NumPy Tutorial

Python NumPy Tutorial numpy.empty() in Python numpy.empty_like() in Python numpy.eye() in Python numpy.identity() in Python numpy.ones() in Python numpy.ones_like() in Python numpy.zeros in Python numpy.zeros_like() in Python numpy.full() in Python numpy.full_like() in Python numpy.asarray() in Python numpy.frombuffer() in Python numpy.fromiter() in Python numpy.fromstring () in Python numpy.asanyarray() in Python with Example numpy.ascontiguousarray() in Python with Example Numpy.asmatrix() in Python with Example Numpy.copy() in Python with Example numpy.loadtxt() Python numpy.arrange() in Python numpy.linspace() in Python numpy.logspace() in Python numpy.geomspace() in Python numpy.meshgrid() in Python numpy.diag() in Python numpy.diagflat() in Python numpy.tri() in Python numpy.tril() in Python numpy.copyto() in Python numpy.reshape() in Python numpy.ravel() in Python numpy.ndarray.flat() in Python numpy.ndarray.flatten() in Python numpy.rollaxis() in Python numpy.swapaxes() in Python numpy.ndarray.T in Python numpy.transpose() in Python numpy.atleast_1d() in Python numpy.atleast_2d() in Python numpy.atleast_3d() in Python numpy.broadcast_to() in Python numpy.broadcast_arrays() in Python numpy.expand_dims() in Python numpy.squeeze() in Python numpy.asarray_chkfinite() in Python numpy.asscalar() in Python numpy.concatenate() in Python numpy.stack() in Python numpy.column_stack() in Python numpy.dstack() in Python numpy.hstack() in Python numpy.vstack() in Python numpy.split() in Python numpy.tile() in Python numpy.repeat() in Python numpy.delete() in Python numpy.append() in Python numpy.resize() in Python numpy.trim_zeros() in Python numpy.unique() in Python numpy.flip() in Python NumPy vs SciPy

Misc

Numpy Attributes

numpy.loadtxt() Python

Python numpy.loadtxt() The loadtxt() function of Python numpy class loads the data from a text fileand provides a fast reader for simple text files. Syntax

numpy.loadtxt(fname, dtype=<class'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None)
Parameter fname : This parameter represents a file, filename, or generator to read. If the extension is .gz or .bz2, the file decompressed. dtype : It is an optional parameter. It depicts the data type of returned array, and by default, it is a float.If it is a structured data-type, the array will be of one-dimensional, whereeach row represents as an element of the array. delimiter : This parameter represents a string to separatethe  values. By default, it can be any whitespace. converters : It signifies a dictionary used for mapping column number to a function that will convert that column to a float. skiprows : This parameter is used for skipping the first skip rows lines.It is an optional field, and its default value is 0. usecols : This parameter states which columns to read, with 0 being the first. It is also an optional field. unpack : It is an optional parameter and represents Boolean value, and by default, it is false.If passed true, the returned array is transposed, so that the parameters may be unpacked using x, y, z = loadtxt(...). When used with a structured data-type, the arrays are returned for each field. ndmin : It returns an array will have at least ndmin dimensions. encoding : It is used forEncoding and later decoding the inputfile. max_rows : It reads the  max_rows lines of content after skiprows lines. Return This function reads the data from the text file. Example 1
# Python program to explain 
# numpy.loadtxt() function
importnumpy as np
# StringIO behaves like a file object
fromio import StringIO
n = StringIO("1 2 \n 4 5 9")
m = np.loadtxt(n)
print(m)
Output
[[ 1.  2.]
[ 4.  5.]]
Example 2
# Python program to explain 
# numpy.loadtxt() function
importnumpy as np
# StringIO behaves like a file object
fromio import StringIO
obj = StringIO("1, 2, 3\n4, 5, 6")
n, m, p = np.loadtxt(obj, delimiter =', ', usecols =(0, 1, 2), unpack = True)
print("value of n: ", n)
print("value of m: ", m)
print("value of p: ", p)
Output
value of n:  [ 1.  4.]
value of m:  [ 2.  5.]
value of p:  [ 3.  6.]