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# numpy.genfromtxt

`numpy.``genfromtxt`(fname, dtype=<type '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=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=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 byte strings in Python 3k. 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 valid line after the first skip_header lines. 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 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 variables 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 = loadtxt(...)` 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. New in version 1.10.0.
  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 the variables are named (either by a flexible dtype or with names, 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.

  References

  [R20] | 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, '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', '|S5')])

  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, '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', '|S5')])