51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2026 Lark Technologies Pte. Ltd.
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# SPDX-License-Identifier: MIT
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"""DataFrame ↔ Feishu Sheet typed-JSON helpers.
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This is the same 7-line snippet the skill docs already inline (see
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`lark-sheets-write-cells` "DataFrame → 协议（5 行 helper）" and
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`lark-sheets-read-data` "输出 → DataFrame（2 行 helper）"), pulled out
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so callers can `import` it instead of copy-pasting:
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from sheets_df import df_to_sheet, sheet_to_df
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Callers run lark-cli themselves; this file is a library, not a CLI.
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"""
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import json
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import pandas as pd
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def df_to_sheet(df, name, formats=None):
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"""Pack one DataFrame into one entry of a `+table-put --sheets` payload."""
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packed = json.loads(df.to_json(orient="split", date_format="iso"))
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# The protocol requires string column names. pandas keeps integer labels
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# (e.g. the default RangeIndex columns 0/1/2) as JSON numbers, while the
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# dtypes dict keys get stringified during JSON serialization — the CLI
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# then rejects `columns` ("cannot unmarshal number into … type string")
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# and the dtype lookup would miss anyway. Stringify every key once, and
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# refuse to continue when that conversion silently merges two columns.
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normalized_labels = [str(c) for c in df.columns]
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columns = [str(c) for c in packed["columns"]]
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if normalized_labels != columns:
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columns = normalized_labels
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if len(set(columns)) != len(columns):
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raise ValueError(
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"column labels collide after str() conversion; "
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"rename the DataFrame columns before packing"
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)
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packed["columns"] = columns
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dtype_values = list(df.dtypes)
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return {
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"name": name,
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**packed,
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"dtypes": {key: str(dtype) for key, dtype in zip(columns, dtype_values)},
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**({"formats": {str(k): v for k, v in formats.items()}} if formats else {}),
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}
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def sheet_to_df(sheet):
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"""Restore one `+table-get` sheet dict into a typed DataFrame."""
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return pd.DataFrame(sheet["data"], columns=sheet["columns"]).astype(sheet["dtypes"])
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