NumPy manual contents — NumPy v1.15 Manual (original) (raw)
-
- Setting up
* What is NumPy?
* Installing NumPy - Quickstart tutorial
* Prerequisites
* The Basics
* An example
* Array Creation
* Printing Arrays
* Basic Operations
* Universal Functions
* Indexing, Slicing and Iterating
* Shape Manipulation
* Changing the shape of an array
* Stacking together different arrays
* Splitting one array into several smaller ones
* Copies and Views
* No Copy at All
* View or Shallow Copy
* Deep Copy
* Functions and Methods Overview
* Less Basic
* Broadcasting rules
* Fancy indexing and index tricks
* Indexing with Arrays of Indices
* Indexing with Boolean Arrays
* The ix_() function
* Indexing with strings
* Linear Algebra
* Simple Array Operations
* Tricks and Tips
* “Automatic” Reshaping
* Vector Stacking
* Histograms
* Further reading - NumPy basics
* Data types
* Array types and conversions between types
* Array Scalars
* Extended Precision
* 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
* Introduction
* Structured Datatypes
* Structured Datatype Creation
* Manipulating and Displaying Structured Datatypes
* Automatic Byte Offsets and Alignment
* Field Titles
* Union types
* Indexing and Assignment to Structured arrays
* Assigning data to a Structured Array
* Assignment from Python Native Types (Tuples)
* Assignment from Scalars
* Assignment from other Structured Arrays
* Assignment involving subarrays
* Indexing Structured Arrays
* Accessing Individual Fields
* Accessing Multiple Fields
* Indexing with an Integer to get a Structured Scalar
* Viewing Structured Arrays Containing Objects
* Structure Comparison
* Record Arrays
* Recarray Helper Functions
* Subclassing ndarray
* 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_ufunc__ for ufuncs
* __array_wrap__ for ufuncs and other functions
* Extra gotchas - custom __del__ methods and ndarray.base
* Subclassing and Downstream Compatibility - Miscellaneous
* IEEE 754 Floating Point Special Values
* How numpy handles numerical exceptions
* Examples
* Interfacing to C
* Interfacing to Fortran:
* Interfacing to C++: - NumPy for Matlab users
* Introduction
* Some Key Differences
* ‘array’ or ‘matrix’? Which should I use?
* Short answer
* Long answer
* Table of Rough MATLAB-NumPy Equivalents
* General Purpose Equivalents
* Linear Algebra Equivalents
* Notes
* Customizing Your Environment
* Links - Building from source
* Prerequisites
* Basic Installation
* Parallel builds
* 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 - 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
* Cython
* Complex addition in Cython
* Image filter in Cython
* 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
* PyFort
* Writing your own ufunc
* Creating a new universal function
* Example Non-ufunc extension
* Example NumPy ufunc for one dtype
* Example NumPy ufunc with multiple dtypes
* Example NumPy ufunc with multiple arguments/return values
* Example NumPy ufunc with structured array dtype arguments
* PyUFunc_FromFuncAndData Specification
* 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
* 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
- Setting up
-
- Array objects
* The N-dimensional array (ndarray)
* Constructing arrays
* numpy.ndarray
* numpy.ndarray.all
* numpy.ndarray.any
* numpy.ndarray.argmax
* numpy.ndarray.argmin
* numpy.ndarray.argpartition
* numpy.ndarray.argsort
* numpy.ndarray.astype
* numpy.ndarray.byteswap
* numpy.ndarray.choose
* numpy.ndarray.clip
* numpy.ndarray.compress
* numpy.ndarray.conj
* numpy.ndarray.conjugate
* numpy.ndarray.copy
* numpy.ndarray.cumprod
* numpy.ndarray.cumsum
* numpy.ndarray.diagonal
* numpy.ndarray.dot
* numpy.ndarray.dump
* numpy.ndarray.dumps
* numpy.ndarray.fill
* numpy.ndarray.flatten
* numpy.ndarray.getfield
* numpy.ndarray.item
* numpy.ndarray.itemset
* numpy.ndarray.max
* numpy.ndarray.mean
* numpy.ndarray.min
* numpy.ndarray.newbyteorder
* numpy.ndarray.nonzero
* numpy.ndarray.partition
* numpy.ndarray.prod
* numpy.ndarray.ptp
* numpy.ndarray.put
* numpy.ndarray.ravel
* numpy.ndarray.repeat
* numpy.ndarray.reshape
* numpy.ndarray.resize
* numpy.ndarray.round
* numpy.ndarray.searchsorted
* numpy.ndarray.setfield
* numpy.ndarray.setflags
* numpy.ndarray.sort
* numpy.ndarray.squeeze
* numpy.ndarray.std
* numpy.ndarray.sum
* numpy.ndarray.swapaxes
* numpy.ndarray.take
* numpy.ndarray.tobytes
* numpy.ndarray.tofile
* numpy.ndarray.tolist
* numpy.ndarray.tostring
* numpy.ndarray.trace
* numpy.ndarray.transpose
* numpy.ndarray.var
* numpy.ndarray.view
* 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.tostring
* numpy.ndarray.tobytes
* 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.partition
* numpy.ndarray.argpartition
* 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, matrix multiplication, and comparison operations
* numpy.ndarray.__lt__
* numpy.ndarray.__le__
* numpy.ndarray.__gt__
* numpy.ndarray.__ge__
* numpy.ndarray.__eq__
* numpy.ndarray.__ne__
* numpy.ndarray.__neg__
* numpy.ndarray.__pos__
* numpy.ndarray.__abs__
* numpy.ndarray.__invert__
* numpy.ndarray.__add__
* numpy.ndarray.__sub__
* numpy.ndarray.__mul__
* 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.__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__
* numpy.ndarray.__matmul__
* 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.__contains__
* numpy.ndarray.__int__
* numpy.ndarray.__float__
* 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.generic.all
* numpy.generic.any
* numpy.generic.argmax
* numpy.generic.argmin
* numpy.generic.argsort
* numpy.generic.astype
* numpy.generic.byteswap
* numpy.generic.choose
* numpy.generic.clip
* numpy.generic.compress
* numpy.generic.conjugate
* numpy.generic.copy
* numpy.generic.cumprod
* numpy.generic.cumsum
* numpy.generic.diagonal
* numpy.generic.dump
* numpy.generic.dumps
* numpy.generic.fill
* numpy.generic.flatten
* numpy.generic.getfield
* numpy.generic.item
* numpy.generic.itemset
* numpy.generic.max
* numpy.generic.mean
* numpy.generic.min
* numpy.generic.newbyteorder
* numpy.generic.nonzero
* numpy.generic.prod
* numpy.generic.ptp
* numpy.generic.put
* numpy.generic.ravel
* numpy.generic.repeat
* numpy.generic.reshape
* numpy.generic.resize
* numpy.generic.round
* numpy.generic.searchsorted
* numpy.generic.setfield
* numpy.generic.setflags
* numpy.generic.sort
* numpy.generic.squeeze
* numpy.generic.std
* numpy.generic.sum
* numpy.generic.swapaxes
* numpy.generic.take
* numpy.generic.tofile
* numpy.generic.tolist
* numpy.generic.tostring
* numpy.generic.trace
* numpy.generic.transpose
* numpy.generic.var
* numpy.generic.view
* 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
* numpy.dtype.newbyteorder
* 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 and Indexing
* Advanced Indexing
* Integer array indexing
* Purely integer array indexing
* Combining advanced and basic indexing
* Boolean array indexing
* Detailed notes
* Field Access
* Flat Iterator indexing
* Iterating Over Arrays
* Single Array Iteration
* Controlling Iteration Order
* Modifying Array Values
* Using an External Loop
* Tracking an Index or Multi-Index
* Buffering the Array Elements
* Iterating as a Specific Data Type
* Broadcasting Array Iteration
* Iterator-Allocated Output Arrays
* Outer Product Iteration
* Reduction Iteration
* Putting the Inner Loop in Cython
* Standard array subclasses
* Special attributes and methods
* Matrix objects
* numpy.matrix.T
* numpy.matrix.H
* numpy.matrix.I
* numpy.matrix.A
* numpy.matrix
* numpy.matrix.all
* numpy.matrix.any
* numpy.matrix.argmax
* numpy.matrix.argmin
* numpy.matrix.argpartition
* numpy.matrix.argsort
* numpy.matrix.astype
* numpy.matrix.byteswap
* numpy.matrix.choose
* numpy.matrix.clip
* numpy.matrix.compress
* numpy.matrix.conj
* numpy.matrix.conjugate
* numpy.matrix.copy
* numpy.matrix.cumprod
* numpy.matrix.cumsum
* numpy.matrix.diagonal
* numpy.matrix.dot
* numpy.matrix.dump
* numpy.matrix.dumps
* numpy.matrix.fill
* numpy.matrix.flatten
* numpy.matrix.getA
* numpy.matrix.getA1
* numpy.matrix.getH
* numpy.matrix.getI
* numpy.matrix.getT
* numpy.matrix.getfield
* numpy.matrix.item
* numpy.matrix.itemset
* numpy.matrix.max
* numpy.matrix.mean
* numpy.matrix.min
* numpy.matrix.newbyteorder
* numpy.matrix.nonzero
* numpy.matrix.partition
* numpy.matrix.prod
* numpy.matrix.ptp
* numpy.matrix.put
* numpy.matrix.ravel
* numpy.matrix.repeat
