NumPy Getting Started Guide: Entering the New World of Data Processing

1. Install NumPy

Install NumPy in the terminal via the pip command:

pip install numpy

2. Import NumPy

Import the NumPy module in the python script:

import numpy as np

3. Create and operate arrays

The core of NumPyThe data structure is ndarray, which can create one-dimensional, two-dimensional or even higher-dimensional arrays:

# 创建一维数组
arr = np.array([1, 2, 3, 4, 5])

# 创建二维数组
matrix = np.array([[1, 2, 3], [4, 5, 6]])

4. Array properties and methods

NumPy arrays have various properties and methods to manipulate and analyze data:

• shape: the shape (dimension and size) of the array
• dtype: Type of elements in the array
• reshape: change the shape of the array
• transpose: transpose array
• sum: Calculate the sum of array elements
• mean: Calculate the average of array elements

5. Array indexing and slicing

NumPy provides flexible indexing and slicing mechanisms to easily access and modify array elements:

# 访问元素
print(arr[2])

# 切片
print(matrix[:, 1:])

6. Basic mathematical operations

NumPy supports basic mathematical operations on arrays, such as addition, subtraction, multiplication and division:

# 加法
result = arr + 1

# 乘法
product = matrix * 2

7. Data broadcast

Data broadcasting in NumPy allows mathematical operations to be performed on arrays of different shapes, simplifying processing of large data sets:

# 将标量广播到数组
print(arr + 5)

# 广播数组
print(matrix + arr)

8. File input/output

NumPy can easily load and save arrays from files via the np.load and np.save functions:

# 从文件中加载数组
data = np.load("data.npy")

# 保存数组到文件
np.save("output.npy", data)

9. Performance optimization

NumPy is optimized for performance on large arrays, which can be further improved by using vectorized operations and NumPy-specific functions:

• Use vectorized operations instead of loops
• Avoid unnecessary array copy
• Using NumPy’s parallelization functions

10. Advanced functions

In addition to basic operations, NumPy also provides more advanced functions, such as:

• Linear algebra operations
• Fourier Transform
• Random number generation
• Image Processing

By mastering these core concepts, beginners can quickly get started with NumPy and become even more powerful in the field of data processing and analysis.

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