Python numpy.append() Function – Easy Array Concatenation

The Python numpy.append() function allows for array concatenation and is especially useful when processing data efficiently. It returns a new array while keeping the original array unchanged. This tutorial explains how to use the function and avoid common errors.

Syntax of numpy.append() Function

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

Parameters:

1. arr: The source array (NumPy array or array-like object).
2. values: The values to be appended (array-like object).
3. axis: The axis along which the values should be appended:
   • None (default): Arrays are flattened and concatenated.
   • 0: Along the first dimension (rows).
   • 1: Along the second dimension (columns).

Return Value:

A new array with the appended values.

Examples of Using numpy.append()

1. Flattening and Concatenating Arrays

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])

arr2 = np.array([[10, 20], [30, 40]])
# Arrays werden abgeflacht und zusammengeführt

arr_flat = np.append(arr1, arr2)

print(“Abgeflachtes Array:”, arr_flat)

Output:

Abgeflachtes Array: [ 1 2 3 4 10 20 30 40]

2. Concatenating Along an Axis

import numpy as np
# Zusammenführen entlang der Achse 0 (Zeilen)

arr_axis0 = np.append([[1, 2], [3, 4]], [[10, 20], [30, 40]], axis=0)

print(“Zusammengeführt entlang Achse 0:\n”, arr_axis0)
# Zusammenführen entlang der Achse 1 (Spalten)

arr_axis1 = np.append([[1, 2], [3, 4]], [[10, 20], [30, 40]], axis=1)

print(“Zusammengeführt entlang Achse 1:\n”, arr_axis1)

Output:

Zusammengeführt entlang Achse 0:

[[ 1 2]

[ 3 4]

[10 20]

[30 40]]
Zusammengeführt entlang Achse 1:

[[ 1 2 10 20]

[ 3 4 30 40]]

3. Concatenating Arrays of Different Shapes

The numpy.append() function works as long as the array dimensions are compatible. If arrays are flattened, they can be concatenated even if their shapes differ.

import numpy as np
# Abflachen und Zusammenführen

arr_flat = np.append([[1, 2]], [[1, 2, 3], [4, 5, 6]])

print(“Zusammenführung von Arrays unterschiedlicher Form:\n”, arr_flat)
# Zusammenführung entlang der Achse 0

arr_axis0 = np.append([[1, 2]], [[3, 4], [5, 6]], axis=0)

print(“Zusammengeführt entlang Achse 0:\n”, arr_axis0)

Output:

Zusammenführung von Arrays unterschiedlicher Form:

[1 2 1 2 3 4 5 6]
Zusammengeführt entlang Achse 0:

[[1 2]

[3 4]

[5 6]]

4. Common Error with Incompatible Dimensions

If the array dimensions are incompatible, numpy.append() raises a ValueError.

import numpy as np
# Fehler: Unterschiedliche Formen entlang der Achse 0

arr_error = np.append([[1, 2]], [[1, 2, 3]], axis=0)

Error:

ValueError: all the input array dimensions except for the concatenation axis must match exactly

Solution: Ensure that the dimensions match along the specified axis.

Tips for Using numpy.append()

1. Check Dimensions:
   Use arr.shape to check the dimensions of arrays before appending
   
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   print(arr1.shape) # Gibt die Form des Arrays zurück

2. Consider Alternatives:
   For appending along specific axes, numpy.concatenate() is often more efficient.
3. Avoid Large Arrays:
   For very large arrays, predefine the array and assign values directly. This avoids the overhead of creating a new array with each call to numpy.append().

Common Use Cases

• Data Analysis: Merging datasets.
• Image Processing: Combining pixel values from multiple images.
• Machine Learning: Preparing training data.

Conclusion