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, encoding='bytes')

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 line after the first skip_header lines. This line can optionally be proceeded by a comment delimeter. 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.

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’.

New in version 1.14.0.

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')])