### Navigation

- index
- modules |
- modules |
- next |
- previous |
- NumPy v1.5 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Array creation routines »

# numpy.fromfile

`numpy.``fromfile`(file, dtype=float, count=-1, sep='')

  Construct an array from data in a text or binary file.

  A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function.

  Parameters : | file : file or str Open file object or filename. dtype : data-type Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. count : int Number of items to read. `-1` means all items (i.e., the complete file). sep : str Separator between items if file is a text file. Empty (“”) separator means the file should be treated as binary. Spaces (” “) in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace.

  See also

  `load`, `save`, `ndarray.tofile`

  `loadtxt`
    More flexible way of loading data from a text file.

  Notes

  Do not rely on the combination of tofile and fromfile for data storage, as the binary files generated are are not platform independent. In particular, no byte-order or data-type information is saved. Data can be stored in the platform independent `.npy` format using save and load instead.

  Examples

  Construct an ndarray:

      >>> dt = np.dtype([('time', [('min', int), ('sec', int)]),
      ...                ('temp', float)])
      >>> x = np.zeros((1,), dtype=dt)
      >>> x['time']['min'] = 10; x['temp'] = 98.25
      >>> x
      array([((10, 0), 98.25)],
            dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

  Save the raw data to disk:

      >>> import os
      >>> fname = os.tmpnam()
      >>> x.tofile(fname)

  Read the raw data from disk:

      >>> np.fromfile(fname, dtype=dt)
      array([((10, 0), 98.25)],
            dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

  The recommended way to store and load data:

      >>> np.save(fname, x)
      >>> np.load(fname + '.npy')
      array([((10, 0), 98.25)],
            dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

### Navigation

- index
- modules |
- modules |
- next |
- previous |
- NumPy v1.5 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Array creation routines »