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Unveiling the Power of NumPy: A Look at Essential Array Functions


Array Creation Routines

These routines are used to create new NumPy arrays. Some commonly used functions include:

  • np.arange: Creates an array with evenly spaced values within a specific range.
  • np.empty: Creates an uninitialized array (faster than np.zeros but potentially contains garbage values).
  • np.ones: Creates an array filled with ones.
  • np.zeros: Creates an array filled with zeros.
  • np.array: Creates an array from a list, tuple, or other iterable.

Array Manipulation Routines

These routines perform operations on existing NumPy arrays. Examples include:

  • np.sort: Sorts the elements of an array.
  • np.split: Splits an array into multiple sub-arrays.
  • np.concatenate: Combines multiple arrays along a specified axis.
  • np.transpose: Swaps the axes of an array.
  • np.reshape: Changes the shape of an array.

Linear Algebra Routines

These routines provide functions for linear algebra operations on arrays. For instance:

  • np.linalg.eig: Computes the eigenvalues and eigenvectors of a matrix.
  • np.linalg.inv: Computes the inverse of a matrix.
  • np.dot: Performs matrix multiplication.

Mathematical Routines

These routines offer various mathematical functions that can be applied element-wise to arrays. Some examples include:

  • np.add, np.subtract, np.multiply, np.divide: Basic arithmetic operations.
  • np.sqrt: Square root function.
  • np.exp, np.log: Exponential and logarithmic functions.
  • np.sin, np.cos, np.tan: Trigonometric functions.

Statistical Routines

These routines perform statistical operations on arrays. For instance:

  • np.percentile: Calculates percentiles of an array.
  • np.median: Finds the median of an array.
  • np.std: Computes the standard deviation of an array.
  • np.mean: Calculates the mean of an array.

Random Number Generation Routines

These routines generate random numbers:

  • np.random.randint: Generates random integers within a specified range.
  • np.random.randn: Generates random numbers from a standard normal distribution.
  • np.random.rand: Generates random floats between 0 and 1.

Array Creation

import numpy as np

# From a list
data = [1, 2, 3, 4, 5]
arr = np.array(data)
print(arr)  # Output: [1 2 3 4 5]

# Zeros and ones with specific shape
zeros_2x3 = np.zeros((2, 3))  # 2 rows, 3 columns filled with zeros
print(zeros_2x3)  

ones_diag = np.ones((3, 3), dtype=bool)  # Diagonal filled with ones (boolean type)
print(ones_diag)

Array Manipulation

# Reshape
arr = np.arange(12).reshape(3, 4)
print(arr)  # Output: [[ 0  1  2  3] [ 4  5  6  7] [ 8  9 10 11]]

# Transpose (swap rows and columns)
transposed = arr.T
print(transposed)  # Output: [[ 0  4  8] [ 1  5  9] [ 2  6 10] [ 3  7 11]]

# Concatenate (vertically)
top = np.array([10, 11, 12])
combined = np.concatenate((arr, top.reshape(1, -1)), axis=0)  # Reshape top for proper axis=0
print(combined)

Linear Algebra

# Matrix multiplication
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
product = np.dot(A, B)
print(product)  # Output: [[19 22] [43 50]]

# Inverse of a matrix
inv_A = np.linalg.inv(A)
print(inv_A)  # Output: [[-2.  1. ] [ 1.5 -0.5]]

Mathematical Routines

arr = np.array([1, 4, 9])

# Sine of each element
sin_values = np.sin(arr)
print(sin_values)  # Output: [ 0.84147098  0.75680249  0.38941834]

# Square root
sqrt_values = np.sqrt(arr)
print(sqrt_values)  # Output: [1.  2.  3.]

data = np.random.rand(10)  # Generate 10 random floats

# Mean
average = np.mean(data)
print(average)  # Random value between 0 and 1

# Standard deviation
stddev = np.std(data)
print(stddev)  # Random value

  • Modules
    NumPy itself is a module, and within it, some routines might be grouped into specific modules based on their functionality (e.g., numpy.random for random number generation).
  • Operations
    This term emphasizes the action performed by the routines on the data.
  • Methods
    While less common in the context of NumPy itself, some routines might be considered methods if they operate on specific objects like arrays.
  • Functions
    This is the most general term and encompasses all functionalities offered by NumPy, including routines for array creation, manipulation, mathematical operations, etc.