NumPy manual contents

• User Guide
  • Setting up
    • What is NumPy?
      • Why is NumPy Fast?
      • Who Else Uses 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
      • Overflow Errors
      • 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
    • Writing custom array containers
    • 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
    • Testing
      • Parallel builds
    • FORTRAN ABI mismatch
      • Choosing the fortran compiler
      • How to check the ABI of blas/lapack/atlas
    • Accelerated BLAS/LAPACK libraries
      • BLAS
      • LAPACK
      • 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
    • 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
• NumPy Reference
  • 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.max
          • 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.__bool__
        • 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.__complex__
        • 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
          • numpy.dtype.base
        • 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
        • Alternative Looping and Element Access
        • 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):
            • numpy.ma.MaskedArray.__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.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
          • 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
        • 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
  • 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
        • numpy.shape
      • 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.char.add
        • numpy.char.multiply
        • numpy.char.mod
        • numpy.char.capitalize
        • numpy.char.center
        • numpy.char.decode
        • numpy.char.encode
        • numpy.char.expandtabs
        • numpy.char.join
        • numpy.char.ljust
        • numpy.char.lower
        • numpy.char.lstrip
        • numpy.char.partition
        • numpy.char.replace
        • numpy.char.rjust
        • numpy.char.rpartition
        • numpy.char.rsplit
        • numpy.char.rstrip
        • numpy.char.split
        • numpy.char.splitlines
        • numpy.char.strip
        • numpy.char.swapcase
        • numpy.char.title
        • numpy.char.translate
        • numpy.char.upper
        • numpy.char.zfill
      • Comparison
        • numpy.char.equal
        • numpy.char.not_equal
        • numpy.char.greater_equal
        • numpy.char.less_equal
        • numpy.char.greater
        • numpy.char.less
        • numpy.char.compare_chararrays
      • String information
        • numpy.char.count
        • numpy.char.endswith
        • numpy.char.find
        • numpy.char.index
        • numpy.char.isalpha
        • numpy.char.isalnum
        • numpy.char.isdecimal
        • numpy.char.isdigit
        • numpy.char.islower
        • numpy.char.isnumeric
        • numpy.char.isspace
        • numpy.char.istitle
        • numpy.char.isupper
        • numpy.char.rfind
        • numpy.char.rindex
        • numpy.char.startswith
        • numpy.char.str_len
      • Convenience class
        • numpy.char.array
        • numpy.char.asarray
        • numpy.char.chararray
          • numpy.char.chararray.astype
          • numpy.char.chararray.argsort
          • numpy.char.chararray.copy
          • numpy.char.chararray.count
          • numpy.char.chararray.decode
          • numpy.char.chararray.dump
          • numpy.char.chararray.dumps
          • numpy.char.chararray.encode
          • numpy.char.chararray.endswith
          • numpy.char.chararray.expandtabs
          • numpy.char.chararray.fill
          • numpy.char.chararray.find
          • numpy.char.chararray.flatten
          • numpy.char.chararray.getfield
          • numpy.char.chararray.index
          • numpy.char.chararray.isalnum
          • numpy.char.chararray.isalpha
          • numpy.char.chararray.isdecimal
          • numpy.char.chararray.isdigit
          • numpy.char.chararray.islower
          • numpy.char.chararray.isnumeric
          • numpy.char.chararray.isspace
          • numpy.char.chararray.istitle
          • numpy.char.chararray.isupper
          • numpy.char.chararray.item
          • numpy.char.chararray.join
          • numpy.char.chararray.ljust
          • numpy.char.chararray.lower
          • numpy.char.chararray.lstrip
          • numpy.char.chararray.nonzero
          • numpy.char.chararray.put
          • numpy.char.chararray.ravel
          • numpy.char.chararray.repeat
