Multidimensional Lists
NumPy Arrays
NumPy arrays are the fundamental data structure for numerical computation in Python. They provide efficient storage and manipulation of numerical data. Key advantages over standard Python lists include:
- Homogeneous data type: All elements in a NumPy array must be of the same data type. This allows for optimized memory usage and faster computations.
- Vectorized operations: NumPy supports vectorized operations, meaning that operations are applied to the entire array at once, rather than element by element. This significantly speeds up calculations.
- Broadcasting: A powerful feature that allows arithmetic operations between arrays of different shapes under certain conditions. Broadcasting in NumPy
- Efficient memory layout: NumPy arrays store data in contiguous memory locations, improving memory access speed.
Creating NumPy Arrays:
NumPy arrays are created using the numpy.array() function.
import numpy as np
# From a list
arr1 = np.array(1, 2, 3, 4, 5)
# From a list of lists (creates a 2D array)
arr2 = np.array(1, 2, 3, 4, 5, 6)
# Using other functions like arange, zeros, ones, etc.
arr3 = np.arange(10) # Creates an array from 0 to 9
arr4 = np.zeros((2,3)) # Creates a 2x3 array filled with zeros
arr5 = np.ones((3,2)) # Creates a 3x2 array filled with ones
print(arr1)
print(arr2)
print(arr3)
print(arr4)
print(arr5)
Array Attributes:
NumPy arrays have several useful attributes:
print(arr2.shape) # Returns the dimensions of the array
print(arr2.dtype) # Returns the data type of the array elements
print(arr2.size) # Returns the total number of elements in the array
print(arr2.ndim) # Returns the number of dimensions (axes) of the array
Array Operations:
NumPy provides a rich set of functions for array manipulation and computation:
# Arithmetic operations
arr6 = arr1 + 2 # Adds 2 to each element
arr7 = arr1 * arr1 # Element-wise multiplication
# Array slicing
arr8 = arr2[:2, :2 # Selects a subarray
# Reshaping arrays
arr9 = arr1.reshape(5,1) # Reshapes arr1 into a 5x1 array
# Linear algebra operations ([NumPy Linear Algebra](./../numpy-linear-algebra/))
# ...
print(arr6)
print(arr7)
print(arr8)
print(arr9)