NumPy API详解大全（持续更新ing...）

诸神缄默不语-个人CSDN博文目录

官方文档：NumPy Reference — NumPy v1.23 Manual

1. Array Objects

1.1 The N-dimensional array ( ndarray )

1.1.1 numpy.ndarray

1. shape：由array每一维度组成的tuple
   如果想要改变array维度，可以直接改变shape，但是不建议这么干
   
   https://numpy.org/doc/stable/reference/generated/numpy.ndarray.shape.html
2. mean() 等如numpy.mean()

2. Constants

3. Universal Functions (ufunc)

4. Routines

4.1 Array creation routines

1. array(object)：创建一个值与object相同的numpy.ndarray对象（元素保持最高精度）
   
2. numpy.diag(v, k=0)
   1. 如v是一维向量：构建以v为值的对角阵
   2. k控制在对角线上方几行
      https://numpy.org/doc/stable/reference/generated/numpy.diag.html

4.2 Array manipulation routines

1. numpy.expand_dims(a, axis)：增加一维（感觉上比较类似PyTorch的unsqueeze()）https://numpy.org/doc/stable/reference/generated/numpy.expand_dims.html
2. numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")：合并多个ndarray
   https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
   常见问题：入参ndarray必须用稠密矩阵，如果使用了稀疏矩阵，将会报ValueError: zero-dimensional arrays cannot be concatenated bug
3. numpy.transpose(a, axes=None)
   https://numpy.org/doc/stable/reference/generated/numpy.transpose.html
   1. 对二维矩阵：转置

4.3 Binary operations

4.4 String operations

4.5 C-Types Foreign Function Interface (numpy.ctypeslib)

4.6 Datetime Support Functions

4.7 Data type routines

4.8 Optionally SciPy-accelerated routines (numpy.dual)

4.9 Mathematical functions

1. numpy.power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])：element-wise计算幂
   https://numpy.org/doc/stable/reference/generated/numpy.power.html

4.10 Floating point error handling

1. numpy.errstate(**kwargs)：上下文管理器或者装饰器
   kwargs{divide, over, under, invalid}
   https://numpy.org/doc/stable/reference/generated/numpy.errstate.html
   
   示例一：

with np.errstate(divide='ignore'):
    d_inv_sqrt = np.power(rowsum, -0.5)

4.11 Discrete Fourier Transform (numpy.fft)

4.12 Functional programming

4.13 NumPy-specific help functions

4.14 Indexing routines

4.15 Input and output

4.16 Linear algebra (numpy.linalg)

1. numpy.dot(a, b, out=None)
   https://numpy.org/doc/stable/reference/generated/numpy.dot.html
   1. 如果a和b都是二维矩阵：返回a和b的矩阵乘法结果

4.17 Logic functions

1. numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)：返回2个array是否所有元素都相同（允许一定误差）
   https://numpy.org/doc/stable/reference/generated/numpy.allclose.html
   可用于检测一个矩阵是否是对称阵：np.allclose(X, X.T)
2. numpy.isinf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])：element-wise计算矩阵元素是否无限（可能是正负无限）
   https://numpy.org/doc/stable/reference/generated/numpy.isinf.html

4.18 Masked array operations

4.19 Mathematical functions

4.20 Matrix library (numpy.matlib)

4.21 Miscellaneous routines

4.22 Padding Arrays

4.23 Polynomials

4.24 Random sampling (numpy.random)

https://numpy.org/doc/stable/reference/random/index.html

1. Generator
   https://numpy.org/doc/stable/reference/random/generator.html
   Generator是代替RandomState的。
   1. numpy.random.default_rng()
      参数：seed{None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
      示例代码：

import numpy as np
rng = np.random.default_rng(12345)

rfloat = rng.random()
#生成一个随机float

rints = rng.integers(low=0, high=10, size=3)
#生成0（包含）-10（不包含）之间的3个integer

2. Generator.choice(a, size=None, replace=True, p=None, axis=0, shuffle=True)
   https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.choice.html
   从传入的一维数组（a）中产生随机抽样
   参数：
   size {int, tuple[int]}, optional：输出的shape
   replace bool, optional：有放回抽样 / 无放回抽样
   示例代码：
   ①从np.arange(5)中生成一个大小为3的抽样

rng.choice(5, 3)
#输出：array([0, 3, 4]) # random
#This is equivalent to rng.integers(0,5,3)

4.24 Set routines

1. numpy.unique(arr)：直接的默认返回值是np.ndarray对象，arr中唯一值从小到大排序
   https://numpy.org/doc/stable/reference/generated/numpy.unique.html

4.25 Sorting, searching, and counting

4.26 Statistics

1. numpy.mean(a)：直接求整个array-like对象的平均值
   https://numpy.org/doc/stable/reference/generated/numpy.mean.html

其他

1. 切片：
   按标准来将ndarray变成布尔张量：example_matrix[example_matrix>=0.98]

其他正文及脚注未提及的参考资料

1. numpy和tensor增加、删除一个维度 - 简书
2. Numpy矩阵操作总结 | zdaiot