NumPy manual contentsΒΆ
- NumPy User Guide
- Setting up
- Quickstart tutorial
- NumPy basics
- Data types
- Array creation
- I/O with NumPy
- 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
- Byte-swapping
- Structured arrays
- Subclassing ndarray
- Introduction
- View casting
- Creating new from template
- Relationship of view casting and new-from-template
- Implications for subclassing
- 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
- NumPy for Matlab users
- Building from source
- Using NumPy C-API
- How to extend NumPy
- Using Python as glue
- Writing your own ufunc
- Beyond the Basics
- NumPy Reference
- Array objects
- The N-dimensional array (
ndarray)- Constructing arrays
- numpy.ndarray
- numpy.ndarray.T
- numpy.ndarray.data
- numpy.ndarray.dtype
- numpy.ndarray.flags
- numpy.ndarray.flat
- numpy.ndarray.imag
- numpy.ndarray.real
- numpy.ndarray.size
- numpy.ndarray.itemsize
- numpy.ndarray.nbytes
- numpy.ndarray.ndim
- numpy.ndarray.shape
- numpy.ndarray.strides
- numpy.ndarray.ctypes
- numpy.ndarray.base
- 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
- numpy.ndarray
- Indexing arrays
- Internal memory layout of an ndarray
- Array attributes
- 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
- Item selection and manipulation
- Calculation
- numpy.ndarray.argmax
- numpy.ndarray.min
- numpy.ndarray.argmin
- numpy.ndarray.ptp
- numpy.ndarray.clip
- numpy.ndarray.conj
- numpy.ndarray.round
- numpy.ndarray.trace
- numpy.ndarray.sum
- numpy.ndarray.cumsum
- numpy.ndarray.mean
- numpy.ndarray.var
- numpy.ndarray.std
- numpy.ndarray.prod
- numpy.ndarray.cumprod
- numpy.ndarray.all
- numpy.ndarray.any
- Array conversion
- 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.__nonzero__
- numpy.ndarray.__neg__
- numpy.ndarray.__pos__
- numpy.ndarray.__abs__
- numpy.ndarray.__invert__
- numpy.ndarray.__add__
- numpy.ndarray.__sub__
- numpy.ndarray.__mul__
- numpy.ndarray.__div__
- numpy.ndarray.__truediv__
- numpy.ndarray.__floordiv__
- numpy.ndarray.__mod__
- numpy.ndarray.__divmod__
- numpy.ndarray.__pow__
- numpy.ndarray.__lshift__
- numpy.ndarray.__rshift__
- numpy.ndarray.__and__
- numpy.ndarray.__or__
- numpy.ndarray.__xor__
- numpy.ndarray.__iadd__
- numpy.ndarray.__isub__
- numpy.ndarray.__imul__
- numpy.ndarray.__idiv__
- numpy.ndarray.__itruediv__
- numpy.ndarray.__ifloordiv__
- numpy.ndarray.__imod__
- numpy.ndarray.__ipow__
- numpy.ndarray.__ilshift__
- numpy.ndarray.__irshift__
- numpy.ndarray.__iand__
- numpy.ndarray.__ior__
- numpy.ndarray.__ixor__
- Special methods
- numpy.ndarray.__copy__
- numpy.ndarray.__deepcopy__
- numpy.ndarray.__reduce__
- numpy.ndarray.__setstate__
- numpy.ndarray.__new__
- numpy.ndarray.__array__
- numpy.ndarray.__array_wrap__
- numpy.ndarray.__len__
- numpy.ndarray.__getitem__
- numpy.ndarray.__setitem__
- numpy.ndarray.__contains__
- numpy.ndarray.__int__
- numpy.ndarray.__long__
- numpy.ndarray.__float__
- numpy.ndarray.__oct__
- numpy.ndarray.__hex__
- numpy.ndarray.__str__
- numpy.ndarray.__repr__
- Constructing arrays
- 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.T
- numpy.generic.base
- numpy.generic.data
- numpy.generic.dtype
- numpy.generic.flags
- numpy.generic.flat
- numpy.generic.imag
- numpy.generic.itemsize
- numpy.generic.nbytes
- numpy.generic.ndim
- numpy.generic.real
- numpy.generic.shape
- numpy.generic.size
- numpy.generic.strides
- 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.conj
- 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.tobytes
- 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
- numpy.generic
- Defining new types
- Data type objects (
dtype)- Specifying and constructing data types
dtype- Attributes
- numpy.dtype.type
- numpy.dtype.kind
- numpy.dtype.char
- numpy.dtype.num
- numpy.dtype.str
- numpy.dtype.name
- numpy.dtype.itemsize
- numpy.dtype.byteorder
- numpy.dtype.fields
- numpy.dtype.names
- numpy.dtype.subdtype
- numpy.dtype.shape
- numpy.dtype.hasobject
- numpy.dtype.flags
- numpy.dtype.isbuiltin
- numpy.dtype.isnative
- numpy.dtype.descr
- numpy.dtype.alignment
- Methods
- Attributes
- Indexing
- Iterating Over Arrays
- 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.A
- numpy.matrix.A1
- numpy.matrix.H
- numpy.matrix.I
