Contents

## Introduction

In python, there are many ways to re-structure the array according to the need of the person. But, there are some cases when we need a **one-dimensional array rather than** two- dimensional array. This type of problem numpy library provides a function by which we can convert a two-dimensional array into a **one-dimensional array**, i.e., **numpy.ndarray.ravel()** The travel() function in the numpy library is one of the most essential and commonly used functions that helps in unraveling the data which has been presented by the user.

## What is the Numpy ravel() Method?

**Numpy.ndarray.ravel() is used when to return contiguous flattened array. It means a 1-d array with all the input elements and with the same type as it. **

## Syntax of Numpy Ravel

`numpy.ravel(order = 'C')`

## Parameters

**order: {‘C,’ ‘F,’ ‘A,’ ‘K’} –** These are optional function in the input. All the elements of the input array are read according to the index order. Firstly, If we use order= ‘C,’ it means the index order will be row-major. The order in memory is slow at the first index and fastest at the last index.

Secondly, If we use order = ‘F,’ it means the index order will be column-major. It is also known as ‘Fortran-style order,’ which changes the fastest at the first index and slowest at the last index. **Remember C and F both do not consider the memory layout of the array and only refer to the order of axis indexing.**

Thirdly, If we use order = ‘A,’ it means reading and writing the array elements in Fortran-Style index order. If an array is Fortran contiguous in memory, otherwise, it will take C-like order.

Fourthly, If we use order = ‘K,’ **it will read the elements in the order they occur in memory. Except for reversing the data. By default, the order is always set to ‘C.’**

## Return value of Numpy Ravel

An array with the same type as the input array with the order as per your requirement.

## Examples of Numpy Ravel Method

Let us understand the **numpy ravel() function** of the numpy module in details with the help of examples:

### 1. Using a 2-d array in Numpy Ravel() Function

In this example, we will be importing the numpy module as np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function to convert a 2-d array into a 1-d array.

```
#python program using a 2-d array
#Import numpy library
import numpy as np
arr = np.array([[1,4,7], [2,5,8]])
output=arr.ravel()
print("Input Array : ",arr)
print("Output Array : ",output)
```

**Output:**

```
Input Array : [[1 4 7]
[2 5 8]]
Output Array : [1 4 7 2 5 8]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the **ravel() function.** At last, we have printed the value of the output array.

### 2. To show that numpy.ravel() is equivalent to reshape

In this example, we will be importing the numpy module as an alias name np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function to convert a 2-d array into a 1-d array. and at last, we will apply the array.reshape() function.

```
#python program using a 2-d array
#Import numpy library
import numpy as np
arr = np.array([[1,4,7], [2,5,8]])
output=arr.ravel()
print("Input Array : ",arr)
print("Output Array : ",output)
arr1 = arr.reshape(-1)
print("Reshape output : ", arr1)
```

**Output:**

```
Input Array : [[1 4 7]
[2 5 8]]
Output Array : [1 4 7 2 5 8]
Reshape output : [1 4 7 2 5 8]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the **ravel() function.** Fourthly, we have printed the value of the output array. Fifthly, we have applied arr. reshape(-1) function and stored the value into new arr1. Finally, we have printed the output of reshape function and seen that numpy.ravel() is equivalent to an array.reshape().

### 3. Using Order = ‘F’ As A Parameter in Numpy Ravel() Function

In this example, we will be importing the numpy module as np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function with the order = ‘F’ to convert a 2-d array into a 1-d array.

```
#python program using a 2-d array
#Import numpy library
#using order = 'F'
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
output=arr.ravel('F')
print("Input Array : ",arr)
print("Output Array : ",output)
```

**Output:**

```
Input Array : [[1 2 3]
[4 5 6]]
Output Array : [1 4 2 5 3 6]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the ravel() function. Fourthly, we have used the order ‘F’ to print the output as column-major. At last, we have printed the value of the output array.

### 4. Using Order = ‘C’ As A Parameter in Numpy Ravel() Function

In this example, we will be importing the numpy module as np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function with the order = ‘C’ to convert a 2-d array into a 1-d array.

```
#Import numpy library
#using order = 'C'
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
output=arr.ravel('C')
print("Input Array : ",arr)
print("Output Array : ",output)
```

**Output:**

```
Input Array : [[1 2 3]
[4 5 6]]
Output Array : [1 2 3 4 5 6]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the ravel() function’s value. Fourthly, we have used the order ‘C’ to print the output as row-major. At last, we have printed the value of the output array.

