class numpy.memmap[source]

Create a memory-map to an array stored in a binary file on disk.

Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. NumPy’s memmap’s are array-like objects. This differs from Python’s mmap module, which uses file-like objects.

This subclass of ndarray has some unpleasant interactions with some operations, because it doesn’t quite fit properly as a subclass. An alternative to using this subclass is to create the mmap object yourself, then create an ndarray with ndarray.__new__ directly, passing the object created in its ‘buffer=’ parameter.

This class may at some point be turned into a factory function which returns a view into an mmap buffer.

Delete the memmap instance to close.


filename : str, file-like object, or pathlib.Path instance

The file name or file object to be used as the array data buffer.

dtype : data-type, optional

The data-type used to interpret the file contents. Default is uint8.

mode : {‘r+’, ‘r’, ‘w+’, ‘c’}, optional

The file is opened in this mode:

‘r’ Open existing file for reading only.
‘r+’ Open existing file for reading and writing.
‘w+’ Create or overwrite existing file for reading and writing.
‘c’ Copy-on-write: assignments affect data in memory, but changes are not saved to disk. The file on disk is read-only.

Default is ‘r+’.

offset : int, optional

In the file, array data starts at this offset. Since offset is measured in bytes, it should normally be a multiple of the byte-size of dtype. When mode != 'r', even positive offsets beyond end of file are valid; The file will be extended to accommodate the additional data. By default, memmap will start at the beginning of the file, even if filename is a file pointer fp and fp.tell() != 0.

shape : tuple, optional

The desired shape of the array. If mode == 'r' and the number of remaining bytes after offset is not a multiple of the byte-size of dtype, you must specify shape. By default, the returned array will be 1-D with the number of elements determined by file size and data-type.

order : {‘C’, ‘F’}, optional

Specify the order of the ndarray memory layout: row-major, C-style or column-major, Fortran-style. This only has an effect if the shape is greater than 1-D. The default order is ‘C’.

See also

Create or load a memory-mapped .npy file.


The memmap object can be used anywhere an ndarray is accepted. Given a memmap fp, isinstance(fp, numpy.ndarray) returns True.

Memory-mapped files cannot be larger than 2GB on 32-bit systems.

When a memmap causes a file to be created or extended beyond its current size in the filesystem, the contents of the new part are unspecified. On systems with POSIX filesystem semantics, the extended part will be filled with zero bytes.


>>> data = np.arange(12, dtype='float32')
>>> data.resize((3,4))

This example uses a temporary file so that doctest doesn’t write files to your directory. You would use a ‘normal’ filename.

>>> from tempfile import mkdtemp
>>> import os.path as path
>>> filename = path.join(mkdtemp(), 'newfile.dat')

Create a memmap with dtype and shape that matches our data:

>>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
>>> fp
memmap([[ 0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.]], dtype=float32)

Write data to memmap array:

>>> fp[:] = data[:]
>>> fp
memmap([[  0.,   1.,   2.,   3.],
        [  4.,   5.,   6.,   7.],
        [  8.,   9.,  10.,  11.]], dtype=float32)
>>> fp.filename == path.abspath(filename)

Deletion flushes memory changes to disk before removing the object:

>>> del fp

Load the memmap and verify data was stored:

>>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> newfp
memmap([[  0.,   1.,   2.,   3.],
        [  4.,   5.,   6.,   7.],
        [  8.,   9.,  10.,  11.]], dtype=float32)

Read-only memmap:

>>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> fpr.flags.writeable

Copy-on-write memmap:

>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
>>> fpc.flags.writeable

It’s possible to assign to copy-on-write array, but values are only written into the memory copy of the array, and not written to disk:

>>> fpc
memmap([[  0.,   1.,   2.,   3.],
        [  4.,   5.,   6.,   7.],
        [  8.,   9.,  10.,  11.]], dtype=float32)
>>> fpc[0,:] = 0
>>> fpc
memmap([[  0.,   0.,   0.,   0.],
        [  4.,   5.,   6.,   7.],
        [  8.,   9.,  10.,  11.]], dtype=float32)

File on disk is unchanged:

>>> fpr
memmap([[  0.,   1.,   2.,   3.],
        [  4.,   5.,   6.,   7.],
        [  8.,   9.,  10.,  11.]], dtype=float32)

Offset into a memmap:

>>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
>>> fpo
memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)


filename (str or pathlib.Path instance) Path to the mapped file.
offset (int) Offset position in the file.
mode (str) File mode.


flush() Write any changes in the array to the file on disk.

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