a65adcc2e5
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>
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
"""
|
||
数据转换器
|
||
"""
|
||
|
||
from typing import Dict, List
|
||
import pandas as pd
|
||
from ppt_station.models.job import Transform, TransformOp
|
||
|
||
|
||
class DataFrameTransformer:
|
||
"""DataFrame 转换器"""
|
||
|
||
@staticmethod
|
||
def apply_transforms(
|
||
dfs: Dict[str, pd.DataFrame], transforms: Dict[str, Transform]
|
||
) -> Dict[str, pd.DataFrame]:
|
||
"""应用所有转换"""
|
||
result = dict(dfs)
|
||
|
||
for name, transform in (transforms or {}).items():
|
||
# 获取源数据
|
||
if isinstance(transform.from_, str):
|
||
source_df = result[transform.from_].copy()
|
||
elif isinstance(transform.from_, list):
|
||
# 多个数据源需要合并
|
||
source_dfs = [result[src].copy() for src in transform.from_]
|
||
source_df = source_dfs[0]
|
||
for df in source_dfs[1:]:
|
||
# 默认按索引合并
|
||
source_df = pd.concat([source_df, df], axis=1)
|
||
else:
|
||
raise ValueError(f"Invalid 'from' type: {type(transform.from_)}")
|
||
|
||
# 应用操作
|
||
for op in transform.ops:
|
||
source_df = DataFrameTransformer._apply_op(source_df, op)
|
||
|
||
result[name] = source_df
|
||
|
||
return result
|
||
|
||
@staticmethod
|
||
def _apply_op(df: pd.DataFrame, op: TransformOp) -> pd.DataFrame:
|
||
"""应用单个转换操作"""
|
||
if op.type == "groupby":
|
||
if not op.by or not op.agg:
|
||
raise ValueError("groupby requires 'by' and 'agg' parameters")
|
||
return df.groupby(op.by).agg(op.agg).reset_index()
|
||
|
||
elif op.type == "pivot":
|
||
if not op.index or not op.columns or not op.values:
|
||
raise ValueError("pivot requires 'index', 'columns', and 'values'")
|
||
return df.pivot(index=op.index, columns=op.columns, values=op.values)
|
||
|
||
elif op.type == "merge":
|
||
# merge 需要两个 DataFrame，这里暂不实现
|
||
raise NotImplementedError("merge operation should be handled at higher level")
|
||
|
||
elif op.type == "compute":
|
||
if not op.expr or not op.output_col:
|
||
raise ValueError("compute requires 'expr' and 'output_col'")
|
||
# 使用 eval 计算表达式
|
||
df[op.output_col] = df.eval(op.expr)
|
||
return df
|
||
|
||
elif op.type == "filter":
|
||
if not op.condition:
|
||
raise ValueError("filter requires 'condition'")
|
||
return df.query(op.condition)
|
||
|
||
elif op.type == "sort":
|
||
if not op.sort_by:
|
||
raise ValueError("sort requires 'sort_by'")
|
||
return df.sort_values(op.sort_by, ascending=op.ascending)
|
||
|
||
elif op.type == "rename":
|
||
if not op.map:
|
||
raise ValueError("rename requires 'map'")
|
||
return df.rename(columns=op.map)
|
||
|
||
else:
|
||
raise ValueError(f"Unknown operation type: {op.type}")
|
||
|