
          • numpy.transpose
          • numpy.var
      • Masked arrays (numpy.ma)
      • Standard container class
        • numpy.lib.user_array.container
      • Array Iterators
        • Default iteration
        • Flat iteration
          • numpy.ndarray.flat
        • N-dimensional enumeration
          • numpy.ndenumerate
        • Iterator for broadcasting
          • numpy.broadcast
    • Masked arrays
      • The numpy.ma module
        • Rationale
        • What is a masked array?
        • The numpy.ma module
      • Using numpy.ma
        • Constructing masked arrays
          • numpy.ma.array
          • numpy.ma.masked_array
          • numpy.ma.asarray
          • numpy.ma.asanyarray
          • numpy.ma.fix_invalid
          • numpy.ma.masked_equal
          • numpy.ma.masked_greater
          • numpy.ma.masked_greater_equal
          • numpy.ma.masked_inside
          • numpy.ma.masked_invalid
          • numpy.ma.masked_less
          • numpy.ma.masked_less_equal
          • numpy.ma.masked_not_equal
          • numpy.ma.masked_object
          • numpy.ma.masked_outside
          • numpy.ma.masked_values
          • numpy.ma.masked_where
        • 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
          • numpy.ma.MaskedArray.base
          • numpy.ma.MaskedArray.ctypes
          • numpy.ma.MaskedArray.dtype
          • numpy.ma.MaskedArray.flags
          • numpy.ma.MaskedArray.itemsize
          • numpy.ma.MaskedArray.nbytes
          • numpy.ma.MaskedArray.ndim
          • numpy.ma.MaskedArray.shape
          • numpy.ma.MaskedArray.size
          • numpy.ma.MaskedArray.strides
          • numpy.ma.MaskedArray.imag
          • numpy.ma.MaskedArray.real
          • numpy.ma.MaskedArray.flat
          • numpy.ma.MaskedArray.__array_priority__
      • MaskedArray methods
        • Conversion
          • numpy.ma.MaskedArray.__float__
          • numpy.ma.MaskedArray.__hex__
          • numpy.ma.MaskedArray.__int__
          • numpy.ma.MaskedArray.__long__
          • numpy.ma.MaskedArray.__oct__
          • numpy.ma.MaskedArray.view
          • numpy.ma.MaskedArray.astype
          • numpy.ma.MaskedArray.byteswap
          • numpy.ma.MaskedArray.compressed
          • numpy.ma.MaskedArray.filled
          • numpy.ma.MaskedArray.tofile
          • numpy.ma.MaskedArray.toflex
          • numpy.ma.MaskedArray.tolist
          • numpy.ma.MaskedArray.torecords
          • numpy.ma.MaskedArray.tostring
        • Shape manipulation
          • numpy.ma.MaskedArray.flatten
          • numpy.ma.MaskedArray.ravel
          • numpy.ma.MaskedArray.reshape
          • numpy.ma.MaskedArray.resize
          • numpy.ma.MaskedArray.squeeze
          • numpy.ma.MaskedArray.swapaxes
          • numpy.ma.MaskedArray.transpose
          • numpy.ma.MaskedArray.T
        • Item selection and manipulation
          • numpy.ma.MaskedArray.argmax
          • numpy.ma.MaskedArray.argmin
          • numpy.ma.MaskedArray.argsort
          • numpy.ma.MaskedArray.choose
          • numpy.ma.MaskedArray.compress
          • numpy.ma.MaskedArray.diagonal
          • numpy.ma.MaskedArray.fill
          • numpy.ma.MaskedArray.item
          • numpy.ma.MaskedArray.nonzero
          • numpy.ma.MaskedArray.put
          • numpy.ma.MaskedArray.repeat
          • numpy.ma.MaskedArray.searchsorted
          • numpy.ma.MaskedArray.sort
          • numpy.ma.MaskedArray.take
        • Pickling and copy
          • numpy.ma.MaskedArray.copy
          • numpy.ma.MaskedArray.dump
          • numpy.ma.MaskedArray.dumps
        • Calculations
          • numpy.ma.MaskedArray.all
          • numpy.ma.MaskedArray.anom
          • numpy.ma.MaskedArray.any
          • numpy.ma.MaskedArray.clip
          • numpy.ma.MaskedArray.conj
          • numpy.ma.MaskedArray.conjugate
          • numpy.ma.MaskedArray.cumprod
          • numpy.ma.MaskedArray.cumsum
          • numpy.ma.MaskedArray.max
          • numpy.ma.MaskedArray.mean
          • numpy.ma.MaskedArray.min
          • numpy.ma.MaskedArray.prod
          • numpy.ma.MaskedArray.product
          • numpy.ma.MaskedArray.ptp
          • numpy.ma.MaskedArray.round
          • numpy.ma.MaskedArray.std
          • numpy.ma.MaskedArray.sum
          • numpy.ma.MaskedArray.trace
          • numpy.ma.MaskedArray.var
        • Arithmetic and comparison operations
          • Comparison operators:
            • numpy.ma.MaskedArray.__lt__
            • numpy.ma.MaskedArray.__le__
            • numpy.ma.MaskedArray.__gt__
            • numpy.ma.MaskedArray.__ge__
            • numpy.ma.MaskedArray.__eq__
            • numpy.ma.MaskedArray.__ne__
          • Truth value of an array (bool):
            • numpy.ma.MaskedArray.__nonzero__
          • Arithmetic:
            • numpy.ma.MaskedArray.__abs__
            • numpy.ma.MaskedArray.__add__
            • numpy.ma.MaskedArray.__radd__
            • numpy.ma.MaskedArray.__sub__
            • numpy.ma.MaskedArray.__rsub__
            • numpy.ma.MaskedArray.__mul__
            • numpy.ma.MaskedArray.__rmul__
            • numpy.ma.MaskedArray.__div__
            • numpy.ma.MaskedArray.__rdiv__
            • numpy.ma.MaskedArray.__truediv__
            • numpy.ma.MaskedArray.__rtruediv__
            • numpy.ma.MaskedArray.__floordiv__
            • numpy.ma.MaskedArray.__rfloordiv__
            • numpy.ma.MaskedArray.__mod__
            • numpy.ma.MaskedArray.__rmod__
            • numpy.ma.MaskedArray.__divmod__
            • numpy.ma.MaskedArray.__rdivmod__
            • numpy.ma.MaskedArray.__pow__
            • numpy.ma.MaskedArray.__rpow__
            • numpy.ma.MaskedArray.__lshift__
            • numpy.ma.MaskedArray.__rlshift__
            • numpy.ma.MaskedArray.__rshift__
            • numpy.ma.MaskedArray.__rrshift__
            • numpy.ma.MaskedArray.__and__
            • numpy.ma.MaskedArray.__rand__
            • numpy.ma.MaskedArray.__or__
            • numpy.ma.MaskedArray.__ror__
            • numpy.ma.MaskedArray.__xor__
