NumPy Arrays in Python

• Python is a versatile programming language known for its readability and extensive libraries.
• Arrays in Python are typically handled using the NumPy library, which provides efficient array operations.

Array Concepts in Python

• An array is a collection of elements of the same data type, residing in contiguous memory locations.
• Arrays are useful for efficiently storing and manipulating large amounts of data.
• Python lists can store elements of different types, while NumPy arrays are designed for homogenous data types.
• NumPy arrays offer better performance for numerical operations compared to Python lists, due to vectorized operations.

Creating Arrays using NumPy

• To create arrays in Python, import the NumPy library: import numpy as np.
• Arrays can be created from Python lists using np.array(): arr = np.array([1, 2, 3, 4, 5]).
• NumPy offers functions to create arrays with specific initial values:
  • np.zeros(shape): Creates an array filled with zeros.
  • np.ones(shape): Creates an array filled with ones.
  • np.empty(shape): Creates an array without initializing values.
  • np.full(shape, value): Creates an array filled with a specified value.
• The shape argument is a tuple that specifies the dimensions of the array (e.g., (3, 4) for a 3x4 array).

Array Attributes

• ndarray.ndim: Represents the number of dimensions (axes) of the array.
• ndarray.shape: Represents the dimensions of the array.
• ndarray.size: Represents the total number of elements in the array.
• ndarray.dtype: Represents the data type of the elements in the array.
• ndarray.itemsize: Represents the size in bytes of each element in the array.

Array Indexing and Slicing

• Array elements can be accessed using their index, starting from 0.
  • For one-dimensional arrays: arr[0] accesses the first element.
  • For multi-dimensional arrays: arr[0, 1] accesses the element at the first row and second column.
• Slicing allows extracting subarrays from an array:
  • arr[start:end] extracts elements from start index to end-1 index.
  • arr[:end] extracts elements from the beginning to end-1 index.
  • arr[start:] extracts elements from start index to the end.
  • arr[:] creates a copy of the entire array.
• For multi-dimensional arrays, slicing can be applied to each dimension: arr[0:2, 1:3].

Array Operations

• NumPy arrays support element-wise operations:
  • Addition: arr1 + arr2
  • Subtraction: arr1 - arr2
  • Multiplication: arr1 * arr2
  • Division: arr1 / arr2
• Scalar operations apply the operation to each element in the array: arr + 5.
• NumPy provides various mathematical functions that can be applied to arrays:
  • np.sin(arr): Calculates the sine of each element.
  • np.cos(arr): Calculates the cosine of each element.
  • np.sqrt(arr): Calculates the square root of each element.
  • np.exp(arr): Calculates the exponential of each element.
• Aggregation functions compute statistics on array elements:
  • np.sum(arr): Calculates the sum of all elements.
  • np.mean(arr): Calculates the mean of all elements.
  • np.median(arr): Calculates the median of all elements.
  • np.min(arr): Finds the minimum value.
  • np.max(arr): Finds the maximum value.
  • np.std(arr): Calculates the standard deviation.

Array Reshaping

• The shape of an array can be changed without changing its data using ndarray.reshape():
  • arr = np.array([1, 2, 3, 4, 5, 6])
  • new_arr = arr.reshape((2, 3)) # Reshapes to a 2x3 array
• ndarray.ravel() or ndarray.flatten() can be used to flatten a multi-dimensional array into a one-dimensional array.
  • ravel() returns a view of the original array if possible, while flatten() always returns a copy.

Array Concatenation and Splitting

• np.concatenate((arr1, arr2), axis) joins two or more arrays along a specified axis.
• np.vstack((arr1, arr2)) stacks arrays vertically (row-wise).
• np.hstack((arr1, arr2)) stacks arrays horizontally (column-wise).
• np.split(arr, indices_or_sections, axis) splits an array into multiple sub-arrays. indices_or_sections can be an integer specifying the number of equal sections or a sorted array of indices where the splits will occur.

Broadcasting

• Broadcasting allows NumPy to perform operations on arrays with different shapes.
• NumPy automatically expands the dimensions of the smaller array to match the larger array.
• Broadcasting rules:
  • If the arrays do not have the same number of dimensions, prepend the shape of the smaller array with 1s until the dimensions match.
  • If the size of a dimension does not match, but one of the dimensions has a size of 1, the array with size 1 is broadcast along that dimension.
  • If neither of the dimensions matches and neither has a size of 1, broadcasting fails.

Array Copying

• Assigning an array to another variable does not create a new array; it creates a new reference to the same array:
  • arr1 = arr2 # arr1 and arr2 point to the same array
• To create a new copy of an array, use ndarray.copy():
  • arr2 = arr1.copy() # arr2 is a new array with the same data as arr1

Example MCQs topics

• Creating a NumPy array from a Python list.
• Accessing elements in a multi-dimensional array using indexing.
• Slicing a NumPy array to extract a subarray.
• Performing element-wise arithmetic operations on arrays.
• Reshaping a NumPy array to a different shape.
• Using boolean indexing to filter array elements.
• Calculating the sum, mean, or other statistics of array elements.
• Concatenating two or more arrays along a specified axis.
• Understanding broadcasting rules for array operations.
• Creating a copy of an array vs. creating a view of an array.

Learn about arrays in Python using NumPy. Explore array creation, attributes, and manipulation methods. NumPy arrays provide efficient storage and operations for numerical data.