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)[source]#
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:
- fnamefile, 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.- dtypedtype, optional
Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually.
- commentsstr, optional
The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded.
- delimiterstr, 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.
- skiprowsint, optional
skiprows was removed in numpy 1.10. Please use skip_header instead.
- skip_headerint, optional
The number of lines to skip at the beginning of the file.
- skip_footerint, optional
The number of lines to skip at the end of the file.
- convertersvariable, 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)}
.- missingvariable, optional
missing was removed in numpy 1.10. Please use missing_values instead.
- missing_valuesvariable, optional
The set of strings corresponding to missing data.
- filling_valuesvariable, optional
The set of values to be used as default when the data are missing.
- usecolssequence, 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.
- excludelistsequence, 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_.
- deletecharsstr, optional
A string combining invalid characters that must be deleted from the names.
- defaultfmtstr, optional
A format used to define default field names, such as “f%i” or “f_%02i”.
- autostripbool, optional
Whether to automatically strip white spaces from the variables.
- replace_spacechar, 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.
- unpackbool, 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.- usemaskbool, optional
If True, return a masked array. If False, return a regular array.
- loosebool, optional
If True, do not raise errors for invalid values.
- invalid_raisebool, 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_rowsint, 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.
- encodingstr, 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.
Changed in version 2.0: Before NumPy 2, the default was
'bytes'
for Python 2 compatibility. The default is nowNone
.- ndminint, optional
Same parameter as
loadtxt
New in version 1.23.0.
- likearray_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:
- outndarray
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')])