# PPtool使用指南（by@zemengchuan）
GitHub链接：http://github.com/zemengchuan/PPtool
## 用途：
PPtool是集成了许多能够便利PPdata包的使用方法的python第三方包
## 安装方式
```python
pip install PPtool
```
## 方法库
## 快速获取目标数据的函数
方法：get_method
描述：通过向get_method方法传入目标数据的描述，可以快速获取你想要的函数名称，并且可以自行选择需要匹配的函数数量，以及是否需要调用
输入参数：
| 名称 | 类型 | 描述 |
| -------- | ---- | ------------------- |
| describe | str | 目标数据的名字 |
| limit | int | 匹配的数量，默认为5 |
输出参数：返回你选择的数据，如果不需要调用则返回0
方法示例：
```python
import PPtool as pt
method = pt.get_method("商品价格指数", limit=5)
print(method)
```
结果示例：
```
您希望获取的数据有可能是:(注：这里的待选项数量由limit参数控制)
0 " 企业商品价格指数"，其调用方法为China_CGPI
1 "商品零售价格指数"，其调用方法为China_retail_price_index
2 "大宗商品价格"，其调用方法为China_commodity_price_index
3 "建材价格指数"，其调用方法为China_construction_price_index
4 "工业品出厂价格指数"，其调用方法为China_PPI
请选择需要调用的函数编号：（不调用则填quit）0
您选择的数据是:“ 企业商品价格指数”
[ 月份 总指数-指数值 总指数-同比增长 总指数-环比增长 农产品-指数值 农产品-同比增长 农产品-环比增长 矿产品-指数值 矿产品-同比增长 矿产品-环比增长 煤油电-指数值 煤油电-同比增长 煤油电-环
比增长
0 2023年01月份 100.4 -5.639098 0.199601 107.4 15.359828 2.972196 101.9 -1.450677 -0.682261 105.8 -10.338983 -2.037037
1 2022年12月份 100.2 -6.790698 0.400802 104.3 7.971014 -0.666667 102.6 -1.535509 1.886792 108.0 -8.937605 0.185529
2 2022年11月份 99.8 -9.107468 -0.299700 105.0 6.924644 -6.166220 100.7 -6.586271 2.650357 107.8 -12.924071 -0.185185
3 2022年10月份 100.1 -9.082652 -1.573255 111.9 21.498371 -0.089286 98.1 -11.938959 2.400835 108.0 -12.691997 -3.398927
4 2022年09月份 101.7 -5.746061 -0.586510 112.0 24.721604 4.089219 95.8 -16.258741 3.232759 111.8 -4.931973 -2.272727
.. ... ... ... ... ... ... ... ... ... ... ... ... ...
195 2006年10月份 103.8 2.843555 0.193050 106.3 8.116355 0.662879 116.0 9.002067 2.112676 107.0 -11.401838 -2.550091
196 2006年09月份 103.6 2.139406 0.680272 105.6 6.850147 1.343570 113.6 4.048361 0.709220 109.8 -9.495549 -0.453309
197 2006年08月份 102.9 0.753941 0.390244 104.2 4.628979 1.165049 112.8 1.768315 0.624442 110.3 -10.492575 -0.090580
198 2006年07月份 102.5 -0.214174 0.195503 103.0 2.763644 0.292113 112.1 1.009191 1.356239 110.4 -10.657927 0.272480
199 2006年06月份 102.3 -0.689253 0.788177 102.7 2.392822 1.884921 110.6 -0.887176 1.589051 110.1 -10.356619 -0.524033
[200 rows x 13 columns]]
```