- numpy.matrix.T
- numpy.matrix.base
- numpy.matrix.ctypes
- numpy.matrix.data
- numpy.matrix.dtype
- numpy.matrix.flags
- numpy.matrix.flat
- numpy.matrix.imag
- numpy.matrix.itemsize
- numpy.matrix.nbytes
- numpy.matrix.ndim
- numpy.matrix.real
- numpy.matrix.shape
- numpy.matrix.size
- numpy.matrix.strides
- 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
- Character arrays (
numpy.char)- numpy.chararray
- numpy.chararray.T
- numpy.chararray.base
- numpy.chararray.ctypes
- numpy.chararray.data
- numpy.chararray.dtype
- numpy.chararray.flags
- numpy.chararray.flat
- numpy.chararray.imag
- numpy.chararray.itemsize
- numpy.chararray.nbytes
- numpy.chararray.ndim
- numpy.chararray.real
- numpy.chararray.shape
- numpy.chararray.size
- numpy.chararray.strides
- numpy.chararray.astype
- 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
- numpy.chararray
- Record arrays (
numpy.rec)- numpy.recarray
- numpy.recarray.T
- numpy.recarray.base
- numpy.recarray.ctypes
- numpy.recarray.data
- numpy.recarray.dtype
- numpy.recarray.flags
- numpy.recarray.flat
- numpy.recarray.imag
- numpy.recarray.itemsize
- numpy.recarray.nbytes
- numpy.recarray.ndim
- numpy.recarray.real
- numpy.recarray.shape
- numpy.recarray.size
- numpy.recarray.strides
- 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.field
- 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.T
- numpy.record.base
- numpy.record.data
- numpy.record.dtype
- numpy.record.flags
- numpy.record.flat
- numpy.record.imag
- numpy.record.itemsize
- numpy.record.nbytes
- numpy.record.ndim
- numpy.record.real
- numpy.record.shape
- numpy.record.size
- numpy.record.strides
- 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.conj
- 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.tobytes
- numpy.record.tofile
- numpy.record.tolist
- numpy.record.tostring
- numpy.record.trace
- numpy.record.transpose
- numpy.record.var
- numpy.record.view
- numpy.recarray
- Masked arrays (
numpy.ma) - Standard container class
- Array Iterators
- Masked arrays
- The
numpy.mamodule - 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
- Indexing and slicing
- Operations on masked arrays
- Constructing masked arrays
- Examples
- Constants of the
numpy.mamodule - The
MaskedArrayclass- 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__
- Attributes and properties of masked arrays
MaskedArraymethods- Conversion
- numpy.ma.MaskedArray.__float__
- numpy.ma.MaskedArray.__hex__
- numpy.ma.MaskedArray.__int__
- numpy.ma.MaskedArray.__long__
- numpy.ma.MaskedArray.__oct__
- numpy.ma.MaskedArray.view
- numpy.ma.MaskedArray.astype
- numpy.ma.MaskedArray.byteswap
- numpy.ma.MaskedArray.compressed
- numpy.ma.MaskedArray.filled
- numpy.ma.MaskedArray.tofile
- numpy.ma.MaskedArray.toflex
- numpy.ma.MaskedArray.tolist
- numpy.ma.MaskedArray.torecords
- numpy.ma.MaskedArray.tostring
- numpy.ma.MaskedArray.tobytes
- Shape manipulation
- 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
- 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:
- Truth value of an array (
bool): - Arithmetic:
- numpy.ma.MaskedArray.__abs__
- numpy.ma.MaskedArray.__add__
- numpy.ma.MaskedArray.__radd__
- numpy.ma.MaskedArray.__sub__
- numpy.ma.MaskedArray.__rsub__
- numpy.ma.MaskedArray.__mul__
- numpy.ma.MaskedArray.__rmul__
- numpy.ma.MaskedArray.__div__
- numpy.ma.MaskedArray.__rdiv__
- numpy.ma.MaskedArray.__truediv__
- numpy.ma.MaskedArray.__rtruediv__
- numpy.ma.MaskedArray.__floordiv__
- numpy.ma.MaskedArray.__rfloordiv__
- numpy.ma.MaskedArray.__mod__
- numpy.ma.MaskedArray.__rmod__
- numpy.ma.MaskedArray.__divmod__
- numpy.ma.MaskedArray.__rdivmod__
- numpy.ma.MaskedArray.__pow__
- numpy.ma.MaskedArray.__rpow__
- numpy.ma.MaskedArray.__lshift__
- numpy.ma.MaskedArray.__rlshift__
- numpy.ma.MaskedArray.__rshift__
- numpy.ma.MaskedArray.__rrshift__
- numpy.ma.MaskedArray.__and__
- numpy.ma.MaskedArray.__rand__
- numpy.ma.MaskedArray.__or__
- numpy.ma.MaskedArray.__ror__
- numpy.ma.MaskedArray.__xor__
- numpy.ma.MaskedArray.__rxor__
- Arithmetic, in-place:
- numpy.ma.MaskedArray.__iadd__
- numpy.ma.MaskedArray.__isub__
- numpy.ma.MaskedArray.__imul__
- numpy.ma.MaskedArray.__idiv__
- numpy.ma.MaskedArray.__itruediv__
- numpy.ma.MaskedArray.__ifloordiv__
