pyarrow.HalfFloatArray#

class pyarrow.HalfFloatArray#

Bases: FloatingPointArray

Concrete class for Arrow arrays of float16 data type.

__init__(*args, **kwargs)#

Methods

`__init__`(args, *kwargs)
`buffers`(self)	Return a list of Buffer objects pointing to this array's physical storage.
`cast`(self[, target_type, safe, options, ...])	Cast array values to another data type
`dictionary_encode`(self[, null_encoding])	Compute dictionary-encoded representation of array.
`diff`(self, Array other)	Compare contents of this array against another one.
`drop_null`(self)	Remove missing values from an array.
`equals`(self, Array other)	Parameters:
`fill_null`(self, fill_value)	See `pyarrow.compute.fill_null()` for usage.
`filter`(self, Array mask, *[, ...])	Select values from an array.
`format`(self, **kwargs)	DEPRECATED, use pyarrow.Array.to_string
`from_buffers`(DataType type, length, buffers)	Construct an Array from a sequence of buffers.
`from_pandas`(obj[, mask, type])	Convert pandas.Series to an Arrow Array.
`get_total_buffer_size`(self)	The sum of bytes in each buffer referenced by the array.
`index`(self, value[, start, end, memory_pool])	Find the first index of a value.
`is_nan`(self)	Return BooleanArray indicating the NaN values.
`is_null`(self, *[, nan_is_null])	Return BooleanArray indicating the null values.
`is_valid`(self)	Return BooleanArray indicating the non-null values.
`slice`(self[, offset, length])	Compute zero-copy slice of this array.
`sort`(self[, order])	Sort the Array
`sum`(self, **kwargs)	Sum the values in a numerical array.
`take`(self, indices)	Select values from an array.
`to_numpy`(self[, zero_copy_only, writable])	Return a NumPy view or copy of this array (experimental).
`to_pandas`(self[, memory_pool, categories, ...])	Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
`to_pylist`(self)	Convert to a list of native Python objects.
`to_string`(self, *, int indent=2, ...)	Render a "pretty-printed" string representation of the Array.
`tolist`(self)	Alias of to_pylist for compatibility with NumPy.
`unique`(self)	Compute distinct elements in array.
`validate`(self, *[, full])	Perform validation checks.
`value_counts`(self)	Compute counts of unique elements in array.
`view`(self, target_type)	Return zero-copy "view" of array as another data type.

Attributes

`nbytes`	Total number of bytes consumed by the elements of the array.
`null_count`
`offset`	A relative position into another array's data.
`type`

buffers(self)#

Return a list of Buffer objects pointing to this array’s physical storage.

To correctly interpret these buffers, you need to also apply the offset multiplied with the size of the stored data type.

cast(self, target_type=None, safe=None, options=None, memory_pool=None)#

Cast array values to another data type

See pyarrow.compute.cast() for usage.

Parameters:

target_typeDataType, default None: Type to cast array to.
safebool, default True: Whether to check for conversion errors such as overflow.
optionsCastOptions, default None: Additional checks pass by CastOptions
memory_poolMemoryPool, optional: memory pool to use for allocations during function execution.

Returns:

castArray

dictionary_encode(self, null_encoding='mask')#

Compute dictionary-encoded representation of array.

See pyarrow.compute.dictionary_encode() for full usage.

Parameters:

null_encodingstr, default “mask”: How to handle null entries.

Returns:

encodedDictionaryArray: A dictionary-encoded version of this array.

diff(self, Array other)#

Compare contents of this array against another one.

Return a string containing the result of diffing this array (on the left side) against the other array (on the right side).

Parameters:

otherArray: The other array to compare this array with.

Returns:

diffstr: A human-readable printout of the differences.

Examples

>>> import pyarrow as pa
>>> left = pa.array(["one", "two", "three"])
>>> right = pa.array(["two", None, "two-and-a-half", "three"])
>>> print(left.diff(right)) 

@@ -0, +0 @@ -“one” @@ -2, +1 @@ +null +”two-and-a-half”

drop_null(self)#: Remove missing values from an array.

equals(self, Array other)#

Parameters:

otherpyarrow.Array

Returns:

bool

fill_null(self, fill_value)#

See pyarrow.compute.fill_null() for usage.

Parameters:

fill_valueany: The replacement value for null entries.

Returns:

resultArray: A new array with nulls replaced by the given value.

filter(self, Array mask, *, null_selection_behavior=u'drop')#

Select values from an array.

See pyarrow.compute.filter() for full usage.

Parameters:

maskArray or array-like: The boolean mask to filter the array with.
null_selection_behaviorstr, default “drop”: How nulls in the mask should be handled.

Returns:

filteredArray: An array of the same type, with only the elements selected by the boolean mask.

format(self, **kwargs)#

DEPRECATED, use pyarrow.Array.to_string

Parameters:

**kwargsdict

Returns:

str

static from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None)#

Construct an Array from a sequence of buffers.