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"description": "# PPtool\u4f7f\u7528\u6307\u5357\uff08by@zemengchuan\uff09\r\n\r\nGitHub\u94fe\u63a5\uff1ahttp://github.com/zemengchuan/PPtool\r\n\r\n## \u7528\u9014\uff1a\r\n\r\nPPtool\u662f\u96c6\u6210\u4e86\u8bb8\u591a\u80fd\u591f\u4fbf\u5229PPdata\u5305\u7684\u4f7f\u7528\u65b9\u6cd5\u7684python\u7b2c\u4e09\u65b9\u5305\r\n\r\n## \u5b89\u88c5\u65b9\u5f0f\r\n\r\n```python\r\npip install PPtool\r\n```\r\n\r\n## \u65b9\u6cd5\u5e93\r\n\r\n## \u5feb\u901f\u83b7\u53d6\u76ee\u6807\u6570\u636e\u7684\u51fd\u6570\r\n\r\n\u65b9\u6cd5\uff1aget_method\r\n\r\n\u63cf\u8ff0\uff1a\u901a\u8fc7\u5411get_method\u65b9\u6cd5\u4f20\u5165\u76ee\u6807\u6570\u636e\u7684\u63cf\u8ff0\uff0c\u53ef\u4ee5\u5feb\u901f\u83b7\u53d6\u4f60\u60f3\u8981\u7684\u51fd\u6570\u540d\u79f0\uff0c\u5e76\u4e14\u53ef\u4ee5\u81ea\u884c\u9009\u62e9\u9700\u8981\u5339\u914d\u7684\u51fd\u6570\u6570\u91cf\uff0c\u4ee5\u53ca\u662f\u5426\u9700\u8981\u8c03\u7528\r\n\r\n\u8f93\u5165\u53c2\u6570\uff1a\r\n\r\n| \u540d\u79f0 | \u7c7b\u578b | \u63cf\u8ff0 |\r\n| -------- | ---- | ------------------- |\r\n| describe | str | \u76ee\u6807\u6570\u636e\u7684\u540d\u5b57 |\r\n| limit | int | \u5339\u914d\u7684\u6570\u91cf\uff0c\u9ed8\u8ba4\u4e3a5 |\r\n\r\n\r\n\u8f93\u51fa\u53c2\u6570\uff1a\u8fd4\u56de\u4f60\u9009\u62e9\u7684\u6570\u636e\uff0c\u5982\u679c\u4e0d\u9700\u8981\u8c03\u7528\u5219\u8fd4\u56de0\r\n\r\n\r\n\u65b9\u6cd5\u793a\u4f8b\uff1a\r\n\r\n```python\r\nimport PPtool as pt\r\n\r\nmethod = pt.get_method(\"\u5546\u54c1\u4ef7\u683c\u6307\u6570\", limit=5)\r\nprint(method)\r\n```\r\n\r\n\u7ed3\u679c\u793a\u4f8b\uff1a\r\n\r\n```\r\n\u60a8\u5e0c\u671b\u83b7\u53d6\u7684\u6570\u636e\u6709\u53ef\u80fd\u662f:(\u6ce8\uff1a\u8fd9\u91cc\u7684\u5f85\u9009\u9879\u6570\u91cf\u7531limit\u53c2\u6570\u63a7\u5236)\r\n0 \" \u4f01\u4e1a\u5546\u54c1\u4ef7\u683c\u6307\u6570\"\uff0c\u5176\u8c03\u7528\u65b9\u6cd5\u4e3aChina_CGPI\r\n1 \"\u5546\u54c1\u96f6\u552e\u4ef7\u683c\u6307\u6570\"\uff0c\u5176\u8c03\u7528\u65b9\u6cd5\u4e3aChina_retail_price_index\r\n2 \"\u5927\u5b97\u5546\u54c1\u4ef7\u683c\"\uff0c\u5176\u8c03\u7528\u65b9\u6cd5\u4e3aChina_commodity_price_index\r\n3 \"\u5efa\u6750\u4ef7\u683c\u6307\u6570\"\uff0c\u5176\u8c03\u7528\u65b9\u6cd5\u4e3aChina_construction_price_index\r\n4 \"\u5de5\u4e1a\u54c1\u51fa\u5382\u4ef7\u683c\u6307\u6570\"\uff0c\u5176\u8c03\u7528\u65b9\u6cd5\u4e3aChina_PPI\r\n\r\n\u8bf7\u9009\u62e9\u9700\u8981\u8c03\u7528\u7684\u51fd\u6570\u7f16\u53f7\uff1a\uff08\u4e0d\u8c03\u7528\u5219\u586bquit\uff090\r\n\r\n\u60a8\u9009\u62e9\u7684\u6570\u636e\u662f:\u201c \u4f01\u4e1a\u5546\u54c1\u4ef7\u683c\u6307\u6570\u201d\r\n[ \u6708\u4efd \u603b\u6307\u6570-\u6307\u6570\u503c \u603b\u6307\u6570-\u540c\u6bd4\u589e\u957f \u603b\u6307\u6570-\u73af\u6bd4\u589e\u957f \u519c\u4ea7\u54c1-\u6307\u6570\u503c \u519c\u4ea7\u54c1-\u540c\u6bd4\u589e\u957f \u519c\u4ea7\u54c1-\u73af\u6bd4\u589e\u957f \u77ff\u4ea7\u54c1-\u6307\u6570\u503c \u77ff\u4ea7\u54c1-\u540c\u6bd4\u589e\u957f \u77ff\u4ea7\u54c1-\u73af\u6bd4\u589e\u957f \u7164\u6cb9\u7535-\u6307\u6570\u503c \u7164\u6cb9\u7535-\u540c\u6bd4\u589e\u957f \u7164\u6cb9\u7535-\u73af\r\n\u6bd4\u589e\u957f\r\n0 2023\u5e7401\u6708\u4efd 100.4 -5.639098 0.199601 107.4 15.359828 2.972196 101.9 -1.450677 -0.682261 105.8 -10.338983 -2.037037\r\n1 2022\u5e7412\u6708\u4efd 100.2 -6.790698 0.400802 104.3 7.971014 -0.666667 102.6 -1.535509 1.886792 108.0 -8.937605 0.185529\r\n2 2022\u5e7411\u6708\u4efd 99.8 -9.107468 -0.299700 105.0 6.924644 -6.166220 100.7 -6.586271 2.650357 107.8 -12.924071 -0.185185\r\n3 2022\u5e7410\u6708\u4efd 100.1 -9.082652 -1.573255 111.9 21.498371 -0.089286 98.1 -11.938959 2.400835 108.0 -12.691997 -3.398927\r\n4 2022\u5e7409\u6708\u4efd 101.7 -5.746061 -0.586510 112.0 24.721604 4.089219 95.8 -16.258741 3.232759 111.8 -4.931973 -2.272727\r\n.. ... ... ... ... ... ... ... ... ... ... ... ... ...\r\n195 2006\u5e7410\u6708\u4efd 103.8 2.843555 0.193050 106.3 8.116355 0.662879 116.0 9.002067 2.112676 107.0 -11.401838 -2.550091\r\n196 2006\u5e7409\u6708\u4efd 103.6 2.139406 0.680272 105.6 6.850147 1.343570 113.6 4.048361 0.709220 109.8 -9.495549 -0.453309\r\n197 2006\u5e7408\u6708\u4efd 102.9 0.753941 0.390244 104.2 4.628979 1.165049 112.8 1.768315 0.624442 110.3 -10.492575 -0.090580\r\n198 2006\u5e7407\u6708\u4efd 102.5 -0.214174 0.195503 103.0 2.763644 0.292113 112.1 1.009191 1.356239 110.4 -10.657927 0.272480\r\n199 2006\u5e7406\u6708\u4efd 102.3 -0.689253 0.788177 102.7 2.392822 1.884921 110.6 -0.887176 1.589051 110.1 -10.356619 -0.524033\r\n\r\n[200 rows x 13 columns]]\r\n```\r\n",
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