Getting Started with NumPy: The Swiss Army Knife of Data Processing

NumPy (Numerical python) is a powerful Python library that provides high-level ## for scientific computing and data processing #tool. It is known for its ability to handle multi-dimensional arrays (called n-dimensional arrays), making it the tool of choice for data scientists, researchers, and engineers. Installation and Import

To use NumPy with Python, install it using pip

:

pip install numpy

Then, import it in your code:

import numpy as np

Basic data types

NumPy provides a variety of data types to store different types of data, including:

Integer types:

int8, int16, int32 and int64
• Floating point types:
float16, float32 and float64
• Complex types:
complex64 and complex128
• Boolean type:
bool
Create array

NumPy arrays are objects that represent multidimensional data. You can create an array using the following methods:

From list or tuple:

• np.array() From scalar values:
• np.full() and np.ones() From file:
• np.loadtxt() Use NumPy functions:
• np.arange(), np.linspace() and np.random.rand()
Array operations

NumPy provides a wide range of array manipulation functions, including:

Arithmetic operations:

Addition ( ), subtraction (-), multiplication (*) and division (/)
• Logical operations:
Equal to (==), Not equal to (!=), Greater than (>) and Less than (<)
• Array operations:
Sum, average, maximum and minimum values
• Boolean mask:
Select elements from an array
Indexing and slicing

You can access elements in an array using indexing

and slicing:

Index:

Use square brackets (
• []) Slicing:
Use colon (
• :)
broadcast

Broadcasting is a powerful feature of NumPy that allows element-wise operations to be performed on arrays of different shapes.

Other useful features

In addition to array operations, NumPy also provides many other useful functions, including:

Linear Algebra:

Matrix multiplication, eigenvalues ​​and eigenvectors
• Random number generation:
Generate random numbers of various distributions
• File input/output:
Saving and loading arrays
• Optimization:
• OptimizationTools for scientific computing
Example

The following is an example of calculating simple statistics using NumPy:

import numpy as np

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

print("平均值：", np.mean(data))
print("标准差：", np.std(data))

in conclusion

NumPy is a versatile and powerful tool that provides a wide range of data processing capabilities. Its ease of use, efficiency and power make it the preferred tool for scientific computing and data analysis

.

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