GSParse

GSParse is a lightweight library for extracting data from public Google Sheets by URL — no API keys, no OAuth, no service accounts. It downloads a spreadsheet's export, parses every worksheet, and gives you a clean, convenient API for cells, rows, columns, ranges, search, and export.

from gsparse import GSParseClient

client = GSParseClient()
spreadsheet = client.load_spreadsheet("https://docs.google.com/spreadsheets/d/<SHEET_ID>/edit")

for worksheet in spreadsheet:
    print(worksheet.name, worksheet.row_count, "x", worksheet.column_count)

---

Table of Contents

• Features
• Installation
• Quick Start
• Usage
  • Loading data
  • Worksheets
  • Cells and ranges
  • Exporting to dictionaries
  • Searching
  • Cleaning data
  • Parsing from a CSV string
• API Reference
• Notes & Limitations
• Development
• Contributing
• License

---

Features

• 📊 Reads all worksheets of a spreadsheet in one call (XLSX)
• 🔗 Loads directly by Google Sheets URL — no credentials required
• 🧩 Convenient object model: Spreadsheet → Worksheet → Cell / Range
• 🔍 Search by exact value or regular expression
• 📋 Export worksheets to lists of dictionaries (records)
• 🧹 Helpers to strip empty rows/columns
• 🔤 Optional preserve_strings mode to keep every value as text
• 🔄 Automatic retries and encoding auto-detection
• ⚡ Supports both XLSX (default) and CSV formats

Installation

pip install gsparse

Or with uv:

uv add gsparse

Requirements: Python ≥ 3.10 · requests · chardet · openpyxl

Quick Start

from gsparse import GSParseClient

client = GSParseClient()

url = "https://docs.google.com/spreadsheets/d/YOUR_SHEET_ID/edit"
spreadsheet = client.load_spreadsheet(url)

# Inspect the workbook
print("Title:", spreadsheet.title)
print("Sheets:", spreadsheet.worksheet_names)

# Grab the first worksheet
worksheet = spreadsheet.get_first_worksheet()

# Read a single cell (1-based coordinates)
cell = worksheet.get_cell(1, 1)  # A1
print(cell.address, "=", cell.value)

# Export rows as dictionaries (first row is used as headers)
for row in worksheet.get_data_as_dict():
    print(row)

Usage

Loading data

# Load the whole spreadsheet (XLSX — reads every worksheet)
spreadsheet = client.load_spreadsheet(url)

# Load a single worksheet by name
worksheet = client.load_worksheet(url, "Sheet1")

# Load the first worksheet
worksheet = client.load_worksheet(url)

By default values keep their native types (numbers stay numbers). Pass preserve_strings=True to the client if you want every cell returned as a string:

client = GSParseClient(preserve_strings=True)

Worksheets

spreadsheet.worksheet_count          # number of sheets
spreadsheet.worksheet_names          # ["Sheet1", "Data", ...]

spreadsheet.get_worksheet("Data")    # by name (or None)
spreadsheet.get_worksheet_by_index(0)
spreadsheet.get_first_worksheet()
spreadsheet.get_last_worksheet()

# Dict-like and iterable access
sheet = spreadsheet["Data"]
if "Data" in spreadsheet:
    ...
for sheet in spreadsheet:
    print(sheet.name)

Cells and ranges

worksheet = spreadsheet.get_first_worksheet()

# Single cell (row, column), both starting at 1
cell = worksheet.get_cell(2, 3)      # C2
print(cell.address, cell.value, cell.is_empty)

# A range: get_range(start_row, end_row, start_column, end_column)
rng = worksheet.get_range(1, 10, 1, 3)          # A1:C10
cells = worksheet.get_cells_in_range(rng)