            • numpy.ma.MaskedArray.__rxor__
          • Arithmetic, in-place:
            • numpy.ma.MaskedArray.__iadd__
            • numpy.ma.MaskedArray.__isub__
            • numpy.ma.MaskedArray.__imul__
            • numpy.ma.MaskedArray.__idiv__
            • numpy.ma.MaskedArray.__itruediv__
            • numpy.ma.MaskedArray.__ifloordiv__
            • numpy.ma.MaskedArray.__imod__
            • numpy.ma.MaskedArray.__ipow__
            • numpy.ma.MaskedArray.__ilshift__
            • numpy.ma.MaskedArray.__irshift__
            • numpy.ma.MaskedArray.__iand__
            • numpy.ma.MaskedArray.__ior__
            • numpy.ma.MaskedArray.__ixor__
        • Representation
          • numpy.ma.MaskedArray.__repr__
          • numpy.ma.MaskedArray.__str__
          • numpy.ma.MaskedArray.ids
          • numpy.ma.MaskedArray.iscontiguous
        • Special methods
          • numpy.ma.MaskedArray.__copy__
          • numpy.ma.MaskedArray.__deepcopy__
          • numpy.ma.MaskedArray.__getstate__
          • numpy.ma.MaskedArray.__reduce__
          • numpy.ma.MaskedArray.__setstate__
          • numpy.ma.MaskedArray.__new__
          • numpy.ma.MaskedArray.__array__
          • numpy.ma.MaskedArray.__array_wrap__
          • numpy.ma.MaskedArray.__len__
          • numpy.ma.MaskedArray.__getitem__
          • numpy.ma.MaskedArray.__setitem__
          • numpy.ma.MaskedArray.__delitem__
          • numpy.ma.MaskedArray.__getslice__
          • numpy.ma.MaskedArray.__setslice__
          • numpy.ma.MaskedArray.__contains__
        • Specific methods
          • Handling the mask
            • numpy.ma.MaskedArray.__setmask__
            • numpy.ma.MaskedArray.harden_mask
            • numpy.ma.MaskedArray.soften_mask
            • numpy.ma.MaskedArray.unshare_mask
            • numpy.ma.MaskedArray.shrink_mask
          • Handling the fill_value
            • numpy.ma.MaskedArray.get_fill_value
            • numpy.ma.MaskedArray.set_fill_value
          • Counting the missing elements
            • numpy.ma.MaskedArray.count
      • Masked array operations
        • Constants
          • numpy.ma.MaskType
        • Creation
          • From existing data
            • numpy.ma.masked_array
            • numpy.ma.array
            • numpy.ma.copy
            • numpy.ma.frombuffer
            • numpy.ma.fromfunction
            • numpy.ma.MaskedArray.copy
          • Ones and zeros
            • numpy.ma.empty
            • numpy.ma.empty_like
            • numpy.ma.masked_all
            • numpy.ma.masked_all_like
            • numpy.ma.ones
            • numpy.ma.zeros
        • Inspecting the array
          • numpy.ma.all
          • numpy.ma.any
          • numpy.ma.count
          • numpy.ma.count_masked
          • numpy.ma.getmask
          • numpy.ma.getmaskarray
          • numpy.ma.getdata
          • numpy.ma.nonzero
          • numpy.ma.shape
          • numpy.ma.size
          • numpy.ma.MaskedArray.data
          • numpy.ma.MaskedArray.mask
          • numpy.ma.MaskedArray.recordmask
          • numpy.ma.MaskedArray.all
          • numpy.ma.MaskedArray.any
          • numpy.ma.MaskedArray.count
          • numpy.ma.MaskedArray.nonzero
          • numpy.ma.shape
          • numpy.ma.size
        • Manipulating a MaskedArray
          • Changing the shape
            • numpy.ma.ravel
            • numpy.ma.reshape
            • numpy.ma.resize
            • numpy.ma.MaskedArray.flatten
            • numpy.ma.MaskedArray.ravel
            • numpy.ma.MaskedArray.reshape
            • numpy.ma.MaskedArray.resize
          • Modifying axes
            • numpy.ma.swapaxes
            • numpy.ma.transpose
            • numpy.ma.MaskedArray.swapaxes
            • numpy.ma.MaskedArray.transpose
          • Changing the number of dimensions
            • numpy.ma.atleast_1d
            • numpy.ma.atleast_2d
            • numpy.ma.atleast_3d
            • numpy.ma.expand_dims
            • numpy.ma.squeeze
            • numpy.ma.MaskedArray.squeeze
            • numpy.ma.column_stack
            • numpy.ma.concatenate
            • numpy.ma.dstack
            • numpy.ma.hstack
            • numpy.ma.hsplit
            • numpy.ma.mr
            • numpy.ma.row_stack
            • numpy.ma.vstack
          • Joining arrays
            • numpy.ma.column_stack
            • numpy.ma.concatenate
            • numpy.ma.dstack
            • numpy.ma.hstack
            • numpy.ma.vstack
        • Operations on masks
          • Creating a mask
            • numpy.ma.make_mask
            • numpy.ma.make_mask_none
            • numpy.ma.mask_or
            • numpy.ma.make_mask_descr
          • Accessing a mask
            • numpy.ma.getmask
            • numpy.ma.getmaskarray
            • numpy.ma.masked_array.mask
          • Finding masked data
            • numpy.ma.flatnotmasked_contiguous
            • numpy.ma.flatnotmasked_edges
            • numpy.ma.notmasked_contiguous
            • numpy.ma.notmasked_edges
          • Modifying a mask
            • numpy.ma.mask_cols
            • numpy.ma.mask_or
            • numpy.ma.mask_rowcols
            • numpy.ma.mask_rows
            • numpy.ma.harden_mask
            • numpy.ma.soften_mask
            • numpy.ma.MaskedArray.harden_mask
            • numpy.ma.MaskedArray.soften_mask
            • numpy.ma.MaskedArray.shrink_mask
            • numpy.ma.MaskedArray.unshare_mask
        • Conversion operations
          • > to a masked array
            • numpy.ma.asarray
            • numpy.ma.asanyarray
            • numpy.ma.fix_invalid
            • numpy.ma.masked_equal
            • numpy.ma.masked_greater
            • numpy.ma.masked_greater_equal
            • numpy.ma.masked_inside
            • numpy.ma.masked_invalid
            • numpy.ma.masked_less
            • numpy.ma.masked_less_equal
            • numpy.ma.masked_not_equal
            • numpy.ma.masked_object
            • numpy.ma.masked_outside
            • numpy.ma.masked_values
            • numpy.ma.masked_where
