Types of NumPy Arrays

One-dimensional Arrays

• These are similar to Python lists but with more functionality and optimized for numerical operations.
• Represented as a single row or column of elements.
• Created using the numpy.array() function.

  import numpy as np

  arr1d = np.array([1, 2, 3, 4, 5])

Two-dimensional Arrays

• These are like tables, with rows and columns.
• You can think of them as arrays of arrays, where each inner array represents a row.
• Created using nested lists or by reshaping a one-dimensional array.

  import numpy as np

  arr2d = np.array([[1, 2, 3], [4, 5, 6]])

Multi-dimensional Arrays

• These are arrays with more than two dimensions, like a cube or higher-dimensional space.
• Useful for representing volumetric data or tensors in machine learning.
• Created by reshaping arrays or using functions like 'numpy.zeros()' or 'numpy.ones()'

  import numpy as np

  arr3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

Structured Arrays

• These are arrays where each element can have different data types.
• Useful for handling structured data, like tables with different types of columns.
• Created using a structured data type descriptor.

  import numpy as np 
  
  dtype = [('name', 'S10'), ('age', int)]
  data = [('Alice', 25), ('Bob', 30)]
  arr_structured = np.array(data, dtype=dtype)

Masked Arrays

• These are arrays that have a mask associated with them, indicating invalid or missing values.
• Useful for handling datasets with missing or invalid data.
• Created using the 'numpy.ma.masked_array()' function.

  import numpy as np 
 
  arr = np.ma.array([1, 2, 3, -99, 5], mask=[0, 0, 0, 1, 0])

Matrix Arrays

• These are special types of 2D arrays used for linear algebra operations.
• Provides convenience functions for matrix operations like matrix multiplication.
• Created using the 'numpy.matrix()' function.

  import numpy as np 

  mat = np.matrix([[1, 2], [3, 4]])

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These are the main types of arrays in NumPy, each designed for specific use cases. Understanding these types will help you effectively utilize NumPy for various numerical computing tasks.