          • numpy.char.chararray.replace
          • numpy.char.chararray.reshape
          • numpy.char.chararray.resize
          • numpy.char.chararray.rfind
          • numpy.char.chararray.rindex
          • numpy.char.chararray.rjust
          • numpy.char.chararray.rsplit
          • numpy.char.chararray.rstrip
          • numpy.char.chararray.searchsorted
          • numpy.char.chararray.setfield
          • numpy.char.chararray.setflags
          • numpy.char.chararray.sort
          • numpy.char.chararray.split
          • numpy.char.chararray.splitlines
          • numpy.char.chararray.squeeze
          • numpy.char.chararray.startswith
          • numpy.char.chararray.strip
          • numpy.char.chararray.swapaxes
          • numpy.char.chararray.swapcase
          • numpy.char.chararray.take
          • numpy.char.chararray.title
          • numpy.char.chararray.tofile
          • numpy.char.chararray.tolist
          • numpy.char.chararray.tostring
          • numpy.char.chararray.translate
          • numpy.char.chararray.transpose
          • numpy.char.chararray.upper
          • numpy.char.chararray.view
          • numpy.char.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
        • numpy.maximum_sctype
    • 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
          • numpy.errstate.__call__
      • 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
      • Type Promotion
      • 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
            • Format Version 3.0
            • 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.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
        • 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
      • 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
        • numpy.conjugate
      • 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
      • Performance tuning
        • numpy.setbufsize
        • numpy.getbufsize
      • Memory ranges
        • numpy.shares_memory
        • numpy.may_share_memory
        • numpy.byte_bounds
      • Array mixins
        • numpy.lib.mixins.NDArrayOperatorsMixin
      • NumPy version comparison
        • numpy.lib.NumpyVersion
      • Utility
        • numpy.get_include
        • numpy.deprecate
        • numpy.deprecate_with_doc
      • Matlab-like Functions
        • numpy.who
        • numpy.disp
    • 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)
      • Quick Start
      • Introduction
        • What’s New or Different
        • Parallel Generation
      • Concepts
        • Random Generator
          • Accessing the BitGenerator
            • numpy.random.Generator.bit_generator
          • Simple random data
            • numpy.random.Generator.integers
            • numpy.random.Generator.random
            • numpy.random.Generator.choice
            • numpy.random.Generator.bytes
          • Permutations
            • numpy.random.Generator.shuffle
            • numpy.random.Generator.permutation
          • Distributions
            • numpy.random.Generator.beta
            • numpy.random.Generator.binomial
            • numpy.random.Generator.chisquare
            • numpy.random.Generator.dirichlet
            • numpy.random.Generator.exponential
            • numpy.random.Generator.f
            • numpy.random.Generator.gamma
            • numpy.random.Generator.geometric
            • numpy.random.Generator.gumbel
            • numpy.random.Generator.hypergeometric
            • numpy.random.Generator.laplace
            • numpy.random.Generator.logistic
            • numpy.random.Generator.lognormal
            • numpy.random.Generator.logseries
            • numpy.random.Generator.multinomial
            • numpy.random.Generator.multivariate_hypergeometric
            • numpy.random.Generator.multivariate_normal
            • numpy.random.Generator.negative_binomial
            • numpy.random.Generator.noncentral_chisquare
            • numpy.random.Generator.noncentral_f
            • numpy.random.Generator.normal
            • numpy.random.Generator.pareto
            • numpy.random.Generator.poisson
            • numpy.random.Generator.power
            • numpy.random.Generator.rayleigh
            • numpy.random.Generator.standard_cauchy
            • numpy.random.Generator.standard_exponential
            • numpy.random.Generator.standard_gamma
            • numpy.random.Generator.standard_normal
            • numpy.random.Generator.standard_t
            • numpy.random.Generator.triangular
            • numpy.random.Generator.uniform
