pandas-dev/pandas · error · ValueError

Inferred frequency from passed values does not conform to…

Error message

Inferred frequency {inferred} from passed values does not conform to passed frequency {freq.freqstr}

What it means

Raised by _validate_frequency when constructing a DatetimeIndex/TimedeltaIndex with an explicit freq= argument whose step does not match what pandas infers from the supplied values (GH#11587). The class generates a range at the declared freq from index[0] and compares asi8 byte arrays; on mismatch (or when index[0] is NaT) it re-raises this targeted message.

Solutions

1. Omit freq= and let pandas infer it, then optionally call .asfreq(desired) to enforce.
2. Clean the source values so they actually match the desired grid before passing freq=.
3. If the data is meant to be irregular, drop the freq argument entirely.
4. Set freq=None on an existing index via idx = idx._with_freq(None) before reuse if you only need the values.

Example fix

// before
idx = pd.DatetimeIndex(['2020-01-01','2020-01-03'], freq='D')  # ValueError

// after
idx = pd.DatetimeIndex(['2020-01-01','2020-01-03'])  # inferred freq=None
# or, to enforce daily:
idx = pd.DatetimeIndex(['2020-01-01','2020-01-02']).asfreq('D')

def make_index(values, freq=None):
    if freq is None:
        return pd.DatetimeIndex(values)
    inferred = pd.DatetimeIndex(values).inferred_freq
    if inferred is not None and inferred != pd.tseries.frequencies.to_offset(freq).freqstr:
        return pd.DatetimeIndex(values)  # let pandas infer; do not force freq
    return pd.DatetimeIndex(values, freq=freq)

def freq_matches(values, freq) -> bool:
    inferred = pd.DatetimeIndex(values).inferred_freq
    return inferred is not None and inferred == pd.tseries.frequencies.to_offset(freq).freqstr

try:
    idx = pd.DatetimeIndex(values, freq=freq)
except ValueError as e:
    if "does not conform to passed frequency" in str(e):
        idx = pd.DatetimeIndex(values)  # fall back to inferred/no freq
    else:
        raise

When it happens

Trigger: pd.DatetimeIndex(values, freq='D') where values are not actually daily-spaced; pd.date_range validation paths that pass both data and a freq; round-tripping data that was filtered/missing rows but retained a stale freq attribute.

Common situations: Hand-editing or subsetting a DatetimeIndex without dropping its freq; concatenating indexes whose spacing differs; loading data with gaps but asserting a business-day or fixed frequency; timezone edge cases for non-Tick offsets.

Related errors

• Must provide freq argument if no data is supplied
• cannot assign without a target object
• Cannot create a from a MultiIndex.
• Cannot directly set timezone. Use tz_localize() or…
• does not have a resolution.

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/99d17d98c0f951de. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/datetimelike.py:1880

                start=index[0],
                end=None,
                periods=len(index),
                freq=freq,
                unit=index.unit,
            )
            if not lib.array_equivalent_bytes(index.asi8, on_freq.asi8):
                raise ValueError
        except ValueError as err:
            if "non-fixed" in str(err):
                # non-fixed frequencies are not meaningful for timedelta64;
                #  we retain that error message
                raise err
            # GH#11587 the main way this is reached is if the `np.array_equal`
            #  check above is False.  This can also be reached if index[0]
            #  is `NaT`, in which case the call to `cls._generate_range` will
            #  raise a ValueError, which we re-raise with a more targeted
            #  message.
            raise ValueError(
                f"Inferred frequency {inferred} from passed values "
                f"does not conform to passed frequency {freq.freqstr}"
            ) from err

    @classmethod
    def _generate_range(
        cls, start, end, periods: int | None, freq, *args, **kwargs
    ) -> Self:
        raise AbstractMethodError(cls)

    # --------------------------------------------------------------

    @cache_readonly
    def _creso(self) -> int:
        return get_unit_from_dtype(self._ndarray.dtype)

    @cache_readonly
    def unit(self) -> TimeUnit:

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