          • > to a ndarray
            • numpy.ma.compress_cols
            • numpy.ma.compress_rowcols
            • numpy.ma.compress_rows
            • numpy.ma.compressed
            • numpy.ma.filled
            • numpy.ma.MaskedArray.compressed
            • numpy.ma.MaskedArray.filled
          • > to another object
            • numpy.ma.MaskedArray.tofile
            • numpy.ma.MaskedArray.tolist
            • numpy.ma.MaskedArray.torecords
            • numpy.ma.MaskedArray.tostring
          • Pickling and unpickling
            • numpy.ma.dump
            • numpy.ma.dumps
            • numpy.ma.load
            • numpy.ma.loads
          • Filling a masked array
            • numpy.ma.common_fill_value
            • numpy.ma.default_fill_value
            • numpy.ma.maximum_fill_value
            • numpy.ma.maximum_fill_value
            • numpy.ma.set_fill_value
            • numpy.ma.MaskedArray.get_fill_value
            • numpy.ma.MaskedArray.set_fill_value
            • numpy.ma.MaskedArray.fill_value
        • Masked arrays arithmetics
          • Arithmetics
            • numpy.ma.anom
            • numpy.ma.anomalies
            • numpy.ma.average
            • numpy.ma.conjugate
            • numpy.ma.corrcoef
            • numpy.ma.cov
            • numpy.ma.cumsum
            • numpy.ma.cumprod
            • numpy.ma.mean
            • numpy.ma.median
            • numpy.ma.power
            • numpy.ma.prod
            • numpy.ma.std
            • numpy.ma.sum
            • numpy.ma.var
            • numpy.ma.MaskedArray.anom
            • numpy.ma.MaskedArray.cumprod
            • numpy.ma.MaskedArray.cumsum
            • numpy.ma.MaskedArray.mean
            • numpy.ma.MaskedArray.prod
            • numpy.ma.MaskedArray.std
            • numpy.ma.MaskedArray.sum
            • numpy.ma.MaskedArray.var
          • Minimum/maximum
            • numpy.ma.argmax
            • numpy.ma.argmin
            • numpy.ma.max
            • numpy.ma.min
            • numpy.ma.ptp
            • numpy.ma.MaskedArray.argmax
            • numpy.ma.MaskedArray.argmin
            • numpy.ma.MaskedArray.max
            • numpy.ma.MaskedArray.min
            • numpy.ma.MaskedArray.ptp
          • Sorting
            • numpy.ma.argsort
            • numpy.ma.sort
            • numpy.ma.MaskedArray.argsort
            • numpy.ma.MaskedArray.sort
          • Algebra
            • numpy.ma.diag
            • numpy.ma.dot
            • numpy.ma.identity
            • numpy.ma.inner
            • numpy.ma.innerproduct
            • numpy.ma.outer
            • numpy.ma.outerproduct
            • numpy.ma.trace
            • numpy.ma.transpose
            • numpy.ma.MaskedArray.trace
            • numpy.ma.MaskedArray.transpose
          • Polynomial fit
            • numpy.ma.vander
            • numpy.ma.polyfit
          • Clipping and rounding
            • numpy.ma.around
            • numpy.ma.clip
            • numpy.ma.round
            • numpy.ma.MaskedArray.clip
            • numpy.ma.MaskedArray.round
          • Miscellanea
            • numpy.ma.allequal
            • numpy.ma.allclose
            • numpy.ma.apply_along_axis
            • numpy.ma.arange
            • numpy.ma.choose
            • numpy.ma.ediff1d
            • numpy.ma.indices
            • numpy.ma.where
    • The Array Interface
      • Python side
      • C-struct access
      • Type description examples
      • Differences with Array interface (Version 2)
  • Universal functions (ufunc)
    • Broadcasting
    • Output type determination
    • Use of internal buffers
      • numpy.setbufsize
    • Error handling
      • numpy.seterr
      • numpy.seterrcall
    • Casting Rules
    • ufunc
      • Optional keyword arguments
      • Attributes
        • numpy.ufunc.nin
        • numpy.ufunc.nout
        • numpy.ufunc.nargs
        • numpy.ufunc.ntypes
        • numpy.ufunc.types
        • numpy.ufunc.identity
      • Methods
        • numpy.ufunc.reduce
        • numpy.ufunc.accumulate
        • numpy.ufunc.reduceat
        • numpy.ufunc.outer
    • Available ufuncs
      • Math operations
      • Trigonometric functions
      • Bit-twiddling functions
      • Comparison functions
      • Floating functions
  • Routines
    • Array creation routines
      • Ones and zeros
        • numpy.empty
        • numpy.empty_like
        • numpy.eye
        • numpy.identity
        • numpy.ones
        • numpy.ones_like
        • numpy.zeros
        • numpy.zeros_like
      • From existing data
        • numpy.array
        • numpy.asarray
        • numpy.asanyarray
        • numpy.ascontiguousarray
        • numpy.asmatrix
        • numpy.copy
        • numpy.frombuffer
        • numpy.fromfile
        • numpy.fromfunction
        • numpy.fromiter
        • numpy.fromstring
        • numpy.loadtxt
      • Creating record arrays (numpy.rec)
        • numpy.core.records.array
        • numpy.core.records.fromarrays
        • numpy.core.records.fromrecords
        • numpy.core.records.fromstring
        • numpy.core.records.fromfile
      • Creating character arrays (numpy.char)
        • numpy.core.defchararray.array
        • numpy.core.defchararray.asarray
      • Numerical ranges
        • numpy.arange
        • numpy.linspace
        • numpy.logspace
        • numpy.meshgrid
        • numpy.mgrid
        • numpy.ogrid
      • Building matrices
        • numpy.diag
        • numpy.diagflat
        • numpy.tri
        • numpy.tril
        • numpy.triu
        • numpy.vander
      • The Matrix class
        • numpy.mat
        • numpy.bmat
    • Array manipulation routines
      • Changing array shape
        • numpy.reshape
        • numpy.ravel
        • numpy.ndarray.flat
        • numpy.ndarray.flatten
      • Transpose-like operations
        • numpy.rollaxis
        • numpy.swapaxes
        • numpy.ndarray.T
        • numpy.transpose
      • Changing number of dimensions
        • numpy.atleast_1d
        • numpy.atleast_2d
