flytekit.types.schema.types_pandas

Directory

Classes

flytekit.types.schema.types_pandas.PandasDataFrameTransformer

Transforms a pd.DataFrame to Schema without column types.

def PandasDataFrameTransformer()

Properties

Methods

assert_type()

def assert_type(
    t: Type[T],
    v: T,
)

async_to_literal()

def async_to_literal(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: pandas.DataFrame,
    python_type: typing.Type[pandas.DataFrame],
    expected: flytekit.models.types.LiteralType,
) -> flytekit.models.literals.Literal

Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type. Implementers should refrain from using type(python_val) instead rely on the passed in python_type. If these do not match (or are not allowed) the Transformer implementer should raise an AssertionError, clearly stating what was the mismatch

async_to_python_value()

def async_to_python_value(
    ctx: flytekit.core.context_manager.FlyteContext,
    lv: flytekit.models.literals.Literal,
    expected_python_type: typing.Type[pandas.DataFrame],
) -> pandas.DataFrame

Converts the given Literal to a Python Type. If the conversion cannot be done an AssertionError should be raised

from_binary_idl()

def from_binary_idl(
    binary_idl_object: Binary,
    expected_python_type: Type[T],
) -> Optional[T]

This function primarily handles deserialization for untyped dicts, dataclasses, Pydantic BaseModels, and attribute access.｀

For untyped dict, dataclass, and pydantic basemodel: Life Cycle (Untyped Dict as example): python val -> msgpack bytes -> binary literal scalar -> msgpack bytes -> python val (to_literal) (from_binary_idl)

For attribute access: Life Cycle: python val -> msgpack bytes -> binary literal scalar -> resolved golang value -> binary literal scalar -> msgpack bytes -> python val (to_literal) (propeller attribute access) (from_binary_idl)

from_generic_idl()

def from_generic_idl(
    generic: Struct,
    expected_python_type: Type[T],
) -> Optional[T]

TODO: Support all Flyte Types. This is for dataclass attribute access from input created from the Flyte Console.

Note:

• This can be removed in the future when the Flyte Console support generate Binary IDL Scalar as input.

get_literal_type()

def get_literal_type(
    t: typing.Type[pandas.DataFrame],
) -> flytekit.models.types.LiteralType

Converts the python type to a Flyte LiteralType

guess_python_type()

def guess_python_type(
    literal_type: LiteralType,
) -> Type[T]

Converts the Flyte LiteralType to a python object type.

isinstance_generic()

def isinstance_generic(
    obj,
    generic_alias,
)

schema_match()

def schema_match(
    schema: dict,
) -> bool

Check if a JSON schema fragment matches this transformer’s python_type.

For BaseModel subclasses, automatically compares the schema’s title, type, and required fields against the type’s own JSON schema. For other types, returns False by default — override if needed.

to_html()

def to_html(
    ctx: flytekit.core.context_manager.FlyteContext,
    python_val: pandas.DataFrame,
    expected_python_type: typing.Type[~T],
)

Converts any python val (dataframe, int, float) to a html string, and it will be wrapped in the HTML div

to_literal()

def to_literal(
    ctx: FlyteContext,
    python_val: typing.Any,
    python_type: Type[T],
    expected: LiteralType,
) -> Literal

Converts a given python_val to a Flyte Literal, assuming the given python_val matches the declared python_type. Implementers should refrain from using type(python_val) instead rely on the passed in python_type. If these do not match (or are not allowed) the Transformer implementer should raise an AssertionError, clearly stating what was the mismatch

to_python_value()

def to_python_value(
    ctx: FlyteContext,
    lv: Literal,
    expected_python_type: Type[T],
) -> Optional[T]

Converts the given Literal to a Python Type. If the conversion cannot be done an AssertionError should be raised

flytekit.types.schema.types_pandas.PandasSchemaReader

class PandasSchemaReader(
    local_dir: str,
    cols: typing.Optional[typing.Dict[str, type]],
    fmt: <enum 'SchemaFormat'>,
)

Properties

Methods

all()

def all(
    kwargs,
) -> T

iter()

def iter(
    kwargs,
) -> typing.Generator[T, None, None]

flytekit.types.schema.types_pandas.PandasSchemaWriter

class PandasSchemaWriter(
    local_dir: str,
    cols: typing.Optional[typing.Dict[str, type]],
    fmt: <enum 'SchemaFormat'>,
)

Properties

Methods

write()

def write(
    dfs,
    kwargs,
)

flytekit.types.schema.types_pandas.ParquetIO

Methods

read()

def read(
    files: os.PathLike,
    columns: typing.Optional[typing.List[str]],
    kwargs,
) -> pandas.DataFrame

write()

def write(
    df: pandas.DataFrame,
    to_file: os.PathLike,
    coerce_timestamps: str,
    allow_truncated_timestamps: bool,
    kwargs,
)

Writes data frame as a chunk to the local directory owned by the Schema object. Will later be uploaded to s3.