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numpy.genfromtxt(fname, dtype=, 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)
[source] -
Load data from a text file, with missing values handled as specified.
Each line past the first
skip_header
lines is split at thedelimiter
character, and characters following thecomments
character are discarded.Parameters: fname : file, str, list of str, generator
File, filename, list, or generator to read. If the filename extension is
gz
orbz2
, the file is first decompressed. Mote 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 useskip_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 usemissing_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 firstskip_header
lines. Ifnames
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. Ifnames
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 becomefile_
.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')])
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numpy.genfromtxt()
2017-01-10 18:14:14
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