- numpy.ma.MaskedArray.__imod__
- numpy.ma.MaskedArray.__ipow__
- numpy.ma.MaskedArray.__ilshift__
- numpy.ma.MaskedArray.__irshift__
- numpy.ma.MaskedArray.__iand__
- numpy.ma.MaskedArray.__ior__
- numpy.ma.MaskedArray.__ixor__
- Representation
- 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
- Conversion
- Masked array operations
- Constants
- Creation
- Inspecting the array
- numpy.ma.all
- numpy.ma.any
- numpy.ma.count
- numpy.ma.count_masked
- numpy.ma.getmask
- numpy.ma.getmaskarray
- numpy.ma.getdata
- numpy.ma.nonzero
- numpy.ma.shape
- numpy.ma.size
- numpy.ma.is_masked
- numpy.ma.is_mask
- numpy.ma.MaskedArray.data
- numpy.ma.MaskedArray.mask
- numpy.ma.MaskedArray.recordmask
- numpy.ma.MaskedArray.all
- numpy.ma.MaskedArray.any
- numpy.ma.MaskedArray.count
- numpy.ma.MaskedArray.nonzero
- numpy.ma.shape
- numpy.ma.size
- Manipulating a MaskedArray
- Operations on masks
- 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
- > to another object
- Pickling and unpickling
- Filling a masked array
- > to a masked array
- 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
- Sorting
- Algebra
- Polynomial fit
- Clipping and rounding
- Miscellanea
- Arithmetics
- The
- The Array Interface
- Datetimes and Timedeltas
- The N-dimensional array (
- Universal functions (
ufunc) - Routines
- Array creation routines
- Array manipulation routines
- Binary operations
- String operations
- String operations
- numpy.core.defchararray.add
- numpy.core.defchararray.multiply
- numpy.core.defchararray.mod
- numpy.core.defchararray.capitalize
- numpy.core.defchararray.center
- numpy.core.defchararray.decode
- numpy.core.defchararray.encode
- numpy.core.defchararray.join
- numpy.core.defchararray.ljust
- numpy.core.defchararray.lower
- numpy.core.defchararray.lstrip
- numpy.core.defchararray.partition
- numpy.core.defchararray.replace
- numpy.core.defchararray.rjust
- numpy.core.defchararray.rpartition
- numpy.core.defchararray.rsplit
- numpy.core.defchararray.rstrip
- numpy.core.defchararray.split
- numpy.core.defchararray.splitlines
- numpy.core.defchararray.strip
- numpy.core.defchararray.swapcase
- numpy.core.defchararray.title
- numpy.core.defchararray.translate
- numpy.core.defchararray.upper
- numpy.core.defchararray.zfill
- Comparison
- String information
- numpy.core.defchararray.count
- numpy.core.defchararray.find
- numpy.core.defchararray.index
- numpy.core.defchararray.isalpha
- numpy.core.defchararray.isdecimal
- numpy.core.defchararray.isdigit
- numpy.core.defchararray.islower
- numpy.core.defchararray.isnumeric
- numpy.core.defchararray.isspace
- numpy.core.defchararray.istitle
- numpy.core.defchararray.isupper
- numpy.core.defchararray.rfind
- numpy.core.defchararray.rindex
- numpy.core.defchararray.startswith
- Convenience class
- numpy.core.defchararray.chararray
- numpy.core.defchararray.chararray.T
- numpy.core.defchararray.chararray.base
- numpy.core.defchararray.chararray.ctypes
- numpy.core.defchararray.chararray.data
- numpy.core.defchararray.chararray.dtype
- numpy.core.defchararray.chararray.flags
- numpy.core.defchararray.chararray.flat
- numpy.core.defchararray.chararray.imag
- numpy.core.defchararray.chararray.itemsize
- numpy.core.defchararray.chararray.nbytes
- numpy.core.defchararray.chararray.ndim
- numpy.core.defchararray.chararray.real
- numpy.core.defchararray.chararray.shape
- numpy.core.defchararray.chararray.size
- numpy.core.defchararray.chararray.strides
- numpy.core.defchararray.chararray.astype
- numpy.core.defchararray.chararray.copy
- numpy.core.defchararray.chararray.count
- numpy.core.defchararray.chararray.decode
- numpy.core.defchararray.chararray.dump
- numpy.core.defchararray.chararray.dumps
- numpy.core.defchararray.chararray.encode
- numpy.core.defchararray.chararray.endswith
- numpy.core.defchararray.chararray.expandtabs
- numpy.core.defchararray.chararray.fill
- numpy.core.defchararray.chararray.find
- numpy.core.defchararray.chararray.flatten
- numpy.core.defchararray.chararray.getfield
- numpy.core.defchararray.chararray.index
- numpy.core.defchararray.chararray.isalnum
- numpy.core.defchararray.chararray.isalpha
- numpy.core.defchararray.chararray.isdecimal
- numpy.core.defchararray.chararray.isdigit