The concrete type returned depends on the datatype.

Parameters:

typeDataType: The value type of the array.
lengthint: The number of values in the array.
buffersList[Buffer]: The buffers backing this array.
null_countint, default -1: The number of null entries in the array. Negative value means that the null count is not known.
offsetint, default 0: The array’s logical offset (in values, not in bytes) from the start of each buffer.
childrenList[Array], default None: Nested type children with length matching type.num_fields.

Returns:

arrayArray

static from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None)#

Convert pandas.Series to an Arrow Array.

This method uses Pandas semantics about what values indicate nulls. See pyarrow.array for more general conversion from arrays or sequences to Arrow arrays.

Parameters:

objndarray, pandas.Series, array-like
maskarray (bool), optional: Indicate which values are null (True) or not null (False).
typepyarrow.DataType: Explicit type to attempt to coerce to, otherwise will be inferred from the data.
safebool, default True: Check for overflows or other unsafe conversions.
memory_poolpyarrow.MemoryPool, optional: If not passed, will allocate memory from the currently-set default memory pool.

Returns:

arraypyarrow.Array or pyarrow.ChunkedArray: ChunkedArray is returned if object data overflows binary buffer.

Notes

Localized timestamps will currently be returned as UTC (pandas’s native representation). Timezone-naive data will be implicitly interpreted as UTC.

get_total_buffer_size(self)#

The sum of bytes in each buffer referenced by the array.

An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer.

If a buffer is referenced multiple times then it will only be counted once.

index(self, value, start=None, end=None, *, memory_pool=None)#

Find the first index of a value.

See pyarrow.compute.index() for full usage.

Parameters:

valueScalar or object: The value to look for in the array.
startint, optional: The start index where to look for value.
endint, optional: The end index where to look for value.
memory_poolMemoryPool, optional: A memory pool for potential memory allocations.

Returns:

indexInt64Scalar: The index of the value in the array (-1 if not found).

is_nan(self)#

Return BooleanArray indicating the NaN values.

Returns:

arraybool Array

is_null(self, *, nan_is_null=False)#

Return BooleanArray indicating the null values.

Parameters:

nan_is_nullbool (optional, default False): Whether floating-point NaN values should also be considered null.

Returns:

arraybool Array

is_valid(self)#: Return BooleanArray indicating the non-null values.

nbytes#

Total number of bytes consumed by the elements of the array.

In other words, the sum of bytes from all buffer ranges referenced.

Unlike get_total_buffer_size this method will account for array offsets.

If buffers are shared between arrays then the shared portion will be counted multiple times.

The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary.

null_count#

offset#

A relative position into another array’s data.

The purpose is to enable zero-copy slicing. This value defaults to zero but must be applied on all operations with the physical storage buffers.

slice(self, offset=0, length=None)#

Compute zero-copy slice of this array.

Parameters:

offsetint, default 0: Offset from start of array to slice.
lengthint, default None: Length of slice (default is until end of Array starting from offset).

Returns:

slicedRecordBatch

sort(self, order='ascending', **kwargs)#

Sort the Array

Parameters:

orderstr, default “ascending”: Which order to sort values in. Accepted values are “ascending”, “descending”.
**kwargsdict, optional: Additional sorting options. As allowed by SortOptions

Returns:

resultArray

sum(self, **kwargs)#

Sum the values in a numerical array.

See pyarrow.compute.sum() for full usage.

Parameters:

**kwargsdict, optional: Options to pass to pyarrow.compute.sum().

Returns:

sumScalar: A scalar containing the sum value.

take(self, indices)#

Select values from an array.

See pyarrow.compute.take() for full usage.

Parameters:

indicesArray or array-like: The indices in the array whose values will be returned.

Returns:

takenArray: An array with the same datatype, containing the taken values.

to_numpy(self, zero_copy_only=True, writable=False)#

Return a NumPy view or copy of this array (experimental).

By default, tries to return a view of this array. This is only supported for primitive arrays with the same memory layout as NumPy (i.e. integers, floating point, ..) and without any nulls.

For the extension arrays, this method simply delegates to the underlying storage array.

Parameters:

zero_copy_onlybool, default True: If True, an exception will be raised if the conversion to a numpy array would require copying the underlying data (e.g. in presence of nulls, or for non-primitive types).
writablebool, default False: For numpy arrays created with zero copy (view on the Arrow data), the resulting array is not writable (Arrow data is immutable). By setting this to True, a copy of the array is made to ensure it is writable.