        • numpy.atleast_3d
        • numpy.broadcast
        • numpy.broadcast_arrays
        • numpy.expand_dims
        • numpy.squeeze
      • Changing kind of array
        • numpy.asarray
        • numpy.asanyarray
        • numpy.asmatrix
        • numpy.asfarray
        • numpy.asfortranarray
        • numpy.asscalar
        • numpy.require
      • Joining arrays
        • numpy.column_stack
        • numpy.concatenate
        • numpy.dstack
        • numpy.hstack
        • numpy.vstack
      • Splitting arrays
        • numpy.array_split
        • numpy.dsplit
        • numpy.hsplit
        • numpy.split
        • numpy.vsplit
      • Tiling arrays
        • numpy.tile
        • numpy.repeat
      • Adding and removing elements
        • numpy.delete
        • numpy.insert
        • numpy.append
        • numpy.resize
        • numpy.trim_zeros
        • numpy.unique
      • Rearranging elements
        • numpy.fliplr
        • numpy.flipud
        • numpy.reshape
        • numpy.roll
        • numpy.rot90
    • Indexing routines
      • Generating index arrays
        • numpy.c
        • numpy.r
        • numpy.s
        • numpy.nonzero
        • numpy.where
        • numpy.indices
        • numpy.ix
        • numpy.ogrid
        • numpy.ravel_multi_index
        • numpy.unravel_index
        • numpy.diag_indices
        • numpy.diag_indices_from
        • numpy.mask_indices
        • numpy.tril_indices
        • numpy.tril_indices_from
        • numpy.triu_indices
        • numpy.triu_indices_from
      • Indexing-like operations
        • numpy.take
        • numpy.choose
        • numpy.compress
        • numpy.diag
        • numpy.diagonal
        • numpy.select
      • Inserting data into arrays
        • numpy.place
        • numpy.put
        • numpy.putmask
        • numpy.fill_diagonal
      • Iterating over arrays
        • numpy.nditer
          • numpy.copy
        • numpy.ndenumerate
        • numpy.ndindex
        • numpy.flatiter
          • numpy.copy
    • Data type routines
      • numpy.can_cast
      • numpy.promote_types
      • numpy.min_scalar_type
      • numpy.result_type
      • numpy.common_type
      • numpy.obj2sctype
      • Creating data types
        • numpy.dtype
        • numpy.format_parser
      • Data type information
        • numpy.finfo
        • numpy.iinfo
        • numpy.MachAr
      • Data type testing
        • numpy.issctype
        • numpy.issubdtype
        • numpy.issubsctype
        • numpy.issubclass
        • numpy.find_common_type
      • Miscellaneous
        • numpy.typename
        • numpy.sctype2char
        • numpy.mintypecode
    • Input and output
      • NPZ files
        • numpy.load
        • numpy.save
        • numpy.savez
      • Text files
        • numpy.loadtxt
        • numpy.savetxt
        • numpy.genfromtxt
        • numpy.fromregex
        • numpy.fromstring
        • numpy.ndarray.tofile
        • numpy.ndarray.tolist
      • String formatting
        • numpy.array_repr
        • numpy.array_str
      • Memory mapping files
        • numpy.memmap
      • Text formatting options
        • numpy.set_printoptions
        • numpy.get_printoptions
        • numpy.set_string_function
      • Base-n representations
        • numpy.binary_repr
        • numpy.base_repr
      • Data sources
        • numpy.DataSource
    • Discrete Fourier Transform (numpy.fft)
      • Standard FFTs
        • numpy.fft.fft
        • numpy.fft.ifft
        • numpy.fft.fft2
        • numpy.fft.ifft2
        • numpy.fft.fftn
        • numpy.fft.ifftn
      • Real FFTs
        • numpy.fft.rfft
        • numpy.fft.irfft
        • numpy.fft.rfft2
        • numpy.fft.irfft2
        • numpy.fft.rfftn
        • numpy.fft.irfftn
      • Hermitian FFTs
        • numpy.fft.hfft
        • numpy.fft.ihfft
      • Helper routines
        • numpy.fft.fftfreq
        • numpy.fft.fftshift
        • numpy.fft.ifftshift
      • Background information
        • Real and Hermitian transforms
        • References
        • Examples
    • Linear algebra (numpy.linalg)
      • Matrix and vector products
        • numpy.dot
        • numpy.vdot
        • numpy.inner
        • numpy.outer
        • numpy.tensordot
        • numpy.einsum
        • numpy.linalg.matrix_power
        • numpy.kron
      • Decompositions
        • numpy.linalg.cholesky
        • numpy.linalg.qr
        • numpy.linalg.svd
      • Matrix eigenvalues
        • numpy.linalg.eig
        • numpy.linalg.eigh
        • numpy.linalg.eigvals
        • numpy.linalg.eigvalsh
      • Norms and other numbers
        • numpy.linalg.norm
        • numpy.linalg.cond
        • numpy.linalg.det
        • numpy.linalg.slogdet
        • numpy.trace
      • Solving equations and inverting matrices
        • numpy.linalg.solve
        • numpy.linalg.tensorsolve
        • numpy.linalg.lstsq
        • numpy.linalg.inv
        • numpy.linalg.pinv
        • numpy.linalg.tensorinv
      • Exceptions
        • numpy.linalg.LinAlgError
    • Random sampling (numpy.random)
      • Simple random data
        • numpy.random.rand
        • numpy.random.randn
        • numpy.random.randint
        • numpy.random.random_integers
        • numpy.random.random_sample
        • numpy.random.bytes
      • Permutations
        • numpy.random.shuffle
        • numpy.random.permutation
      • Distributions
        • numpy.random.beta
        • numpy.random.binomial
        • numpy.random.chisquare
        • numpy.random.mtrand.dirichlet
        • numpy.random.exponential
        • numpy.random.f
        • numpy.random.gamma
        • numpy.random.geometric
        • numpy.random.gumbel
        • numpy.random.hypergeometric
        • numpy.random.laplace
        • numpy.random.logistic
        • numpy.random.lognormal
        • numpy.random.logseries
        • numpy.random.multinomial
        • numpy.random.multivariate_normal
        • numpy.random.negative_binomial
        • numpy.random.noncentral_chisquare
        • numpy.random.noncentral_f
