pandas-dev/pandas · error · NotImplementedError

Converting strings to {pa_type} is not implemented.

Error message

Converting strings to {pa_type} is not implemented.

What it means

Raised by _from_sequence_of_strings when the target pa_type is not one of the string-convertible types handled (string/binary/timestamp/date/duration/time/bool/int/float/decimal). This path is used when constructing an ArrowExtensionArray from a sequence of strings with a given ArrowDtype. Any pyarrow type outside the enumerated set (e.g. list_, struct, fixed_size_binary, month_day_nano_interval, large_binary variants not special-cased) raises NotImplementedError.

Source

Thrown at pandas/core/arrays/arrow/array.py:496

            scalars = pc.if_else(pc.equal(scalars, "0.0"), "0", scalars)
            scalars = scalars.cast(pa.bool_())
        elif (
            pa.types.is_integer(pa_type)
            or pa.types.is_floating(pa_type)
            or pa.types.is_decimal(pa_type)
        ):
            from pandas.core.tools.numeric import to_numeric

            scalars = to_numeric(strings, errors="raise")
            if is_pa_array:
                scalars = strings.cast(pa_type)
            else:
                mask = isna(strings)
                if mask is not None:
                    scalars = pa.array(scalars, mask=mask, type=pa_type)

        else:
            raise NotImplementedError(
                f"Converting strings to {pa_type} is not implemented."
            )
        return cls._from_sequence(scalars, dtype=pa_type, copy=copy)

    def _from_pyarrow_array(self, pa_array):
        """
        Construct from a pyarrow Array/ChunkedArray result of an operation.

        Avoids full __init__ overhead by reusing the dtype when the pyarrow
        type is unchanged.
        """
        assert isinstance(pa_array, (pa.Array, pa.ChunkedArray))
        obj = type(self).__new__(type(self))
        if isinstance(pa_array, pa.Array):
            pa_array = pa.chunked_array([pa_array])
        obj._pa_array = pa_array
        pa_type = pa_array.type
        obj._dtype = (

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Pre-convert the strings to a pyarrow array yourself and pass values, not strings: pa.array(parsed_lists, type=pa.list_(pa.int64())).
2. Use a supported intermediate dtype first (e.g. object) then cast.
3. Switch to a non-pyarrow dtype for the conversion step and convert_dtypes() afterwards.
4. For list/struct types, build the pa.Array from already-typed Python objects via _from_sequence.

Example fix

# before
import pyarrow as pa
import pandas as pd
arr = pd.array(['[1,2]','[3]'], dtype=pd.ArrowDtype(pa.list_(pa.int64())))  # NotImplementedError
# after - parse first, then build
parsed = [[1,2],[3]]
arr = pd.array(parsed, dtype=pd.ArrowDtype(pa.list_(pa.int64())))

import pyarrow as pa

SUPPORTED_FOR_STR_CONV = (
    pa.types.is_string, pa.types.is_large_string, pa.types.is_binary,
    pa.types.is_timestamp, pa.types.is_date, pa.types.is_duration,
    pa.types.is_time, pa.types.is_boolean, pa.types.is_integer,
    pa.types.is_floating, pa.types.is_decimal,
)

def can_convert_strings(pa_type) -> bool:
    return any(check(pa_type) for check in SUPPORTED_FOR_STR_CONV)

# before: pd.array(strings, dtype=pd.ArrowDtype(pa_type))
if not can_convert_strings(pa_type):
    parsed = pre_parse(strings, pa_type)  # your parser
    arr = pd.array(parsed, dtype=pd.ArrowDtype(pa_type))
else:
    arr = pd.array(strings, dtype=pd.ArrowDtype(pa_type))

import pyarrow as pa

def is_string_convertible_arrow_type(t) -> bool:
    checks = (
        pa.types.is_string, pa.types.is_large_string, pa.types.is_binary,
        pa.types.is_timestamp, pa.types.is_date, pa.types.is_duration,
        pa.types.is_time, pa.types.is_boolean, pa.types.is_integer,
        pa.types.is_floating, pa.types.is_decimal,
    )
    return any(c(t) for c in checks)

try:
    arr = pd.array(strings, dtype=pd.ArrowDtype(pa_type))
except NotImplementedError as e:
    if 'Converting strings to' in str(e):
        parsed = pre_parse(strings, pa_type)
        arr = pd.array(parsed, dtype=pd.ArrowDtype(pa_type))
    else:
        raise

When it happens

Trigger: Building a pyarrow-backed array from strings into an unsupported dtype: `pd.array(['1','2'], dtype=ArrowDtype(pa.list_(pa.int64())))`, or `_from_sequence_of_strings(strings, dtype=ArrowDtype(pa.struct([...])))`, or interval/temporal-with-timezone types not yet handled.

Common situations: Parsing CSV/string columns into complex Arrow types (lists, structs) expecting automatic inference; using pd.Series([...strings...], dtype=ArrowDtype(some_complex_type)); upgrading pandas/pyarrow and hitting a newly-added pa_type not yet supported by the string-conversion ladder.

Related errors

• replace is not supported with a re.Pattern, callable repl, c
• contains not implemented with {flags=}
• {op.__name__} not implemented for {type(other)}
• repeat is not implemented when repeats is {type(repeats).__n
• Only flags=0 is implemented.

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/93233f43a5f5dec8. Report an issue: GitHub.