1. What is NumPy (是什么)#

NumPy is a library that provides the ndarray (多维数组) object for storing and manipulating numerical data efficiently.

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import numpy as np
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x = np.array([[1, 2], [3, 4]])
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print(x)
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print(type(x))
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print(x.shape)

2. Why Learn NumPy (为什么需要学习)#

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import numpy as np
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a = np.array([1, 2, 3])
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b = np.array([4, 5, 6])
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print(a + b)  # element-wise addition (逐元素加法)

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import numpy as np
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a = np.array([1, 2, 3])
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print(a * 2)  # vectorized operation (向量化操作)

3. Comparison#

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import numpy as np
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x = np.array([1, 2, 3])
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print(x * 2)

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import torch
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x = torch.tensor([1, 2, 3], dtype=torch.float32)
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print(x * 2)

4. Basic Operations (基础操作)#

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import numpy as np
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a = np.zeros((2, 3))   # all zeros (全0)
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b = np.ones((2, 3))    # all ones (全1)
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c = np.arange(0, 10)   # range (范围)

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import numpy as np
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x = np.array([1, 2, 3, 4])
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y = x.reshape(2, 2)
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print(y)

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import numpy as np
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x = np.array([[1, 2], [3, 4]])
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print(x[0, 1])  # access element (访问元素)

1. What is NumPy (是什么)#

NumPy is a numerical computing library (数值计算库) in Python that provides the ndarray (多维数组) for efficient computation.

2. What is ndarray (多维数组)#

ndarray is a homogeneous array (同质数组) that stores elements of the same data type in contiguous memory (连续内存).

3. Difference between list and ndarray (区别)#

list allows mixed types (可混合类型), while ndarray requires a single type (统一类型) and supports vectorized operations (向量化运算).

4. What is vectorization (向量化)#

Vectorization is performing operations on entire arrays without explicit loops (无需显式循环).

5. What is broadcasting (广播机制)#

Broadcasting allows arrays of different shapes (不同形状) to be operated together automatically.

6. Why is NumPy faster than Python (为什么更快)#

Because it uses C implementation (底层C实现), contiguous memory (连续内存), and vectorization (向量化).

7. How to create an array (创建数组)#

Use functions like np.array(), np.zeros(), np.ones(), np.arange().

8. What is shape (形状)#

shape describes the dimensions (维度) of an array.

9. What is reshape (重塑)#

reshape changes the shape (改变形状) of an array without changing its data.

10. Difference between copy and view (拷贝 vs 视图)#

copy creates new memory (新内存), while view shares memory (共享内存).

11. What is slicing (切片)#

Slicing extracts a subset (子数组) of an array.

12. What is dtype (数据类型)#

dtype defines the type (数据类型) of elements in an array.

13. What is axis (轴)#

axis specifies the direction (方向) along which operations are performed.

14. What is flatten (展平)#

flatten converts a multi-dimensional array (多维数组) into one dimension.

15. Difference between arange and linspace (区别)#

arange uses step size (步长), while linspace uses number of points (点的数量).

16. What is indexing (索引)#

Indexing accesses specific elements (访问元素) using positions.

17. What is boolean indexing (布尔索引)#

Boolean indexing selects elements based on conditions (条件筛选).

18. What is aggregation (聚合)#

Aggregation performs operations like sum, mean on arrays.

19. What is matrix multiplication (矩阵乘法)#

Matrix multiplication follows:

using np.dot() or @.

20. What is the main purpose of NumPy (核心作用)#

NumPy is used for efficient numerical computation (高效数值计算) and is the foundation of scientific computing (科学计算基础).