Saving and Loading Arrays
NumPy allows us to save and retrieve arrays efficiently in binary or text format.
a) Saving and Loading a NumPy Array (Binary Format)
# Create an array
a = np.array([1, 2, 3, 4, 5])
# Save array to a binary file
np.save('data.npy', a)
# Load the array from the file
b = np.load('data.npy')
print("Loaded Array:", b)
Why Use .npy Format?
- Stores data in a compact binary format.
- Faster loading and saving compared to CSV or text files.
- Retains data type and structure of arrays.
b) Saving and Loading Multiple Arrays
We can save multiple arrays in a single file using np.savez().
# Create multiple arrays
arr1 = np.array([1, 2, 3])
arr2 = np.array([10, 20, 30])
# Save both arrays in a single file
np.savez('multi_data.npz', array1=arr1, array2=arr2)
# Load the arrays from file
loaded_data = np.load('multi_data.npz')
print("Array 1:", loaded_data['array1'])
print("Array 2:", loaded_data['array2'])
When to Use .npz Format?
- When working with large datasets where multiple arrays need to be stored efficiently.
- Reduces disk space usage while maintaining fast I/O speeds.
c) Saving and Loading CSV Files
Many real-world datasets are stored in CSV format. NumPy makes it easy to save and load CSV files.
Saving a NumPy Array as a CSV File
# Create a sample array
data = np.array([[1, 2, 3], [4, 5, 6]])
# Save to CSV
np.savetxt('data.csv', data, delimiter=',')
Loading Data from a CSV File
# Load CSV file
loaded_data = np.loadtxt('data.csv', delimiter=',')
print("Loaded CSV Data:\n", loaded_data)
Use Case:
- CSV files are widely used in data science, machine learning, and finance.
