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='bytes', *, like=None)
Each line past the first :None:None:`skip_header` lines is split at the :None:None:`delimiter` character, and characters following the :None:None:`comments` character are discarded.
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 :None:None:`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.
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.
Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually.
The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded.
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.
:None:None:`skiprows` was removed in numpy 1.10. Please use :None:None:`skip_header` instead.
The number of lines to skip at the beginning of the file.
The number of lines to skip at the end of the file.
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)}
.
:None:None:`missing` was removed in numpy 1.10. Please use :None:None:`missing_values` instead.
The set of strings corresponding to missing data.
The set of values to be used as default when the data are missing.
Which columns to read, with 0 being the first. For example, usecols = (1, 4, 5)
will extract the 2nd, 5th and 6th columns.
If :None:None:`names` is True, the field names are read from the first line after the first :None:None:`skip_header` lines. This line can optionally be preceded by a comment delimiter. If :None:None:`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 :None:None:`names` is None, the names of the dtype fields will be used, if any.
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, :None:None:`file` would become :None:None:`file_`.
A string combining invalid characters that must be deleted from the names.
A format used to define default field names, such as "f%i" or "f_%02i".
Whether to automatically strip white spaces from the variables.
Character(s) used in replacement of white spaces in the variable names. By default, use a '_'.
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.
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.
If True, return a masked array. If False, return a regular array.
If True, do not raise errors for invalid values.
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.
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 used to decode the inputfile. Does not apply when :None:None:`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'.
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.
Data read from the text file. If :None:None:`usemask` is True, this is a masked array.
Load data from a text file, with missing values handled as specified.
numpy.loadtxt
equivalent function when no data is missing.
>>> from io import StringIO
... import numpy as np
Comma delimited file with mixed dtype
>>> s = StringIO(u"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, b'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, b'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])
An example with fixed-width columns
>>> s = StringIO(u"11.3abcde")
... data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
... delimiter=[1,3,5])
... data array((1, 1.3, b'abcde'), dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', 'S5')])
An example to show comments
>>> f = StringIO('''See :
... 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')])
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.lib.npyio.recfromtxt
numpy.lib.npyio.recfromcsv
numpy.lib._iotools.ConversionWarning
numpy.loadtxt
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