Python library to interact with Smartsheet objects
Project description
Simple Smartsheet
Python library to interact with Smartsheet API easily
Installation
Requires Python 3.6+
pip install simple-smartsheet
Why not smartsheet-python-sdk
smartsheet-python-sdk
has very wide object coverage and maps to Smartsheet API very nicely, but it does not have any additional features (for example, easy access to cells by column titles).
simple-smartsheet
library is focused on user experience first in expense of feature coverage.
As of now, you can only interact with Sheets and nested objects (rows, columns, cells).
Usage
from datetime import date
from pprint import pprint
from simple_smartsheet import Smartsheet
from simple_smartsheet.models import Sheet, Column, Row, Cell
TOKEN = "my-secret-token"
smartsheet = Smartsheet(TOKEN)
# creating new Sheet
new_sheet = Sheet(
name="My New Sheet",
columns=[
Column(primary=True, title="Full Name", type="TEXT_NUMBER"),
Column(title="Number of read books", type="TEXT_NUMBER"),
Column(title="Birth date", type="DATE"),
],
)
# print the sheet object as a dictionary which will be used in REST API
pprint(new_sheet.dump())
# adding the sheet via API
smartsheet.sheets.create(new_sheet)
# getting a simplified view of sheets
sheets = smartsheet.sheets.list()
pprint(sheets)
# getting the sheet by name
sheet = smartsheet.sheets.get("My New Sheet")
# printing the sheet object attributes
pprint(sheet.__dict__)
# or printing the sheet object as a dictionary which will be used in REST API
pprint(sheet.dump())
# getting columns details by column title (case-sensitive)
full_name_column = sheet.get_column("Full Name")
pprint(full_name_column.__dict__)
num_books_column = sheet.get_column("Number of read books")
pprint(num_books_column.__dict__)
# adding rows (cells created using different ways):
sheet.add_rows(
[
Row(
to_top=True,
cells=[
Cell(column_id=full_name_column.id, value="Alice Smith"),
Cell(column_id=num_books_column.id, value=5),
],
),
Row(
to_top=True,
cells=sheet.make_cells(
{"Full Name": "Bob Lee", "Number of read books": 2}
)
),
Row(
to_top=True,
cells=[
sheet.make_cell("Full Name", "Charlie Brown"),
sheet.make_cell("Number of read books", 1),
sheet.make_cell("Birth date", date(1990, 1, 1)),
],
),
]
)
# sort rows now by column "Full Name" descending / returns updated sheet
sheet = sheet.sort_rows([{"column_title": "Full Name", "descending": True}])
# or getting an updated sheet again
# sheet = smartsheet.sheets.get("My New Sheet")
print("\nSheet after adding rows:")
# all sheet attributes
pprint(sheet.__dict__)
# or just a list of dictionaries containing column titles and values
pprint(sheet.as_list())
# getting a specific cell and updating it:
row_id_to_delete = None
rows_to_update = []
for row in sheet.rows:
full_name = row.get_cell("Full Name").value
num_books = row.get_cell("Number of read books").value
print(f"{full_name} has read {num_books} books")
if full_name.startswith("Charlie"):
num_books_cell = row.get_cell("Number of read books")
num_books_cell.value += 1
rows_to_update.append(row)
elif full_name.startswith("Bob"):
row_id_to_delete = row.id # used later
# update rows
sheet.update_rows(rows_to_update)
# or a single row
# sheet.update_rows(rows_to_update[0])
# getting an updated sheet
sheet = smartsheet.sheets.get("My New Sheet")
print("\nSheet after updating rows:")
pprint(sheet.as_list())
# deleting row by id
sheet.delete_row(row_id_to_delete)
# getting an updated sheet
sheet = smartsheet.sheets.get("My New Sheet")
print("\nSheet after deleting rows:")
pprint(sheet.as_list())
# deleting Sheet
# sheet = smartsheet.sheets.delete('My New Sheet')
sheets = smartsheet.sheets.list()
pprint(sheets)
Docs
While a separate docs page is work in progress, available public API is described here
Class simple_smartsheet.Smartsheet
This class a main entry point for the library
Methods:
def __init__(token: str)
: constructor for the class
Attributes:
token
: Smartsheet API token, obtained in Personal Settings -> API accesssession
: requests.Session object which stores headers based on the tokensheets
:simple_smartsheet.models.sheet.SheetsCRUD
object which provides methods to interact with Sheets
Class simple_smartsheet.models.sheet.SheetsCRUD
Methods:
def get(name: Optional[str], id: Optional[int], index_keys: Optional[Dict[str, Any]])
: fetches Sheet by name or ID. It can also build an index for several fields to do quick rows lookup (see section "Custom Indexes")def list()
: fetches a list of all sheets (summary only)def create(obj: Sheet)
: adds a new sheetdef update(obj: Sheet)
: updates a sheetdef delete(name: Optional[str], id: Optional[int])
: deletes a sheet by name or ID
Class simple_smartsheet.models.Sheet
Attributes (converted from camelCase to snake_case):
