Import Text Files Into Numpy Arrays – Python

We have to import data from text files into Numpy arrays in Python. By using the numpy.loadtxt() and numpy.genfromtxt() functions, we can efficiently read data from text files and store it as arrays for further processing.

• numpy.loadtxt( ) – Used to load text file data
• numpy.genfromtxt( ) – Used to load data from a text file, with missing values handled as defined.

Note: numpy.loadtxt( ) is equivalent function to numpy.genfromtxt( ) when no data is missing.

Using numpy.loadtxt()

numpy.loadtxt() function is one of the most commonly used methods to import data from a text file into a NumPy array. It can read a variety of text files that have structured data like numbers or strings, and it can handle delimiter-separated values such as CSV, TSV, or space-separated files.

Example 1: Importing Text file into Numpy arrays

The following ‘example1.txt’ text file is considered in this example.  

import numpy as np

# Text file data converted to integer data type
d = np.loadtxt("example1.txt", dtype=int)
print(d)

Output :

[[ 1  2]
[ 3  4]
[ 5  6]
[ 7  8]
[ 9 10]]

Explanation: This code uses the numpy.loadtxt() function to read data from the text file example1.txt and convert the data into a NumPy array with integer data type (dtype=int). The resulting array is then printed.

Example 2: Importing text file into NumPy array by skipping first row 

import numpy as np

# skipping first row
# converting file data to string
d = np.loadtxt("example2.txt", skiprows=1, dtype='str')
print(d)

Output :

[['2' 'Bunty']
['3' 'Tinku']
['4' 'Rina']]

Explanation: This code uses numpy.loadtxt() to read data from the text file example2.txt, skipping the first row and converting the remaining data into a NumPy array of strings (dtype=’str’). The resulting data is then printed.

Example 3: Importing only the first column(Names) of text file into numpy arrays

The indexing in NumPy arrays starts from 0. Hence, the Roll column in the text file is the 0^th column, Names column is the 1^st column and the Marks are the 2^nd column in the text file ‘example3.txt’.

import numpy as np

# only column1 data is imported into numpy
# array from text file
d = np.loadtxt("example3.txt", usecols=1, skiprows=1, dtype='str')

for each in d:
    print(each)

Output :

Ankit
Bunty
Tinku
Rina
Rajesh

Explanation: This code uses numpy.loadtxt() to read data from the text file example3.txt, skipping the first row and importing only the data from column 1 (usecols=1). The data is converted into strings (dtype=’str’). It then prints each value in the resulting array.

Using numpy.genfromtxt()

numpy.genfromtxt() is a more flexible function that can handle missing data, non-numeric data, and more complex file formats. It’s generally used when dealing with more complex text files.

Example 1: Importing Data with np.genfromtxt() in NumPy

import numpy as np

d = np.genfromtxt("example4.txt", dtype=str, encoding=None, delimiter=",")
print(d)

Output :

[['a' 'b' 'c' 'd']
['e' 'f' 'g' 'h']]

Explanation: This code uses the numpy.genfromtxt() function to read data from the text file example4.txt. The data is read as strings (dtype=str), with no specific encoding (encoding=None), and is assumed to be comma-separated (delimiter=”,”). The resulting array is then printed.

Example 2: Importing text file into numpy arrays by skipping last row 

import numpy as np

# skipping last line in the file
d = np.genfromtxt("example5.txt", dtype=str, encoding=None, skip_footer=1)
print(d)

Output :

[['This' 'is' 'GeeksForGeeks' 'Website']
['How' 'are' 'You' 'Geeks?']
['Geeks' 'for' 'Geeks' 'GFG']]

Explanation: This code uses the numpy.genfromtxt() function to read data from the text file example5.txt, skipping the last line of the file (skip_footer=1). The data is read as strings (dtype=str) and no specific encoding is used (encoding=None). The resulting array is then printed.