* numpy.matrix.reshape
* numpy.matrix.resize
* numpy.matrix.round
* numpy.matrix.searchsorted
* numpy.matrix.setfield
* numpy.matrix.setflags
* numpy.matrix.sort
* numpy.matrix.squeeze
* numpy.matrix.std
* numpy.matrix.sum
* numpy.matrix.swapaxes
* numpy.matrix.take
* numpy.matrix.tobytes
* numpy.matrix.tofile
* numpy.matrix.tolist
* numpy.matrix.tostring
* numpy.matrix.trace
* numpy.matrix.transpose
* numpy.matrix.var
* numpy.matrix.view
* numpy.asmatrix
* numpy.bmat
* Memory-mapped file arrays
* numpy.memmap
* numpy.memmap.flush
* numpy.memmap.flush
* Character arrays (numpy.char)
* numpy.chararray
* numpy.chararray.astype
* numpy.chararray.argsort
* numpy.chararray.copy
* numpy.chararray.count
* numpy.chararray.decode
* numpy.chararray.dump
* numpy.chararray.dumps
* numpy.chararray.encode
* numpy.chararray.endswith
* numpy.chararray.expandtabs
* numpy.chararray.fill
* numpy.chararray.find
* numpy.chararray.flatten
* numpy.chararray.getfield
* numpy.chararray.index
* numpy.chararray.isalnum
* numpy.chararray.isalpha
* numpy.chararray.isdecimal
* numpy.chararray.isdigit
* numpy.chararray.islower
* numpy.chararray.isnumeric
* numpy.chararray.isspace
* numpy.chararray.istitle
* numpy.chararray.isupper
* numpy.chararray.item
* numpy.chararray.join
* numpy.chararray.ljust
* numpy.chararray.lower
* numpy.chararray.lstrip
* numpy.chararray.nonzero
* numpy.chararray.put
* numpy.chararray.ravel
* numpy.chararray.repeat
* numpy.chararray.replace
* numpy.chararray.reshape
* numpy.chararray.resize
* numpy.chararray.rfind
* numpy.chararray.rindex
* numpy.chararray.rjust
* numpy.chararray.rsplit
* numpy.chararray.rstrip
* numpy.chararray.searchsorted
* numpy.chararray.setfield
* numpy.chararray.setflags
* numpy.chararray.sort
* numpy.chararray.split
* numpy.chararray.splitlines
* numpy.chararray.squeeze
* numpy.chararray.startswith
* numpy.chararray.strip
* numpy.chararray.swapaxes
* numpy.chararray.swapcase
* numpy.chararray.take
* numpy.chararray.title
* numpy.chararray.tofile
* numpy.chararray.tolist
* numpy.chararray.tostring
* numpy.chararray.translate
* numpy.chararray.transpose
* numpy.chararray.upper
* numpy.chararray.view
* numpy.chararray.zfill
* numpy.core.defchararray.array
* Record arrays (numpy.rec)
* numpy.recarray
* numpy.recarray.all
* numpy.recarray.any
* numpy.recarray.argmax
* numpy.recarray.argmin
* numpy.recarray.argpartition
* numpy.recarray.argsort
* numpy.recarray.astype
* numpy.recarray.byteswap
* numpy.recarray.choose
* numpy.recarray.clip
* numpy.recarray.compress
* numpy.recarray.conj
* numpy.recarray.conjugate
* numpy.recarray.copy
* numpy.recarray.cumprod
* numpy.recarray.cumsum
* numpy.recarray.diagonal
* numpy.recarray.dot
* numpy.recarray.dump
* numpy.recarray.dumps
* numpy.recarray.fill
* numpy.recarray.flatten
* numpy.recarray.getfield
* numpy.recarray.item
* numpy.recarray.itemset
* numpy.recarray.max
* numpy.recarray.mean
* numpy.recarray.min
* numpy.recarray.newbyteorder
* numpy.recarray.nonzero
* numpy.recarray.partition
* numpy.recarray.prod
* numpy.recarray.ptp
* numpy.recarray.put
* numpy.recarray.ravel
* numpy.recarray.repeat
* numpy.recarray.reshape
* numpy.recarray.resize
* numpy.recarray.round
* numpy.recarray.searchsorted
* numpy.recarray.setfield
* numpy.recarray.setflags
* numpy.recarray.sort
* numpy.recarray.squeeze
* numpy.recarray.std
* numpy.recarray.sum
* numpy.recarray.swapaxes
* numpy.recarray.take
* numpy.recarray.tobytes
* numpy.recarray.tofile
* numpy.recarray.tolist
* numpy.recarray.tostring
* numpy.recarray.trace
* numpy.recarray.transpose
* numpy.recarray.var
* numpy.recarray.view
* numpy.record
* numpy.record.all
* numpy.record.any
* numpy.record.argmax
* numpy.record.argmin
* numpy.record.argsort
* numpy.record.astype
* numpy.record.byteswap
* numpy.record.choose
* numpy.record.clip
* numpy.record.compress
* numpy.record.conjugate
* numpy.record.copy
* numpy.record.cumprod
* numpy.record.cumsum
* numpy.record.diagonal
* numpy.record.dump
* numpy.record.dumps
* numpy.record.fill
* numpy.record.flatten
* numpy.record.getfield
* numpy.record.item
* numpy.record.itemset
* numpy.record.max
* numpy.record.mean
* numpy.record.min
* numpy.record.newbyteorder
* numpy.record.nonzero
* numpy.record.pprint
* numpy.record.prod
* numpy.record.ptp
* numpy.record.put
* numpy.record.ravel
* numpy.record.repeat
* numpy.record.reshape
* numpy.record.resize
* numpy.record.round
* numpy.record.searchsorted
* numpy.record.setfield
* numpy.record.setflags
* numpy.record.sort
* numpy.record.squeeze
* numpy.record.std
* numpy.record.sum
* numpy.record.swapaxes
* numpy.record.take
* numpy.record.tofile
* numpy.record.tolist
* numpy.record.tostring
* numpy.record.trace
* numpy.record.transpose
* numpy.record.var
* numpy.record.view
* 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
* numpy.ndenumerate.next
* Iterator for broadcasting
* numpy.broadcast
* numpy.broadcast.reset
* 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.__int__
* numpy.ma.MaskedArray.__long__
* 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
* numpy.ma.MaskedArray.tobytes
* 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):
* 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.__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.__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.is_masked
* numpy.ma.is_mask
* 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.stack
* 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.stack
* numpy.ma.column_stack
* numpy.ma.concatenate
* numpy.ma.append
* 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
* numpy.ma.clump_masked
* numpy.ma.clump_unmasked
* 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.tobytes
* 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)
* Datetimes and Timedeltas
* Basic Datetimes
* Datetime and Timedelta Arithmetic
* Datetime Units
* Business Day Functionality
* np.is_busday():
* np.busday_count():
* Custom Weekmasks
* Changes with NumPy 1.11
* Differences Between 1.6 and 1.7 Datetimes
* String Parsing
* Unit Conversion
* Datetime Arithmetic - Constants
- Universal functions (ufunc)
* Broadcasting
* Output type determination
* Use of internal buffers
* numpy.setbufsize
* Error handling
* numpy.seterr
* numpy.seterrcall
* Casting Rules
* Overriding Ufunc behavior
* ufunc
* Optional keyword arguments
* Attributes
* numpy.ufunc.nin
* numpy.ufunc.nout
* numpy.ufunc.nargs
* numpy.ufunc.ntypes
* numpy.ufunc.types
* numpy.ufunc.identity
* numpy.ufunc.signature
* Methods
* numpy.ufunc.reduce
* numpy.ufunc.accumulate
* numpy.ufunc.reduceat
* numpy.ufunc.outer
* numpy.ufunc.at
* 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
* numpy.full
* numpy.full_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.geomspace
* 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
* Basic operations
* numpy.copyto
* Changing array shape
* numpy.reshape
* numpy.ravel
* numpy.ndarray.flat
* numpy.ndarray.flatten
* Transpose-like operations
* numpy.moveaxis
* 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.reset
* numpy.broadcast_to
* numpy.broadcast_arrays
* numpy.expand_dims
* numpy.squeeze
* Changing kind of array
* numpy.asarray
* numpy.asanyarray
* numpy.asmatrix
* numpy.asfarray
* numpy.asfortranarray
* numpy.ascontiguousarray
* numpy.asarray_chkfinite
* numpy.asscalar
* numpy.require
* Joining arrays
* numpy.concatenate
* numpy.stack
* numpy.column_stack
* numpy.dstack
* numpy.hstack
* numpy.vstack
* numpy.block
* Splitting arrays
* numpy.split
* numpy.array_split
* numpy.dsplit
* numpy.hsplit
* 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.flip
* numpy.fliplr
* numpy.flipud
* numpy.reshape
* numpy.roll
* numpy.rot90
* 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
* 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.chararray.astype
* numpy.core.defchararray.chararray.argsort
* numpy.core.defchararray.chararray.copy
* numpy.core.defchararray.chararray.count
* numpy.core.defchararray.chararray.decode
* numpy.core.defchararray.chararray.dump
* numpy.core.defchararray.chararray.dumps
* numpy.core.defchararray.chararray.encode
* numpy.core.defchararray.chararray.endswith
* numpy.core.defchararray.chararray.expandtabs
* numpy.core.defchararray.chararray.fill
* numpy.core.defchararray.chararray.find
* numpy.core.defchararray.chararray.flatten
* numpy.core.defchararray.chararray.getfield
* numpy.core.defchararray.chararray.index
