smartspread 1.1.3

A Python library for Google Sheets with high-level API, automatic type inference, and efficient caching.

SmartSpread

A Python library for Google Sheets that extends gspread with a high-level API, automatic type inference, and efficient caching.

Features

• Simple API: Intuitive interface for spreadsheet and tab operations
• Multiple Data Formats: Work with DataFrames, list of dicts, or list of lists
• Automatic Type Inference: Smart conversion of numeric, string, and None values
• Efficient Caching: Minimizes API calls to stay within rate limits
• Pandas Integration: Seamless DataFrame read/write operations
• Row Operations: Update or insert rows based on column patterns

Installation

pip install smartspread

Quick Start

Authentication

1. Create a Google Cloud Project
2. Enable the Google Sheets API
3. Create a service account and download credentials JSON
4. Share your spreadsheet with the service account email

Basic Usage

from smartspread import SmartSpread

# Initialize with credentials
spread = SmartSpread(
    sheet_identifier="your-spreadsheet-id-or-name",
    key_file="path/to/credentials.json"
)

# Get or create a tab
tab = spread.tab("MyTab")

# Read data as DataFrame
df = tab.read_data()

# Modify data
tab.data["new_column"] = "value"

# Write back to Google Sheets
tab.write_data(overwrite_tab=True)

Update Rows by Pattern

# Update existing row or insert new one
tab.update_row_by_column_pattern(
    column="ID",
    value=123,
    updates={"Status": "completed", "Updated": "2024-01-01"}
)
tab.write_data(overwrite_tab=True)

Filter Data

# Filter rows by pattern
filtered = tab.filter_rows_by_column("Name", "Alice")
print(filtered)

Work with Different Formats

# DataFrame format (default)
tab_df = spread.tab("Sheet1", data_format="DataFrame")
df = tab_df.data  # pandas DataFrame

# List of dicts format
tab_dict = spread.tab("Sheet2", data_format="dict")
data = tab_dict.data  # [{"col1": "val1", ...}, ...]

# List of lists format
tab_list = spread.tab("Sheet3", data_format="list")
data = tab_list.data  # [["header1", "header2"], ["val1", "val2"], ...]

Refresh Data

# Reload data after external changes
tab.refresh()

# Refresh spreadsheet metadata
spread.refresh()

API Reference

SmartSpread

• SmartSpread(sheet_identifier, key_file=None, service_account_data=None, user_email=None)
• spread.tab(tab_name, data_format="DataFrame", keep_number_formatting=False) - Get or create tab
• spread.tab_names - List all tab names
• spread.tab_exists(tab_name) - Check if tab exists
• spread.url - Get spreadsheet URL
• spread.grant_access(email, role="owner") - Grant access to user
• spread.refresh() - Clear cache and reload metadata

SmartTab

• tab.read_data() - Read data from Google Sheets
• tab.write_data(overwrite_tab=False, as_table=False) - Write data to Google Sheets
• tab.update_row_by_column_pattern(column, value, updates) - Update or insert row
• tab.filter_rows_by_column(column, pattern) - Filter rows by pattern
• tab.refresh() - Reload data from Google Sheets
• tab.data - Access the data (DataFrame, list of dicts, or list of lists)

Notes

• Google Sheets API has rate limits (60 requests/minute for free tier)
• SmartSpread uses caching to minimize API calls
• Empty cells are represented as None in DataFrames
• Integer columns use nullable Int64 dtype to preserve None values

Changelog

v1.1.3 (2024)

• Fixed: pd.NA values now properly sanitized to None in list and dict output formats

v1.1.2 (2024)

• Changed: Package renamed to smartspread (no underscore) for cleaner imports
• Added: Backwards compatibility for from smart_spread import ... with deprecation warning

v1.1.1 (2024)

• Fixed: JSON serialization error when using data_format="list" with nullable Int64 columns containing pd.NA values

License

MIT License - see LICENSE file for details.

Links

• GitHub Repository
• PyPI Package