Numpy fromfile() Function

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The Numpy fromfile() function is used to read data from a binary or text file into a NumPy array. The function efficiently reads binary data with a known data type or parses simply formatted text files, depending on the mode specified. Data written using the numpy.tofile() method can typically be read back using this function, provided the data format matches.

Syntax

Following is the syntax of the Numpy fromfile() function −

numpy.fromfile(file, dtype=float, count=-1, sep='', offset=0, like=None)

Parameters

Following are the parameters of the Numpy fromfile() function −

• file: The file or file-like object to read. This can be a string representing the file path or an open file object.
• dtype(optional): The data type of the returned array. By default, it is set to float, but it can be specified as any NumPy dtype, such as int, float32, etc.
• count(optional): The number of items to read. If -1 (default), all items in the file will be read.
• sep(optional):The separator used if reading from a text file. An empty string ('') indicates binary mode. A string (e.g., a comma or space) indicates text mode.
• offset(optional): The offset (in bytes) from the files current position. By default offset is assigned to 0. Only permitted for binary files.
• like (optional): It allows the creation of an array which is like the input object but uses an existing array-like object (like another NumPy array).

Return Value

The function returns a NumPy array populated with the data read from the file.

Example

Following is a basic example to read binary data from a file using Numpy fromfile() function −

import numpy as np
# Create a binary file for the example
data = np.array([11, 22, 33, 44], dtype=np.int32)
data.tofile('example_data.bin')
# Now read the binary data using numpy.fromfile()
read_data = np.fromfile('example_data.bin', dtype=np.int32)
print("Binary Data:", read_data)

Output

Following is the output of the above code −

Binary Data: [11 22 33 44]

Example : Reading Text Data

In the following example, we read data from a text file into a NumPy array using the numpy.fromfile() function −

import numpy as np
# Create a text file for the example
with open('example_data.txt', 'w') as file:
    file.write('85 25 15 24 9')
   
# reading the text data using numpy.fromfile()
read_data = np.fromfile('example_data.txt', dtype=np.float64, sep=' ')
print("Text Data:", read_data)

Output

Following is the output of the above code −

Text Data: [85. 25. 15. 24.  9.]

Example : Reading a Specific Number of Items

We can specify the count parameter to limit the number of items to read from the file. In the following example, we read only the first 4 elements from a binary file −

import numpy as np
# Create a binary file for the example
data = np.array([18, 80, 24, 99, 56, 45, 79, 31], dtype=np.int32)
data.tofile('example_data.bin')

# Read only the first 4 elements using numpy.fromfile()
read_data = np.fromfile('example_data.bin', dtype=np.int32, count=4)
print("Read First 4 Elements:", read_data)

Output

Following is the output of the above code −

Read First 4 Elements: [18 80 24 99]

Example : Binary vs. Text Mode

When the sep parameter is an empty string (sep=''), the file is read in binary mode. When sep is specified with a character (e.g., a space or comma), the file is read in text mode.

In the following example, we have demonstrated the difference between reading in binary mode and text mode using numpy.fromfile() −

import numpy as np

# Write some binary data
data = np.array([56, 29, 66, 89, 77], dtype=np.int32)
data.tofile('binary_data.bin')

# Read in binary mode
binary_read = np.fromfile('binary_data.bin', dtype=np.int32)
print("Read Binary Mode:", binary_read)

# Write some text data
with open('text_data.txt', 'w') as file:
    file.write('45, 77, 25, 19, 85, 96')

# Read in text mode
text_read = np.fromfile('text_data.txt', dtype=np.int32, sep=',')
print("Read Text Mode:", text_read)

Output

Following is the output of the above code −

Read Binary Mode: [56 29 66 89 77]
Read Text Mode: [45 77 25 19 85 96]