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`252`	`252`	`\| 171 \| [子类继承父类的静态方法吗?](md/171.md) \| staticmethod \| V1.0 \| ⭐️⭐⭐ \|`
`253`	`253`	`\| 172 \| [NumPy 的pad填充方法](md/172.md) \| NumPy pad \| V1.0 \| ⭐️⭐⭐⭐ \|`
`254`	`254`	`\| 173 \| [创建下对角线为1、2、3、4的对角矩阵](md/173.md) \| NumPy diag \| V1.0 \| ⭐️⭐⭐ \|`
	`255`	`+\| 174 \| [cut 数据分箱](md/174.md) \| Pandas cut \| v1.0 \| ⭐️⭐⭐ \|`
`255`	`256`
`256`	`257`	`### Python 实战`
`257`	`258`

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`1`	`1`
`2`	`2`	```markdown
`3`	`3`	`@author jackzhenguo`
`4`		`-@desc`
	`4`	`+@desc cut 数据分箱`
`5`	`5`	`@tag`
`6`	`6`	`@version`
`7`		`-@date 2020/03/12`
	`7`	`+@date 2020/11/28`
`8`	`8`	```
`9`		`-`
	`9`	`+`
	`10`	`+第174个小例子:cut 数据分箱`
	`11`	`+`
	`12`	`+将百分制分数转为A,B,C,D四个等级,bins 被分为 [0,60,75,90,100],labels 等于['D', 'C', 'B', 'A']:`
	`13`	`+`
	`14`	+```python
	`15`	`+# 生成20个[0,100]的随机整数`
	`16`	`+In [30]: a = np.random.randint(1,100,20)`
	`17`	`+In [31]: a`
	`18`	`+Out[31]:`
	`19`	`+array([48, 22, 46, 84, 13, 52, 36, 35, 27, 99, 31, 37, 15, 31, 5, 46, 98,99, 60, 43])`
	`20`	`+`
	`21`	`+# cut分箱`
	`22`	`+In [33]: pd.cut(a, [0,60,75,90,100], labels = ['D', 'C', 'B', 'A'])`
	`23`	`+Out[33]:`
	`24`	`+[D, D, D, B, D, ..., D, A, A, D, D]`
	`25`	`+Length: 20`
	`26`	`+Categories (4, object): [D < C < B < A]`
	`27`	+```
	`28`	`+`
	`29`	`+分箱后,48分对应D,22分对应D,46对应D,84分对应B,...`

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