In this tutorial we want to learn about Numpy Mathematical Functions, NumPy is the fundamental library for numerical computing in Python, It offers a rich collection of mathematical functions that enable efficient and easy numerical operations. These functions provides powerful tools for performing mathematical computations on arrays, also it enables you to manipulate, transform and analyze data.

**Numpy Mathematical Functions**

NumPy provides different math functions that operate on single elements or entire arrays. Let’s talk about some of the commonly used mathematical functions:

Numpy Basic Arithmetic Operations:

NumPy allows you to perform basic arithmetic operations such as addition, subtraction, multiplication and division on arrays element. This capability is known as *vectorization* and can significantly improve the performance of your computations.

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import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) # Addition result = np.add(arr1, arr2) print(result) # Subtraction result = np.subtract(arr1, arr2) print(result) # Multiplication result = np.multiply(arr1, arr2) print(result) # Division result = np.divide(arr1, arr2) print(result) |

This will be the result

Numpy Trigonometric Functions:

NumPy provides different trigonometric functions, including sine, cosine, tangent and their inverse counterparts. These functions are useful for different type of applications, such as signal processing, geometry and physics.

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import numpy as np angle = np.pi/4 # Sine result = np.sin(angle) print(result) # Cosine result = np.cos(angle) print(result) # Tangent result = np.tan(angle) print(result) # Inverse Sine result = np.arcsin(0.5) print(result) |

This will be the result

Numpy Exponential and Logarithmic Functions:

NumPy provides functions to calculate exponential and logarithmic values. These functions are invaluable for different mathematical and scientific computations, such as growth models, probability distributions and signal processing.

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import numpy as np x = 2 # Exponential result = np.exp(x) print(result) # Natural Logarithm result = np.log(x) print(result) # Logarithm (base 10) result = np.log10(x) print(result) |

This will be the output

Numpy Statistical Functions:

NumPy includes statistical functions that enable you to calculate different statistical measures, such as mean, standard deviation, minimum, maximum and many more. These functions facilitate data analysis, hypothesis testing and modeling.

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import numpy as np data = np.array([1, 2, 3, 4, 5]) # Mean result = np.mean(data) print(result) # Standard Deviation result = np.std(data) print(result) # Minimum result = np.min(data) print(result) # Maximum result = np.max(data) print(result) |

This will be the output