            • numpy.random.Generator.vonmises
            • numpy.random.Generator.wald
            • numpy.random.Generator.weibull
            • numpy.random.Generator.zipf
        • Legacy Generator (RandomState)
          • Seeding and State
            • numpy.random.RandomState.get_state
            • numpy.random.RandomState.set_state
            • numpy.random.RandomState.seed
          • Simple random data
            • numpy.random.RandomState.rand
            • numpy.random.RandomState.randn
            • numpy.random.RandomState.randint
            • numpy.random.RandomState.random_integers
            • numpy.random.RandomState.random_sample
            • numpy.random.RandomState.choice
            • numpy.random.RandomState.bytes
          • Permutations
            • numpy.random.RandomState.shuffle
            • numpy.random.RandomState.permutation
          • Distributions
            • numpy.random.RandomState.beta
            • numpy.random.RandomState.binomial
            • numpy.random.RandomState.chisquare
            • numpy.random.RandomState.dirichlet
            • numpy.random.RandomState.exponential
            • numpy.random.RandomState.f
            • numpy.random.RandomState.gamma
            • numpy.random.RandomState.geometric
            • 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.poisson
            • numpy.random.RandomState.power
            • numpy.random.RandomState.rayleigh
            • 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.triangular
            • numpy.random.RandomState.uniform
            • numpy.random.RandomState.vonmises
            • numpy.random.RandomState.wald
            • numpy.random.RandomState.weibull
            • numpy.random.RandomState.zipf
          • Functions in numpy.random
            • numpy.random.beta
            • numpy.random.binomial
            • numpy.random.bytes
            • numpy.random.chisquare
            • numpy.random.choice
            • numpy.random.dirichlet
            • numpy.random.exponential
            • numpy.random.f
            • numpy.random.gamma
            • numpy.random.geometric
            • numpy.random.get_state
            • 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.permutation
            • numpy.random.poisson
            • numpy.random.power
            • numpy.random.rand
            • numpy.random.randint
            • numpy.random.randn
            • numpy.random.random
            • numpy.random.random_integers
            • numpy.random.random_sample
            • numpy.random.ranf
            • numpy.random.rayleigh
            • numpy.random.sample
            • numpy.random.seed
            • numpy.random.set_state
            • numpy.random.shuffle
            • 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
        • Bit Generators
          • Supported BitGenerators
            • numpy.random.BitGenerator
              • numpy.random.BitGenerator.random_raw
            • MT19937
              • State
                • numpy.random.MT19937.state
              • Parallel generation
                • numpy.random.MT19937.jumped
              • Extending
                • numpy.random.MT19937.cffi
                • numpy.random.MT19937.ctypes
            • PCG64
              • State
                • numpy.random.PCG64.state
              • Parallel generation
                • numpy.random.PCG64.advance
                • numpy.random.PCG64.jumped
              • Extending
                • numpy.random.PCG64.cffi
                • numpy.random.PCG64.ctypes
            • Philox
              • State
                • numpy.random.Philox.state
              • Parallel generation
                • numpy.random.Philox.advance
                • numpy.random.Philox.jumped
              • Extending
                • numpy.random.Philox.cffi
                • numpy.random.Philox.ctypes
            • SFC64
              • State
                • numpy.random.SFC64.state
              • Extending
                • numpy.random.SFC64.cffi
                • numpy.random.SFC64.ctypes
        • Seeding and Entropy
          • numpy.random.SeedSequence
            • numpy.random.SeedSequence.generate_state
            • numpy.random.SeedSequence.spawn
      • Features
        • Parallel Applications
          • SeedSequence spawning
          • Independent Streams
          • Jumping the BitGenerator state
        • Multithreaded Generation
        • What’s New or Different
        • Comparing Performance
