Output formats

Choose the shape your pipeline expects.

xbbg returns tidy long output by default, with typed and semi-long alternatives for serialization, analytics, and quick inspection.

xbbg returns data in a tidy long format by default. The format parameter on any data function lets you select a different shape. Pass a Format enum value or its string equivalent.

from xbbg import blp, Format

df = blp.bdp('AAPL US Equity', ['PX_LAST', 'VOLUME'], format=Format.LONG_TYPED)
# or equivalently
df = blp.bdp('AAPL US Equity', ['PX_LAST', 'VOLUME'], format='long_typed')

Quick Reference

Format	String alias	Columns	Use case
Format.LONG	'long'	ticker, field, value	Default. Joins, aggregations, tidy data
Format.LONG_TYPED	'long_typed'	ticker, field, value_f64, value_i64, value_str, value_date, value_ts, value_bool	Type-safe analysis without casting
Format.LONG_WITH_METADATA	'long_metadata'	ticker, field, value, dtype	Serialization, debugging, data catalogs
Format.SEMI_LONG	'semi_long'	ticker (+ date for bdh), one column per field	Quick inspection, closest to wide behavior

INFO

Lazy backends (Polars lazy, narwhals lazy, SQLFrame) support LONG, SEMI_LONG, LONG_TYPED, and LONG_WITH_METADATA.

---

Format.LONG (default)

All field values are returned as strings. This is the default and is backwards-compatible with all backends.

Columns: ticker, field, value

from xbbg import blp

df = blp.bdp(
    ['AAPL US Equity', 'MSFT US Equity'],
    ['PX_LAST', 'VOLUME'],
)

            ticker    field       value
0  AAPL US Equity  PX_LAST      189.30
1  AAPL US Equity   VOLUME  58234100.0
2  MSFT US Equity  PX_LAST      415.12
3  MSFT US Equity   VOLUME  22183400.0

Because value is always a string, downstream code that needs numeric types must cast explicitly. This is intentional: LONG makes no assumptions about the field type, so it works correctly regardless of Bloomberg's metadata for a field.

Best for: tidy-data workflows, groupby/join operations, writing to columnar stores, passing data to ML pipelines that apply their own type inference.

---

Format.LONG_TYPED

Each row is placed in the value column matching its Arrow type. Unmatched columns are null for that row. No casting is required by callers.

Columns: ticker, field, value_f64, value_i64, value_str, value_bool, value_date, value_ts

from xbbg import blp, Format

df = blp.bdp(
    ['AAPL US Equity', 'MSFT US Equity'],
    ['PX_LAST', 'VOLUME', 'SECURITY_NAME'],
    format=Format.LONG_TYPED,
)

            ticker          field  value_f64  value_i64    value_str  value_bool value_date value_ts
0  AAPL US Equity        PX_LAST     189.30       null         null        null       null     null
1  AAPL US Equity         VOLUME       null   58234100         null        null       null     null
2  AAPL US Equity  SECURITY_NAME       null       null    Apple Inc        null       null     null
3  MSFT US Equity        PX_LAST     415.12       null         null        null       null     null
4  MSFT US Equity         VOLUME       null   22183400         null        null       null     null
5  MSFT US Equity  SECURITY_NAME       null       null  Microsoft Co        null       null     null

Field types come from Bloomberg's field metadata and are resolved at query time. You can override the type for specific fields with field_types:

df = blp.bdp(
    'AAPL US Equity',
    ['PX_LAST', 'VOLUME'],
    format=Format.LONG_TYPED,
    field_types={'VOLUME': 'float64'},  # treat VOLUME as f64 instead of i64
)

Best for: type-safe downstream analysis, Arrow/Parquet pipelines, anything where you need to branch on numeric vs. string values without inspecting dtype metadata at runtime.

---

Format.LONG_WITH_METADATA

Like LONG, but adds a dtype column containing the Arrow type name for each value. The value column is still a string.