        • numpy.random.normal
        • numpy.random.pareto
        • numpy.random.poisson
        • numpy.random.power
        • numpy.random.rayleigh
        • numpy.random.standard_cauchy
        • numpy.random.standard_exponential
        • numpy.random.standard_gamma
        • numpy.random.standard_normal
        • numpy.random.standard_t
        • numpy.random.triangular
        • numpy.random.uniform
        • numpy.random.vonmises
        • numpy.random.wald
        • numpy.random.weibull
        • numpy.random.zipf
      • Random generator
        • numpy.random.mtrand.RandomState
          • numpy.random.mtrand.dirichlet
        • numpy.random.seed
        • numpy.random.get_state
        • numpy.random.set_state
    • Sorting, searching, and counting
      • Sorting
        • numpy.sort
        • numpy.lexsort
        • numpy.argsort
        • numpy.ndarray.sort
        • numpy.msort
        • numpy.sort_complex
      • Searching
        • numpy.argmax
        • numpy.nanargmax
        • numpy.argmin
        • numpy.nanargmin
        • numpy.argwhere
        • numpy.nonzero
        • numpy.flatnonzero
        • numpy.where
        • numpy.searchsorted
        • numpy.extract
      • Counting
        • numpy.count_nonzero
    • Logic functions
      • Truth value testing
        • numpy.all
        • numpy.any
      • Array contents
        • numpy.isfinite
        • numpy.isinf
        • numpy.isnan
        • numpy.isneginf
        • numpy.isposinf
      • Array type testing
        • numpy.iscomplex
        • numpy.iscomplexobj
        • numpy.isfortran
        • numpy.isreal
        • numpy.isrealobj
        • numpy.isscalar
      • Logical operations
        • numpy.logical_and
        • numpy.logical_or
        • numpy.logical_not
        • numpy.logical_xor
      • Comparison
        • numpy.allclose
        • numpy.array_equal
        • numpy.array_equiv
        • numpy.greater
        • numpy.greater_equal
        • numpy.less
        • numpy.less_equal
        • numpy.equal
        • numpy.not_equal
    • Binary operations
      • Elementwise bit operations
        • numpy.bitwise_and
        • numpy.bitwise_or
        • numpy.bitwise_xor
        • numpy.invert
        • numpy.left_shift
        • numpy.right_shift
      • Bit packing
        • numpy.packbits
        • numpy.unpackbits
      • Output formatting
        • numpy.binary_repr
    • Statistics
      • Extremal values
        • numpy.amin
        • numpy.amax
        • numpy.nanmax
        • numpy.nanmin
        • numpy.ptp
      • Averages and variances
        • numpy.average
        • numpy.mean
        • numpy.median
        • numpy.std
        • numpy.var
      • Correlating
        • numpy.corrcoef
        • numpy.correlate
        • numpy.cov
      • Histograms
        • numpy.histogram
        • numpy.histogram2d
        • numpy.histogramdd
        • numpy.bincount
        • numpy.digitize
    • Mathematical functions
      • Trigonometric functions
        • numpy.sin
        • numpy.cos
        • numpy.tan
        • numpy.arcsin
        • numpy.arccos
        • numpy.arctan
        • numpy.hypot
        • numpy.arctan2
        • numpy.degrees
        • numpy.radians
        • numpy.unwrap
        • numpy.deg2rad
        • numpy.rad2deg
      • Hyperbolic functions
        • numpy.sinh
        • numpy.cosh
        • numpy.tanh
        • numpy.arcsinh
        • numpy.arccosh
        • numpy.arctanh
      • Rounding
        • numpy.around
        • numpy.round
        • numpy.rint
        • numpy.fix
        • numpy.floor
        • numpy.ceil
        • numpy.trunc
      • Sums, products, differences
        • numpy.prod
        • numpy.sum
        • numpy.nansum
        • numpy.cumprod
        • numpy.cumsum
        • numpy.diff
        • numpy.ediff1d
        • numpy.gradient
        • numpy.cross
        • numpy.trapz
      • Exponents and logarithms
        • numpy.exp
        • numpy.expm1
        • numpy.exp2
        • numpy.log
        • numpy.log10
        • numpy.log2
        • numpy.log1p
        • numpy.logaddexp
        • numpy.logaddexp2
      • Other special functions
        • numpy.i0
        • numpy.sinc
      • Floating point routines
        • numpy.signbit
        • numpy.copysign
        • numpy.frexp
        • numpy.ldexp
      • Arithmetic operations
        • numpy.add
        • numpy.reciprocal
        • numpy.negative
        • numpy.multiply
        • numpy.divide
        • numpy.power
        • numpy.subtract
        • numpy.true_divide
        • numpy.floor_divide
        • numpy.fmod
        • numpy.mod
        • numpy.modf
        • numpy.remainder
      • Handling complex numbers
        • numpy.angle
        • numpy.real
        • numpy.imag
        • numpy.conj
      • Miscellaneous
        • numpy.convolve
        • numpy.clip
        • numpy.sqrt
        • numpy.square
        • numpy.absolute
        • numpy.fabs
        • numpy.sign
        • numpy.maximum
        • numpy.minimum
        • numpy.nan_to_num
        • numpy.real_if_close
        • numpy.interp
    • Functional programming
      • numpy.apply_along_axis
      • numpy.apply_over_axes
      • numpy.vectorize
      • numpy.frompyfunc
      • numpy.piecewise
    • Polynomials
      • Basics
        • numpy.poly1d
        • numpy.polyval
        • numpy.poly
        • numpy.roots
      • Fitting
        • numpy.polyfit
      • Calculus
        • numpy.polyder
        • numpy.polyint
      • Arithmetic
        • numpy.polyadd
        • numpy.polydiv
        • numpy.polymul
        • numpy.polysub
      • Warnings
        • numpy.RankWarning
    • Financial functions
      • Simple financial functions
        • numpy.fv
        • numpy.pv
        • numpy.npv
        • numpy.pmt
        • numpy.ppmt
        • numpy.ipmt
        • numpy.irr
        • numpy.mirr
        • numpy.nper
        • numpy.rate
    • Set routines
      • Making proper sets
        • numpy.unique
      • Boolean operations
        • numpy.in1d
        • numpy.intersect1d
