NumPy (short for Numerical Python) is a powerful open-source library in Python, primarily used for numerical computing and data manipulation. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions to operate on these arrays efficiently.
Key Features of NumPy:
- Array-Oriented Computing: NumPy introduces the concept of n-dimensional arrays (ndarray), which are more efficient and flexible compared to Python lists. These arrays allow for fast element-wise operations and broadcasting, where operations are applied to arrays of different shapes automatically.
- Mathematical Functions: NumPy provides a comprehensive set of mathematical tools such as linear algebra, Fourier transforms, statistical functions, and random number generation.
- Efficiency: NumPy is highly optimized for performance, allowing it to handle large datasets with minimal memory overhead. It’s implemented in C, and operations on NumPy arrays are performed at C-level speed, making it much faster than standard Python loops and list-based operations.
- Broadcasting: Broadcasting in NumPy allows for arithmetic operations between arrays of different shapes. For example, you can add a scalar to an array or add arrays with different dimensions, and NumPy will automatically handle the dimension mismatches.
- Integration with Other Libraries: NumPy is foundational for many other scientific libraries in Python like Pandas (for data analysis), Matplotlib (for data visualization), TensorFlow, and PyTorch (for machine learning), making it a core tool in data science and machine learning.
Installing NumPy
If you don’t have NumPy installed yet, you can install it using:
pip install numpy
2. Importing NumPy
import numpy as np
3. Creating Arrays
NumPy arrays are the foundation of NumPy, enabling fast and efficient data storage and manipulation.
Creating a 1D array:
arr1 = np.array([1, 2, 3])
print(arr1)
# Output: [1 2 3]
Creating a 2D array (matrix):
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2)
# Output:
# [[1 2 3]
# [4 5 6]]
Creating an array of zeros:
zeros = np.zeros((2, 3)) # 2x3 matrix filled with zeros
print(zeros)
# Output:
# [[0. 0. 0.]
# [0. 0. 0.]]
Creating an array of ones:
ones = np.ones((3, 2)) # 3x2 matrix filled with ones
print(ones)
# Output:
# [[1. 1.]
# [1. 1.]
# [1. 1.]]
Creating an empty array:
empty = np.empty((2, 2)) # Uninitialized array with random values
print(empty)
# Output: Random values, shape (2,2)
4. Array Properties
NumPy arrays have some useful attributes.
print(arr2.ndim) # Number of dimensions
# Output: 2
print(arr2.shape) # Shape of the array
# Output: (2, 3)
print(arr2.size) # Total number of elements
# Output: 6
print(arr2.dtype) # Data type of the elements
# Output: int64 (or int32 depending on your system)
5. Array Creation: Special Methods
arange(): Array with a range of values
arr = np.arange(0, 10, 2) # From 0 to 10, step by 2
print(arr)
# Output: [0 2 4 6 8]
linspace(): Array with evenly spaced values
lin = np.linspace(0, 10, 5) # 5 equally spaced numbers between 0 and 10
print(lin)
# Output: [ 0. 2.5 5. 7.5 10. ]
6. Reshaping Arrays
Reshaping the array into different dimensions:
reshaped = arr2.reshape((3, 2)) # Reshape 2x3 to 3x2
print(reshaped)
# Output:
# [[1 2]
# [3 4]
# [5 6]]
7. Basic Operations
Element-wise operations:
arr = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr + arr2) # Add element-wise
# Output: [5 7 9]
print(arr * arr2) # Multiply element-wise
# Output: [ 4 10 18]
print(arr ** 2) # Square each element
# Output: [1 4 9]
Broadcasting: Operations between arrays of different shapes
arr = np.array([1, 2, 3])
scalar = 3
print(arr * scalar)
# Output: [3 6 9]
8. Statistical Methods
Basic statistical functions:
arr = np.array([1, 2, 3, 4, 5])
print(arr.mean()) # Mean of the array
# Output: 3.0
print(arr.sum()) # Sum of elements
# Output: 15
print(arr.min()) # Minimum value
# Output: 1
print(arr.max()) # Maximum value
# Output: 5
print(arr.std()) # Standard deviation
# Output: 1.4142135623730951
9. Indexing and Slicing
1D array indexing:
arr = np.array([10, 20, 30, 40, 50])
print(arr[0]) # First element
# Output: 10
print(arr[-1]) # Last element
# Output: 50
2D array indexing:
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2[0, 1]) # Element in the first row, second column
# Output: 2
print(arr2[:, 1]) # All rows, second column
# Output: [2 5]
Slicing arrays:
arr = np.array([1, 2, 3, 4, 5])
print(arr[1:4]) # Elements from index 1 to 3 (4 excluded)
# Output: [2 3 4]
10. Array Copying
Simple assignment (creates a reference, not a copy):
arr_copy = arr
arr_copy[0] = 100
print(arr) # arr is also modified
# Output: [100 2 3 4 5]
Deep copy:
arr_copy = arr.copy()
arr_copy[0] = 1
print(arr) # arr is not modified
# Output: [100 2 3 4 5]
11. Array Operations
Transposing an array:
arr2 = np.array([[1, 2], [3, 4], [5, 6]])
print(arr2.T) # Transpose
# Output:
# [[1 3 5]
# [2 4 6]]
Dot product of two arrays:
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
dot_product = np.dot(a, b)
print(dot_product)
# Output:
# [[19 22]
# [43 50]]
12. Concatenation and Stacking
Horizontal stacking:
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
hstack = np.hstack((arr1, arr2))
print(hstack)
# Output: [1 2 3 4 5 6]
Vertical stacking:
vstack = np.vstack((arr1, arr2))
print(vstack)
# Output:
# [[1 2 3]
# [4 5 6]]
Concatenate along an axis:
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6]])
concat = np.concatenate((arr1, arr2), axis=0)
print(concat)
# Output:
# [[1 2]
# [3 4]
# [5 6]]
13. Random Numbers
Generate random values:
rand_arr = np.random.rand(3, 3) # Random values in a 3x3 array
print(rand_arr)
# Output: Random 3x3 matrix
Generate random integers:
rand_int = np.random.randint(0, 10, size=(3, 3)) # Random integers between 0 and 10
print(rand_int)
# Output: Random 3x3 matrix of integers
14. Boolean Indexing
arr = np.array([1, 2, 3, 4, 5])
bool_idx = arr > 3 # Condition to filter elements
print(bool_idx)
# Output: [False False False True True]
filtered_arr = arr[bool_idx] # Apply the condition to filter the array
print(filtered_arr)
# Output: [4 5]
15. Sorting Arrays
arr = np.array([3, 1, 5, 2, 4])
sorted_arr = np.sort(arr)
print(sorted_arr)
# Output: [1 2 3 4 5]