I learn of NumPy while seeking one way to read a text file effectively. Therefore, I decide to put some notes here for convenient access.
1. Input and Output
1.1 Load data from a text file
np.loadtxt
is used to load data from a simply formatted text file and return an array called ndarry
.
import numpy as np
np.loadtxt(fname, # file or str, File, filename, or generator to read.
dtype=<type 'float'>, # data-type, data-type of the resulting array; default: float.
comments='#', # str or sequence, the characters or list of characters used to indicate the start of a comment.
delimiter=None, # str, by default, this is any whitespace.
converters=None, # dict, mapping column number to a function. E.g., if column 0 is a date string: converters = {0: datestr2num}.
skiprows=0, # int, skip the first skiprows lines
usecols=None, # sequence, identify which columns to read. E.g, `usecols = (1,4,5)` will extract the 2nd, 5th and 6th columns.
unpack=False, # If True, the returned array is transposed, so that arguments may be unpacked using x, y, z = loadtxt(...).
ndmin=0) # int, the returned array will have at least ndmin dimensions. The reLegal values: 0 (default), 1 or 2.
1.2 Save an array to a text file
np.savetxt
is used to save an array to a text file.
np.savetxt(fname, # filename or file handle, '.gz' is automatically saved in compressed gzip format.
X, # array_like, data to be saved to a text file.
fmt='%.18e', # str or sequence of strs, e.g. [‘%.3e + %.3ej’, ‘(%.15e%+.15ej)’] for 2 columns (a list of specifiers, one per column)
delimiter=' ', # str
newline='\n', # str, string or character separating lines.
header='', # str
footer='', # str
comments='# ') # str, string that will be prepended to the header and footer strings, to mark them as comments.
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