- numpy.core.defchararray.chararray.islower
- numpy.core.defchararray.chararray.isnumeric
- numpy.core.defchararray.chararray.isspace
- numpy.core.defchararray.chararray.istitle
- numpy.core.defchararray.chararray.isupper
- numpy.core.defchararray.chararray.item
- numpy.core.defchararray.chararray.join
- numpy.core.defchararray.chararray.ljust
- numpy.core.defchararray.chararray.lower
- numpy.core.defchararray.chararray.lstrip
- numpy.core.defchararray.chararray.nonzero
- numpy.core.defchararray.chararray.put
- numpy.core.defchararray.chararray.ravel
- numpy.core.defchararray.chararray.repeat
- numpy.core.defchararray.chararray.replace
- numpy.core.defchararray.chararray.reshape
- numpy.core.defchararray.chararray.resize
- numpy.core.defchararray.chararray.rfind
- numpy.core.defchararray.chararray.rindex
- numpy.core.defchararray.chararray.rjust
- numpy.core.defchararray.chararray.rsplit
- numpy.core.defchararray.chararray.rstrip
- numpy.core.defchararray.chararray.searchsorted
- numpy.core.defchararray.chararray.setfield
- numpy.core.defchararray.chararray.setflags
- numpy.core.defchararray.chararray.sort
- numpy.core.defchararray.chararray.split
- numpy.core.defchararray.chararray.splitlines
- numpy.core.defchararray.chararray.squeeze
- numpy.core.defchararray.chararray.startswith
- numpy.core.defchararray.chararray.strip
- numpy.core.defchararray.chararray.swapaxes
- numpy.core.defchararray.chararray.swapcase
- numpy.core.defchararray.chararray.take
- numpy.core.defchararray.chararray.title
- numpy.core.defchararray.chararray.tofile
- numpy.core.defchararray.chararray.tolist
- numpy.core.defchararray.chararray.tostring
- numpy.core.defchararray.chararray.translate
- numpy.core.defchararray.chararray.transpose
- numpy.core.defchararray.chararray.upper
- numpy.core.defchararray.chararray.view
- numpy.core.defchararray.chararray.zfill
- numpy.core.defchararray.chararray
- String operations
- C-Types Foreign Function Interface (
numpy.ctypeslib) - Datetime Support Functions
- Data type routines
- Optionally Scipy-accelerated routines (
numpy.dual) - Mathematical functions with automatic domain (
numpy.emath) - Floating point error handling
- Discrete Fourier Transform (
numpy.fft) - Financial functions
- Functional programming
- NumPy-specific help functions
- Indexing routines
- Input and output
- Linear algebra (
numpy.linalg) - Logic functions
- Masked array operations
- Constants
- Creation
- Inspecting the array
- numpy.ma.all
- numpy.ma.any
- numpy.ma.count
- numpy.ma.count_masked
- numpy.ma.getmask
- numpy.ma.getmaskarray
- numpy.ma.getdata
- numpy.ma.nonzero
- numpy.ma.shape
- numpy.ma.size
- numpy.ma.is_masked
- numpy.ma.is_mask
- numpy.ma.MaskedArray.data
- numpy.ma.MaskedArray.mask
- numpy.ma.MaskedArray.recordmask
- numpy.ma.MaskedArray.all
- numpy.ma.MaskedArray.any
- numpy.ma.MaskedArray.count
- numpy.ma.MaskedArray.nonzero
- numpy.ma.shape
- numpy.ma.size
- Manipulating a MaskedArray
- Operations on masks
- 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
- > to another object
- Pickling and unpickling
- Filling a masked array
- > to a masked array
- 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
- Sorting
- Algebra
- Polynomial fit
- Clipping and rounding
- Miscellanea
- Arithmetics
- Mathematical functions
- Matrix library (
numpy.matlib) - Miscellaneous routines
- Padding Arrays
- Polynomials
- Transition notice
- Polynomial Package
- Using the Convenience Classes
- 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
- numpy.polynomial.polynomial.Polynomial
- 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
- Calculus
- Algebra
- Miscellaneous
- Polynomial Class
- 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
- numpy.polynomial.chebyshev.Chebyshev
- Basics
- Fitting
- Calculus
- Algebra
- Quadrature
- 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
- Chebyshev Class
- 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
- numpy.polynomial.legendre.Legendre
- Basics
- Fitting
- Calculus
- Algebra
- Quadrature
- 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
- Legendre Class
- 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
- numpy.polynomial.laguerre.Laguerre
- Basics
- Fitting
- Calculus
- Algebra
