Array Operations

Arithmetic Operations

NumPy supports element-wise arithmetic operations like addition, subtraction, multiplication, and division:

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)  # [5 7 9]
print(a - b)  # [-3 -3 -3]
print(a * b)  # [4 10 18]
print(a / b)  # [0.25 0.4 0.5]

Broadcasting

Broadcasting allows operations between arrays of different shapes by stretching the smaller array to match the larger one:

import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.array([10, 20, 30])
print(a + b)  # [[11 22 33] [14 25 36]]

Aggregate Functions

NumPy provides functions to compute aggregates like sum, mean, and standard deviation:

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.sum(arr))    # 15
print(np.mean(arr))   # 3.0
print(np.std(arr))    # 1.4142135623730951
print(np.min(arr))    # 1
print(np.max(arr))    # 5

Element-wise Operations and Comparison

Perform operations and comparisons element by element:

import numpy as np
a = np.array([1, 2, 3])
b = np.array([2, 2, 2])
print(a ** 2)        # [1 4 9]
print(a > b)         # [False False True]
print(np.equal(a, b))  # [False True False]

Dot Product, Matrix Multiplication

NumPy supports dot products and matrix multiplication:

import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
print(np.dot(a, b))  # [[19 22] [43 50]]
print(a @ b)         # Same as np.dot, [[19 22] [43 50]]

NumPy Universal Functions (ufuncs)

Unary Functions

Unary ufuncs operate on each element of an array:

import numpy as np
arr = np.array([1, 4, 9])
print(np.sqrt(arr))  # [1. 2. 3.]
print(np.exp(arr))   # [2.71828183 54.59815003 8103.08392758]
print(np.log(arr))   # [0. 1.38629436 2.19722458]

Binary Functions

Binary ufuncs operate on two arrays element-wise:

import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.add(a, b))       # [5 7 9]
print(np.subtract(a, b))  # [-3 -3 -3]
print(np.multiply(a, b))  # [4 10 18]
print(np.divide(a, b))    # [0.25 0.4 0.5]

Performance Benefits

Ufuncs are implemented in C, offering significant performance benefits over Python loops. They enable vectorized operations, reducing execution time and improving efficiency for large arrays.

Shape Manipulation

reshape, transpose, T

Modify array shapes without changing data:

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
print(arr.reshape(2, 3))  # [[1 2 3] [4 5 6]]
print(arr.reshape(3, 2))  # [[1 2] [3 4] [5 6]]
print(arr.reshape(2, 3).transpose())  # [[1 4] [2 5] [3 6]]
print(arr.reshape(2, 3).T)  # Same as transpose, [[1 4] [2 5] [3 6]]

resize, swapaxes, expand_dims, squeeze

Additional shape manipulation tools:

import numpy as np
arr = np.array([1, 2, 3, 4])
# Resize (modifies in place, may repeat or truncate)
arr2 = np.array([1, 2, 3, 4])
arr2.resize(2, 3)
print(arr2)  # [[1 2 3] [4 0 0]]
# Swap axes
arr3 = np.array([[1, 2], [3, 4]])
print(np.swapaxes(arr3, 0, 1))  # [[1 3] [2 4]]
# Expand dimensions
arr4 = np.array([1, 2, 3])
print(np.expand_dims(arr4, axis=0))  # [[1 2 3]]
# Squeeze (remove single-dimensional entries)
arr5 = np.array([[1], [2], [3]])
print(np.squeeze(arr5))  # [1 2 3]

Flattening Arrays (flatten, ravel)

Convert multi-dimensional arrays to 1D:

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr.flatten())  # [1 2 3 4] (returns copy)
print(arr.ravel())    # [1 2 3 4] (returns view, faster)