* numpy.core.defchararray.chararray.isalnum
* numpy.core.defchararray.chararray.isalpha
* numpy.core.defchararray.chararray.isdecimal
* numpy.core.defchararray.chararray.isdigit
* numpy.core.defchararray.chararray.islower
* numpy.core.defchararray.chararray.isnumeric
* numpy.core.defchararray.chararray.isspace
* numpy.core.defchararray.chararray.istitle
* numpy.core.defchararray.chararray.isupper
* numpy.core.defchararray.chararray.item
* numpy.core.defchararray.chararray.join
* numpy.core.defchararray.chararray.ljust
* numpy.core.defchararray.chararray.lower
* numpy.core.defchararray.chararray.lstrip
* numpy.core.defchararray.chararray.nonzero
* numpy.core.defchararray.chararray.put
* numpy.core.defchararray.chararray.ravel
* numpy.core.defchararray.chararray.repeat
* numpy.core.defchararray.chararray.replace
* numpy.core.defchararray.chararray.reshape
* numpy.core.defchararray.chararray.resize
* numpy.core.defchararray.chararray.rfind
* numpy.core.defchararray.chararray.rindex
* numpy.core.defchararray.chararray.rjust
* numpy.core.defchararray.chararray.rsplit
* numpy.core.defchararray.chararray.rstrip
* numpy.core.defchararray.chararray.searchsorted
* numpy.core.defchararray.chararray.setfield
* numpy.core.defchararray.chararray.setflags
* numpy.core.defchararray.chararray.sort
* numpy.core.defchararray.chararray.split
* numpy.core.defchararray.chararray.splitlines
* numpy.core.defchararray.chararray.squeeze
* numpy.core.defchararray.chararray.startswith
* numpy.core.defchararray.chararray.strip
* numpy.core.defchararray.chararray.swapaxes
* numpy.core.defchararray.chararray.swapcase
* numpy.core.defchararray.chararray.take
* numpy.core.defchararray.chararray.title
* numpy.core.defchararray.chararray.tofile
* numpy.core.defchararray.chararray.tolist
* numpy.core.defchararray.chararray.tostring
* numpy.core.defchararray.chararray.translate
* numpy.core.defchararray.chararray.transpose
* numpy.core.defchararray.chararray.upper
* numpy.core.defchararray.chararray.view
* numpy.core.defchararray.chararray.zfill
* C-Types Foreign Function Interface (numpy.ctypeslib)
* Datetime Support Functions
* numpy.datetime_as_string
* numpy.datetime_data
* Business Day Functions
* numpy.busdaycalendar
* numpy.is_busday
* numpy.busday_offset
* numpy.busday_count
* 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.dtype.newbyteorder
* 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
* Optionally Scipy-accelerated routines (numpy.dual)
* Linear algebra
* FFT
* Other
* Mathematical functions with automatic domain (numpy.emath)
* 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
* 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.rfftfreq
* numpy.fft.fftshift
* numpy.fft.ifftshift
* Background information
* Implementation details
* Normalization
* Real and Hermitian transforms
* Higher dimensions
* References
* Examples
* 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
* Functional programming
* numpy.apply_along_axis
* numpy.apply_over_axes
* numpy.vectorize
* numpy.vectorize.__call__
* numpy.frompyfunc
* numpy.piecewise
* NumPy-specific help functions
* Finding help
* numpy.lookfor
* Reading help
* numpy.info
* numpy.source
* 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.take_along_axis
* numpy.choose
* numpy.compress
* numpy.diag
* numpy.diagonal
* numpy.select
* numpy.lib.stride_tricks.as_strided
* Inserting data into arrays
* numpy.place
* numpy.put
* numpy.put_along_axis
* numpy.putmask
* numpy.fill_diagonal
* Iterating over arrays
* numpy.nditer
* numpy.nditer.close
* numpy.nditer.copy
* numpy.nditer.debug_print
* numpy.nditer.enable_external_loop
* numpy.nditer.iternext
* numpy.nditer.remove_axis
* numpy.nditer.remove_multi_index
* numpy.nditer.reset
* numpy.ndenumerate
* numpy.ndenumerate.next
* numpy.ndindex
* numpy.ndindex.ndincr
* numpy.ndindex.next
* numpy.nested_iters
* numpy.flatiter
* numpy.flatiter.copy
* numpy.lib.Arrayterator
* Input and output
* NumPy binary files (NPY, NPZ)
* numpy.load
* numpy.save
* numpy.savez
* numpy.savez_compressed
* Text files
* numpy.loadtxt
* numpy.savetxt
* numpy.genfromtxt
* numpy.fromregex
* numpy.fromstring
* numpy.ndarray.tofile
* numpy.ndarray.tolist
* Raw binary files
* String formatting
* numpy.array2string
* numpy.array_repr
* numpy.array_str
* numpy.format_float_positional
* numpy.format_float_scientific
* Memory mapping files
* numpy.memmap
* numpy.memmap.flush
* Text formatting options
* numpy.set_printoptions
* numpy.get_printoptions
* numpy.set_string_function
* numpy.printoptions
* Base-n representations
* numpy.binary_repr
* numpy.base_repr
* Data sources
* numpy.DataSource
* numpy.DataSource.abspath
* numpy.DataSource.exists
* numpy.DataSource.open
* Binary Format Description
* numpy.lib.format
* NPY format
* Capabilities
* Limitations
* File extensions
* Version numbering
* Format Version 1.0
* Format Version 2.0
* Notes
* Linear algebra (numpy.linalg)
* Matrix and vector products
* numpy.dot
* numpy.linalg.multi_dot
* numpy.vdot
* numpy.inner
* numpy.outer
* numpy.matmul
* numpy.tensordot
* numpy.einsum
* numpy.einsum_path
* 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.matrix_rank
* 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
* Linear algebra on several matrices at once
* Logic functions
* Truth value testing
* numpy.all
* numpy.any
* Array contents
* numpy.isfinite
* numpy.isinf
* numpy.isnan
* numpy.isnat
* 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.isclose
* numpy.array_equal
* numpy.array_equiv
* numpy.greater
* numpy.greater_equal
* numpy.less
* numpy.less_equal
* numpy.equal
* numpy.not_equal
* 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.is_masked
* numpy.ma.is_mask
* 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.stack
* 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.stack
* numpy.ma.column_stack
* numpy.ma.concatenate
* numpy.ma.append
* 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
* numpy.ma.clump_masked
* numpy.ma.clump_unmasked
* 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.tobytes
* 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
* 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.nanprod
* numpy.nansum
* numpy.cumprod
* numpy.cumsum
* numpy.nancumprod
* numpy.nancumsum
* 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
* numpy.nextafter
* numpy.spacing
* Rational routines
* numpy.lcm
* numpy.gcd
* Arithmetic operations
* numpy.add
* numpy.reciprocal
* numpy.positive
* numpy.negative
* numpy.multiply
* numpy.divide
* numpy.power
* numpy.subtract
* numpy.true_divide
* numpy.floor_divide
* numpy.float_power
* numpy.fmod
* numpy.mod
* numpy.modf
* numpy.remainder
* numpy.divmod
* Handling complex numbers
* numpy.angle
* numpy.real
* numpy.imag
* numpy.conj
* Miscellaneous
* numpy.convolve
* numpy.clip
* numpy.sqrt
* numpy.cbrt
* numpy.square
* numpy.absolute
* numpy.fabs
* numpy.sign
* numpy.heaviside
* numpy.maximum
* numpy.minimum
* numpy.fmax
* numpy.fmin
* numpy.nan_to_num
* numpy.real_if_close
* numpy.interp
* Matrix library (numpy.matlib)
* numpy.matlib.empty
* numpy.matlib.zeros
* numpy.matlib.ones
* numpy.matlib.eye
* numpy.matlib.identity
* numpy.matlib.repmat
* numpy.matlib.rand
* numpy.matlib.randn
* Miscellaneous routines
* Buffer objects
* Performance tuning
* numpy.setbufsize
* numpy.getbufsize
* Memory ranges
* numpy.shares_memory
* numpy.may_share_memory
* Array mixins
* numpy.lib.mixins.NDArrayOperatorsMixin
* NumPy version comparison
* numpy.lib.NumpyVersion
* Padding Arrays
* numpy.pad
* Polynomials
* Transition notice
* Polynomial Package
* Using the Convenience Classes
* Basics
* Calculus
* Other Polynomial Constructors
* Fitting
* Polynomial Module (numpy.polynomial.polynomial)
* Polynomial Class
* numpy.polynomial.polynomial.Polynomial
* numpy.polynomial.polynomial.Polynomial.__call__
* numpy.polynomial.polynomial.Polynomial.basis
* numpy.polynomial.polynomial.Polynomial.cast
* numpy.polynomial.polynomial.Polynomial.convert
* numpy.polynomial.polynomial.Polynomial.copy
* numpy.polynomial.polynomial.Polynomial.cutdeg
* numpy.polynomial.polynomial.Polynomial.degree
* numpy.polynomial.polynomial.Polynomial.deriv
* numpy.polynomial.polynomial.Polynomial.fit
* numpy.polynomial.polynomial.Polynomial.fromroots
* numpy.polynomial.polynomial.Polynomial.has_samecoef
* numpy.polynomial.polynomial.Polynomial.has_samedomain