        • numpy.setdiff1d
        • numpy.setxor1d
        • numpy.union1d
    • Window functions
      • Various windows
        • numpy.bartlett
        • numpy.blackman
        • numpy.hamming
        • numpy.hanning
        • numpy.kaiser
    • Floating point error handling
      • Setting and getting error handling
        • numpy.seterr
        • numpy.geterr
        • numpy.seterrcall
        • numpy.geterrcall
        • numpy.errstate
      • Internal functions
        • numpy.seterrobj
        • numpy.geterrobj
    • Masked array operations
      • Constants
        • numpy.ma.MaskType
      • Creation
        • From existing data
          • numpy.ma.masked_array
          • numpy.ma.array
          • numpy.ma.copy
          • numpy.ma.frombuffer
          • numpy.ma.fromfunction
          • numpy.ma.MaskedArray.copy
        • Ones and zeros
          • numpy.ma.empty
          • numpy.ma.empty_like
          • numpy.ma.masked_all
          • numpy.ma.masked_all_like
          • numpy.ma.ones
          • numpy.ma.zeros
      • Inspecting the array
        • numpy.ma.all
        • numpy.ma.any
        • numpy.ma.count
        • numpy.ma.count_masked
        • numpy.ma.getmask
        • numpy.ma.getmaskarray
        • numpy.ma.getdata
        • numpy.ma.nonzero
        • numpy.ma.shape
        • numpy.ma.size
        • numpy.ma.MaskedArray.data
        • numpy.ma.MaskedArray.mask
        • numpy.ma.MaskedArray.recordmask
        • numpy.ma.MaskedArray.all
        • numpy.ma.MaskedArray.any
        • numpy.ma.MaskedArray.count
        • numpy.ma.MaskedArray.nonzero
        • numpy.ma.shape
        • numpy.ma.size
      • Manipulating a MaskedArray
        • Changing the shape
          • numpy.ma.ravel
          • numpy.ma.reshape
          • numpy.ma.resize
          • numpy.ma.MaskedArray.flatten
          • numpy.ma.MaskedArray.ravel
          • numpy.ma.MaskedArray.reshape
          • numpy.ma.MaskedArray.resize
        • Modifying axes
          • numpy.ma.swapaxes
          • numpy.ma.transpose
          • numpy.ma.MaskedArray.swapaxes
          • numpy.ma.MaskedArray.transpose
        • Changing the number of dimensions
          • numpy.ma.atleast_1d
          • numpy.ma.atleast_2d
          • numpy.ma.atleast_3d
          • numpy.ma.expand_dims
          • numpy.ma.squeeze
          • numpy.ma.MaskedArray.squeeze
          • numpy.ma.column_stack
          • numpy.ma.concatenate
          • numpy.ma.dstack
          • numpy.ma.hstack
          • numpy.ma.hsplit
          • numpy.ma.mr
          • numpy.ma.row_stack
          • numpy.ma.vstack
        • Joining arrays
          • numpy.ma.column_stack
          • numpy.ma.concatenate
          • numpy.ma.dstack
          • numpy.ma.hstack
          • numpy.ma.vstack
      • Operations on masks
        • Creating a mask
          • numpy.ma.make_mask
          • numpy.ma.make_mask_none
          • numpy.ma.mask_or
          • numpy.ma.make_mask_descr
        • Accessing a mask
          • numpy.ma.getmask
          • numpy.ma.getmaskarray
          • numpy.ma.masked_array.mask
        • Finding masked data
          • numpy.ma.flatnotmasked_contiguous
          • numpy.ma.flatnotmasked_edges
          • numpy.ma.notmasked_contiguous
          • numpy.ma.notmasked_edges
        • Modifying a mask
          • numpy.ma.mask_cols
          • numpy.ma.mask_or
          • numpy.ma.mask_rowcols
          • numpy.ma.mask_rows
          • numpy.ma.harden_mask
          • numpy.ma.soften_mask
          • numpy.ma.MaskedArray.harden_mask
          • numpy.ma.MaskedArray.soften_mask
          • numpy.ma.MaskedArray.shrink_mask
          • numpy.ma.MaskedArray.unshare_mask
      • Conversion operations
        • > to a masked array
          • numpy.ma.asarray
          • numpy.ma.asanyarray
          • numpy.ma.fix_invalid
          • numpy.ma.masked_equal
          • numpy.ma.masked_greater
          • numpy.ma.masked_greater_equal
          • numpy.ma.masked_inside
          • numpy.ma.masked_invalid
          • numpy.ma.masked_less
          • numpy.ma.masked_less_equal
          • numpy.ma.masked_not_equal
          • numpy.ma.masked_object
          • numpy.ma.masked_outside
          • numpy.ma.masked_values
          • numpy.ma.masked_where
        • > to a ndarray
          • numpy.ma.compress_cols
          • numpy.ma.compress_rowcols
          • numpy.ma.compress_rows
          • numpy.ma.compressed
          • numpy.ma.filled
          • numpy.ma.MaskedArray.compressed
          • numpy.ma.MaskedArray.filled
        • > to another object
          • numpy.ma.MaskedArray.tofile
          • numpy.ma.MaskedArray.tolist
          • numpy.ma.MaskedArray.torecords
          • numpy.ma.MaskedArray.tostring
        • Pickling and unpickling
          • numpy.ma.dump
          • numpy.ma.dumps
          • numpy.ma.load
          • numpy.ma.loads
        • Filling a masked array
          • numpy.ma.common_fill_value
          • numpy.ma.default_fill_value
          • numpy.ma.maximum_fill_value
          • numpy.ma.maximum_fill_value
          • numpy.ma.set_fill_value
          • numpy.ma.MaskedArray.get_fill_value
          • numpy.ma.MaskedArray.set_fill_value
          • numpy.ma.MaskedArray.fill_value
      • Masked arrays arithmetics
        • Arithmetics
          • numpy.ma.anom
          • numpy.ma.anomalies
          • numpy.ma.average
          • numpy.ma.conjugate
          • numpy.ma.corrcoef
          • numpy.ma.cov
          • numpy.ma.cumsum
          • numpy.ma.cumprod
          • numpy.ma.mean
          • numpy.ma.median
          • numpy.ma.power
          • numpy.ma.prod
          • numpy.ma.std
          • numpy.ma.sum
          • numpy.ma.var
          • numpy.ma.MaskedArray.anom
          • numpy.ma.MaskedArray.cumprod
          • numpy.ma.MaskedArray.cumsum
          • numpy.ma.MaskedArray.mean
          • numpy.ma.MaskedArray.prod
          • numpy.ma.MaskedArray.std
          • numpy.ma.MaskedArray.sum
          • numpy.ma.MaskedArray.var