- Quadrature
- 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
- Laguerre Class
- 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
- numpy.polynomial.hermite.Hermite
- Basics
- Fitting
- Calculus
- Algebra
- Quadrature
- 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
- Hermite Class
- 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
- numpy.polynomial.hermite_e.HermiteE
- Basics
- Fitting
- Calculus
- Algebra
- Quadrature
- 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
- HermiteE Class
- Polyutils
- Poly1d
- Polynomial Package
- Transition notice
- Random sampling (
numpy.random)- Simple random data
- Permutations
- Distributions
- numpy.random.beta
- numpy.random.binomial
- numpy.random.chisquare
- numpy.random.dirichlet
- numpy.random.exponential
- numpy.random.f
- numpy.random.gamma
- numpy.random.geometric
- numpy.random.gumbel
- numpy.random.hypergeometric
- numpy.random.laplace
- numpy.random.logistic
- numpy.random.lognormal
- numpy.random.logseries
- numpy.random.multinomial
- numpy.random.multivariate_normal
- numpy.random.negative_binomial
- numpy.random.noncentral_chisquare
- numpy.random.noncentral_f
- numpy.random.normal
- numpy.random.pareto
- numpy.random.poisson
- numpy.random.power
- numpy.random.rayleigh
- numpy.random.standard_cauchy
- numpy.random.standard_exponential
- numpy.random.standard_gamma
- numpy.random.standard_normal
- numpy.random.standard_t
- numpy.random.triangular
- numpy.random.uniform
- numpy.random.vonmises
- numpy.random.wald
- numpy.random.weibull
- numpy.random.zipf
- Random generator
- numpy.random.RandomState
- numpy.random.RandomState.beta
- numpy.random.RandomState.binomial
- numpy.random.RandomState.bytes
- numpy.random.RandomState.chisquare
- numpy.random.RandomState.choice
- numpy.random.RandomState.dirichlet
- numpy.random.RandomState.exponential
- numpy.random.RandomState.f
- numpy.random.RandomState.gamma
- numpy.random.RandomState.geometric
- numpy.random.RandomState.get_state
- numpy.random.RandomState.gumbel
- numpy.random.RandomState.hypergeometric
- numpy.random.RandomState.laplace
- numpy.random.RandomState.logistic
- numpy.random.RandomState.lognormal
- numpy.random.RandomState.logseries
- numpy.random.RandomState.multinomial
- numpy.random.RandomState.multivariate_normal
- numpy.random.RandomState.negative_binomial
- numpy.random.RandomState.noncentral_chisquare
- numpy.random.RandomState.noncentral_f
- numpy.random.RandomState.normal
- numpy.random.RandomState.pareto
- numpy.random.RandomState.permutation
- numpy.random.RandomState.poisson
- numpy.random.RandomState.power
- numpy.random.RandomState.rand
- numpy.random.RandomState.randint
- numpy.random.RandomState.randn
- numpy.random.RandomState.random_integers
- numpy.random.RandomState.random_sample
- numpy.random.RandomState.rayleigh
- numpy.random.RandomState.seed
- numpy.random.RandomState.set_state
- numpy.random.RandomState.shuffle
- numpy.random.RandomState.standard_cauchy
- numpy.random.RandomState.standard_exponential
- numpy.random.RandomState.standard_gamma
- numpy.random.RandomState.standard_normal
- numpy.random.RandomState.standard_t
- numpy.random.RandomState.tomaxint
- numpy.random.RandomState.triangular
- numpy.random.RandomState.uniform
- numpy.random.RandomState.vonmises
- numpy.random.RandomState.wald
- numpy.random.RandomState.weibull
- numpy.random.RandomState.zipf
- numpy.random.seed
- numpy.random.get_state
- numpy.random.set_state
- numpy.random.RandomState
- Set routines
- Sorting, searching, and counting
- Statistics
- 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
- Test Running
- Asserts
- Window functions
- Packaging (
numpy.distutils)- Modules in
numpy.distutils- misc_util
- numpy.distutils.misc_util.get_numpy_include_dirs
- numpy.distutils.misc_util.dict_append
- numpy.distutils.misc_util.appendpath
- numpy.distutils.misc_util.allpath
- numpy.distutils.misc_util.dot_join
- numpy.distutils.misc_util.generate_config_py
- numpy.distutils.misc_util.get_cmd
- numpy.distutils.misc_util.terminal_has_colors
- numpy.distutils.misc_util.red_text
- numpy.distutils.misc_util.green_text
- numpy.distutils.misc_util.yellow_text
- numpy.distutils.misc_util.blue_text
- numpy.distutils.misc_util.cyan_text