* numpy.polynomial.polynomial.Polynomial.has_sametype
* numpy.polynomial.polynomial.Polynomial.has_samewindow
* numpy.polynomial.polynomial.Polynomial.identity
* numpy.polynomial.polynomial.Polynomial.integ
* numpy.polynomial.polynomial.Polynomial.linspace
* numpy.polynomial.polynomial.Polynomial.mapparms
* numpy.polynomial.polynomial.Polynomial.roots
* numpy.polynomial.polynomial.Polynomial.trim
* numpy.polynomial.polynomial.Polynomial.truncate
* Basics
* numpy.polynomial.polynomial.polyval
* numpy.polynomial.polynomial.polyval2d
* numpy.polynomial.polynomial.polyval3d
* numpy.polynomial.polynomial.polygrid2d
* numpy.polynomial.polynomial.polygrid3d
* numpy.polynomial.polynomial.polyroots
* numpy.polynomial.polynomial.polyfromroots
* numpy.polynomial.polynomial.polyvalfromroots
* Fitting
* numpy.polynomial.polynomial.polyfit
* numpy.polynomial.polynomial.polyvander
* numpy.polynomial.polynomial.polyvander2d
* numpy.polynomial.polynomial.polyvander3d
* Calculus
* numpy.polynomial.polynomial.polyder
* numpy.polynomial.polynomial.polyint
* Algebra
* numpy.polynomial.polynomial.polyadd
* numpy.polynomial.polynomial.polysub
* numpy.polynomial.polynomial.polymul
* numpy.polynomial.polynomial.polymulx
* numpy.polynomial.polynomial.polydiv
* numpy.polynomial.polynomial.polypow
* Miscellaneous
* numpy.polynomial.polynomial.polycompanion
* numpy.polynomial.polynomial.polydomain
* numpy.polynomial.polynomial.polyzero
* numpy.polynomial.polynomial.polyone
* numpy.polynomial.polynomial.polyx
* numpy.polynomial.polynomial.polytrim
* numpy.polynomial.polynomial.polyline
* Chebyshev Module (numpy.polynomial.chebyshev)
* Chebyshev Class
* numpy.polynomial.chebyshev.Chebyshev
* numpy.polynomial.chebyshev.Chebyshev.__call__
* numpy.polynomial.chebyshev.Chebyshev.basis
* numpy.polynomial.chebyshev.Chebyshev.cast
* numpy.polynomial.chebyshev.Chebyshev.convert
* numpy.polynomial.chebyshev.Chebyshev.copy
* numpy.polynomial.chebyshev.Chebyshev.cutdeg
* numpy.polynomial.chebyshev.Chebyshev.degree
* numpy.polynomial.chebyshev.Chebyshev.deriv
* numpy.polynomial.chebyshev.Chebyshev.fit
* numpy.polynomial.chebyshev.Chebyshev.fromroots
* numpy.polynomial.chebyshev.Chebyshev.has_samecoef
* numpy.polynomial.chebyshev.Chebyshev.has_samedomain
* numpy.polynomial.chebyshev.Chebyshev.has_sametype
* numpy.polynomial.chebyshev.Chebyshev.has_samewindow
* numpy.polynomial.chebyshev.Chebyshev.identity
* numpy.polynomial.chebyshev.Chebyshev.integ
* numpy.polynomial.chebyshev.Chebyshev.interpolate
* numpy.polynomial.chebyshev.Chebyshev.linspace
* numpy.polynomial.chebyshev.Chebyshev.mapparms
* numpy.polynomial.chebyshev.Chebyshev.roots
* numpy.polynomial.chebyshev.Chebyshev.trim
* numpy.polynomial.chebyshev.Chebyshev.truncate
* Basics
* numpy.polynomial.chebyshev.chebval
* numpy.polynomial.chebyshev.chebval2d
* numpy.polynomial.chebyshev.chebval3d
* numpy.polynomial.chebyshev.chebgrid2d
* numpy.polynomial.chebyshev.chebgrid3d
* numpy.polynomial.chebyshev.chebroots
* numpy.polynomial.chebyshev.chebfromroots
* Fitting
* numpy.polynomial.chebyshev.chebfit
* numpy.polynomial.chebyshev.chebvander
* numpy.polynomial.chebyshev.chebvander2d
* numpy.polynomial.chebyshev.chebvander3d
* Calculus
* numpy.polynomial.chebyshev.chebder
* numpy.polynomial.chebyshev.chebint
* Algebra
* numpy.polynomial.chebyshev.chebadd
* numpy.polynomial.chebyshev.chebsub
* numpy.polynomial.chebyshev.chebmul
* numpy.polynomial.chebyshev.chebmulx
* numpy.polynomial.chebyshev.chebdiv
* numpy.polynomial.chebyshev.chebpow
* Quadrature
* numpy.polynomial.chebyshev.chebgauss
* numpy.polynomial.chebyshev.chebweight
* Miscellaneous
* numpy.polynomial.chebyshev.chebcompanion
* numpy.polynomial.chebyshev.chebdomain
* numpy.polynomial.chebyshev.chebzero
* numpy.polynomial.chebyshev.chebone
* numpy.polynomial.chebyshev.chebx
* numpy.polynomial.chebyshev.chebtrim
* numpy.polynomial.chebyshev.chebline
* numpy.polynomial.chebyshev.cheb2poly
* numpy.polynomial.chebyshev.poly2cheb
* Legendre Module (numpy.polynomial.legendre)
* Legendre Class
* numpy.polynomial.legendre.Legendre
* numpy.polynomial.legendre.Legendre.__call__
* numpy.polynomial.legendre.Legendre.basis
* numpy.polynomial.legendre.Legendre.cast
* numpy.polynomial.legendre.Legendre.convert
* numpy.polynomial.legendre.Legendre.copy
* numpy.polynomial.legendre.Legendre.cutdeg
* numpy.polynomial.legendre.Legendre.degree
* numpy.polynomial.legendre.Legendre.deriv
* numpy.polynomial.legendre.Legendre.fit
* numpy.polynomial.legendre.Legendre.fromroots
* numpy.polynomial.legendre.Legendre.has_samecoef
* numpy.polynomial.legendre.Legendre.has_samedomain
* numpy.polynomial.legendre.Legendre.has_sametype
* numpy.polynomial.legendre.Legendre.has_samewindow
* numpy.polynomial.legendre.Legendre.identity
* numpy.polynomial.legendre.Legendre.integ
* numpy.polynomial.legendre.Legendre.linspace
* numpy.polynomial.legendre.Legendre.mapparms
* numpy.polynomial.legendre.Legendre.roots
* numpy.polynomial.legendre.Legendre.trim
* numpy.polynomial.legendre.Legendre.truncate
* Basics
* numpy.polynomial.legendre.legval
* numpy.polynomial.legendre.legval2d
* numpy.polynomial.legendre.legval3d
* numpy.polynomial.legendre.leggrid2d
* numpy.polynomial.legendre.leggrid3d
* numpy.polynomial.legendre.legroots
* numpy.polynomial.legendre.legfromroots
* Fitting
* numpy.polynomial.legendre.legfit
* numpy.polynomial.legendre.legvander
* numpy.polynomial.legendre.legvander2d
* numpy.polynomial.legendre.legvander3d
* Calculus
* numpy.polynomial.legendre.legder
* numpy.polynomial.legendre.legint
* Algebra
* numpy.polynomial.legendre.legadd
* numpy.polynomial.legendre.legsub
* numpy.polynomial.legendre.legmul
* numpy.polynomial.legendre.legmulx
* numpy.polynomial.legendre.legdiv
* numpy.polynomial.legendre.legpow
* Quadrature
* numpy.polynomial.legendre.leggauss
* numpy.polynomial.legendre.legweight
* Miscellaneous
* numpy.polynomial.legendre.legcompanion
* numpy.polynomial.legendre.legdomain
* numpy.polynomial.legendre.legzero
* numpy.polynomial.legendre.legone
* numpy.polynomial.legendre.legx
* numpy.polynomial.legendre.legtrim
* numpy.polynomial.legendre.legline
* numpy.polynomial.legendre.leg2poly
* numpy.polynomial.legendre.poly2leg
* Laguerre Module (numpy.polynomial.laguerre)
* Laguerre Class
* numpy.polynomial.laguerre.Laguerre
* numpy.polynomial.laguerre.Laguerre.__call__
* numpy.polynomial.laguerre.Laguerre.basis
* numpy.polynomial.laguerre.Laguerre.cast
* numpy.polynomial.laguerre.Laguerre.convert
* numpy.polynomial.laguerre.Laguerre.copy
* numpy.polynomial.laguerre.Laguerre.cutdeg
* numpy.polynomial.laguerre.Laguerre.degree
* numpy.polynomial.laguerre.Laguerre.deriv
* numpy.polynomial.laguerre.Laguerre.fit
* numpy.polynomial.laguerre.Laguerre.fromroots
* numpy.polynomial.laguerre.Laguerre.has_samecoef
* numpy.polynomial.laguerre.Laguerre.has_samedomain
* numpy.polynomial.laguerre.Laguerre.has_sametype
* numpy.polynomial.laguerre.Laguerre.has_samewindow
* numpy.polynomial.laguerre.Laguerre.identity
* numpy.polynomial.laguerre.Laguerre.integ
* numpy.polynomial.laguerre.Laguerre.linspace
* numpy.polynomial.laguerre.Laguerre.mapparms
* numpy.polynomial.laguerre.Laguerre.roots
* numpy.polynomial.laguerre.Laguerre.trim
* numpy.polynomial.laguerre.Laguerre.truncate
* Basics
* numpy.polynomial.laguerre.lagval
* numpy.polynomial.laguerre.lagval2d
* numpy.polynomial.laguerre.lagval3d
* numpy.polynomial.laguerre.laggrid2d
* numpy.polynomial.laguerre.laggrid3d
* numpy.polynomial.laguerre.lagroots
* numpy.polynomial.laguerre.lagfromroots
* Fitting
* numpy.polynomial.laguerre.lagfit
* numpy.polynomial.laguerre.lagvander
* numpy.polynomial.laguerre.lagvander2d
* numpy.polynomial.laguerre.lagvander3d
* Calculus
* numpy.polynomial.laguerre.lagder
* numpy.polynomial.laguerre.lagint
* Algebra
* numpy.polynomial.laguerre.lagadd
* numpy.polynomial.laguerre.lagsub
* numpy.polynomial.laguerre.lagmul
* numpy.polynomial.laguerre.lagmulx
* numpy.polynomial.laguerre.lagdiv
* numpy.polynomial.laguerre.lagpow
* Quadrature
* numpy.polynomial.laguerre.laggauss
* numpy.polynomial.laguerre.lagweight
* Miscellaneous
* numpy.polynomial.laguerre.lagcompanion
* numpy.polynomial.laguerre.lagdomain
* numpy.polynomial.laguerre.lagzero
* numpy.polynomial.laguerre.lagone
* numpy.polynomial.laguerre.lagx
* numpy.polynomial.laguerre.lagtrim
* numpy.polynomial.laguerre.lagline
* numpy.polynomial.laguerre.lag2poly
* numpy.polynomial.laguerre.poly2lag