          • Recommendation
          • Timings
          • Performance on different Operating Systems
            • 64-bit Linux
            • 64-bit Windows
            • 32-bit Windows
        • Cython API for random
        • Examples of using Numba, Cython, CFFI
          • Numba
          • Cython
          • CFFI
          • New Basic RNGs
          • Examples
            • Numba
            • CFFI + Numba
            • Cython
              • setup.py
              • extending.pyx
              • extending_distributions.pyx
            • CFFI
        • Original Source of the Generator and BitGenerators
    • 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.dec.deprecated
        • numpy.testing.dec.knownfailureif
        • numpy.testing.dec.setastest
        • numpy.testing.dec.skipif
        • numpy.testing.dec.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
      • distutils.misc_util
      • numpy.distutils.ccompiler
      • numpy.distutils.cpuinfo.cpu
      • numpy.distutils.core.Extension
      • numpy.distutils.exec_command
      • numpy.distutils.log.set_verbosity
      • numpy.distutils.system_info.get_info
      • numpy.distutils.system_info.get_standard_file
    • Configuration class
    • Building Installable C libraries
      • npy-pkg-config files
      • Reusing a C library from another package
    • Conversion of .src files
  • NumPy Distutils - Users Guide
    • SciPy structure
    • Requirements for SciPy packages
    • The setup.py file
      • SciPy pure Python package example
      • Configuration instance attributes
      • Configuration instance methods
      • Conversion of .src files using Templates
      • Fortran files
        • Named repeat rule
        • Short repeat rule
        • Pre-defined names
      • Other files
      • Useful functions in numpy.distutils.misc_util
      • numpy.distutils.system_info module
      • numpy.distutils.cpuinfo module
      • numpy.distutils.log module
      • numpy.distutils.exec_command module
    • The __init__.py file
    • Extra features in NumPy Distutils
      • Specifying config_fc options for libraries in setup.py script
      • Getting extra Fortran 77 compiler options from source
  • NumPy C-API
    • Python Types and C-Structures
      • New Python Types Defined
        • PyArray_Type and PyArrayObject
        • PyArrayDescr_Type and PyArray_Descr
        • PyUFunc_Type and PyUFuncObject
        • PyArrayIter_Type and PyArrayIterObject
        • PyArrayMultiIter_Type and PyArrayMultiIterObject
        • PyArrayNeighborhoodIter_Type and PyArrayNeighborhoodIterObject
        • PyArrayFlags_Type and PyArrayFlagsObject
        • ScalarArrayTypes
      • Other C-Structures
        • PyArray_Dims
        • PyArray_Chunk
        • PyArrayInterface
        • Internally used structures
    • System configuration
      • Data type sizes
      • Platform information
      • Compiler directives
      • Interrupt Handling
    • 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
        • Complex functions
        • 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
    • Memory Alignment
      • Numpy Alignment Goals
      • Variables in Numpy which control and describe alignment
      • Consequences of alignment
    • 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
• Contributing to NumPy
  • Development process - summary
    • Divergence between upstream/master and your feature branch
    • Guidelines
    • Stylistic Guidelines
    • Test coverage
    • Building docs
      • Requirements
      • Fixing Warnings
  • Development process - details
    • NumPy Code of Conduct
      • Introduction
      • Specific Guidelines
      • Diversity Statement
      • Reporting Guidelines
      • Incident reporting resolution & Code of Conduct enforcement
      • Endnotes
    • Git Basics
      • Install git
      • Get the local copy of the code
      • Updating the code
      • 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
      • Additional Git Resources
        • Tutorials and summaries
        • Advanced git workflow
        • Manual pages online
    • Setting up and using your development environment
      • Recommended development setup
      • Testing builds
      • Building in-place
      • Other build options
      • Using virtualenvs
      • Running tests
      • Rebuilding & cleaning the workspace
      • Debugging
      • Understanding the code & getting started
    • 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
    • NumPy benchmarks
      • Usage
