Numpy numpy.loadtxt() Function

  1. Syntax of numpy.loadtxt():
  2. Example Codes: NumPy Read txt File Using numpy.loadtxt() Function
  3. Example Codes: Set dtype Parameter in numpy.loadtxt() Function While Reading the txt File
  4. Example Codes: Set delimiter Parameter in numpy.loadtxt() Function While Reading txt Files
  5. Example Codes: Set usecols Parameter in numpy.loadtxt() Function While Reading the txt File
  6. Example Codes: Set unpack Parameter in numpy.loadtxt() Function While Reading txt Files

Python Numpy numpy.loadtxt() function loads data from a text file and provides fast approach for simple text files.

Syntax of numpy.loadtxt():

numpy.loadtxt(fname, 
              dtype=<class 'float'>, 
              comments='#', 
              delimiter=None, 
              converters=None, 
              skiprows=0, 
              usecols=None, 
              unpack=False, 
              ndmin=0, 
              encoding='bytes', 
              max_rows=None)

Parameters

fname Path of txt file to be imported
dtype Data type of the resulting array
comments Characters or list of characters used to indicate the start of a comment
delimiter Delimiter to use for parsing the content of txt file
converters Dictionary mapping column number to a function that will parse the column string into the desired value.
skiprows Which row/rows to skip
usecols The column indices to be read
unpack Transpose returned array, so that arguments may be unpacked using x, y, z = loadtxt(...). [unpack=True]
ndim Minimum number of dimensions in returned array
encoding Encoding used to decode the input file.
max_rows Maximum number of rows to read after skiprows lines

Return

N-dimensional array read from the txt file.

Example Codes: NumPy Read txt File Using numpy.loadtxt() Function

import numpy as np
  
from io import StringIO    
  
f = StringIO("3 6 8 \n12 9 1 \n 2 3 4") 
a = np.loadtxt(f) 

print("The loaded array is:")
  
print(a)

Output:

 The loaded array is:
[[ 3.  6.  8.]
 [12.  9.  1.]
 [ 2.  3.  4.]]

It loads the txt file into the NumPy array .

Here, StringIO acts like a file object.

We can also provide file path as an argument to the np.loadtxt function using both absolute and relative paths.

Example Codes: Set dtype Parameter in numpy.loadtxt() Function While Reading the txt File

By default, the data type of values of array read from a txt file is float. We can manually set datatype of elements using the dtype parameter.

import numpy as np
  
from io import StringIO    
  
f = StringIO("3 6 8 \n12 9 1 \n 2 3 4") 
a = np.loadtxt(f,dtype="int") 

print("The loaded array is:")
  
print(a)

Output:

The loaded array is:
[[ 3  6  8]
 [12  9  1]
 [ 2  3  4]]

The above code loads all the elements into an array from txt file as integers.

Example Codes: Set delimiter Parameter in numpy.loadtxt() Function While Reading txt Files

By default, the delimiter to separate the values is whitespace. We can manually set delimiter using the delimiter parameter.

import numpy as np
  
from io import StringIO    
  
f = StringIO("3, 6, 8 \n12, 9, 1 \n 2, 3, 4") 
a = np.loadtxt(f,dtype="int",delimiter=",") 

print("The loaded array is:")
  
print(a) 

Output:

The loaded array is:
[[ 3  6  8]
 [12  9  1]
 [ 2  3  4]]

As the txt file’s values are separated by ,, we have to use , as the delimiter to separate values while reading from the txt file into the array.

Example Codes: Set usecols Parameter in numpy.loadtxt() Function While Reading the txt File

import numpy as np
  
from io import StringIO    
  
f = StringIO("3 6 8 \n12 9 1 \n 2 3 4") 
a = np.loadtxt(f,dtype="int",usecols =(0, 1)) 

print("The loaded array is:")
  
print(a)  

Output:

The loaded array is:
[[ 3  6]
 [12  9]
 [ 2  3]]

The usecols specifies which columns to be read from the txt file.

It reads only the 1st and 2nd column from the txt file into the array.

Example Codes: Set unpack Parameter in numpy.loadtxt() Function While Reading txt Files

import numpy as np
  
from io import StringIO    
  
f = StringIO("3 6 8 \n12 9 1 \n 2 3 4") 
(x,y,z) = np.loadtxt(f,dtype="int",unpack=True) 

print(x)
print(y)
print(z) 

Output:

[ 3 12  2]
[6 9 3]
[8 1 4]

It transposes the array and unpacks the rows of the transposed array into specified variables.

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