* Hermite Module, “Physicists’” (numpy.polynomial.hermite)
* Hermite Class
* numpy.polynomial.hermite.Hermite
* numpy.polynomial.hermite.Hermite.__call__
* numpy.polynomial.hermite.Hermite.basis
* numpy.polynomial.hermite.Hermite.cast
* numpy.polynomial.hermite.Hermite.convert
* numpy.polynomial.hermite.Hermite.copy
* numpy.polynomial.hermite.Hermite.cutdeg
* numpy.polynomial.hermite.Hermite.degree
* numpy.polynomial.hermite.Hermite.deriv
* numpy.polynomial.hermite.Hermite.fit
* numpy.polynomial.hermite.Hermite.fromroots
* numpy.polynomial.hermite.Hermite.has_samecoef
* numpy.polynomial.hermite.Hermite.has_samedomain
* numpy.polynomial.hermite.Hermite.has_sametype
* numpy.polynomial.hermite.Hermite.has_samewindow
* numpy.polynomial.hermite.Hermite.identity
* numpy.polynomial.hermite.Hermite.integ
* numpy.polynomial.hermite.Hermite.linspace
* numpy.polynomial.hermite.Hermite.mapparms
* numpy.polynomial.hermite.Hermite.roots
* numpy.polynomial.hermite.Hermite.trim
* numpy.polynomial.hermite.Hermite.truncate
* Basics
* numpy.polynomial.hermite.hermval
* numpy.polynomial.hermite.hermval2d
* numpy.polynomial.hermite.hermval3d
* numpy.polynomial.hermite.hermgrid2d
* numpy.polynomial.hermite.hermgrid3d
* numpy.polynomial.hermite.hermroots
* numpy.polynomial.hermite.hermfromroots
* Fitting
* numpy.polynomial.hermite.hermfit
* numpy.polynomial.hermite.hermvander
* numpy.polynomial.hermite.hermvander2d
* numpy.polynomial.hermite.hermvander3d
* Calculus
* numpy.polynomial.hermite.hermder
* numpy.polynomial.hermite.hermint
* Algebra
* numpy.polynomial.hermite.hermadd
* numpy.polynomial.hermite.hermsub
* numpy.polynomial.hermite.hermmul
* numpy.polynomial.hermite.hermmulx
* numpy.polynomial.hermite.hermdiv
* numpy.polynomial.hermite.hermpow
* Quadrature
* numpy.polynomial.hermite.hermgauss
* numpy.polynomial.hermite.hermweight
* Miscellaneous
* numpy.polynomial.hermite.hermcompanion
* numpy.polynomial.hermite.hermdomain
* numpy.polynomial.hermite.hermzero
* numpy.polynomial.hermite.hermone
* numpy.polynomial.hermite.hermx
* numpy.polynomial.hermite.hermtrim
* numpy.polynomial.hermite.hermline
* numpy.polynomial.hermite.herm2poly
* numpy.polynomial.hermite.poly2herm
* HermiteE Module, “Probabilists’” (numpy.polynomial.hermite_e)
* HermiteE Class
* numpy.polynomial.hermite_e.HermiteE
* numpy.polynomial.hermite_e.HermiteE.__call__
* numpy.polynomial.hermite_e.HermiteE.basis
* numpy.polynomial.hermite_e.HermiteE.cast
* numpy.polynomial.hermite_e.HermiteE.convert
* numpy.polynomial.hermite_e.HermiteE.copy
* numpy.polynomial.hermite_e.HermiteE.cutdeg
* numpy.polynomial.hermite_e.HermiteE.degree
* numpy.polynomial.hermite_e.HermiteE.deriv
* numpy.polynomial.hermite_e.HermiteE.fit
* numpy.polynomial.hermite_e.HermiteE.fromroots
* numpy.polynomial.hermite_e.HermiteE.has_samecoef
* numpy.polynomial.hermite_e.HermiteE.has_samedomain
* numpy.polynomial.hermite_e.HermiteE.has_sametype
* numpy.polynomial.hermite_e.HermiteE.has_samewindow
* numpy.polynomial.hermite_e.HermiteE.identity
* numpy.polynomial.hermite_e.HermiteE.integ
* numpy.polynomial.hermite_e.HermiteE.linspace
* numpy.polynomial.hermite_e.HermiteE.mapparms
* numpy.polynomial.hermite_e.HermiteE.roots
* numpy.polynomial.hermite_e.HermiteE.trim
* numpy.polynomial.hermite_e.HermiteE.truncate
* Basics
* numpy.polynomial.hermite_e.hermeval
* numpy.polynomial.hermite_e.hermeval2d
* numpy.polynomial.hermite_e.hermeval3d
* numpy.polynomial.hermite_e.hermegrid2d
* numpy.polynomial.hermite_e.hermegrid3d
* numpy.polynomial.hermite_e.hermeroots
* numpy.polynomial.hermite_e.hermefromroots
* Fitting
* numpy.polynomial.hermite_e.hermefit
* numpy.polynomial.hermite_e.hermevander
* numpy.polynomial.hermite_e.hermevander2d
* numpy.polynomial.hermite_e.hermevander3d
* Calculus
* numpy.polynomial.hermite_e.hermeder
* numpy.polynomial.hermite_e.hermeint
* Algebra
* numpy.polynomial.hermite_e.hermeadd
* numpy.polynomial.hermite_e.hermesub
* numpy.polynomial.hermite_e.hermemul
* numpy.polynomial.hermite_e.hermemulx
* numpy.polynomial.hermite_e.hermediv
* numpy.polynomial.hermite_e.hermepow
* Quadrature
* numpy.polynomial.hermite_e.hermegauss
* numpy.polynomial.hermite_e.hermeweight
* Miscellaneous
* numpy.polynomial.hermite_e.hermecompanion
* numpy.polynomial.hermite_e.hermedomain
* numpy.polynomial.hermite_e.hermezero
* numpy.polynomial.hermite_e.hermeone
* numpy.polynomial.hermite_e.hermex
* numpy.polynomial.hermite_e.hermetrim
* numpy.polynomial.hermite_e.hermeline
* numpy.polynomial.hermite_e.herme2poly
* numpy.polynomial.hermite_e.poly2herme
* Polyutils
* Error objects
* numpy.polynomial.polyutils.PolyError
* numpy.polynomial.polyutils.PolyDomainError
* Warning objects
* numpy.polynomial.polyutils.RankWarning
* Base class
* numpy.polynomial.polyutils.PolyBase
* Functions
* numpy.polynomial.polyutils.as_series
* numpy.polynomial.polyutils.trimseq
* numpy.polynomial.polyutils.trimcoef
* numpy.polynomial.polyutils.getdomain
* numpy.polynomial.polyutils.mapdomain
* numpy.polynomial.polyutils.mapparms
* Poly1d
* Basics
* numpy.poly1d
* numpy.poly1d.__call__
* numpy.poly1d.deriv
* numpy.poly1d.integ
* 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
* 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.random
* numpy.random.ranf
* numpy.random.sample
* numpy.random.choice
* numpy.random.bytes
* Permutations
* numpy.random.shuffle
* numpy.random.permutation
* Distributions
* numpy.random.beta
* numpy.random.binomial
* numpy.random.chisquare
* numpy.random.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.RandomState
* numpy.random.RandomState.beta
* numpy.random.RandomState.binomial
* numpy.random.RandomState.bytes
* numpy.random.RandomState.chisquare
* numpy.random.RandomState.choice
* numpy.random.RandomState.dirichlet
* numpy.random.RandomState.exponential
* numpy.random.RandomState.f
* numpy.random.RandomState.gamma
* numpy.random.RandomState.geometric
* numpy.random.RandomState.get_state
* numpy.random.RandomState.gumbel
* numpy.random.RandomState.hypergeometric
* numpy.random.RandomState.laplace
* numpy.random.RandomState.logistic
* numpy.random.RandomState.lognormal
* numpy.random.RandomState.logseries
* numpy.random.RandomState.multinomial
* numpy.random.RandomState.multivariate_normal
* numpy.random.RandomState.negative_binomial
* numpy.random.RandomState.noncentral_chisquare
* numpy.random.RandomState.noncentral_f
* numpy.random.RandomState.normal
* numpy.random.RandomState.pareto
* numpy.random.RandomState.permutation
* numpy.random.RandomState.poisson
* numpy.random.RandomState.power
* numpy.random.RandomState.rand
* numpy.random.RandomState.randint
* numpy.random.RandomState.randn
* numpy.random.RandomState.random_integers
* numpy.random.RandomState.random_sample
* numpy.random.RandomState.rayleigh
* numpy.random.RandomState.seed
* numpy.random.RandomState.set_state
* numpy.random.RandomState.shuffle
* numpy.random.RandomState.standard_cauchy
* numpy.random.RandomState.standard_exponential
* numpy.random.RandomState.standard_gamma
* numpy.random.RandomState.standard_normal
* numpy.random.RandomState.standard_t
* numpy.random.RandomState.tomaxint
* numpy.random.RandomState.triangular
* numpy.random.RandomState.uniform
* numpy.random.RandomState.vonmises
* numpy.random.RandomState.wald
* numpy.random.RandomState.weibull
* numpy.random.RandomState.zipf
* numpy.random.seed
* numpy.random.get_state
* numpy.random.set_state
* Set routines
* Making proper sets
* numpy.unique
* Boolean operations
* numpy.in1d
* numpy.intersect1d
* numpy.isin
* numpy.setdiff1d
* numpy.setxor1d
* numpy.union1d
* Sorting, searching, and counting
* Sorting
* numpy.sort
* numpy.lexsort
* numpy.argsort
* numpy.ndarray.sort
* numpy.msort
* numpy.sort_complex
* numpy.partition
* numpy.argpartition
* 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
* Statistics
* Order statistics
* numpy.amin
* numpy.amax
* numpy.nanmin
* numpy.nanmax
* numpy.ptp
* numpy.percentile
* numpy.nanpercentile
* numpy.quantile
* numpy.nanquantile
* Averages and variances
* numpy.median
* numpy.average
* numpy.mean
* numpy.std
* numpy.var
* numpy.nanmedian
* numpy.nanmean
* numpy.nanstd
* numpy.nanvar
* Correlating
* numpy.corrcoef
* numpy.correlate
* numpy.cov
* Histograms
* numpy.histogram
* numpy.histogram2d
* numpy.histogramdd
* numpy.bincount
* numpy.histogram_bin_edges
* numpy.digitize
* Test Support (numpy.testing)
* Asserts
