NumPy ndarray.dump() Method

NumPy ndarray.dump()

The numpy.ndarray.dump() method is used to serialize a NumPy array and save it as a binary file in .npy format. This method allows saving array data efficiently for later use.

Syntax

ndarray.dump(file)

Parameters

Parameter	Type	Description
file	str or file-like object	File path or an open file where the array will be saved in .npy format.

Return Value

This method does not return any value. It writes the array data directly to the specified file.

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Examples

1. Saving a NumPy Array to a File

This example demonstrates how to save a NumPy array to a binary .npy file using ndarray.dump().

import numpy as np

# Create a NumPy array
arr = np.array([[1, 2, 3], 
                [4, 5, 6]])

# Specify the file name to save the array
file_name = "array_data.npy"

# Save the array using dump() method
arr.dump(file_name)

print(f"Array successfully saved to {file_name}")

Output:

Array successfully saved to array_data.npy

The array is stored in a binary format in the array_data.npy file. It can be loaded later using numpy.load().

2. Loading a Saved NumPy Array

Once an array is saved using dump(), it can be reloaded using numpy.load().

import numpy as np  # Import NumPy library

# Specify the file name where the array was saved
file_name = "array_data.npy"

# Load the saved array
loaded_array = np.load(file_name)

# Print the loaded array
print("Loaded array:")
print(loaded_array)

Output:

Loaded array:
[[1 2 3]
 [4 5 6]]

The previously saved array is successfully reloaded and displayed.

3. Saving and Loading a Large NumPy Array

When working with large datasets, ndarray.dump() efficiently stores arrays in a compressed binary format.

import numpy as np  # Import NumPy library

# Create a large NumPy array with random values
large_array = np.random.rand(1000, 1000)  # 1000x1000 random float values

# File to save the large array
file_name = "large_array.npy"

# Save the large array
large_array.dump(file_name)

print(f"Large array successfully saved to {file_name}")

# Load the saved large array
loaded_large_array = np.load(file_name)

# Verify the shape of the loaded array
print("Shape of loaded array:", loaded_large_array.shape)

Output:

Large array successfully saved to large_array.npy
Shape of loaded array: (1000, 1000)

The large array is stored and retrieved with the same shape, ensuring data integrity.