Python read ascii file numpy
I have an Show
when I printed the first line of the file it looks like :
how could I read such a file into a Load data from a text file. Each row in the text file must have the same number of values. Parametersfnamefile, str, pathlib.Path, list of str, generatorFile, filename, list, or generator to read. If the filename extension is Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. commentsstr or sequence of str or None, optionalThe characters or list of characters used to indicate the start of a comment. None implies no comments. For backwards compatibility, byte strings will be decoded as ‘latin1’. The default is ‘#’. delimiterstr, optionalThe string used to separate values. For backwards compatibility, byte strings will be decoded as ‘latin1’. The default is whitespace. convertersdict or callable, optionalA function to parse all columns strings into the desired value, or a dictionary mapping column number to a parser function. E.g. if column 0 is a date string: Skip the first skiprows lines, including comments; default: 0. usecolsint or sequence, optionalWhich columns to read, with 0 being the first. For example, Changed in version 1.11.0: When a single column has to be read it is possible to use an integer instead of a tuple. E.g If True, the returned array is transposed, so that arguments may be unpacked using The returned array will have at least ndmin dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. New in version 1.6.0. encodingstr, optionalEncoding used to decode the inputfile. Does not apply to input streams. The special value ‘bytes’ enables backward compatibility workarounds that ensures you receive byte arrays as results if 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. max_rowsint, optionalRead max_rows lines of content after skiprows lines. The default is to read all the lines. New in version 1.16.0. quotecharunicode character or None, optionalThe character used to denote the start and end of a quoted item. Occurrences of the delimiter or comment characters are ignored within a quoted item. The default value is If two consecutive instances of quotechar are found within a quoted field, the first is treated as an escape character. See examples. New in version 1.23.0. likearray_like, optionalReference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as New in version 1.20.0. Data read from the text file. Notes This function aims to be a fast reader for simply formatted files. The New in version 1.10.0. The strings produced by the Python float.hex method can be used as input for floats. Examples >>> from io import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\n2 3") >>> np.loadtxt(c) array([[0., 1.], [2., 3.]]) >>> d = StringIO("M 21 72\nF 35 58") >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'), ... 'formats': ('S1', 'i4', 'f4')}) array([(b'M', 21, 72.), (b'F', 35, 58.)], dtype=[('gender', 'S1'), ('age', ' >>> c = StringIO("1,0,2\n3,0,4") >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True) >>> x array([1., 3.]) >>> y array([2., 4.]) The converters argument is used to specify functions to preprocess the text prior to parsing. converters can be a dictionary that maps preprocessing functions to each column: >>> s = StringIO("1.618, 2.296\n3.141, 4.669\n") >>> conv = { ... 0: lambda x: np.floor(float(x)), # conversion fn for column 0 ... 1: lambda x: np.ceil(float(x)), # conversion fn for column 1 ... } >>> np.loadtxt(s, delimiter=",", converters=conv) array([[1., 3.], [3., 5.]]) converters can be a callable instead of a dictionary, in which case it is applied to all columns: >>> s = StringIO("0xDE 0xAD\n0xC0 0xDE") >>> import functools >>> conv = functools.partial(int, base=16) >>> np.loadtxt(s, converters=conv) array([[222., 173.], [192., 222.]]) This example shows how converters can be used to convert a field with a trailing minus sign into a negative number. >>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94') >>> def conv(fld): ... return -float(fld[:-1]) if fld.endswith(b'-') else float(fld) ... >>> np.loadtxt(s, converters=conv) array([[ 10.01, -31.25], [ 19.22, 64.31], [-17.57, 63.94]]) Using a callable as the converter can be particularly useful for handling values with different formatting, e.g. floats with underscores: >>> s = StringIO("1 2.7 100_000") >>> np.loadtxt(s, converters=float) array([1.e+00, 2.7e+00, 1.e+05]) This idea can be extended to automatically handle values specified in many different formats: >>> def conv(val): ... try: ... return float(val) ... except ValueError: ... return float.fromhex(val) >>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2") >>> np.loadtxt(s, delimiter=",", converters=conv, encoding=None) array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00]) Note that with the default
>>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94') >>> conv = lambda x: -float(x[:-1]) if x.endswith('-') else float(x) >>> np.loadtxt(s, converters=conv, encoding=None) array([[ 10.01, -31.25], [ 19.22, 64.31], [-17.57, 63.94]]) Support for quoted fields is enabled with the quotechar parameter. Comment and delimiter characters are ignored when they appear within a quoted item delineated by quotechar: >>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n') >>> dtype = np.dtype([("label", "U12"), ("value", float)]) >>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"') array([('alpha, #42', 10.), ('beta, #64', 2.)], dtype=[('label', ' Two consecutive quote characters within a quoted field are treated as a single escaped character: >>> s = StringIO('"Hello, my name is ""Monty""!"') >>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"') array('Hello, my name is "Monty"!', dtype=' How do I read ASCII data in Python?Reading and Writing files in Pure Python. import urllib2 url = 'http://python4esac.github.com/_downloads/data.txt' open('data.txt', 'wb'). ... . In [2]: print(f. ... . In [4]: f = open('data.txt', 'r') # We need to re-open the file In [5]: data = f.. How do I read an ASCII file?You can open an ASCII file in most text editors or word processors, including:. Microsoft Notepad.. Apple TextEdit.. GitHub Atom.. Microsoft Word.. Apple Pages.. How do I open a text file in Python NumPy?To import Text files into Numpy Arrays, we have two functions in Numpy:. numpy. loadtxt( ) – Used to load text file data.. numpy. genfromtxt( ) – Used to load data from a text file, with missing values handled as defined.. What does the function Loadtxt () do in NumPy?loadtxt() function. The loadtxt() function is used to load data from a text file. Each row in the text file must have the same number of values.
|