- numpy.distutils.misc_util.cyg2win32
- numpy.distutils.misc_util.all_strings
- numpy.distutils.misc_util.has_f_sources
- numpy.distutils.misc_util.has_cxx_sources
- numpy.distutils.misc_util.filter_sources
- numpy.distutils.misc_util.get_dependencies
- numpy.distutils.misc_util.is_local_src_dir
- numpy.distutils.misc_util.get_ext_source_files
- numpy.distutils.misc_util.get_script_files
- Other modules
- misc_util
- Building Installable C libraries
- Conversion of
.srcfiles
- Modules in
- NumPy C-API
- Python Types and C-Structures
- System configuration
- Data Type API
- Array API
- Array Iterator API
- UFunc API
- Generalized Universal Function API
- NumPy core libraries
- C API Deprecations
- NumPy internals
- NumPy and SWIG
- Acknowledgements
- Array objects
- F2PY Users Guide and Reference Manual
- Contributing to NumPy
- Working with NumPy source code
- Setting up and using your development environment
- NumPy governance
- NumPy Enhancement Proposals
- Implemented NEPs
- A Mechanism for Overriding Ufuncs
- Generalized Universal Functions
- Optimizing Iterator/UFunc Performance
- A Simple File Format for NumPy Arrays
- Other NEPs
- Missing Data Functionality in NumPy
- Table of Contents
- Abstract
- Definition of Missing Data
- Implementation Techniques For Missing Values
- Glossary of Terms
- Missing Values as Seen in Python
- Working With Missing Values
- Accessing a Boolean Mask
- Creating NA-Masked Arrays
- NA-Masks When Constructing From Lists
- Mask Implementation Details
- New ndarray Methods
- Element-wise UFuncs With Missing Values
- Reduction UFuncs With Missing Values
- Parameterized NA Data Types
- Future Expansion to multi-NA Payloads
- Differences with numpy.ma
- Boolean Indexing
- PEP 3118
- Cython
- Hard Masks
- Shared Masks
- Interaction With Pre-existing C API Usage
- C Implementation Details
- C Iterator API Changes: Iteration With Masks
- Rejected Alternative
- Acknowledgments
- Cleaning the math configuration of numpy.core
- A proposal for adding groupby functionality to NumPy
- A proposal to build numpy without warning with a big set of warning flags
- Replacing Trac with a different bug tracker
- Deferred UFunc Evaluation
- Structured array extensions
- A proposal for implementing some date/time types in NumPy
- A (third) proposal for implementing some date/time types in NumPy
- Plan for dropping Python 2.7 support
- Missing Data Functionality in NumPy
- Implemented NEPs
- Release Notes
- NumPy 1.14.0 Release Notes
- Highlights
- New functions
- Deprecations
- Future Changes
- Compatibility notes
- The mask of a masked array view is also a view rather than a copy
np.ma.maskedis no longer writeablenp.mafunctions producing ``fill_value``s have changeda.flat.__array__()returns non-writeable arrays whenais non-contiguousnp.tensordotnow returns zero array when contracting over 0-length dimensionnumpy.testingreorganizednp.asfarrayno longer accepts non-dtypes through thedtypeargument- 1D
np.linalg.normpreserves float input types, even for arbitrary orders count_nonzero(arr, axis=())now counts over no axes, not all axes__init__.pyfiles added to test directories.astype(bool)on unstructured void arrays now callsboolon each elementMaskedArray.squeezenever returnsnp.ma.masked- Renamed first parameter of
can_castfromfromtofrom_ isnatraisesTypeErrorwhen passed wrong typedtype.__getitem__raisesTypeErrorwhen 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
- New Features
- Encoding argument for text IO functions
- External
noseplugins are usable bynumpy.testing.Tester parametrizedecorator added tonumpy.testingchebinterpolatefunction added tonumpy.polynomial.chebyshev- Support for reading lzma compressed text files in Python 3
signoption added tonp.setprintoptionsandnp.array2stringhermitianoption added to``np.linalg.matrix_rank``thresholdandedgeitemsoptions added tonp.array2stringconcatenateandstackgained anoutargument- Support for PGI flang compiler on Windows
- Improvements
- Numerator degrees of freedom in
random.noncentral_fneed only be positive. - The GIL is released for all
np.einsumvariations - The np.einsum function will use BLAS when possible and optimize by default
