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# numpy.genfromtxt

numpy.genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=" !#$%&'()*+, -./:;<=>?@[\\]^{|}~", replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding=None, *, ndmin=0, like=None)

  Load data from a text file, with missing values handled as specified.

  Each line past the first skip_header lines is split at the delimiter character, and characters following the comments character are discarded.

  Parameters:
    fname file, str, pathlib.Path, list of str, generator

      File, filename, list, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators must return bytes or strings. The strings in a list or produced by a generator are treated as lines.

    dtype dtype, optional

      Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually.

    comments str, optional

      The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded.

    delimiter str, int, or sequence, optional

      The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field.

    skiprows int, optional

      skiprows was removed in numpy 1.10. Please use skip_header instead.

    skip_header int, optional

      The number of lines to skip at the beginning of the file.

    skip_footer int, optional

      The number of lines to skip at the end of the file.

    converters variable, optional

      The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: `converters = {3: lambda s: float(s or 0)}`.

    missing variable, optional

      missing was removed in numpy 1.10. Please use missing_values instead.

    missing_values variable, optional

      The set of strings corresponding to missing data.

    filling_values variable, optional

      The set of values to be used as default when the data are missing.

    usecols sequence, optional

      Which columns to read, with 0 being the first. For example, `usecols = (1, 4, 5)` will extract the 2nd, 5th and 6th columns.

    names{None, True, str, sequence}, optional

      If names is True, the field names are read from the first line after the first skip_header lines. This line can optionally be preceded by a comment delimiter. Any content before the comment delimiter is discarded. If names is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If names is None, the names of the dtype fields will be used, if any.

    excludelist sequence, optional

      A list of names to exclude. This list is appended to the default list [‘return’,’file’,’print’]. Excluded names are appended with an underscore: for example, file would become file_.

    deletechars str, optional

      A string combining invalid characters that must be deleted from the names.

    defaultfmt str, optional

      A format used to define default field names, such as “f%i” or “f_%02i”.

    autostrip bool, optional

      Whether to automatically strip white spaces from the variables.

    replace_space char, optional

      Character(s) used in replacement of white spaces in the variable names. By default, use a ‘_’.

    case_sensitive{True, False, ‘upper’, ‘lower’}, optional

      If True, field names are case sensitive. If False or ‘upper’, field names are converted to upper case. If ‘lower’, field names are converted to lower case.

    unpack bool, optional

      If True, the returned array is transposed, so that arguments may be unpacked using `x, y, z = genfromtxt(...)`. When used with a structured data-type, arrays are returned for each field. Default is False.

    usemask bool, optional

      If True, return a masked array. If False, return a regular array.

    loose bool, optional

      If True, do not raise errors for invalid values.

    invalid_raise bool, optional

      If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped.

    max_rows int, optional

      The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file.

    encoding str, optional

      Encoding used to decode the inputfile. Does not apply when fname is a file object. The special value ‘bytes’ enables backward compatibility workarounds that ensure that you receive byte arrays when possible and passes latin1 encoded strings to converters. Override this value to receive unicode arrays and pass strings as input to converters. If set to None the system default is used. The default value is ‘bytes’.

      Changed in version 2.0: Before NumPy 2, the default was `'bytes'` for Python 2 compatibility. The default is now `None`.

    ndmin int, optional

      Same parameter as `loadtxt`

      New in version 1.23.0.

    like array_like, optional

      Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as `like` supports the `__array_function__` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

      New in version 1.20.0.

  Returns:
    out ndarray

      Data read from the text file. If usemask is True, this is a masked array.

  See also

  `numpy.loadtxt`

    equivalent function when no data is missing.

  Notes

  - When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields.

  - When variables are named (either by a flexible dtype or with a names sequence), there must not be any header in the file (else a ValueError exception is raised).

  - Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces.

  - Custom converters may receive unexpected values due to dtype discovery.

  References

  [1]

  NumPy User Guide, section I/O with NumPy.

  Examples

      >>> from io import StringIO
      >>> import numpy as np

  Comma delimited file with mixed dtype

      >>> s = StringIO("1,1.3,abcde")
      >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
      ... ('mystring','S5')], delimiter=",")
      >>> data
      array((1, 1.3, b'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

  Using dtype = None

      >>> _ = s.seek(0) # needed for StringIO example only
      >>> data = np.genfromtxt(s, dtype=None,
      ... names = ['myint','myfloat','mystring'], delimiter=",")
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '<U5')])

  Specifying dtype and names

      >>> _ = s.seek(0)
      >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
      ... names=['myint','myfloat','mystring'], delimiter=",")
      >>> data
      array((1, 1.3, b'abcde'),
            dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

  An example with fixed-width columns

      >>> s = StringIO("11.3abcde")
      >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
      ...     delimiter=[1,3,5])
      >>> data
      array((1, 1.3, 'abcde'),
            dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '<U5')])

  An example to show comments

      >>> f = StringIO('''
      ... text,# of chars
      ... hello world,11
      ... numpy,5''')
      >>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
      array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
        dtype=[('f0', 'S12'), ('f1', 'S12')])