### 5. Using Order = ‘A’ As A Parameter in Numpy Ravel() Function

In this example, we will be importing the numpy module as np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function with the order = ‘A’ to convert a 2-d array into a 1-d array.

```
#Import numpy library
#using order = 'A'
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
output=arr.ravel('A')
print("Input Array : ",arr)
print("Output Array : ",output)
```

**Output:**

```
Input Array : [[1 2 3]
[4 5 6]]
Output Array : [1 2 3 4 5 6]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the ravel() function. Fourthly, we have used the order ‘A’ to print the output as row-major. At last, we have printed the value of the output array.

### 6. Using Order = ‘K’ As A Parameter in Numpy Ravel() Function

In this example, we will be importing the numpy module as np. Then, we will take the input array as a 2-d array. Finally, we will apply the numpy ravel() function with the order = ‘K’ to convert a 2-d array into a 1-d array.

```
#Import numpy library
#using order = 'K'
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
output=arr.ravel('K')
print("Input Array : ",arr)
print("Output Array : ",output)
```

**Output:**

```
Input Array : [[1 2 3]
[4 5 6]]
Output Array : [1 2 3 4 5 6]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have created a 2- d array using the array function. Thirdly, we have created the variable as output and assigned it the ravel() function. Fourthly, we have used the order ‘K’ to print the output as row-major. At last, we have printed the value of the output array.

### 7. Using reshape() with swapping axis

In this example, we will be importing the numpy module as np. Then, we will arrange an array with the help of reshape function and swapaxes function.

```
#import numpy library
import numpy as np
x = np.arange(18).reshape(3,3,2).swapaxes(1,2)
y=np.ravel(x, order='C')
z=np.ravel(x, order='K')
m=np.ravel(x, order='A')
print(x)
print(y)
print(z)
print(m)
```

**Output:**

```
[[[ 0 2 4]
[ 1 3 5]]
[[ 6 8 10]
[ 7 9 11]]
[[12 14 16]
[13 15 17]]]
[ 0 2 4 1 3 5 6 8 10 7 9 11 12 14 16 13 15 17]
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17]
[ 0 2 4 1 3 5 6 8 10 7 9 11 12 14 16 13 15 17]
```

**Explanation:**

Here firstly, we have imported the numpy module with the alias name as np. Secondly, we have arranged an array till 18 to help reshape() function and swapaxes() function. Thirdly, we have applied the ravel function with all the orders and stored them into the new variable. At last, we have printed all the variables and seen the difference in the output.

## Difference between flatten() and Numpy ravel()

These both the function are used to convert a multi-dimensional array into a one-dimensional array. But, there are some differences through which they differ in themselves.

### Numpy Ravel()

- Numpy Ravel function always returns the reference of the original array.
- It is a library-level function.
- Ravel is faster than flatten().
- If the value is modified, the original array will also get affected.

### Numpy Flatten()

- Flatten method() always returns a copy of the original array.
- It is a function of ndarray object.
- It is slower than ravel as it takes more memory than ravel().
- If the value is modified, the original array will not get affected.

```
#flatten and ravel function in python example
import numpy as np
arr = np.array([[1,2,3], [4,5,6]])
print("Input Array : ",arr)
print("Dimension of array : ",(arr.ndim))
print("\n")
arr1 = arr.ravel()
print("Ravel output : ",arr1)
arr1[0] = 50
print("updated ravel array : ",arr1)
print("Input array : ",arr)
print("\n")
arr2 = arr.flatten()
print("Flatten output : ",arr2)
arr2[0] = 100
print("Updated flatten array : ",arr2)
print("Input array : ",arr)
```

**Output:**

```
Input Array : [[1 2 3]
[4 5 6]]
Dimension of array : 2
Ravel output : [1 2 3 4 5 6]
updated ravel array : [50 2 3 4 5 6]
Input array : [[50 2 3]
[ 4 5 6]]
Flatten output : [50 2 3 4 5 6]
Updated flatten array : [100 2 3 4 5 6]
Input array : [[50 2 3]
[ 4 5 6]]
```

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have taken the input array and printed the input array and the array’s dimension. Thirdly, we have applied the ravel() function and printed the ravel() function’s output. We have then updated the value of arr2[0] = 50 and again printed the ravel output and printed the input array. So we can easily see the changes. The same done with the flatten() function also updated the value, but the input array doesn’t change.

## Conclusion

In this tutorial, we have discussed the ravel() function of the numpy module. All the parameters and their values are explained with the help of examples in detail. Examples will help you to understand the concept more accurately. You can use the order as per your requirement of the output one-dimensional array.