      • Writing benchmarks
    • NumPy C Style Guide
      • Introduction
      • C dialect
      • Code lay-out
      • Naming conventions
      • Function documentation
    • Releasing a Version
      • How to Prepare a Release
        • Current build and release info
          • Source tree
          • NumPy Docs
          • SciPy.org wiki
          • Doc wiki
          • Release Scripts
        • Supported platforms and versions
          • OS X
          • Windows
          • Linux
          • BSD / Solaris
        • Tool chain
          • Compilers
          • Building source archives and wheels
          • Building docs
          • Uploading to PyPI
          • Generating author/pr lists
          • Virtualenv
        • What is released
          • Wheels
          • Other
          • Source distribution
        • Release process
          • Agree on a release schedule
          • Make sure current branch builds a package correctly
          • Check deprecations
          • Check the C API version number
          • Check the release notes
          • Update the release status and create a release “tag”
          • Apply patch to fix bogus strides
          • Update the version of the master branch
          • Trigger the wheel builds on travis-ci and Appveyor
          • Make the release
          • Build and archive documentation
          • Update PyPI
          • Push the release tag and commit
          • Update scipy.org
          • Announce to the lists
          • Announce to Linux Weekly News
          • After the final release
      • Step-by-Step Directions
        • Release Walkthrough
          • Backport Pull Requests
          • Update Release documentation
          • Finish the Release Note
          • Prepare the release commit
          • Build source releases
          • Build wheels
          • Download wheels
          • Generate the README files
          • Tag the release
          • Reset the maintenance branch into a development state
          • Upload to PyPI
          • Upload files to github
          • Upload documents to numpy.org
          • Announce the release on scipy.org
          • Announce to mailing lists
          • Post-Release Tasks
    • 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
• Release Notes
  • 1.17.4
    • Highlights
    • Contributors
    • Pull requests merged
  • 1.17.3
    • Highlights
    • Compatibility notes
    • Contributors
    • Pull requests merged
  • 1.17.2
    • Contributors
    • Pull requests merged
  • 1.17.1
    • Contributors
    • Pull requests merged
  • 1.17.0
    • Highlights
    • New functions
    • Deprecations
      • numpy.polynomial functions warn when passed float in place of int
      • Deprecate numpy.distutils.exec_command and temp_file_name
      • Writeable flag of C-API wrapped arrays
      • numpy.nonzero should no longer be called on 0d arrays
      • Writing to the result of numpy.broadcast_arrays will warn
    • Future Changes
      • Shape-1 fields in dtypes won’t be collapsed to scalars in a future version
    • Compatibility notes
      • float16 subnormal rounding
      • Signed zero when using divmod
      • MaskedArray.mask now returns a view of the mask, not the mask itself
      • Do not lookup __buffer__ attribute in numpy.frombuffer
      • out is buffered for memory overlaps in take, choose, put
      • Unpickling while loading requires explicit opt-in
      • Potential changes to the random stream in old random module
      • i0 now always returns a result with the same shape as the input
      • can_cast no longer assumes all unsafe casting is allowed
      • ndarray.flags.writeable can be switched to true slightly more often
    • C API changes
      • dimension or stride input arguments are now passed by npy_intp const*
    • New Features
      • New extensible numpy.random module with selectable random number generators
      • libFLAME
      • User-defined BLAS detection order
      • User-defined LAPACK detection order
      • ufunc.reduce and related functions now accept a where mask
      • Timsort and radix sort have replaced mergesort for stable sorting
      • packbits and unpackbits accept an order keyword
      • unpackbits now accepts a count parameter
      • linalg.svd and linalg.pinv can be faster on hermitian inputs
      • divmod operation is now supported for two timedelta64 operands
      • fromfile now takes an offset argument
      • New mode “empty” for pad
      • empty_like and related functions now accept a shape argument
