ak.to_parquet

Defined in awkward.operations.convert on line 2960.

ak.to_parquet(array, where, explode_records=False, list_to32=False, string_to32=True, bytestring_to32=True)
Parameters

  • array – Data to write to a Parquet file.

  • where (str, Path, file-like object) – Where to write the Parquet file.

  • explode_records (bool) – If True, lists of records are written as records of lists, so that nested fields become top-level fields (which can be zipped when read back).

  • list_to32 (bool) – If True, convert Awkward lists into 32-bit Arrow lists if they’re small enough, even if it means an extra conversion. Otherwise, signed 32-bit ak.layout.ListOffsetArray maps to Arrow ListType and all others map to Arrow LargeListType.

  • string_to32 (bool) – Same as the above for Arrow string and large_string.

  • bytestring_to32 (bool) – Same as the above for Arrow binary and large_binary.

  • options – All other options are passed to pyarrow.parquet.ParquetWriter. In particular, if no schema is given, a schema is derived from the array type.

Writes an Awkward Array to a Parquet file (through pyarrow).

>>> array1 = ak.Array([[1, 2, 3], [], [4, 5], [], [], [6, 7, 8, 9]])
>>> ak.to_parquet(array1, "array1.parquet")

If the array does not contain records at top-level, the Arrow table will consist of one field whose name is "".

Parquet files can maintain the distinction between “option-type but no elements are missing” and “not option-type” at all levels, including the top level. However, there is no distinction between ?union[X, Y, Z]] type and union[?X, ?Y, ?Z] type. Be aware of these type distinctions when passing data through Arrow or Parquet.

To make a partitioned Parquet dataset, use this function to write each Parquet file to a directory (as separate invocations, probably in parallel with multiple processes), then give them common metadata by calling ak.to_parquet.dataset.

>>> ak.to_parquet(array1, "directory-name/file1.parquet")
>>> ak.to_parquet(array2, "directory-name/file2.parquet")
>>> ak.to_parquet(array3, "directory-name/file3.parquet")
>>> ak.to_parquet.dataset("directory-name")

Then all of the flies in the collection can be addressed as one array. For example,

>>> dataset = ak.from_parquet("directory_name", lazy=True)

(If it is large, you will likely want to load it lazily.)

See also ak.to_arrow, which is used as an intermediate step. See also ak.from_parquet.