DataFrame and Arrow Compatibility

perspective-python accepts a Table constructor argument from any of the common Python columnar data libraries. In all three cases, perspective.table (and Table.update()) consume the input directly — there is no need to serialize to Apache Arrow IPC bytes yourself. However, note is still the most efficient way to bulk load data into Table.

PyArrow

python

import pyarrow as pa
import perspective

arrow_table = pa.table({
    "int": pa.array([1, 2, 3], type=pa.int64()),
    "float": pa.array([1.5, 2.5, 3.5], type=pa.float64()),
    "string": pa.array(["a", "b", "c"], type=pa.string()),
})

table = perspective.table(arrow_table)

The same applies to Table.update():

python

table.update(arrow_table)

If you have Arrow data already in IPC format (e.g. read from disk, received over the wire, or produced by another tool), pass the raw bytes directly — both stream and file formats are auto-detected:

python

with open("data.arrow", "rb") as f:
    table = perspective.table(f.read())

Polars

python

import polars as pl
import perspective

df = pl.DataFrame({
    "a": [1, 2, 3, 4, 5],
    "b": ["x", "y", "z", "x", "y"],
})

table = perspective.table(df)

Internally, the DataFrame is converted to a pyarrow.Table before ingestion, so Polars columns inherit the Arrow type mapping above.

See also Perspective Virtual Server support for polars.DataFrame

Pandas

pandas.DataFrame is supported via pyarrow.Table.from_pandas, which dictates behavior including type support — see the pyarrow pandas docs for details on which pandas dtypes round-trip cleanly.

python

from datetime import date, datetime
import numpy as np
import pandas as pd
import perspective

data = pd.DataFrame({
    "int": np.arange(100),
    "float": [i * 1.5 for i in range(100)],
    "bool": [True for i in range(100)],
    "date": [date.today() for i in range(100)],
    "datetime": [datetime.now() for i in range(100)],
    "string": [str(i) for i in range(100)],
})

table = perspective.table(data, index="float")