numpy.genfromtxt

numpy.genfromtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True)

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

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

Parameters:

fname : file or str

File or filename to read. If the filename extension is gz or bz2, the file is first decompressed.

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.

skip_header : int, optional

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

skip_footer : int, optional

The numbers of lines to skip at the end of the file

converters : variable or None, 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_values : variable or None, optional

The set of strings corresponding to missing data.

filling_values : variable or None, optional

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

usecols : sequence or None, 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 skiprows 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.

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.

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.

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.

Examples

>>> from StringIO 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')])