pathwaycom/pathway · error · ValueError

DateTimeNaive cannot contain timezone information. Use pw.Da

Error message

DateTimeNaive cannot contain timezone information. Use pw.DateTimeUtc for datetimes with a timezone.

What it means

DateTimeNaive is Pathway's pandas.Timestamp subclass that forbids timezone awareness. Its __new__ checks obj.tz after construction and raises if the parsed value carries tzinfo, directing users to DateTimeUtc for timezone-carrying timestamps. This enforces the naive/UTC type distinction used in Pathway's temporal APIs.

Source

Thrown at python/pathway/internals/datetime_types.py:12

# Copyright © 2026 Pathway

import pandas as pd


class DateTimeNaive(pd.Timestamp):
    """Type for storing datetime without timezone information. Extends `pandas.Timestamp` type."""

    def __new__(cls, *args, **kwargs):
        obj = super().__new__(cls, *args, **kwargs)
        if obj.tz is not None:
            raise ValueError(
                "DateTimeNaive cannot contain timezone information. Use pw.DateTimeUtc for datetimes with a timezone."
            )
        return obj


class DateTimeUtc(pd.Timestamp):
    """Type for storing datetime with default timezone. Extends `pandas.Timestamp` type."""

    def __new__(cls, *args, **kwargs):
        obj = super().__new__(cls, *args, **kwargs)
        if obj.tz is None:
            raise ValueError(
                "DateTimeUtc must contain timezone information. Use pw.DateTimeNaive for naive datetimes."
            )
        return obj


class Duration(pd.Timedelta):

View on GitHub (pinned to fa2f74a464)

Solutions

1. If times are UTC or zone-aware, change the schema type to pw.DateTimeUtc.
2. Strip the timezone before ingestion: pd.to_datetime(series).dt.tz_localize(None) for the naive column.
3. Convert to UTC aware for DateTimeUtc: pd.to_datetime(series, utc=True).

Example fix

# before
class Schema(pw.Schema):
    ts: pw.DateTimeNaive
df["ts"] = pd.to_datetime(df["ts"], utc=True)  # aware -> raises on read

# after
class Schema(pw.Schema):
    ts: pw.DateTimeUtc
df["ts"] = pd.to_datetime(df["ts"], utc=True)

def to_naive(ts: pd.Timestamp) -> pd.Timestamp:
    return ts.tz_localize(None) if ts.tz is not None else ts

assert value.tz is None before constructing pw.DateTimeNaive

import pandas as pd

def is_naive_timestamp(v) -> bool:
    return isinstance(v, pd.Timestamp) and v.tz is None

When it happens

Trigger: pw.DateTimeNaive("2024-01-01T10:00:00+02:00"), pw.DateTimeNaive(pd.Timestamp("2024-01-01", tz="UTC")), or DateTimeNaive.fromtimestamp(ts, tz=...) with a tz; values landing in a DateTimeNaive-typed column from UDFs that return tz-aware timestamps.

Common situations: Schema declares DateTimeNaive but the source data (CSV/kafka ISO strings) includes offsets; pandas parsing with utc=True feeding Pathway; mixing naive and aware timestamps when doing as-of joins.

Related errors

• DateTimeUtc must contain timezone information. Use pw.DateTi
• Unsupported type {input_type}, use pw.DATE_TIME_UTC or pw.DA
• If fmt is not a string, you need to specify whether objects
• Failed to detect the region of S3 bucket {bucket!r} (HTTP st
• SchemaRegistryHeader.value must be a str, got {type(self.val

AI-assisted analysis of pathwaycom/pathway@fa2f74a464 (2026-08-15). Data as JSON: /api/errors/d006c9fbb1946ba2. Report an issue: GitHub.