A numpy array is used to store numerical data for manipulation.

You can save and load a numpy array in python using the `numpy.save()`

and `numpy.load()`

methods.

**Basic Example**

```
import numpy as np
numpyArr = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
np.
````save`(`'myNumpyArray.npy'`, numpyArr)
loadedArray= np.`load`('myNumpyArray.npy')

- The save method saves the array in a
`npy`

format, a standard binary format for storing the numpy arrays.

This tutorial teaches you the different methods to store the numpy array and load it for later use in machine-learning activities.

Table of Contents

## Using Numpy Save and Load

- The NumPy save() method serializes the NumPy array into the disk
- The NumPy load() method deserialises the array and converts its back to the NumPy array.

*This method stores the endianness of the data, and precision is also kept.*

Use this method when you want to store the array in a platform-independent format and want to preserve the precision of the data.

**Code**

```
import numpy as np
numpyArr = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
np.save('myNumpyArray
````.npy`', numpyArr)
loadedArray= np.load(`'myNumpyArray.npy'`)
print(loadedArray)
print(numpyArr == loadedArray)

**Output**

```
[[1 2 3]
[4 5 6]
[7 8 9]]
[[ True True True]
[ True True True]
[ True True True]]
```

## Using SaveTxt and LoadTxt

- The NumPy saveTxt() method writes the numpy array as a text file to the disk
- The NumPy loadtxt() method reads the text file and converts it into a NumPy array.

*The saveTxt() method writes data in “%.18e” format by default if the fmt parameter is not specified. You can also specify other formats such as decimal to store the number in decimal format*

Use this method when you want to store the array in a text format and have numbers in different forms such as decimal, float, or String.

**Code**

```
import numpy as np
numpyArr = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
np.
````savetxt`('myNumpyArray`.txt`', numpyArr, `fmt='%d'`)
loadedArray = np.loadtxt('myNumpyArray.txt', `dtype=int`)
print(loadedArray)
print(numpyArr == loadedArray)

**Output**

```
[[1 2 3]
[4 5 6]
[7 8 9]]
[[ True True True]
[ True True True]
[ True True True]]
```

## Using ToFile and FromFile

- The NumPy tofile() method writes the numpy array as a text file to the disk in an efficient way
- The NumPy fromfile() method reads the text file and converts it into a NumPy array.

*This is the highly efficient way to write and read a NumPy array. However, it doesn’t maintain the endianness and the precession of the data.*

Use this method when storing the data, Byte order and data-type maintenance are unnecessary.

**Code**

```
import numpy as np
numpyArr = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
numpyArr.
````tofile`('numpyArr`.dat`')
loadedArray = np.fromfile('numpyArr.dat', `dtype=int`)
print(loadedArray)

**Output**

` [1 2 3 4 5 6 7 8 9]`

## Using savez_compressed And Load

The savez_compressed() saves the single/multiple NumPy array into a single **compressed** file.

Use this method when you want to store data in a compressed format or when you want to compress more than one NumPy array into a single file and load it back.

**Code**

```
import numpy as np
numpyArr = np.asarray([ [1,2,3], [4,5,6], [7,8,9] ])
np.
````savez_compressed`('myNumpyArray`.npz`', numpyArr)
loadedArray = np.load('myNumpyArray`.npz'`)
data = loadedArray['arr_0']
print(data)

**Output**

```
[[1 2 3]
[4 5 6]
[7 8 9]]
```

## Save Multiple Numpy Arrays

This section teaches you how to save multiple NumPy arrays into a single **uncompressed** NPY file using the savez() method.

- Invoke the
`savez()`

method and pass the name for the`npz`

file. - Pass the arrays you want to save and provide a key name for each array that needs to be stored in the file
- While reading, a dictionary-like object is returned, and you can access each array using the key provided earlier.

**Code**

```
import numpy as np
array1 = np.asarray([ [1,2,3], [4,5,6] ])
array2 = np.asarray([ [7,8,9], [10,11,12] ])
np.
````savez`('myNumpyArray.npz', `name1=array1`, `name2`=array2)
data = np.load('myNumpyArray.npz')
print(data[`'name1'`])
print(data['name2'])

**Output**

```
[[1 2 3]
[4 5 6]]
[[ 7 8 9]
[10 11 12]]
```

This is how you can save and load NumPy arrays for machine learning purposes properly.