* numpy.testing.assert_almost_equal
* numpy.testing.assert_approx_equal
* numpy.testing.assert_array_almost_equal
* numpy.testing.assert_allclose
* numpy.testing.assert_array_almost_equal_nulp
* numpy.testing.assert_array_max_ulp
* numpy.testing.assert_array_equal
* numpy.testing.assert_array_less
* numpy.testing.assert_equal
* numpy.testing.assert_raises
* numpy.testing.assert_raises_regex
* 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
* numpy.testing.suppress_warnings
* numpy.testing.suppress_warnings.__call__
* numpy.testing.suppress_warnings.filter
* numpy.testing.suppress_warnings.record
* Guidelines
* Testing Guidelines
* Introduction
* Writing your own tests
* Labeling tests
* Easier setup and teardown functions / methods
* Parametric tests
* Doctests
* tests/
* __init__.py and setup.py
* Tips & Tricks
* Creating many similar tests
* Known failures & skipping tests
* Tests on random data
* Window functions
* Various windows
* numpy.bartlett
* numpy.blackman
* numpy.hamming
* numpy.hanning
* numpy.kaiser - 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 NPY_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
* Auxiliary Data With Object Semantics
* 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
* Simple Iteration Example
* Simple Multi-Iteration Example
* Iterator Data Types
* Construction and Destruction
* Functions For Iteration
* Converting from Previous NumPy Iterators
* 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
* Linking against the core math library in an extension
* Half-precision functions
* C API Deprecations
* Background
* Deprecation Mechanism NPY_NO_DEPRECATED_API - NumPy internals
* NumPy C Code Explanations
* Memory model
* Data-type encapsulation
* N-D Iterators
* Broadcasting
* Array Scalars
* Indexing
* Advanced indexing
* 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
* Argout View Arrays
* Memory Managed Argout View 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
- Array objects
F2PY Users Guide and Reference Manual
- Three ways to wrap - getting started
* The quick way
* The smart way
* The quick and smart way - Signature file
* Python module block
* Fortran/C routine signatures
* Type declarations
* Statements
* Attributes
* Extensions
* F2PY directives
* C expressions
* Multiline blocks - Using F2PY bindings in Python
* Scalar arguments
* String arguments
* Array arguments
* Call-back arguments
* Resolving arguments to call-back functions
* Common blocks
* Fortran 90 module data
* Allocatable arrays - Using F2PY
* Command f2py
* Python module f2py2e - Using via numpy.distutils
- Advanced F2PY usages
* Adding self-written functions to F2PY generated modules
* Modifying the dictionary of a F2PY generated module
- Three ways to wrap - getting started
-
- 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
* 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
* Two and three dots in difference specs
* Development workflow
* Basic workflow
* Making a new feature branch
* The editing workflow
* Overview
* In more detail
* Writing the commit message
* Asking for your changes to be merged with the main repo
* Rebasing on master
* Recovering from mess-ups
* Additional things you might want to do
* Rewriting commit history
* Deleting a branch on github
* Several people sharing a single repository
* Exploring your repository
* Backporting
* Pushing changes to the main repo
* Additional Git Resources
* Tutorials and summaries
* Advanced git workflow
* Manual pages online - Setting up and using your development environment
* Recommended development setup
* Building in-place
* Other build options
* Using virtualenvs
* Running tests
* Rebuilding & cleaning the workspace
* Debugging
* Understanding the code & getting started - NumPy governance
* NumPy project governance and decision-making
* Summary
* The Project
* Governance
* Consensus-based decision making by the community
* Steering Council
* Council decision making
* Council membership
* Conflict of interest
* Private communications of the Council
* Subcommittees
* NumFOCUS Subcommittee
* Institutional Partners and Funding
* Document history
* Acknowledgements
* License
* Current steering council and institutional partners
* Steering council
* Emeritus members
* NumFOCUS Subcommittee
* Institutional Partners
* Document history
- Working with NumPy source code
-
- NumPy 1.15.0 Release Notes
* Highlights
* New functions
* Deprecations
* Future Changes
* Compatibility notes
* Compiled testing modules renamed and made private
* The NpzFile returned by np.savez is now a collections.abc.Mapping
* Under certain conditions, nditer must be used in a context manager
* Numpy has switched to using pytest instead of nose for testing
* Numpy no longer monkey-patches ctypes with __array_interface__
* np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists
* np.squeeze restores old behavior of objects that cannot handle an axis argument
* unstructured void array’s .item method now returns a bytes object
* copy.copy and copy.deepcopy no longer turn masked into an array
* Multifield Indexing of Structured Arrays will still return a copy
* C API changes
* New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
* Changes to PyArray_GetDTypeTransferFunction
* New Features
* np.gcd and np.lcm ufuncs added for integer and objects types
* Support for cross-platform builds for iOS
* return_indices keyword added for np.intersect1d
* np.quantile and np.nanquantile
* Build system
* Improvements
* np.einsum updates
* np.ufunc.reduce and related functions now accept an initial value
* np.flip can operate over multiple axes
* histogram and histogramdd functions have moved to np.lib.histograms
* histogram will accept NaN values when explicit bins are given
* histogram works on datetime types, when explicit bin edges are given
* histogram “auto” estimator handles limited variance better
* The edges retuned by histogram` and histogramdd now match the data float type
* histogramdd allows explicit ranges to be given in a subset of axes
* The normed arguments of histogramdd and histogram2d have been renamed
* np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked
* np.ptp accepts a keepdims argument, and extended axis tuples
* MaskedArray.astype now is identical to ndarray.astype
* Enable AVX2/AVX512 at compile time
* nan_to_num always returns scalars when receiving scalar or 0d inputs
* np.flatnonzero works on numpy-convertible types
* np.interp returns numpy scalars rather than builtin scalars
* Allow dtype field names to be unicode in Python 2
* Comparison ufuncs accept dtype=object, overriding the default bool
* sort functions accept kind='stable'
* Do not make temporary copies for in-place accumulation
* linalg.matrix_power can now handle stacks of matrices
* Increased performance in random.permutation for multidimensional arrays
* Generalized ufuncs now accept axes, axis and keepdims arguments
* float128 values now print correctly on ppc systems
* New np.take_along_axis and np.put_along_axis functions - NumPy 1.14.5 Release Notes
* Contributors
* Pull requests merged - NumPy 1.14.4 Release Notes
* Contributors
* Pull requests merged - NumPy 1.14.3 Release Notes
* Contributors
* Pull requests merged - NumPy 1.14.2 Release Notes
* Contributors
* Pull requests merged - NumPy 1.14.1 Release Notes
* Contributors
* Pull requests merged - NumPy 1.14.0 Release Notes
* Highlights
* New functions
* Deprecations
* Future Changes
* Compatibility notes
* The mask of a masked array view is also a view rather than a copy
* np.ma.masked is no longer writeable
* np.ma functions producing ``fill_value``s have changed
* a.flat.__array__() returns non-writeable arrays when a is non-contiguous
* np.tensordot now returns zero array when contracting over 0-length dimension
* numpy.testing reorganized
* np.asfarray no longer accepts non-dtypes through the dtype argument
* 1D np.linalg.norm preserves float input types, even for arbitrary orders
* count_nonzero(arr, axis=()) now counts over no axes, not all axes
* __init__.py files added to test directories
* .astype(bool) on unstructured void arrays now calls bool on each element
* MaskedArray.squeeze never returns np.ma.masked
* Renamed first parameter of can_cast from from to from_
* isnat raises TypeError when passed wrong type
* dtype.__getitem__ raises TypeError when passed wrong type
