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Reading and writing files is an important aspect of building programs that are used by many users. Python provides a range of methods that can be used for resource handling. These methods may differ slightly depending on the format of the file and the operation being performed.

In addition to the traditional way of creating a file by clicking a button, we can also open()create a file using built-in functions such as the function. Note that open()the function will only create the file if it does not exist; otherwise, it essentially opens the file.

Writing an array to a text file using the open()and functions in Pythonclose()

Since open()the write function is not used alone, by combining it with other functions, we can perform more file operations such as writing and modifying or overwriting files. These functions include the write write()and overwrite close()functions. Using these functions, we will create an array using NumPy and write it to a text file using the write function as shown in the following program.

import numpy as np

sample_list = [23, 22, 24, 25]
new_array = np.array(sample_list)

# Displaying the array

file = open("sample.txt", "w+")

# Saving the array in a text file
content = str(new_array)
file.write(content)
file.close()

# Displaying the contents of the text file
file = open("sample.txt", "r")
content = file.read()

print("Array contents in sample.txt: ", content)
file.close()

Output:

Array contents in text_sample.txt:  [23 22 24 25]

In the above example, we have created a text file named using the open function in write mode sample.txt. We then proceeded to convert the array into a string format and then writewrite its contents to the text file using the write function. Using open()the write function, we open the contents of the text file in read mode. The text file contents are displayed in the terminal and can also be viewed by physically opening the text file.

Similarly, we can also create a multidimensional array and save it to a text file, as shown below.

import numpy as np

sample_list = [[23, 22, 24, 25], [13, 14, 15, 19]]
new_array = np.array(sample_list)

# Displaying the array

file = open("sample.txt", "w+")

# Saving the array in a text file
content = str(new_array)
file.write(content)
file.close()

# Displaying the contents of the text file
file = open("sample.txt", "r")
content = file.read()

print("Array contents in sample.txt: ", content)
file.close()

Output:

Array contents in sample.txt:  [[23 22 24 25]
[13 14 15 19]]

Writing an array to a text file using the Content Manager in Python

Alternatively, we can use a context manager to write the array to a text file. Unlike open()the __context__ function, where we have to close()close the file after opening it using the __context__ function, a content manager allows us to open and close the file exactly when we need to. In Python, using context managers is considered a better practice when managing resources rather than using __context__ open()and __context__ functions. A context manager can be implemented close()using the __context__ keyword as shown below .with

import numpy as np

new_list = [23, 25, 27, 29, 30]
new_array = np.array(new_list)
print(new_array)

with open("sample.txt", "w+") as f:
    data = f.read()
    f.write(str(new_array))

Output:

[23 25 27 29 30]

In the above example, the context manager opens the text file sample.txtand since the file does not exist, the context manager creates it. Within the scope of the context manager, we write the array to the text file while converting it to a string. The context manager automatically closes the file once we choose to exit the indentation. Similarly, we can also write a multidimensional array to a text file using a context manager as shown below.

import numpy as np

new_list = [[23, 25, 27, 29], [30, 31, 32, 34]]
new_array = np.array(new_list)
print(new_array)

with open("sample.txt", "w+") as f:
    data = f.read()
    f.write(str(new_array))

Output:

[[23 25 27 29]
[30 31 32 34]]

NumPy is a scientific library that provides a range of functions for working with arrays. We can numpy.savetxt()save an array to a text file using the function. The function accepts several parameters including the file name, format, encoding format, delimiter to separate columns, header, footer, and comments to accompany the file.

In addition, NumPy also provides numpy.loadtxt()functions to load text files.

We can save the array to a text file and load it using these two functions as shown in the following code.

import numpy as np

new_list = [23, 24, 25, 26, 28]
new_array = np.array(new_list)
print(new_array)

np.savetxt("sample.txt", new_array, delimiter=", ")

content = np.loadtxt("sample.txt")
print(content)

Output:

[23 24 25 26 28]

As shown below, we can also use these functions to save multidimensional arrays into text files.

import numpy as np

new_list = [[23, 24, 25, 26, 28], [34, 45, 46, 49, 48]]
new_array = np.array(new_list)
print(new_array)

np.savetxt("sample7.txt", new_array, delimiter=", ")

content = np.loadtxt("sample7.txt")
print(content)

Output:

[[23 24 25 26 28]
 [34 45 46 49 48]]