Returns:

arraynumpy.ndarray

to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, unicode maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False)#

Convert to a pandas-compatible NumPy array or DataFrame, as appropriate

Parameters:

memory_poolMemoryPool, default None

Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed.

categorieslist, default empty

List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.

strings_to_categoricalbool, default False

Encode string (UTF8) and binary types to pandas.Categorical.

zero_copy_onlybool, default False

Raise an ArrowException if this function call would require copying the underlying data.

integer_object_nullsbool, default False

Cast integers with nulls to objects

date_as_objectbool, default True

Cast dates to objects. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Note: in pandas version < 2.0, only datetime64[ns] conversion is supported.

timestamp_as_objectbool, default False

Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that don’t fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). Non-nanosecond timestamps are supported in pandas version 2.0. If False, all timestamps are converted to datetime64 dtype.

use_threadsbool, default True

Whether to parallelize the conversion using multiple threads.

deduplicate_objectsbool, default True

Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.

ignore_metadatabool, default False

If True, do not use the ‘pandas’ metadata to reconstruct the DataFrame index, if present

safebool, default True

For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not.

split_blocksbool, default False

If True, generate one internal “block” for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger “consolidation” which may balloon memory use.

self_destructbool, default False

EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program.

Note that you may not see always memory usage improvements. For example, if multiple columns share an underlying allocation, memory can’t be freed until all columns are converted.

maps_as_pydictsstr, optional, default None

Valid values are None, ‘lossy’, or ‘strict’. The default behavior (None), is to convert Arrow Map arrays to Python association lists (list-of-tuples) in the same order as the Arrow Map, as in [(key1, value1), (key2, value2), …].

If ‘lossy’ or ‘strict’, convert Arrow Map arrays to native Python dicts. This can change the ordering of (key, value) pairs, and will deduplicate multiple keys, resulting in a possible loss of data.

If ‘lossy’, this key deduplication results in a warning printed when detected. If ‘strict’, this instead results in an exception being raised when detected.

types_mapperfunction, default None

A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or None if the default conversion should be used for that type. If you have a dictionary mapping, you can pass dict.get as function.

coerce_temporal_nanosecondsbool, default False

Only applicable to pandas version >= 2.0. A legacy option to coerce date32, date64, duration, and timestamp time units to nanoseconds when converting to pandas. This is the default behavior in pandas version 1.x. Set this option to True if you’d like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise).

Returns:

pandas.Series or pandas.DataFrame depending on type of object

Examples

>>> import pyarrow as pa
>>> import pandas as pd

Convert a Table to pandas DataFrame:

>>> table = pa.table([
...    pa.array([2, 4, 5, 100]),
...    pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
...    ], names=['n_legs', 'animals'])
>>> table.to_pandas()
   n_legs        animals
0       2       Flamingo
1       4          Horse
2       5  Brittle stars
3     100      Centipede
>>> isinstance(table.to_pandas(), pd.DataFrame)
True

Convert a RecordBatch to pandas DataFrame:

>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.record_batch([n_legs, animals],
...                         names=["n_legs", "animals"])
>>> batch
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> batch.to_pandas()
   n_legs        animals
0       2       Flamingo
1       4          Horse
2       5  Brittle stars
3     100      Centipede
>>> isinstance(batch.to_pandas(), pd.DataFrame)
True

Convert a Chunked Array to pandas Series:

>>> import pyarrow as pa
>>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]])
>>> n_legs.to_pandas()
0      2
1      2
2      4
3      4
4      5
5    100
dtype: int64
>>> isinstance(n_legs.to_pandas(), pd.Series)
True

to_pylist(self)#

Convert to a list of native Python objects.

Returns:

lstlist

to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False)#

Render a “pretty-printed” string representation of the Array.

Parameters:

indentint, default 2: How much to indent the internal items in the string to the right, by default 2.
top_level_indentint, default 0: How much to indent right the entire content of the array, by default 0.
windowint: How many primitive items to preview at the begin and end of the array when the array is bigger than the window. The other items will be ellipsed.
container_windowint: How many container items (such as a list in a list array) to preview at the begin and end of the array when the array is bigger than the window.
skip_new_linesbool: If the array should be rendered as a single line of text or if each element should be on its own line.

tolist(self)#: Alias of to_pylist for compatibility with NumPy.

type#

unique(self)#

Compute distinct elements in array.

Returns:

uniqueArray: An array of the same data type, with deduplicated elements.

validate(self, *, full=False)#

Perform validation checks. An exception is raised if validation fails.

By default only cheap validation checks are run. Pass full=True for thorough validation checks (potentially O(n)).

Parameters:

fullbool, default False: If True, run expensive checks, otherwise cheap checks only.

Raises:

ArrowInvalid

value_counts(self)#

Compute counts of unique elements in array.

Returns:

StructArray: An array of <input type “Values”, int64 “Counts”> structs

view(self, target_type)#

Return zero-copy “view” of array as another data type.

The data types must have compatible columnar buffer layouts

Parameters:

target_typeDataType: Type to construct view as.

Returns:

viewArray