pyarrow.ChunkedArray

class pyarrow.ChunkedArray

Bases: pyarrow.lib._PandasConvertible

An array-like composed from a (possibly empty) collection of pyarrow.Arrays

Warning

Do not call this class’s constructor directly.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
cast(self, target_type[, safe])	Cast array values to another data type
chunk(self, i)	Select a chunk by its index
combine_chunks(self, MemoryPool memory_pool=None)	Flatten this ChunkedArray into a single non-chunked array.
dictionary_encode(self[, null_encoding])	Compute dictionary-encoded representation of array
drop_null(self)	Remove missing values from a chunked array.
equals(self, ChunkedArray other)	Return whether the contents of two chunked arrays are equal.
fill_null(self, fill_value)	See pyarrow.compute.fill_null docstring for usage.
filter(self, mask[, null_selection_behavior])	Select values from a chunked array.
flatten(self, MemoryPool memory_pool=None)	Flatten this ChunkedArray.
format(self, **kwargs)
get_total_buffer_size(self)	The sum of bytes in each buffer referenced by the chunked array.
index(self, value[, start, end, memory_pool])	Find the first index of a value.
is_null(self, *[, nan_is_null])	Return boolean array indicating the null values.
is_valid(self)	Return boolean array indicating the non-null values.
iterchunks(self)
length(self)
slice(self[, offset, length])	Compute zero-copy slice of this ChunkedArray
take(self, indices)	Select values from a chunked array.
to_numpy(self)	Return a NumPy 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=0, ...)	Render a "pretty-printed" string representation of the ChunkedArray
unify_dictionaries(self, ...)	Unify dictionaries across all chunks.
unique(self)	Compute distinct elements in array
validate(self, *[, full])	Perform validation checks.
value_counts(self)	Compute counts of unique elements in array.

Attributes

chunks
data
nbytes	Total number of bytes consumed by the elements of the chunked array.
null_count	Number of null entries
num_chunks	Number of underlying chunks
type

cast(self, target_type, safe=True)

Cast array values to another data type

See pyarrow.compute.cast for usage

chunk(self, i)

Select a chunk by its index

Parameters

iint

Returns

pyarrow.Array

chunks

combine_chunks(self, MemoryPool memory_pool=None)

Flatten this ChunkedArray into a single non-chunked array.

Parameters

memory_poolMemoryPool, default None

For memory allocations, if required, otherwise use default pool

Returns

resultArray

data

dictionary_encode(self, null_encoding='mask')

Compute dictionary-encoded representation of array

Returns

pyarrow.ChunkedArray

Same chunking as the input, all chunks share a common dictionary.

drop_null(self)

Remove missing values from a chunked array. See pyarrow.compute.drop_null for full description.

equals(self, ChunkedArray other)

Return whether the contents of two chunked arrays are equal.

Parameters

otherpyarrow.ChunkedArray

Chunked array to compare against.

Returns

are_equalbool

fill_null(self, fill_value)

See pyarrow.compute.fill_null docstring for usage.

filter(self, mask, null_selection_behavior='drop')

Select values from a chunked array. See pyarrow.compute.filter for full usage.

flatten(self, MemoryPool memory_pool=None)

Flatten this ChunkedArray. If it has a struct type, the column is flattened into one array per struct field.

Parameters

memory_poolMemoryPool, default None

For memory allocations, if required, otherwise use default pool

Returns

resultlist of ChunkedArray

format(self, **kwargs)

get_total_buffer_size(self)

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

is_null(self, *, nan_is_null=False)

Return boolean array indicating the null values.

Parameters

nan_is_nullbool (optional, default False)

Whether floating-point NaN values should also be considered null.

Returns

arraybool Array or ChunkedArray

is_valid(self)

Return boolean array indicating the non-null values.

iterchunks(self)

length(self)

nbytes

Total number of bytes consumed by the elements of the chunked 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 only 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

Number of null entries

Returns

int

num_chunks

Number of underlying chunks

Returns

int

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

Compute zero-copy slice of this ChunkedArray

Parameters

offsetint, default 0

Offset from start of array to slice

lengthint, default None

Length of slice (default is until end of batch starting from offset)

Returns

slicedChunkedArray

take(self, indices)

Select values from a chunked array. See pyarrow.compute.take for full usage.

to_numpy(self)

Return a NumPy copy of this array (experimental).

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, types_mapper=None)

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 is not passed.

strings_to_categoricalbool, default False

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

categories: list, default empty

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

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[ns] dtype.

timestamp_as_objectbool, default False

Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful if you have timestamps that don’t fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). If False, all timestamps are converted to datetime64[ns] dtype.

use_threadsbool, default True

Whether to parallelize the conversion using multiple threads.

deduplicate_objectsbool, default False

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.

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.

Returns

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

to_pylist(self)

Convert to a list of native Python objects.

to_string(self, *, int indent=0, int window=10, bool skip_new_lines=False)

Render a “pretty-printed” string representation of the ChunkedArray

Parameters

indentint

How much to indent right the content of the array, by default 0.

windowint

How many items to preview at the begin and end of the array when the arrays is bigger than the window. The other elements will be ellipsed.

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.

type

unify_dictionaries(self, MemoryPool memory_pool=None)

Unify dictionaries across all chunks.

This method returns an equivalent chunked array, but where all chunks share the same dictionary values. Dictionary indices are transposed accordingly.

If there are no dictionaries in the chunked array, it is returned unchanged.

Parameters

memory_poolMemoryPool, default None

For memory allocations, if required, otherwise use default pool

Returns

resultChunkedArray

unique(self)

Compute distinct elements in array

Returns

pyarrow.Array

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

full: bool, default False

If True, run expensive checks, otherwise cheap checks only.

Raises

ArrowInvalid

value_counts(self)

Compute counts of unique elements in array.

Returns

An array of <input type “Values”, int64_t “Counts”> structs

previous

pyarrow.table

next

pyarrow.RecordBatch