* User-defined types now need to implement __str__ and __repr__
* Many changes to array printing, disableable with the new “legacy” printing mode
* C API changes
* PyPy compatible alternative to UPDATEIFCOPY arrays
* New Features
* Encoding argument for text IO functions
* External nose plugins are usable by numpy.testing.Tester
* parametrize decorator added to numpy.testing
* chebinterpolate function added to numpy.polynomial.chebyshev
* Support for reading lzma compressed text files in Python 3
* sign option added to np.setprintoptions and np.array2string
* hermitian option added to``np.linalg.matrix_rank``
* threshold and edgeitems options added to np.array2string
* concatenate and stack gained an out argument
* Support for PGI flang compiler on Windows
* Improvements
* Numerator degrees of freedom in random.noncentral_f need only be positive.
* The GIL is released for all np.einsum variations
* The np.einsum function will use BLAS when possible and optimize by default
* f2py now handles arrays of dimension 0
* numpy.distutils supports using MSVC and mingw64-gfortran together
* np.linalg.pinv now works on stacked matrices
* numpy.save aligns data to 64 bytes instead of 16
* NPZ files now can be written without using temporary files
* Better support for empty structured and string types
* Support for decimal.Decimal in np.lib.financial
* Float printing now uses “dragon4” algorithm for shortest decimal representation
* void datatype elements are now printed in hex notation
* printing style for void datatypes is now independently customizable
* Reduced memory usage of np.loadtxt
* Changes
* Multiple-field indexing/assignment of structured arrays
* Integer and Void scalars are now unaffected by np.set_string_function
* 0d array printing changed, style arg of array2string deprecated
* Seeding RandomState using an array requires a 1-d array
* MaskedArray objects show a more useful repr
* The repr of np.polynomial classes is more explicit - NumPy 1.13.3 Release Notes
* Contributors
* Pull requests merged - NumPy 1.13.2 Release Notes
* Contributors
* Pull requests merged - NumPy 1.13.1 Release Notes
* Pull requests merged
* Contributors - NumPy 1.13.0 Release Notes
* Highlights
* New functions
* Deprecations
* Future Changes
* Build System Changes
* Compatibility notes
* Error type changes
* Tuple object dtypes
* DeprecationWarning to error
* FutureWarning to changed behavior
* dtypes are now always true
* __getslice__ and __setslice__ are no longer needed in ndarray subclasses
* Indexing MaskedArrays/Constants with ... (ellipsis) now returns MaskedArray
* C API changes
* GUfuncs on empty arrays and NpyIter axis removal
* PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API
* New Features
* __array_ufunc__ added
* New positive ufunc
* New divmod ufunc
* np.isnat ufunc tests for NaT special datetime and timedelta values
* np.heaviside ufunc computes the Heaviside function
* np.block function for creating blocked arrays
* isin function, improving on in1d
* Temporary elision
* axes argument for unique
* np.gradient now supports unevenly spaced data
* Support for returning arrays of arbitrary dimensions in apply_along_axis
* .ndim property added to dtype to complement .shape
* Support for tracemalloc in Python 3.6
* NumPy may be built with relaxed stride checking debugging
* Improvements
* Ufunc behavior for overlapping inputs
* Partial support for 64-bit f2py extensions with MinGW
* Performance improvements for packbits and unpackbits
* Fix for PPC long double floating point information
* Better default repr for ndarray subclasses
* More reliable comparisons of masked arrays
* np.matrix with booleans elements can now be created using the string syntax
* More linalg operations now accept empty vectors and matrices
* Bundled version of LAPACK is now 3.2.2
* reduce of np.hypot.reduce and np.logical_xor allowed in more cases
* Better repr of object arrays
* Changes
* argsort on masked arrays takes the same default arguments as sort
* average now preserves subclasses
* array == None and array != None do element-wise comparison
* np.equal, np.not_equal for object arrays ignores object identity
* Boolean indexing changes
* np.random.multivariate_normal behavior with bad covariance matrix
* assert_array_less compares np.inf and -np.inf now
* assert_array_ and masked arrays assert_equal hide less warnings
* offset attribute value in memmap objects
* np.real and np.imag return scalars for scalar inputs
* The polynomial convenience classes cannot be passed to ufuncs
* Output arguments to ufuncs can be tuples also for ufunc methods - NumPy 1.12.1 Release Notes
* Bugs Fixed - NumPy 1.12.0 Release Notes
* Highlights
* Dropped Support
* Added Support
* Build System Changes
* Deprecations
* Assignment of ndarray object’s data attribute
* Unsafe int casting of the num attribute in linspace
* Insufficient bit width parameter to binary_repr
* Future Changes
* Multiple-field manipulation of structured arrays
* Compatibility notes
* DeprecationWarning to error
* FutureWarning to changed behavior
* power and ** raise errors for integer to negative integer powers
* Relaxed stride checking is the default
* The np.percentile ‘midpoint’ interpolation method fixed for exact indices
* keepdims kwarg is passed through to user-class methods
* bitwise_and identity changed
* ma.median warns and returns nan when unmasked invalid values are encountered
* Greater consistency in assert_almost_equal
* NoseTester behaviour of warnings during testing
* assert_warns and deprecated decorator more specific
* C API
* New Features
* Writeable keyword argument for as_strided
* axes keyword argument for rot90
* Generalized flip
* BLIS support in numpy.distutils
* Hook in numpy/__init__.py to run distribution-specific checks
* New nanfunctions nancumsum and nancumprod added
* np.interp can now interpolate complex values
* New polynomial evaluation function polyvalfromroots added
* New array creation function geomspace added
* New context manager for testing warnings
* New masked array functions ma.convolve and ma.correlate added
* New float_power ufunc
* np.loadtxt now supports a single integer as usecol argument
* Improved automated bin estimators for histogram
* np.roll can now roll multiple axes at the same time
* The __complex__ method has been implemented for the ndarrays
* pathlib.Path objects now supported
* New bits attribute for np.finfo
* New signature argument to np.vectorize
* Emit py3kwarnings for division of integer arrays
* numpy.sctypes now includes bytes on Python3 too
* Improvements
* bitwise_and identity changed
* Generalized Ufuncs will now unlock the GIL
* Caches in np.fft are now bounded in total size and item count
* Improved handling of zero-width string/unicode dtypes
* Integer ufuncs vectorized with AVX2
* Order of operations optimization in np.einsum
* quicksort has been changed to an introsort
* ediff1d improved performance and subclass handling
* Improved precision of ndarray.mean for float16 arrays
* Changes
* All array-like methods are now called with keyword arguments in fromnumeric.py
* Operations on np.memmap objects return numpy arrays in most cases
* stacklevel of warnings increased - NumPy 1.11.3 Release Notes
* Contributors to maintenance/1.11.3
* Pull Requests Merged - NumPy 1.11.2 Release Notes
* Pull Requests Merged - NumPy 1.11.1 Release Notes
* Fixes Merged - NumPy 1.11.0 Release Notes
* Highlights
* Build System Changes
* Future Changes
* Compatibility notes
* datetime64 changes
* linalg.norm return type changes
* polynomial fit changes
* np.dot now raises TypeError instead of ValueError
* FutureWarning to changed behavior
* % and // operators
* C API
* object dtype detection for old-style classes
* New Features
* Improvements
* np.gradient now supports an axis argument
* np.lexsort now supports arrays with object data-type
* np.ma.core.MaskedArray now supports an order argument
* Memory and speed improvements for masked arrays
* ndarray.tofile now uses fallocate on linux
* Optimizations for operations of the form A.T @ A and A @ A.T
* np.testing.assert_warns can now be used as a context manager
* Speed improvement for np.random.shuffle
* Changes
* Pyrex support was removed from numpy.distutils
* np.broadcast can now be called with a single argument
* np.trace now respects array subclasses
* np.dot now raises TypeError instead of ValueError
* linalg.norm return type changes