        • Minimum/maximum
          • numpy.ma.argmax
          • numpy.ma.argmin
          • numpy.ma.max
          • numpy.ma.min
          • numpy.ma.ptp
          • numpy.ma.MaskedArray.argmax
          • numpy.ma.MaskedArray.argmin
          • numpy.ma.MaskedArray.max
          • numpy.ma.MaskedArray.min
          • numpy.ma.MaskedArray.ptp
        • Sorting
          • numpy.ma.argsort
          • numpy.ma.sort
          • numpy.ma.MaskedArray.argsort
          • numpy.ma.MaskedArray.sort
        • Algebra
          • numpy.ma.diag
          • numpy.ma.dot
          • numpy.ma.identity
          • numpy.ma.inner
          • numpy.ma.innerproduct
          • numpy.ma.outer
          • numpy.ma.outerproduct
          • numpy.ma.trace
          • numpy.ma.transpose
          • numpy.ma.MaskedArray.trace
          • numpy.ma.MaskedArray.transpose
        • Polynomial fit
          • numpy.ma.vander
          • numpy.ma.polyfit
        • Clipping and rounding
          • numpy.ma.around
          • numpy.ma.clip
          • numpy.ma.round
          • numpy.ma.MaskedArray.clip
          • numpy.ma.MaskedArray.round
        • Miscellanea
          • numpy.ma.allequal
          • numpy.ma.allclose
          • numpy.ma.apply_along_axis
          • numpy.ma.arange
          • numpy.ma.choose
          • numpy.ma.ediff1d
          • numpy.ma.indices
          • numpy.ma.where
    • Numpy-specific help functions
      • Finding help
        • numpy.lookfor
      • Reading help
        • numpy.info
        • numpy.source
    • Miscellaneous routines
      • Buffer objects
        • numpy.getbuffer
        • numpy.newbuffer
      • Performance tuning
        • numpy.alterdot
        • numpy.restoredot
        • numpy.setbufsize
        • numpy.getbufsize
    • Test Support (numpy.testing)
    • Asserts
      • numpy.testing.assert_almost_equal
      • numpy.testing.assert_approx_equal
      • numpy.testing.assert_array_almost_equal
      • numpy.testing.assert_array_equal
      • numpy.testing.assert_array_less
      • numpy.testing.assert_equal
      • numpy.testing.assert_raises
      • numpy.testing.assert_warns
      • numpy.testing.assert_string_equal
      • Decorators
        • numpy.testing.decorators.deprecated
        • numpy.testing.decorators.knownfailureif
        • numpy.testing.decorators.setastest
        • numpy.testing.decorators.skipif
        • numpy.testing.decorators.slow
        • numpy.testing.decorate_methods
      • Test Running
        • numpy.testing.Tester
        • numpy.testing.run_module_suite
        • numpy.testing.rundocs
    • 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)
    • String operations
      • String operations
        • numpy.core.defchararray.add
        • numpy.core.defchararray.multiply
        • numpy.core.defchararray.mod
        • numpy.core.defchararray.capitalize
        • numpy.core.defchararray.center
        • numpy.core.defchararray.decode
        • numpy.core.defchararray.encode
        • numpy.core.defchararray.join
        • numpy.core.defchararray.ljust
        • numpy.core.defchararray.lower
        • numpy.core.defchararray.lstrip
        • numpy.core.defchararray.partition
        • numpy.core.defchararray.replace
        • numpy.core.defchararray.rjust
        • numpy.core.defchararray.rpartition
        • numpy.core.defchararray.rsplit
        • numpy.core.defchararray.rstrip
        • numpy.core.defchararray.split
        • numpy.core.defchararray.splitlines
        • numpy.core.defchararray.strip
        • numpy.core.defchararray.swapcase
        • numpy.core.defchararray.title
        • numpy.core.defchararray.translate
        • numpy.core.defchararray.upper
        • numpy.core.defchararray.zfill
      • Comparison
        • numpy.core.defchararray.equal
        • numpy.core.defchararray.not_equal
        • numpy.core.defchararray.greater_equal
        • numpy.core.defchararray.less_equal
        • numpy.core.defchararray.greater
        • numpy.core.defchararray.less
      • String information
        • numpy.core.defchararray.count
        • numpy.core.defchararray.find
        • numpy.core.defchararray.index
        • numpy.core.defchararray.isalpha
        • numpy.core.defchararray.isdecimal
        • numpy.core.defchararray.isdigit
        • numpy.core.defchararray.islower
        • numpy.core.defchararray.isnumeric
        • numpy.core.defchararray.isspace
        • numpy.core.defchararray.istitle
        • numpy.core.defchararray.isupper
        • numpy.core.defchararray.rfind
        • numpy.core.defchararray.rindex
        • numpy.core.defchararray.startswith
      • Convenience class
        • numpy.core.defchararray.chararray
          • numpy.core.defchararray.count
          • numpy.core.defchararray.decode
          • numpy.core.defchararray.encode
          • numpy.core.defchararray.find
          • numpy.core.defchararray.index
          • numpy.core.defchararray.isalpha
          • numpy.core.defchararray.isdecimal
          • numpy.core.defchararray.isdigit
          • numpy.core.defchararray.islower
          • numpy.core.defchararray.isnumeric
          • numpy.core.defchararray.isspace
          • numpy.core.defchararray.istitle
          • numpy.core.defchararray.isupper
          • numpy.core.defchararray.join
          • numpy.core.defchararray.ljust
          • numpy.core.defchararray.lower
          • numpy.core.defchararray.lstrip
          • numpy.core.defchararray.replace
          • numpy.core.defchararray.rfind
          • numpy.core.defchararray.rindex
          • numpy.core.defchararray.rjust
          • numpy.core.defchararray.rsplit
          • numpy.core.defchararray.rstrip
          • numpy.core.defchararray.split
          • numpy.core.defchararray.splitlines
          • numpy.core.defchararray.startswith
          • numpy.core.defchararray.strip
          • numpy.core.defchararray.swapcase
          • numpy.core.defchararray.title
          • numpy.core.defchararray.translate