f2pynow handles arrays of dimension 0numpy.distutilssupports using MSVC and mingw64-gfortran togethernp.linalg.pinvnow works on stacked matricesnumpy.savealigns 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.Decimalinnp.lib.financial - Float printing now uses “dragon4” algorithm for shortest decimal representation
voiddatatype elements are now printed in hex notation- printing style for
voiddatatypes is now independently customizable - Reduced memory usage of
np.loadtxt
- Numerator degrees of freedom in
- Changes
- Multiple-field indexing/assignment of structured arrays
- Integer and Void scalars are now unaffected by
np.set_string_function - 0d array printing changed,
stylearg of array2string deprecated - Seeding
RandomStateusing an array requires a 1-d array MaskedArrayobjects show a more usefulrepr- The
reprofnp.polynomialclasses is more explicit
- NumPy 1.13.3 Release Notes
- NumPy 1.13.2 Release Notes
- NumPy 1.13.1 Release Notes
- NumPy 1.13.0 Release Notes
- Highlights
- New functions
- Deprecations
- Future Changes
- Build System Changes
- Compatibility notes
- C API changes
- New Features
__array_ufunc__added- New
positiveufunc - New
divmodufunc np.isnatufunc tests for NaT special datetime and timedelta valuesnp.heavisideufunc computes the Heaviside functionnp.blockfunction for creating blocked arraysisinfunction, improving onin1d- Temporary elision
axesargument foruniquenp.gradientnow supports unevenly spaced data- Support for returning arrays of arbitrary dimensions in
apply_along_axis .ndimproperty added todtypeto 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
packbitsandunpackbits - Fix for PPC long double floating point information
- Better default repr for
ndarraysubclasses - More reliable comparisons of masked arrays
- np.matrix with booleans elements can now be created using the string syntax
- More
linalgoperations now accept empty vectors and matrices - Bundled version of LAPACK is now 3.2.2
reduceofnp.hypot.reduceandnp.logical_xorallowed in more cases- Better
reprof object arrays
- Changes
argsorton masked arrays takes the same default arguments assortaveragenow preserves subclassesarray == Noneandarray != Nonedo element-wise comparisonnp.equal, np.not_equalfor object arrays ignores object identity- Boolean indexing changes
np.random.multivariate_normalbehavior with bad covariance matrixassert_array_lesscomparesnp.infand-np.infnowassert_array_and masked arraysassert_equalhide less warningsoffsetattribute value inmemmapobjectsnp.realandnp.imagreturn scalars for scalar inputs- The polynomial convenience classes cannot be passed to ufuncs
- Output arguments to ufuncs can be tuples also for ufunc methods
- NumPy 1.12.1 Release Notes
- NumPy 1.12.0 Release Notes
- Highlights
- Dropped Support
- Added Support
- Build System Changes
- Deprecations
- Future Changes
- Compatibility notes
- DeprecationWarning to error
- FutureWarning to changed behavior
powerand**raise errors for integer to negative integer powers- Relaxed stride checking is the default
- The
np.percentile‘midpoint’ interpolation method fixed for exact indices keepdimskwarg is passed through to user-class methodsbitwise_andidentity changed- ma.median warns and returns nan when unmasked invalid values are encountered
- Greater consistancy in
assert_almost_equal NoseTesterbehaviour of warnings during testingassert_warnsanddeprecateddecorator more specific- C API
- New Features
- Writeable keyword argument for
as_strided axeskeyword argument forrot90- Generalized
flip - BLIS support in
numpy.distutils - Hook in
numpy/__init__.pyto run distribution-specific checks - New nanfunctions
nancumsumandnancumprodadded np.interpcan now interpolate complex values- New polynomial evaluation function
polyvalfromrootsadded - New array creation function
geomspaceadded - New context manager for testing warnings
- New masked array functions
ma.convolveandma.correlateadded - New
float_powerufunc np.loadtxtnow supports a single integer asusecolargument- Improved automated bin estimators for
histogram np.rollcan now roll multiple axes at the same time- The
__complex__method has been implemented for the ndarrays pathlib.Pathobjects now supported- New
bitsattribute fornp.finfo - New
signatureargument tonp.vectorize - Emit py3kwarnings for division of integer arrays
