dataframe-to-online-spreadsheet 0.1.9

Export a pandas dataframe to a online spreadsheet.

Export a pandas dataframe to an online spreadsheet

The online spreadsheet such as Google Sheets, Feishu Docs, is a good way to share data with others and is a powerful tool for data analysis and visualization. This package provides a simple way to export a pandas dataframe to a online spreadsheet.

Installation

You can install this package from PyPI using pip:

pip install dataframe-to-online-spreadsheet

Usage

Feishu Docs

1. You need to register a Feishu team and get the manager_ids from Feishu Admin. Fortunately, it is free for a small team.
2. You need to create an app and get the app_id and app_secret from Feishu Open Platform.

import pandas as pd
import dataframe_to_online_spreadsheet.feishu
import os

def test_feishu():

    app_id = os.getenv("APP_ID")
    app_secret = os.getenv("APP_SECRET")

    sheet1_data = pd.read_csv("./tests/test_data1.csv")
    token = sheet1_data.feishu.to_spreadsheet(app_id, app_secret, title="Daily Report", data={'title': 'sheet_name1', 'data': sheet1_data}, manager_ids=['xxx'])

    sheet2_data = pd.read_csv("./tests/test_data2.csv")
    token = sheet2_data.feishu.to_spreadsheet(app_id, app_secret, title="Daily Report", data={'title': 'sheet_name2', 'data': sheet2_data}, manager_ids=['xxx'], spreadsheet_token=token)

    sheet3_data = pd.read_csv("./tests/test_data3.csv")
    token = sheet3_data.feishu.to_spreadsheet(app_id, app_secret, title="Daily Report", data={'title': 'sheet_name3', 'data': sheet3_data}, manager_ids=['xxx'], spreadsheet_token=token)
    logging.info(f"spreadsheet token: {token}")
    assert token

Wecom Docs

1. You need login Wecom Developer.
2. You need to create an app and get the app_id and app_secret.
3. Create a smartsheet and get the doc_id and sheet_id.

import pandas as pd
import dataframe_to_online_spreadsheet.wecom
import os

def test_wecom():

    app_id = os.getenv("APP_ID")
    app_secret = os.getenv("APP_SECRET")
    doc_id = os.getenv("DOC_ID")
    sheet_id = os.getenv("SHEET_ID")

    sheet1_data = pd.read_csv("./tests/test_data1_wecom.csv")

    fields_ids = {
        "f3jXlv": "FIELD_TYPE_NUMBER",
        "f8BRn6": "FIELD_TYPE_TEXT",
        "fBgV4J": "FIELD_TYPE_TEXT",
        "fD1c0G": "FIELD_TYPE_TEXT",
        "fGJwzi": "FIELD_TYPE_TEXT",
        "fMtBZy": "FIELD_TYPE_TEXT",
        "fQONOH": "FIELD_TYPE_TEXT",
        "fQQyQY": "FIELD_TYPE_DATE_TIME",
        "fRq1I2": "FIELD_TYPE_DATE_TIME",
        "fSmqgA": "FIELD_TYPE_TEXT",
        "fSu8nr": "FIELD_TYPE_NUMBER",
        "fWY1jO": "FIELD_TYPE_TEXT",
        "fYrjfz": "FIELD_TYPE_NUMBER",
        "fZH5aH": "FIELD_TYPE_TEXT",
        "fZZ5uE": "FIELD_TYPE_NUMBER",
        "fZilZm": "FIELD_TYPE_TEXT",
        "faGRO9": "FIELD_TYPE_TEXT",
        "fcIPid": "FIELD_TYPE_TEXT",
        "ffPlXC": "FIELD_TYPE_NUMBER",
        "fgxTYC": "FIELD_TYPE_TEXT",
        "fpqHBM": "FIELD_TYPE_NUMBER",
        "fwLOij": "FIELD_TYPE_TEXT",
        "fxRfyy": "FIELD_TYPE_NUMBER",
        "fzNsma": "FIELD_TYPE_TEXT",
        "fwF1k7": "FIELD_TYPE_USER",
    }
    records = sheet1_data.wecom.to_spreadsheet(
        app_id, app_secret, doc_id, sheet_id, fields_ids
    )

    logging.info(records)
    assert records