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# Learn NumPy for Data Science (Course II)

## Learn NumPy for Data Science (Course II)

### Input/Output operations in NumPy

NumPy provides built-in functions for efficiently saving NumPy arrays as external files in the disk and also for loading the NumPy array from a file into your Python code. In this chapter, you will learn various ways of performing I/O operations in NumPy.

#### NPY and NPZ files in NumPy

Generally, the arrays are saved in files with the format `.npy` and .`npz`. The `.npy` files store all the information required to reconstruct an array on any computer, which includes dtype and shape information whereas, several arrays are contained into a single file in uncompressed `.npz` format.

The numpy.save() function is used to save the array in `.npy` format, whereas numpy.savez() is used to save the array in `.npz` format. Likewise, the numpy.load() function is used to load the array into the Python code. While loading the .npz file, the individual array can be accessed by passing “arr_0, arr_1, ….. arr_n”, a as a key to the loaded object.

```import numpy as np

# Creating two 1-D numpy arrays
a = np.arange(start=1, stop=5, step=1)
b = np.arange(start=6, stop=10, step=1)

# Printing the arrays
print("a: ", a)
print("b: ", b)
print("\n")

# Saving the a to .npy format
np.save('a.npy', a)

# Saving the arrays a and b  to .npz format
np.savez('ab.npz', a, b)

```a: [1 2 3 4]
b: [6 7 8 9]

First array in the ab_loaded: [1 2 3 4]
Second array in the ab_loaded: [6 7 8 9]```

#### TXT Files in NumPy

NumPy arrays can also be stored in plain `.txt` file formats. The numpy.savetxt() and numpy.loadtxt() methods are used to save and load arrays respectively.

```import numpy as np

# Creating a 1-D numpy arrays
a = np.arange(start=1, stop=5, step=1)

# Saving array as a txt file
np.savetxt("a.txt", a)

`Loaded Array: [1. 2. 3. 4.]` 