Columns: ticker, field, value, dtype

from xbbg import blp, Format

df = blp.bdp(
    ['AAPL US Equity', 'MSFT US Equity'],
    ['PX_LAST', 'VOLUME', 'SECURITY_NAME'],
    format=Format.LONG_WITH_METADATA,
)

            ticker          field         value   dtype
0  AAPL US Equity        PX_LAST        189.30  float64
1  AAPL US Equity         VOLUME      58234100    int64
2  AAPL US Equity  SECURITY_NAME     Apple Inc   string
3  MSFT US Equity        PX_LAST        415.12  float64
4  MSFT US Equity         VOLUME      22183400    int64
5  MSFT US Equity  SECURITY_NAME  Microsoft Co   string

The dtype values are Arrow type names: float64, int64, string, bool, date32, timestamp[us]. These are stable across backends.

Best for: serialization to JSON/CSV where typed columns are not available, debugging unexpected type resolutions, building data catalogs that track field-level types.

---

Format.SEMI_LONG

One row per ticker (for bdp) or per ticker/date (for bdh). Fields become columns. Values are typed according to each field's Bloomberg metadata.

Columns (bdp): ticker, then one column per requested field
Columns (bdh): ticker, date, then one column per requested field

from xbbg import blp, Format

# Reference data
df = blp.bdp(
    ['AAPL US Equity', 'MSFT US Equity'],
    ['PX_LAST', 'VOLUME', 'SECURITY_NAME'],
    format=Format.SEMI_LONG,
)

            ticker  PX_LAST    VOLUME  SECURITY_NAME
0  AAPL US Equity   189.30  58234100      Apple Inc
1  MSFT US Equity   415.12  22183400   Microsoft Co

# Historical data
df = blp.bdh(
    'SPX Index',
    ['PX_LAST', 'PX_VOLUME'],
    '2024-01-02',
    '2024-01-05',
    format=Format.SEMI_LONG,
)

      ticker        date   PX_LAST   PX_VOLUME
0  SPX Index  2024-01-02  4742.83  2134500000
1  SPX Index  2024-01-03  4704.81  2011800000
2  SPX Index  2024-01-04  4697.24  2198200000
3  SPX Index  2024-01-05  4697.24  1834600000

SEMI_LONG retains the ticker column as an identifier rather than pivoting tickers into column headers. Multi-ticker queries produce stacked rows, not expanded columns.

Best for: quick visual inspection, passing to charting libraries, situations where you want named field columns but not a fully pivoted multi-ticker layout.

Excel presentation aliases for bdh()

bdh() also accepts the Excel-style presentation aliases from the Bloomberg add-in. These are not sent to Bloomberg; xbbg applies them locally after the raw historical response returns.

Alias family	Values	Effect
Dts, Dates, show_date	Show/S/True, Hide/H/False	Keep or remove date-style columns
DtFmt, DateFormat, date_format	D/Date, P/Periodic, B/Both	Use date labels, period labels, or both
Sort, sort	A/Ascend/Chronological, R/Reverse/Descend	Sort historical rows by date
Orientation, Direction, Dir, orientation	V/Vertical, H/Horizontal	Select long or semi-long output when format= is not explicit

hist = blp.bdh(
    'SPX Index',
    'PX_LAST',
    '2024-01-01',
    '2024-12-31',
    Per='M',
    DtFmt='Both',
    Sort='Reverse',
    Direction='H',
)

Explicit format= still wins over Direction/Orientation; use the alias only when you want Excel-like call sites to choose the default shape.

---

---

Which Format to Choose

LONG

You are doing joins, groupby, or aggregations. You need maximum backend compatibility. You will cast values yourself or don't need typed access.

LONG_TYPED

You need typed numeric/string/date values without writing casts. You are writing to Parquet or Arrow. You branch logic on value type.

LONG_WITH_METADATA

You need to know the Arrow type of each value alongside its string representation. Useful for serialization layers and debug workflows.

SEMI_LONG

You want fields as columns for immediate inspection or charting. Multi-ticker output is acceptable as stacked rows.

When in doubt, use the default (LONG). It is stable, backend-agnostic, and the easiest to reason about. Promote to LONG_TYPED when type safety is required, or to SEMI_LONG when a column-per-field layout simplifies downstream code.

Setting a session-wide default

If all calls in your script should use the same format, pass format to each call rather than relying on a global — it keeps each call self-documenting. For exploratory work, the string aliases are concise:

from xbbg import blp

df = blp.bdp('AAPL US Equity', ['PX_LAST', 'VOLUME', 'SECURITY_NAME'], format='long_typed')
df = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31', format='semi_long')