import numpy as np

array = np.array([20, 30, 40, 50, 60])

print(np.sum(array))

print(np.mean(array))

print(np.min(array))

print(np.max(array))

print(np.std(array))

print(np.var(array))

📊 NumPy Operators and Aggregation Functions

This repository showcases how to perform mathematical operations and aggregation functions using NumPy arrays. With these basic concepts, you can efficiently work with data and perform complex mathematical tasks.

📘 Table of Contents

1. Introduction

2. Mathematical Operators

3. Aggregation Functions

4. Conclusion

1. Introduction

NumPy is a powerful library in Python used for numerical computing. It allows users to perform vectorized operations and matrix computations with ease. In this guide, we'll explore mathematical operators and aggregation functions available in NumPy.

2. Mathematical Operators

NumPy provides a wide range of mathematical operators that can be applied directly to NumPy arrays.

Common Mathematical Operations:

Operator Description

+ Addition

- Subtraction

* Multiplication

/ Division

** Exponentiation

// Floor Division

% Modulo (Remainder)

These operations are element-wise, which means the operation is applied to each element of the array.

3. Aggregation Functions

NumPy provides several functions to aggregate and summarize data in arrays, such as:

Function Description

np.sum() Sum of all elements

np.mean() Mean (average) of elements

np.min() Minimum value of the array

np.max() Maximum value of the array

np.std() Standard deviation of the elements

np.var() Variance (square of standard deviation)

These functions help to quickly compute summaries and statistics over an array.

4. Conclusion

NumPy simplifies performing mathematical operations and aggregating results on large datasets. With the operators and functions covered in this guide, you'll be able to:

Perform quick element-wise operations on arrays.

Summarize data using powerful aggregation functions.

For more advanced NumPy features, feel free to explore official NumPy documentation.

💡 Key Takeaways:

NumPy allows for efficient mathematical operations on arrays.

Aggregation functions such as np.sum(), np.mean(), etc., provide quick statistical summaries of data.

Element-wise operations and broadcasting enable efficient handling of large datasets.