skills/tushare-data-1.0.5/references/数据接口/宏观经济/国内宏观/价格指数/居民消费价格指数(CPI).md
wangyitong a65adcc2e5 Initial commit: merged, deduplicated, and vetted skill collection
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:

- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
  ../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
  zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
  - docx family: kept docx (official, full toolchain) + docx-cn
    (GB/T 9704 Chinese official-document constants),
    dropped docx_writer (no scripts, name collided with docx)
  - humanizer family: kept humanizer-zh (6 zh reference docs),
    dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
  ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
  claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
  live credentials

Result: 25 skills, 572 files, ~7.5 MB.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-13 14:47:12 +08:00

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## 居民消费价格指数
----
接口cn_cpi
描述获取CPI居民消费价格数据包括全国、城市和农村的数据
限量单次最大5000行一次可以提取全部数据
权限用户积累600积分可以使用具体请参阅[积分获取办法](https://tushare.pro/document/1?doc_id=13)
<br>
<br>
**输入参数**
名称 | 类型 | 必选 | 描述
---- | ----- | ---- | ----
m | str | N | 月份YYYYMM下同支持多个月份同时输入逗号分隔
start_m | str | N | 开始月份
end_m | str | N | 结束月份
<br>
<br>
**输出参数**
名称 | 类型 | 默认显示 | 描述
--- | ---- | ---- | ----
month | str | Y | 月份YYYYMM
nt_val | float | Y | 全国当月值
nt_yoy | float | Y | 全国同比(%
nt_mom | float | Y | 全国环比(%
nt_accu | float | Y | 全国累计值
town_val | float | Y | 城市当月值
town_yoy | float | Y | 城市同比(%
town_mom | float | Y | 城市环比(%
town_accu | float | Y | 城市累计值
cnt_val | float | Y | 农村当月值
cnt_yoy | float | Y | 农村同比(%
cnt_mom | float | Y | 农村环比(%
cnt_accu | float | Y | 农村累计值
<br>
<br>
**接口调用**
```python
pro = ts.pro_api()
df = pro.cn_cpi(start_m='201801', end_m='201903')
#获取指定字段
df = pro.cn_cpi(start_q='201801', end_q='201903', fields='month,nt_val,nt_yoy')
```
<br>
**数据样例**
month nt_val nt_yoy nt_mom nt_accu town_val town_yoy town_mom town_accu cnt_val cnt_yoy cnt_mom cnt_accu
0 201903 102.30 2.30 -0.40 101.80 102.30 2.30 -0.40 101.90 102.30 2.30 -0.30 101.80
1 201902 101.50 1.50 1.00 101.60 101.50 1.50 1.00 101.60 101.40 1.40 0.90 101.50
2 201901 101.70 1.70 0.50 101.70 101.80 1.80 0.50 101.80 101.70 1.70 0.40 101.70
3 201812 101.90 1.90 0.00 102.10 101.90 1.90 0.00 102.10 101.90 1.90 0.00 102.10
4 201811 102.20 2.20 -0.30 102.10 102.20 2.20 -0.40 102.10 102.20 2.20 -0.30 102.10
5 201810 102.50 2.50 0.20 102.10 102.50 2.50 0.20 102.10 102.60 2.60 0.20 102.10
6 201809 102.50 2.50 0.70 102.10 102.40 2.40 0.70 102.10 102.50 2.50 0.80 102.00
7 201808 102.30 2.30 0.70 102.00 102.30 2.30 0.60 102.00 102.30 2.30 0.80 102.00
8 201807 102.10 2.10 0.30 102.00 102.10 2.10 0.40 102.00 102.00 2.00 0.10 101.90
9 201806 101.90 1.90 -0.10 102.00 101.80 1.80 0.00 102.00 101.90 1.90 -0.10 101.90
10 201805 101.80 1.80 -0.20 102.00 101.80 1.80 -0.20 102.00 101.70 1.70 -0.10 101.90
11 201804 101.80 1.80 -0.20 102.10 101.80 1.80 -0.20 102.10 101.70 1.70 -0.30 101.90
12 201803 102.10 2.10 -1.10 102.10 102.10 2.10 -1.10 102.20 101.90 1.90 -1.20 102.00
13 201802 102.90 2.90 1.20 102.20 103.00 3.00 1.30 102.20 102.70 2.70 1.10 102.10
14 201801 101.50 1.50 0.60 101.50 101.50 1.50 0.60 101.50 101.50 1.50 0.60 101.50