- numpy.sctypes now includes bytes on Python3 too
- Writeable keyword argument for
- Improvements
bitwise_andidentity 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
ediff1dimproved performance and subclass handling- Improved precision of
ndarray.meanfor float16 arrays
- Changes
- NumPy 1.11.3 Release Notes
- NumPy 1.11.2 Release Notes
- NumPy 1.11.1 Release Notes
- NumPy 1.11.0 Release Notes
- Highlights
- Build System Changes
- Future Changes
- Compatibility notes
- New Features
- Improvements
np.gradientnow supports anaxisargumentnp.lexsortnow supports arrays with object data-typenp.ma.core.MaskedArraynow supports anorderargument- Memory and speed improvements for masked arrays
ndarray.tofilenow uses fallocate on linux- Optimizations for operations of the form
A.T @ AandA @ A.T np.testing.assert_warnscan now be used as a context manager- Speed improvement for np.random.shuffle
- Changes
- Deprecations
- FutureWarnings
- NumPy 1.10.4 Release Notes
- NumPy 1.10.3 Release Notes
- NumPy 1.10.2 Release Notes
- NumPy 1.10.1 Release Notes
- NumPy 1.10.0 Release Notes
- Highlights
- Dropped Support
- Future Changes
- Compatibility notes
- Default casting rule change
- numpy version string
- relaxed stride checking
- Concatenation of 1d arrays along any but
axis=0raisesIndexError - 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_rowsargument - New function np.broadcast_to for invoking array broadcasting
- New context manager clear_and_catch_warnings for testing warnings
- cov has new
fweightsandaweightsarguments - Support for the ‘@’ operator in Python 3.5+
- New argument
normto 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_widthandconstant_values - np.argmax and np.argmin now support an
outargument - More system C99 complex functions detected and used
- np.loadtxt support for the strings produced by the
float.hexmethod - 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
- Deprecations
- NumPy 1.9.2 Release Notes
- NumPy 1.9.1 Release Notes
- NumPy 1.9.0 Release Notes
- Highlights
- Dropped Support
- Future Changes
- Compatibility notes
- The diagonal and diag functions return readonly views.
- Special scalar float values don’t cause upcast to double anymore
- Percentile output changes
- ndarray.tofile exception type
- Invalid fill value exceptions
- Polynomial Classes no longer derived from PolyBase
- Using numpy.random.binomial may change the RNG state vs. numpy < 1.9
- Random seed enforced to be a 32 bit unsigned integer
- Argmin and argmax out argument
- Einsum
- Indexing
- Non-integer reduction axis indexes are deprecated
promote_typesand string dtypecan_castand string dtype- astype and string dtype
- npyio.recfromcsv keyword arguments change
- The
doc/swigdirectory moved - The
npy_3kcompat.hheader changed - Negative indices in C-Api
sq_itemandsq_ass_itemsequence methods - NDIter
zeros_likefor 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.linspaceandnp.logspace - More general
np.triuandnp.trilbroadcasting tobytesalias fortostringmethod- Build system
- Compatibility to python
numbersmodule increasingparameter added tonp.vanderunique_countsparameter added tonp.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
- NumPy 1.8.2 Release Notes
- NumPy 1.8.1 Release Notes
- NumPy 1.8.0 Release Notes
- Highlights
- Dropped Support
- Future Changes
- Compatibility notes
- 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
- Changes
- Deprecations
- Authors
- NumPy 1.7.2 Release Notes
- NumPy 1.7.1 Release Notes
- NumPy 1.7.0 Release Notes
- Highlights
- Compatibility notes
- New features
- Reduction UFuncs Generalize axis= Parameter
- Reduction UFuncs New keepdims= Parameter
- Datetime support
- Custom formatter for printing arrays
- New function numpy.random.choice
- New function isclose
- Preliminary multi-dimensional support in the polynomial package
- Ability to pad rank-n arrays
- New argument to searchsorted
- Build system
- C API
- Changes
- Deprecations
- NumPy 1.6.2 Release Notes
- NumPy 1.6.1 Release Notes
- NumPy 1.6.0 Release Notes
- NumPy 1.5.0 Release Notes
- NumPy 1.4.0 Release Notes
- NumPy 1.3.0 Release Notes
- NumPy 1.14.0 Release Notes
- About NumPy
- About this documentation
- Reporting bugs
- NumPy License
- Glossary