A simple format for saving numpy arrays to disk with the full information about them.
.npy format is the standard binary file format in NumPy for
persisting a single arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
.npz format is the standard format for persisting multiple NumPy
arrays on disk. A
.npz file is a zip file containing multiple
files, one for each array.
Can represent all NumPy arrays including nested record arrays and object arrays.
Represents the data in its native binary form.
Supports Fortran-contiguous arrays directly.
Stores all of the necessary information to reconstruct the array including shape and dtype on a machine of a different architecture. Both little-endian and big-endian arrays are supported, and a file with little-endian numbers will yield a little-endian array on any machine reading the file. The types are described in terms of their actual sizes. For example, if a machine with a 64-bit C “long int” writes out an array with “long ints”, a reading machine with 32-bit C “long ints” will yield an array with 64-bit integers.
Is straightforward to reverse engineer. Datasets often live longer than the programs that created them. A competent developer should be able to create a solution in their preferred programming language to read most
.npyfiles that he has been given without much documentation.
Allows memory-mapping of the data. See open_memmep.
Can be read from a filelike stream object instead of an actual file.
Stores object arrays, i.e. arrays containing elements that are arbitrary Python objects. Files with object arrays are not to be mmapable, but can be read and written to disk.
Arbitrary subclasses of numpy.ndarray are not completely preserved. Subclasses will be accepted for writing, but only the array data will be written out. A regular numpy.ndarray object will be created upon reading the file.
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by ‘f0’, ‘f1’,
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
We recommend using the
.npz extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using
The version numbering of these formats is independent of NumPy version numbering. If the format is upgraded, the code in numpy.io will still be able to read and write Version 1.0 files.
Format Version 1.0¶
The first 6 bytes are a magic string: exactly
The next 1 byte is an unsigned byte: the major version number of the file
The next 1 byte is an unsigned byte: the minor version number of the file
\x00. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array’s
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (
\n) and padded with
\x20) to make the total of
len(magic string) + 2 + len(length) + HEADER_LEN be evenly divisible
by 64 for alignment purposes.
The dictionary contains three keys:
An object that can be passed as an argument to the
numpy.dtypeconstructor to create the array’s dtype.
Whether the array data is Fortran-contiguous or not. Since Fortran-contiguous arrays are a common form of non-C-contiguity, we allow them to be written directly to disk for efficiency.
- “shape”tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in alphabetic order. This is for convenience only. A writer SHOULD implement this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
dtype.hasobject is True), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on
fortran_order) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that
shape=() means there is
1 element) by
Format Version 2.0¶
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
numpy.save will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become: “The next 4 bytes form a little-endian unsigned int: the length of the header data HEADER_LEN.”
Format Version 3.0¶
This version replaces the ASCII string (which in practice was latin1) with a utf8-encoded string, so supports structured types with any unicode field names.
.npy format, including motivation for creating it and a comparison of
alternatives, is described in the “npy-format” NEP, however details have
evolved with time and this document is more current.