# ...or build a range from an A1 address
rng = worksheet.get_range_by_address("A1:C10")

# Whole rows / columns
first_row = worksheet.get_row(1)
first_col = worksheet.get_column(1)

# Raw values as lists of lists
rows = worksheet.get_rows()
columns = worksheet.get_columns()

Exporting to dictionaries

# A single worksheet -> list of records
records = worksheet.get_data_as_dict(headers_row=1)

# Every worksheet at once -> {sheet_name: [records, ...]}
data = spreadsheet.export_to_dict(headers_row=1)

# Straight from a URL
data = client.export_to_dict(url, headers_row=1)

Searching

# Exact value across all worksheets
cells = client.find_data(url, "Moscow")

# Regular expression
numbers = client.find_by_pattern(url, r"^\d+$")

# Or on already-loaded objects
worksheet.find_cells_by_value("Moscow")
worksheet.find_cells_by_pattern(r"@\w+\.\w+")     # emails
spreadsheet.find_cells_by_value(42)

Cleaning data

# Return a new, cleaned worksheet (originals untouched)
clean = worksheet.remove_empty_rows()
clean = worksheet.remove_empty_columns()
clean = worksheet.clean_data()                     # both

# ...or mutate in place
worksheet.clean_data_inplace()

Parsing from a CSV string

Useful for tests or data you already have in memory. The delimiter is auto-detected:

csv_data = """Name,Age,City
John,25,Moscow
Mary,30,St. Petersburg"""

worksheet = client.load_from_csv_string(csv_data, "My Data")
print(worksheet.get_data_as_dict())

API Reference

GSParseClient(timeout=30, max_retries=3, preserve_strings=False)

Method	Description
load_spreadsheet(url, format_type="xlsx")	Load the whole spreadsheet
load_worksheet(url, worksheet_name=None, format_type="xlsx")	Load one worksheet (first if name is None)
load_from_csv_string(csv_string, worksheet_name="Sheet1")	Parse a worksheet from a CSV string
export_to_dict(url, headers_row=1, format_type="xlsx")	Export all worksheets to records
find_data(url, value, format_type="xlsx")	Find cells by exact value
find_by_pattern(url, pattern, format_type="xlsx")	Find cells by regular expression
get_sheet_info(url)	Basic info about the spreadsheet
list_worksheets(url)	Map of {worksheet_name: gid}

Spreadsheet

Properties: title, worksheets, url, worksheet_count, worksheet_names

Methods: get_worksheet(name), get_worksheet_by_index(index), get_first_worksheet(), get_last_worksheet(), add_worksheet(ws), remove_worksheet(name), get_all_cells(), get_data_summary(), find_cells_by_value(value), find_cells_by_pattern(pattern), export_to_dict(headers_row=1)

Also supports iteration (for ws in spreadsheet), membership (name in spreadsheet) and indexing (spreadsheet[name]).

Worksheet

Properties: name, data, row_count, column_count

Methods: get_cell(row, column), get_range(start_row, end_row, start_column, end_column), get_range_by_address(address), get_all_cells(), get_cells_in_range(range_obj), get_row(n), get_column(n), get_rows(), get_columns(), get_data_as_dict(headers_row=1), find_cells_by_value(value), find_cells_by_pattern(pattern), remove_empty_rows(), remove_empty_columns(), clean_data() (plus *_inplace() variants)

Cell

Properties: row, column, value, formatted_value, formula, address (e.g. "B2"), is_empty

Range

Properties: start_row, end_row, start_column, end_column, worksheet_name, address, row_count, column_count, cell_count

Methods: contains_cell(row, column), get_cells(data), Range.from_address("Sheet1!A1:B2")

Notes & Limitations

• The spreadsheet must be publicly accessible ("Anyone with the link"). GSParse reads the public export endpoint and does not authenticate.
• All coordinates are 1-based — get_cell(1, 1) is A1.
• Prefer XLSX (the default): it reads every worksheet. The csv format is deprecated — Google's CSV export only returns a single sheet, so it emits a DeprecationWarning.
• If an XLSX download fails, the client automatically falls back to CSV.

Development

# Install the project with its dev dependency group
uv sync                     # installs the "dev" group by default

# Run the test suite
uv run pytest               # or just: pytest

# Lint
uv run ruff check src/      # or just: ruff check src/

Using plain pip? Install the package in editable mode and add the tools manually: pip install -e . pytest ruff (the dev tools live in a PEP 735 [dependency-groups] block, which uv sync picks up automatically).

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request on GitHub.

License

Released under the MIT License.