Methods:
def update_index()
: rebuilds mapping tables for rows and columns for quick lookupdef get_row(row_num: Optional[int], row_id: Optional[int], filter: Optional[Dict[str, Any]])
: returns a Row object by row number, ID or by filter, if a unique index was built (see section "Custom Indexes")def get_rows(index_query: Dict[str, Any])
: returns list of Row objects by filter, if an index was built (see section "Custom Indexes")def get_column(column_title: Optional[str], column_id: Optional[int])
: returns a Column object by column title or iddef add_rows(rows: Sequence[Row])
: adds rows to the sheetdef add_row(row: Row)
: add a single row to the sheetdef update_rows(rows: Sequence[Row])
: updates several rows in the sheetdef update_row(row: Row)
: updates a single rowdef delete_rows(row_ids: Sequence[int])
: delete several rows with provided idsdef delete_row(row_id: int)
: delete a single row with a provided iddef sort_rows(order: List[Dict[str, Any]])
: sorts sheet rows with the specified order. An argument example:
[
{"column_title": "Birth date", "descending": True},
{"column_title": "Full Name"}
]
def make_cell(column_title: str, field_value: Union[float, str, datetime, None])
: creates a Cell object with provided column title and an associated valuedef make_cells(fields: Dict[str, Union[float, str, datetime, None]])
: creates a list of Cell objects from an input dictionary where column title is key associated with the field valuedef as_list()
: returns a list of dictionaries where column title is key associated with the field value
Class simple_smartsheet.models.Row
Attributes (converted from camelCase to snake_case):
- http://smartsheet-platform.github.io/api-docs/#rows
rowNumber
is mapped tonum
Methods:
def get_cell(column_title: Optional[str], column_id: Optional[int])
- returns a Cell object by column title (case-sensitive) or column iddef as_dict()
- returns a dictionary of column title to cell value mappings
Class simple_smartsheet.models.Column
Attributes (converted from camelCase to snake_case):
Class simple_smartsheet.models.Cell
Attributes (converted from camelCase to snake_case):
Custom Indexes
When you are retrieving a smartsheet, it is possible to build an index to enable quick rows lookups.
This is controlled using index_key
argument in get
method. This argument is a dictionary with two keys columns
and unique
. columns
should contain a tuple with column titles (case sensitive). unique
controls if the index always points to a single row (value True
, lookups are done using get_row
method) or multiple rows (value False
, lookups are done using get_rows
method).
Below you can find a code example:
from simple_smartsheet import Smartsheet
from pprint import pprint
TOKEN = 'my-token'
smartsheet = Smartsheet(TOKEN)
INDEX_KEYS = [
{"columns": ("Company Name",), "unique": False},
{"columns": ("Company Name", "Full Name"), "unique": True},
{"columns": ("Email Address",), "unique": True},
]
sheet = smartsheet.sheets.get("Index Test Sheet", index_keys=INDEX_KEYS)
pprint(sheet.indexes)
# >
# defaultdict(<class 'dict'>,
# {('Company Name',): {('ACME',): [Row(id=525791232583556, num=1),
# Row(id=5029390859954052, num=2)],
# ('Globex',): [Row(id=2777591046268804, num=3)]},
# ('Company Name', 'Full Name'): {('ACME', 'Alice Smith'): Row(id=525791232583556, num=1),
# ('ACME', 'Bob Lee'): Row(id=5029390859954052, num=2),
# ('Globex', 'Charlie Brown'): Row(id=2777591046268804, num=3)},
# ('Email Address',): {('alice.smith@acme.com',): Row(id=525791232583556, num=1),
# ('bob.lee@acme.com',): Row(id=5029390859954052, num=2),
# ('charlie.brown@globex.com',): Row(id=2777591046268804, num=3)}})
pprint([row.as_dict() for row in sheet.rows])
# >
# [{'Company Name': 'ACME',
# 'Email Address': 'alice.smith@acme.com',
# 'Full Name': 'Alice Smith'},
# {'Company Name': 'ACME',
# 'Email Address': 'bob.lee@acme.com',
# 'Full Name': 'Bob Lee'},
# {'Company Name': 'Globex',
# 'Email Address': 'charlie.brown@globex.com',
# 'Full Name': 'Charlie Brown'}]
pprint(sheet.get_row(filter={"Email Address": "charlie.brown@globex.com"}).as_dict())
# >
# {'Company Name': 'Globex',
# 'Email Address': 'charlie.brown@globex.com',
# 'Full Name': 'Charlie Brown'}
pprint(
sheet.get_row(filter={"Full Name": "Alice Smith", "Company Name": "ACME"}).as_dict()
)
# >
# {'Company Name': 'ACME',
# 'Email Address': 'alice.smith@acme.com',
# 'Full Name': 'Alice Smith'}
pprint([row.as_dict() for row in sheet.get_rows(filter={"Company Name": "ACME"})])
# >
# [{'Company Name': 'ACME',
# 'Email Address': 'alice.smith@acme.com',
# 'Full Name': 'Alice Smith'},
# {'Company Name': 'ACME',
# 'Email Address': 'bob.lee@acme.com',
# 'Full Name': 'Bob Lee'}]
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