NumPy Append Values to Array
In real-world programming, data is often dynamic.
You may need to add new values to an existing array while working with NumPy.
NumPy provides a simple function called:
np.append()
This function allows you to append values to arrays efficiently.
What is Array Appending?
Appending means:
Adding new elements to the end of an array
Example:
Before: [1, 2, 3]
After: [1, 2, 3, 4]
Why Use np.append()?
- Easy to add new data
- Works with 1D and multi-dimensional arrays
- Useful in data collection
- Helps in dynamic datasets
- Common in data preprocessing
1. Basic Array Append
import numpy as np
arr = np.array([1, 2, 3])
new_arr = np.append(arr, 4)
print(new_arr)
Output
[1 2 3 4]
2. Appending Multiple Values
arr = np.array([10, 20, 30])
new_arr = np.append(arr, [40, 50, 60])
print(new_arr)
Output
[10 20 30 40 50 60]
3. Appending to 2D Arrays
arr = np.array([[1, 2], [3, 4]])
new_arr = np.append(arr, [[5, 6]], axis=0)
print(new_arr)
Output
[[1 2]
[3 4]
[5 6]]
4. Appending Columns
arr = np.array([[1, 2], [3, 4]])
new_arr = np.append(arr, [[5], [6]], axis=1)
print(new_arr)
Output
[[1 2 5]
[3 4 6]]
5. Appending Without Axis
If axis is not defined, array is flattened.
arr = np.array([[1, 2], [3, 4]])
new_arr = np.append(arr, [5, 6])
print(new_arr)
Output
[1 2 3 4 5 6]
6. Appending Floating Values
arr = np.array([1.5, 2.5])
new_arr = np.append(arr, 3.5)
print(new_arr)
7. Appending String Values
arr = np.array(["A", "B"])
new_arr = np.append(arr, "C")
print(new_arr)
8. Real-World Example: Student Scores
scores = np.array([80, 85, 90])
scores = np.append(scores, 95)
print(scores)
9. Real-World Example: Sales Data
sales = np.array([100, 200, 300])
sales = np.append(sales, [400, 500])
print(sales)
10. Important Note
❗ np.append() does NOT modify the original array.
It returns a new array.
arr = np.array([1, 2, 3])
arr = np.append(arr, 4)
Comparison Table
| Function | Purpose |
|---|---|
| np.append() | Add values |
| np.concatenate() | Merge arrays |
| np.insert() | Add at position |
Advantages of np.append()
- Simple syntax
- Flexible usage
- Works with different dimensions
- Useful in data preprocessing
- Beginner-friendly
Summary
NumPy append() allows you to add new values to arrays easily. It is widely used in data manipulation, preprocessing, and dynamic array building.
This functionality is part of NumPy and widely used in applications built with Python.
Conclusion
Appending values in NumPy is essential when working with dynamic datasets. With np.append(), you can easily extend arrays and manage data efficiently in Python.
0 Comments