Skip to main content
Version: 0.3.25

common.libs.pyarrow

remove_null_columns

def remove_null_columns(item: TAnyArrowItem) -> TAnyArrowItem

[view_source]

Remove all columns of datatype pyarrow.null() from the table or record batch

remove_columns

def remove_columns(item: TAnyArrowItem,
columns: Sequence[str]) -> TAnyArrowItem

[view_source]

Remove columns from Arrow item

append_column

def append_column(item: TAnyArrowItem, name: str, data: Any) -> TAnyArrowItem

[view_source]

Appends new column to Table or RecordBatch

rename_columns

def rename_columns(item: TAnyArrowItem,
new_column_names: Sequence[str]) -> TAnyArrowItem

[view_source]

Rename arrow columns on Table or RecordBatch, returns same data but with renamed schema

normalize_py_arrow_schema

def normalize_py_arrow_schema(
item: TAnyArrowItem, columns: TTableSchemaColumns,
naming: NamingConvention,
caps: DestinationCapabilitiesContext) -> TAnyArrowItem

[view_source]

Normalize arrow item schema according to the columns.

  1. arrow schema field names will be normalized according to naming
  2. arrows columns will be reordered according to columns
  3. empty columns will be inserted if they are missing, types will be generated using caps

get_normalized_arrow_fields_mapping

def get_normalized_arrow_fields_mapping(item: TAnyArrowItem,
naming: NamingConvention) -> StrStr

[view_source]

Normalizes schema field names and returns mapping from original to normalized name. Raises on name clashes

py_arrow_to_table_schema_columns

def py_arrow_to_table_schema_columns(
schema: pyarrow.Schema) -> TTableSchemaColumns

[view_source]

Convert a PyArrow schema to a table schema columns dict.

Arguments:

  • schema pyarrow.Schema - pyarrow schema

Returns:

  • TTableSchemaColumns - table schema columns

get_row_count

def get_row_count(parquet_file: TFileOrPath) -> int

[view_source]

Get the number of rows in a parquet file.

Arguments:

  • parquet_file str - path to parquet file

Returns:

  • int - number of rows

to_arrow_scalar

def to_arrow_scalar(value: Any, arrow_type: pyarrow.DataType) -> Any

[view_source]

Converts python value to an arrow compute friendly version

from_arrow_scalar

def from_arrow_scalar(arrow_value: pyarrow.Scalar) -> Any

[view_source]

Converts arrow scalar into Python type. Currently adds "UTC" to naive date times and converts all others to UTC

TNewColumns

Sequence of tuples: (field index, field, generating function)

pq_stream_with_new_columns

def pq_stream_with_new_columns(
parquet_file: TFileOrPath,
columns: TNewColumns,
row_groups_per_read: int = 1) -> Iterator[pyarrow.Table]

[view_source]

Add column(s) to the table in batches.

The table is read from parquet row_groups_per_read row groups at a time

Arguments:

  • parquet_file - path or file object to parquet file
  • columns - list of columns to add in the form of (insertion index, pyarrow.Field, column_value_callback) The callback should accept a pyarrow.Table and return an array of values for the column.
  • row_groups_per_read - number of row groups to read at a time. Defaults to 1.

Yields:

pyarrow.Table objects with the new columns added.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.