* Deprecations
* Views of arrays in Fortran order
* Invalid arguments for array ordering
* Random number generator in the testing namespace
* Random integer generation on a closed interval
* FutureWarnings
* Assigning to slices/views of MaskedArray - NumPy 1.10.4 Release Notes
* Compatibility notes
* Issues Fixed
* Merged PRs - NumPy 1.10.3 Release Notes
- NumPy 1.10.2 Release Notes
* Compatibility notes
* Relaxed stride checking is no longer the default
* Fix swig bug in numpy.i
* Deprecate views changing dimensions in fortran order
* Issues Fixed
* Merged PRs
* Notes - NumPy 1.10.1 Release Notes
- NumPy 1.10.0 Release Notes
* Highlights
* Dropped Support
* Future Changes
* Compatibility notes
* Default casting rule change
* numpy version string
* relaxed stride checking
* Concatenation of 1d arrays along any but axis=0 raises IndexError
* np.ravel, np.diagonal and np.diag now preserve subtypes
* rollaxis and swapaxes always return a view
* nonzero now returns base ndarrays
* C API
* recarray field return types
* recarray views
* ‘out’ keyword argument of ufuncs now accepts tuples of arrays
* byte-array indices now raises an IndexError
* Masked arrays containing objects with arrays
* Median warns and returns nan when invalid values are encountered
* Functions available from numpy.ma.testutils have changed
* New Features
* Reading extra flags from site.cfg
* np.cbrt to compute cube root for real floats
* numpy.distutils now allows parallel compilation
* genfromtxt has a new max_rows argument
* New function np.broadcast_to for invoking array broadcasting
* New context manager clear_and_catch_warnings for testing warnings
* cov has new fweights and aweights arguments
* Support for the ‘@’ operator in Python 3.5+
* New argument norm to fft functions
* Improvements
* np.digitize using binary search
* np.poly now casts integer inputs to float
* np.interp can now be used with periodic functions
* np.pad supports more input types for pad_width and constant_values
* np.argmax and np.argmin now support an out argument
* More system C99 complex functions detected and used
* np.loadtxt support for the strings produced by the float.hex method
* np.isclose properly handles minimal values of integer dtypes
* np.allclose uses np.isclose internally.
* np.genfromtxt now handles large integers correctly
* np.load, np.save have pickle backward compatibility flags
* MaskedArray support for more complicated base classes
* Changes
* dotblas functionality moved to multiarray
* stricter check of gufunc signature compliance
* views returned from np.einsum are writeable
* np.argmin skips NaT values
* Deprecations
* Array comparisons involving strings or structured dtypes
* SafeEval
* alterdot, restoredot
* pkgload, PackageLoader
* bias, ddof arguments to corrcoef
* dtype string representation changes - NumPy 1.9.2 Release Notes
* Issues fixed - NumPy 1.9.1 Release Notes
* Issues fixed - NumPy 1.9.0 Release Notes
* Highlights
* Dropped Support
* Future Changes
* Compatibility notes
* The diagonal and diag functions return readonly views.
* Special scalar float values don’t cause upcast to double anymore
* Percentile output changes
* ndarray.tofile exception type
* Invalid fill value exceptions
* Polynomial Classes no longer derived from PolyBase
* Using numpy.random.binomial may change the RNG state vs. numpy < 1.9
* Random seed enforced to be a 32 bit unsigned integer
* Argmin and argmax out argument
* Einsum
* Indexing
* Non-integer reduction axis indexes are deprecated
* promote_types and string dtype
* can_cast and string dtype
* astype and string dtype
* npyio.recfromcsv keyword arguments change
* The doc/swig directory moved
* The npy_3kcompat.h header changed
* Negative indices in C-Api sq_item and sq_ass_item sequence methods
* NDIter
* zeros_like for string dtypes now returns empty strings
* New Features
* Percentile supports more interpolation options
* Generalized axis support for median and percentile
* Dtype parameter added to np.linspace and np.logspace
* More general np.triu and np.tril broadcasting
* tobytes alias for tostring method
* Build system
* Compatibility to python numbers module
* increasing parameter added to np.vander
* unique_counts parameter added to np.unique
* Support for median and percentile in nanfunctions
* NumpyVersion class added
* Allow saving arrays with large number of named columns
* Full broadcasting support for np.cross
* Improvements
* Better numerical stability for sum in some cases
* Percentile implemented in terms of np.partition
* Performance improvement for np.array
* Performance improvement for np.searchsorted
* Optional reduced verbosity for np.distutils
* Covariance check in np.random.multivariate_normal
* Polynomial Classes no longer template based
* More GIL releases
* MaskedArray support for more complicated base classes
* C-API
* Deprecations
* Non-integer scalars for sequence repetition
* select input deprecations
* rank function
* Object array equality comparisons
* C-API - NumPy 1.8.2 Release Notes
* Issues fixed - NumPy 1.8.1 Release Notes
* Issues fixed
* Changes
* NDIter
* Optional reduced verbosity for np.distutils
* Deprecations
* C-API - NumPy 1.8.0 Release Notes
* Highlights
* Dropped Support
* Future Changes
* Compatibility notes
* NPY_RELAXED_STRIDES_CHECKING
* Binary operations with non-arrays as second argument
* Function median used with overwrite_input only partially sorts array
* Fix to financial.npv
* Runtime warnings when comparing NaN numbers
* New Features
* Support for linear algebra on stacked arrays
* In place fancy indexing for ufuncs
* New functions partition and argpartition
* New functions nanmean, nanvar and nanstd
* New functions full and full_like
* IO compatibility with large files
* Building against OpenBLAS
* New constant
* New modes for qr
* New invert argument to in1d
* Advanced indexing using np.newaxis
* C-API
* runtests.py
* Improvements
* IO performance improvements
* Performance improvements to pad
* Performance improvements to isnan, isinf, isfinite and byteswap
* Performance improvements via SSE2 vectorization
* Performance improvements to median
* Overrideable operand flags in ufunc C-API
* Changes
* General
* C-API Array Additions
* C-API Ufunc Additions
* C-API Developer Improvements
* Deprecations
* General
* Authors - NumPy 1.7.2 Release Notes
* Issues fixed - NumPy 1.7.1 Release Notes
* Issues fixed - NumPy 1.7.0 Release Notes
* Highlights
* Compatibility notes
* New features
* Reduction UFuncs Generalize axis= Parameter
* Reduction UFuncs New keepdims= Parameter
* Datetime support
* Custom formatter for printing arrays
* New function numpy.random.choice
* New function isclose
* Preliminary multi-dimensional support in the polynomial package
* Ability to pad rank-n arrays
* New argument to searchsorted
* Build system
* C API
* Changes
* General
* Casting Rules
* Deprecations
* General
* C-API - NumPy 1.6.2 Release Notes
* Issues fixed
* numpy.core
* numpy.lib
* numpy.distutils
* numpy.random
* Changes
* numpy.f2py
* numpy.poly - NumPy 1.6.1 Release Notes
* Issues Fixed - NumPy 1.6.0 Release Notes
* Highlights
* New features
* New 16-bit floating point type
* New iterator
* Legendre, Laguerre, Hermite, HermiteE polynomials in numpy.polynomial
* Fortran assumed shape array and size function support in numpy.f2py
* Other new functions
* Changes
* default error handling
* numpy.distutils
* numpy.testing
* C API
* Deprecated features
* Removed features
* numpy.fft
* numpy.memmap
* numpy.lib
* numpy.ma
* numpy.distutils - NumPy 1.5.0 Release Notes
* Highlights
* Python 3 compatibility
* PEP 3118 compatibility
* New features
* Warning on casting complex to real
* Dot method for ndarrays
* linalg.slogdet function
* new header
* Changes
* polynomial.polynomial
* polynomial.chebyshev
* histogram
* correlate - NumPy 1.4.0 Release Notes
* Highlights
* New features
* Extended array wrapping mechanism for ufuncs
* Automatic detection of forward incompatibilities
* New iterators
* New polynomial support
* New C API
* New ufuncs
* New defines
* Testing
* Reusing npymath
* Improved set operations
* Improvements
* Deprecations
* Internal changes
* Use C99 complex functions when available
* split multiarray and umath source code
* Separate compilation
* Separate core math library - 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
- NumPy 1.15.0 Release Notes