      • Floating point scalars implement as_integer_ratio to match the builtin float
      • Structured dtype objects can be indexed with multiple fields names
      • .npy files support unicode field names
    • Improvements
      • Array comparison assertions include maximum differences
      • Replacement of the fftpack based fft module by the pocketfft library
      • Further improvements to ctypes support in numpy.ctypeslib
      • numpy.errstate is now also a function decorator
      • numpy.exp and numpy.log speed up for float32 implementation
      • Improve performance of numpy.pad
      • numpy.interp handles infinities more robustly
      • Pathlib support for fromfile, tofile and ndarray.dump
      • Specialized isnan, isinf, and isfinite ufuncs for bool and int types
      • isfinite supports datetime64 and timedelta64 types
      • New keywords added to nan_to_num
      • MemoryErrors caused by allocated overly large arrays are more descriptive
      • floor, ceil, and trunc now respect builtin magic methods
      • quantile now works on fraction.Fraction and decimal.Decimal objects
      • Support of object arrays in matmul
    • Changes
      • median and percentile family of functions no longer warn about nan
      • timedelta64 % 0 behavior adjusted to return NaT
      • NumPy functions now always support overrides with __array_function__
      • lib.recfunctions.structured_to_unstructured does not squeeze single-field views
      • clip now uses a ufunc under the hood
      • __array_interface__ offset now works as documented
      • Pickle protocol in savez set to 3 for force zip64 flag
      • Structured arrays indexed with non-existent fields raise KeyError not ValueError
  • 1.16.5
    • Contributors
    • Pull requests merged
  • 1.16.4
    • New deprecations
      • Writeable flag of C-API wrapped arrays
    • Compatibility notes
      • Potential changes to the random stream
    • Changes
      • numpy.lib.recfunctions.structured_to_unstructured does not squeeze single-field views
    • Contributors
    • Pull requests merged
  • 1.16.3
    • Compatibility notes
      • Unpickling while loading requires explicit opt-in
    • Improvements
      • Covariance in random.mvnormal cast to double
    • Changes
      • __array_interface__ offset now works as documented
  • 1.16.2
    • Compatibility notes
      • Signed zero when using divmod
    • Contributors
    • Pull requests merged
  • 1.16.1
    • Contributors
    • Enhancements
    • Compatibility notes
    • New Features
      • divmod operation is now supported for two timedelta64 operands
    • Improvements
      • Further improvements to ctypes support in np.ctypeslib
      • Array comparison assertions include maximum differences
    • Changes
      • timedelta64 % 0 behavior adjusted to return NaT
  • 1.16.0
    • Highlights
    • New functions
    • New deprecations
    • Expired deprecations
    • Future changes
    • Compatibility notes
      • f2py script on Windows
      • NaT comparisons
      • complex64/128 alignment has changed
      • nd_grid __len__ removal
      • np.unravel_index now accepts shape keyword argument
      • multi-field views return a view instead of a copy
    • C API changes
    • New Features
      • Integrated squared error (ISE) estimator added to histogram
      • max_rows keyword added for np.loadtxt
      • modulus operator support added for np.timedelta64 operands
    • Improvements
      • no-copy pickling of numpy arrays
      • build shell independence
      • np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks
      • randint and choice now work on empty distributions
      • linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays
      • Chain exceptions to give better error messages for invalid PEP3118 format strings
      • Einsum optimization path updates and efficiency improvements
      • numpy.angle and numpy.expand_dims now work on ndarray subclasses
      • NPY_NO_DEPRECATED_API compiler warning suppression
      • np.diff Added kwargs prepend and append
      • ARM support updated
      • Appending to build flags
      • Generalized ufunc signatures now allow fixed-size dimensions
      • Generalized ufunc signatures now allow flexible dimensions
      • np.clip and the clip method check for memory overlap
      • New value unscaled for option cov in np.polyfit
      • Detailed docstrings for scalar numeric types
      • __module__ attribute now points to public modules
      • Large allocations marked as suitable for transparent hugepages
