atoti.Session.read_numpy()

Session.read_numpy(array, /, *, columns, table_name, keys=frozenset({}), partitioning=None, types={}, default_values={})

Read a NumPy 2D array into a new table.

Parameters:

• array (ndarray[Any, dtype[Any]]) – The NumPy 2D ndarray to read the data from.

• columns (Sequence[str]) – The names to use for the table’s columns. They must be in the same order as the values in the NumPy array.

• table_name (str) – The name of the table to create.

• keys (Set[str] | Sequence[str]) –

  The columns that will become keys of the table.

  If a Set is given, the keys will be ordered as the table columns.

• partitioning (str | None) –

  The description of how the data will be split across partitions of the table.

  Default rules:

  • Only non-joined tables are automatically partitioned.

  • Tables are automatically partitioned by hashing their key columns. If there are no key columns, all the dictionarized columns are hashed.

  • Joined tables can only use a sub-partitioning of the table referencing them.

  • Automatic partitioning is done modulo the number of available cores.

  Example

  modulo4(country) splits the data across 4 partitions based on the country column’s dictionarized value.

• types (Mapping[str, DataType]) – Types for some or all columns of the table. Types for non specified columns will be inferred from numpy data types.

• default_values (Mapping[str, bool | date | datetime | float | int | Sequence[int] | Sequence[float] | str | time | None]) – Mapping from column name to column default_value.

Return type:

Table