Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
oxen.data_frame

​
DataFrame Objects

class DataFrame()
The DataFrame class allows you to perform CRUD operations on a remote data frame. If you pass in a Workspace or a RemoteRepo the data is indexed into DuckDB on an oxen-server without downloading the data locally.

​
Examples

​
CRUD Operations

Index a data frame in a workspace. 
from oxen import DataFrame

# Connect to and index the data frame
# Note: This must be an existing file committed to the repo
#       indexing may take a while for large files
data_frame = DataFrame("datasets/SpamOrHam", "data.tsv")

# Add a row
row_id = data_frame.insert_row({"category": "spam", "message": "Hello, do I have an offer for you!"})

# Get a row by id
row = data_frame.get_row_by_id(row_id)
print(row)

# Update a row
row = data_frame.update_row(row_id, {"category": "ham"})
print(row)

# Delete a row
data_frame.delete_row(row_id)

# Get the current changes to the data frame
status = data_frame.diff()
print(status.added_files())

# Commit the changes
data_frame.commit("Updating data.csv")

​
__init__

def __init__(remote: Union[str, RemoteRepo, Workspace],
             path: str,
             host: str = "hub.oxen.ai",
             branch: Optional[str] = None,
             scheme: str = "https",
             workspace_name: Optional[str] = None)
Initialize the DataFrame class. Will index the data frame into duckdb on init. Will throw an error if the data frame does not exist. Arguments:
  • remote - str, RemoteRepo, or Workspace The workspace or remote repo the data frame is in.
  • path - str The path of the data frame file in the repository.
  • host - str The host of the oxen-server. Defaults to “hub.oxen.ai”.
  • branch - Optional[str] The branch of the remote repo. Defaults to None.
  • scheme - str The scheme of the remote repo. Defaults to “https”.

​
workspace_url

def workspace_url(host: str = "oxen.ai", scheme: str = "https") -> str
Get the url of the data frame.

​
size

def size() -> tuple[int, int]
Get the size of the data frame. Returns a tuple of (rows, columns)

​
page_size

def page_size() -> int
Get the page size of the data frame for pagination in list() command. Returns: The page size of the data frame.

​
total_pages

def total_pages() -> int
Get the total number of pages in the data frame for pagination in list() command. Returns: The total number of pages in the data frame.

​
list_page

def list_page(page_num: int = 1) -> List[dict]
List the rows within the data frame. Arguments:
  • page_num - int The page number of the data frame to list. We default to page size of 100 for now.
Returns: A list of rows from the data frame.

​
insert_row

def insert_row(data: dict)
Insert a single row of data into the data frame. Arguments:
  • data - dict A dictionary representing a single row of data. The keys must match a subset of the columns in the data frame. If a column is not present in the dictionary, it will be set to an empty value.
Returns: The id of the row that was inserted.

​
where_sql_from_dict

def where_sql_from_dict(attributes: dict, operator: str = "AND") -> str
Generate the SQL from the attributes.

​
select_sql_from_dict

def select_sql_from_dict(attributes: dict,
                         columns: Optional[List[str]] = None) -> str
Generate the SQL from the attributes.

​
get_embeddings

def get_embeddings(attributes: dict, column: str = "embedding") -> List[float]
Get the embedding from the data frame.

​
is_nearest_neighbors_enabled

def is_nearest_neighbors_enabled(column="embedding")
Check if the embeddings column is indexed in the data frame.

​
enable_nearest_neighbors

def enable_nearest_neighbors(column: str = "embedding")
Index the embeddings in the data frame.

​
query

def query(sql: Optional[str] = None,
          find_embedding_where: Optional[dict] = None,
          embedding: Optional[list[float]] = None,
          sort_by_similarity_to: Optional[str] = None,
          page_num: int = 1,
          page_size: int = 10)
Sort the data frame by the embedding. 
def nearest_neighbors_search(find_embedding_where: dict,
                             sort_by_similarity_to: str = "embedding")
Get the nearest neighbors to the embedding.

​
get_by

def get_by(attributes: dict)
Get a single row of data by attributes.

​
get_row

def get_row(idx: int)
Get a single row of data by index. Arguments:
  • idx - int The index of the row to get.
Returns: A dictionary representing the row.

​
get_row_by_id

def get_row_by_id(id: str)
Get a single row of data by id. Arguments:
  • id - str The id of the row to get.
Returns: A dictionary representing the row.

​
update_row

def update_row(id: str, data: dict)
Update a single row of data by id. Arguments:
  • id - str The id of the row to update.
  • data - dict A dictionary representing a single row of data. The keys must match a subset of the columns in the data frame. If a column is not present in the dictionary, it will be set to an empty value.
Returns: The updated row as a dictionary.

​
delete_row

def delete_row(id: str)
Delete a single row of data by id. Arguments:
  • id - str The id of the row to delete.

​
restore

def restore()
Unstage any changes to the schema or contents of a data frame

​
commit

def commit(message: str, branch: Optional[str] = None)
Commit the current changes to the data frame. Arguments:
  • message - str The message to commit the changes.
  • branch - str The branch to commit the changes to. Defaults to the current branch.