          • numpy.core.defchararray.upper
          • numpy.core.defchararray.zfill
  • Packaging (numpy.distutils)
    • Modules in numpy.distutils
      • misc_util
        • numpy.distutils.misc_util.get_numpy_include_dirs
        • numpy.distutils.misc_util.dict_append
        • numpy.distutils.misc_util.appendpath
        • numpy.distutils.misc_util.allpath
        • numpy.distutils.misc_util.dot_join
        • numpy.distutils.misc_util.generate_config_py
        • numpy.distutils.misc_util.get_cmd
        • numpy.distutils.misc_util.terminal_has_colors
        • numpy.distutils.misc_util.red_text
        • numpy.distutils.misc_util.green_text
        • numpy.distutils.misc_util.yellow_text
        • numpy.distutils.misc_util.blue_text
        • numpy.distutils.misc_util.cyan_text
        • numpy.distutils.misc_util.cyg2win32
        • numpy.distutils.misc_util.all_strings
        • numpy.distutils.misc_util.has_f_sources
        • numpy.distutils.misc_util.has_cxx_sources
        • numpy.distutils.misc_util.filter_sources
        • numpy.distutils.misc_util.get_dependencies
        • numpy.distutils.misc_util.is_local_src_dir
        • numpy.distutils.misc_util.get_ext_source_files
        • numpy.distutils.misc_util.get_script_files
      • Other modules
        • numpy.distutils.system_info.get_info
        • numpy.distutils.system_info.get_standard_file
        • numpy.distutils.cpuinfo.cpu
        • numpy.distutils.log.set_verbosity
        • numpy.distutils.exec_command
    • Building Installable C libraries
      • npy-pkg-config files
      • Reusing a C library from another package
    • 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
        • PyArrayNeighborhoodIter_Type
        • PyArrayFlags_Type
        • ScalarArrayTypes
      • Other C-Structures
        • PyArray_Dims
        • PyArray_Chunk
        • PyArrayInterface
        • Internally used structures
    • System configuration
      • Data type sizes
      • Platform information
    • 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)
      • Neighborhood iterator
      • Array Scalars
      • Data-type descriptors
      • Conversion Utilities
        • For use with PyArg_ParseTuple
        • Other conversions
      • Miscellaneous
        • Importing the API
        • Checking the API Version
        • Internal Flexibility
        • Memory management
        • Threading support
          • Group 1
          • Group 2
        • Priority
        • Default buffers
        • Other constants
        • Miscellaneous Macros
        • Enumerated Types
    • Array Iterator API
      • Array Iterator
      • Converting from Previous NumPy Iterators
      • Simple Iteration Example
      • Simple Multi-Iteration Example
      • Iterator Data Types
      • Construction and Destruction
      • Functions For Iteration
    • UFunc API
      • Constants
      • Macros
      • Functions
      • Generic functions
      • Importing the API
    • Generalized Universal Function API
      • Definitions
      • Details of Signature
      • C-API for implementing Elementary Functions
    • Numpy core libraries
      • Numpy core math library
        • Floating point classification
        • Useful math constants
        • Low-level floating point manipulation
        • Complex functions
        • Linking against the core math library in an extension
        • Half-precision functions
  • 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
  • Numpy and SWIG
    • Numpy.i: a SWIG Interface File for NumPy
      • Introduction
      • Using numpy.i
      • Available Typemaps
        • Input Arrays
        • In-Place Arrays
        • Argout Arrays
        • Argoutview Arrays
        • Output Arrays
        • Other Common Types: bool
        • Other Common Types: complex
      • NumPy Array Scalars and SWIG
        • Why is There a Second File?
      • Helper Functions
        • Macros
        • Routines
      • Beyond the Provided Typemaps
        • A Common Example
        • Other Situations
        • A Final Note
      • Summary
    • Testing the numpy.i Typemaps
      • Introduction
      • Testing Organization
      • Testing Header Files
      • Testing Source Files
      • Testing SWIG Interface Files
      • Testing Python Scripts
  • Acknowledgements
• Contributing to Numpy
  • Working with NumPy source code
    • Introduction
      • Install git
        • Overview
        • In detail
    • Following the latest source
      • Get the local copy of the code
      • Updating the code
    • Making a patch
      • Making patches
        • Overview
        • In detail
      • Moving from patching to development
    • Git for development
      • Getting started with Git development
        • Basic Git setup
        • Making your own copy (fork) of NumPy
          • Set up and configure a github account
          • Create your own forked copy of NumPy
        • Set up your fork
          • Overview
          • In detail
            • Clone your fork
            • Linking your repository to the upstream repo
      • Git configuration
        • Overview
        • In detail
          • user.name and user.email
          • Aliases
          • Editor
          • Merging
      • Development workflow
        • Basic workflow
          • Making a new feature branch
          • The editing workflow
            • Overview
            • In more detail
          • Rebasing on master
            • Recovering from mess-ups
          • Asking for your changes to be merged with the main repo
            • Asking for code review
            • Filing a pull request
          • Pushing changes to the main repo
        • Additional things you might want to do
          • Rewriting commit history
          • Deleting a branch on github
          • Several people sharing a single repository
          • Exploring your repository
    • git resources
      • Tutorials and summaries
      • Advanced git workflow
      • Manual pages online
• 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