      • Alpine Linux (and other musl c library distros) support
      • Speedup np.block for large arrays
        • arr.ctypes.data_as(...) holds a reference to arr
      • Speedup np.take for read-only arrays
      • Support path-like objects for more functions
      • Better behaviour of ufunc identities during reductions
      • Improved conversion from ctypes objects
      • A new ndpointer.contents member
      • matmul is now a ufunc
      • Start and stop arrays for linspace, logspace and geomspace
      • CI extended with additional services
    • Changes
      • Comparison ufuncs will now error rather than return NotImplemented
      • Positive will now raise a deprecation warning for non-numerical arrays
      • NDArrayOperatorsMixin now implements matrix multiplication
      • The scaling of the covariance matrix in np.polyfit is different
      • maximum and minimum no longer emit warnings
      • Umath and multiarray c-extension modules merged into a single module
      • getfield validity checks extended
      • NumPy functions now support overrides with __array_function__
      • Arrays based off readonly buffers cannot be set writeable
  • 1.15.4
    • Compatibility Note
    • Contributors
    • Pull requests merged
  • 1.15.3
    • Compatibility Note
    • Contributors
    • Pull requests merged
  • 1.15.2
    • Compatibility Note
    • Contributors
    • Pull requests merged
  • 1.15.1
    • Compatibility Note
    • Contributors
    • Pull requests merged
  • 1.15.0
    • 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
  • 1.14.6
    • Contributors
    • Pull requests merged
  • 1.14.5
    • Contributors
    • Pull requests merged
  • 1.14.4
    • Contributors
    • Pull requests merged
  • 1.14.3
    • Contributors
    • Pull requests merged
  • 1.14.2
    • Contributors
    • Pull requests merged
  • 1.14.1
    • Contributors
    • Pull requests merged
  • 1.14.0
    • 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
  • 1.13.3
    • Contributors
    • Pull requests merged
  • 1.13.2
    • Contributors
    • Pull requests merged
  • 1.13.1
    • Pull requests merged
    • Contributors
  • 1.13.0
    • 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
  • 1.12.1
    • Bugs Fixed
  • 1.12.0
    • 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
  • 1.11.3
    • Contributors to maintenance/1.11.3
    • Pull Requests Merged
  • 1.11.2
    • Pull Requests Merged
  • 1.11.1
    • Fixes Merged
  • 1.11.0
    • 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
  • 1.10.4
    • Compatibility notes
    • Issues Fixed
    • Merged PRs
  • 1.10.3
  • 1.10.2
    • 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
  • 1.10.1
  • 1.10.0
    • 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
  • 1.9.2
    • Issues fixed
  • 1.9.1
    • Issues fixed
  • 1.9.0
    • 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
  • 1.8.2
    • Issues fixed
  • 1.8.1
    • Issues fixed
    • Changes
      • NDIter
      • Optional reduced verbosity for np.distutils
    • Deprecations
      • C-API
  • 1.8.0
    • 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
  • 1.7.2
    • Issues fixed
  • 1.7.1
    • Issues fixed
  • 1.7.0
    • 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
  • 1.6.2
    • Issues fixed
      • numpy.core
      • numpy.lib
      • numpy.distutils
      • numpy.random
    • Changes
      • numpy.f2py
      • numpy.poly
  • 1.6.1
    • Issues Fixed
  • 1.6.0
    • 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
  • 1.5.0
    • 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
  • 1.4.0
    • 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
  • 1.3.0
    • Highlights
      • Python 2.6 support
      • Generalized ufuncs
      • Experimental Windows 64 bits support
    • New features
      • Formatting issues
      • Nan handling in max/min
      • Nan handling in sign
      • New ufuncs
      • Masked arrays
      • gfortran support on windows
      • Arch option for windows binary
    • Deprecated features
      • Histogram
    • Documentation changes
    • New C API
      • Multiarray API
      • Ufunc API
      • New defines
      • Portable NAN, INFINITY, etc…
    • Internal changes
      • numpy.core math configuration revamp
      • umath refactor
      • Improvements to build warnings
      • Separate core math library
      • CPU arch detection
• About NumPy
  • NumPy community
• About this documentation
  • Conventions
• Reporting bugs
• Glossary

User Guide