## Numpy Array Append

Numpy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. One of the common operations when working with numpy arrays is appending data. Appending refers to adding elements to the end of an array. This article will explore various ways to append data to numpy arrays, along with detailed examples.

## Understanding Numpy Array Append

Appending data to a numpy array can be done in several ways. The most straightforward method is using the `numpy.append()`

function. This function adds values at the end of an array. It is important to note that `numpy.append()`

does not modify the original array; instead, it returns a new array that includes the appended values.

### Basic Usage of `numpy.append()`

The basic syntax of `numpy.append()`

is:

```
numpy.append(arr, values, axis=None)
```

`arr`

is the array to which`values`

are appended.`values`

are the values to be appended. This can be a list, another array, or any other sequence.`axis`

specifies the axis along which values are appended. If the axis is not provided, both`arr`

and`values`

are flattened before use.

#### Example 1: Appending Elements to a 1D Array

```
import numpy as np
arr = np.array([1, 2, 3])
values = [4, 5]
result = np.append(arr, values)
print(result)
```

Output:

#### Example 2: Appending Elements to a 2D Array Without Axis

```
import numpy as np
arr = np.array([[1, 2], [3, 4]])
values = [[5, 6], [7, 8]]
result = np.append(arr, values)
print(result)
```

Output:

#### Example 3: Appending Elements to a 2D Array Along Axis 0

```
import numpy as np
arr = np.array([[1, 2], [3, 4]])
values = [[5, 6]]
result = np.append(arr, values, axis=0)
print(result)
```

Output:

#### Example 4: Appending Elements to a 2D Array Along Axis 1

```
import numpy as np
arr = np.array([[1, 2], [3, 4]])
values = [[5], [6]]
result = np.append(arr, values, axis=1)
print(result)
```

Output:

### Handling Different Dimensions

When appending data, it’s crucial that the dimensions of the original array and the values being appended match according to the specified axis. If they do not match, numpy will raise an error.

#### Example 5: Appending with Dimension Mismatch

```
import numpy as np
arr = np.array([[1, 2], [3, 4]])
values = [5, 6, 7] # This will cause an error
try:
result = np.append(arr, values, axis=0)
except ValueError as e:
print(e)
```

Output:

### Performance Considerations

Using `numpy.append()`

in a loop can be inefficient, especially for large arrays. Each append operation involves creating a new array and copying data from the old array to the new one, which can be computationally expensive.

#### Example 6: Inefficient Appending in a Loop

```
import numpy as np
arr = np.array([1, 2, 3])
for i in range(4, 1000):
arr = np.append(arr, i)
print(arr)
```

Output:

### Alternatives to `numpy.append()`

For better performance, especially in loops, it is recommended to use other methods such as `numpy.concatenate()`

or building a list and converting it to an array at the end.

#### Example 7: Using `numpy.concatenate()`

for Better Performance

```
import numpy as np
arr = np.array([1, 2, 3])
values = np.array([4, 5, 6])
result = np.concatenate([arr, values])
print(result)
```

Output:

## Numpy Array Append Conclusion

Appending to numpy arrays is a common operation that can be achieved using the `numpy.append()`

function. However, it is important to understand the function’s behavior, especially regarding its performance implications and how it handles dimensions. For operations involving large arrays or frequent append operations, alternatives like `numpy.concatenate()`

or list operations can provide more efficient solutions.