pyarrow.Int16Array¶
-
class
pyarrow.Int16Array¶ Bases:
pyarrow.lib.IntegerArrayConcrete class for Arrow arrays of int16 data type.
-
__init__(*args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(*args, **kwargs)Initialize self.
buffers(self)Return a list of Buffer objects pointing to this array’s physical storage.
cast(self, target_type[, safe])Cast array values to another data type
dictionary_encode(self)Compute dictionary-encoded representation of array.
diff(self, Array other)Compare contents of this array against another one.
equals(self, Array other)fill_null(self, fill_value)See pyarrow.compute.fill_null for usage.
filter(self, Array mask[, …])Select values from an array.
format(self, **kwargs)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.
is_null(self)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.
sum(self)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=0, int window=10)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
Total number of bytes consumed by the elements of the array.
A relative position into another array’s data.
-
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, safe=True)¶ Cast array values to another data type
See pyarrow.compute.cast for usage
-
dictionary_encode(self)¶ Compute dictionary-encoded representation of array.
-
diff(self, Array other)¶ Compare contents of this array against another one.
Return string containing the result of arrow::Diff comparing contents of this array against the other array.
-
equals(self, Array other)¶
-
fill_null(self, fill_value)¶ See pyarrow.compute.fill_null for usage.
-
filter(self, Array mask, null_selection_behavior=u'drop')¶ Select values from an array. See pyarrow.compute.filter for full usage.
-
format(self, **kwargs)¶
-
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
type (DataType) – The value type of the array.
length (int) – The number of values in the array.
buffers (List[Buffer]) – The buffers backing this array.
null_count (int, default -1) – The number of null entries in the array. Negative value means that the null count is not known.
offset (int, default 0) – The array’s logical offset (in values, not in bytes) from the start of each buffer.
children (List[Array], default None) – Nested type children with length matching type.num_fields.
- Returns
array (Array)
-
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
sequence (ndarray, pandas.Series, array-like) –
mask (array (boolean), optional) – Indicate which values are null (True) or not null (False).
type (pyarrow.DataType) – Explicit type to attempt to coerce to, otherwise will be inferred from the data.
safe (bool, default True) – Check for overflows or other unsafe conversions.
memory_pool (pyarrow.MemoryPool, optional) – If not passed, will allocate memory from the currently-set default memory pool.
Notes
Localized timestamps will currently be returned as UTC (pandas’s native representation). Timezone-naive data will be implicitly interpreted as UTC.
- Returns
array (pyarrow.Array or pyarrow.ChunkedArray) – ChunkedArray is returned if object data overflows binary buffer.
-
is_null(self)¶ Return BooleanArray indicating the null values.
-
is_valid(self)¶ Return BooleanArray indicating the non-null values.
-
nbytes¶ Total number of bytes consumed by the elements of the array.
-
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
offset (int, default 0) – Offset from start of array to slice.
length (int, default None) – Length of slice (default is until end of Array starting from offset).
- Returns
sliced (RecordBatch)
-
sum(self)¶ Sum the values in a numerical array.
-
take(self, indices)¶ Select values from an array. See pyarrow.compute.take for full usage.
-
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.
- Parameters
zero_copy_only (bool, 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).
writable (bool, 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
array (numpy.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_pool (MemoryPool, default None) – Arrow MemoryPool to use for allocations. Uses the default memory pool is not passed.
strings_to_categorical (bool, 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_only (bool, default False) – Raise an ArrowException if this function call would require copying the underlying data.
integer_object_nulls (bool, default False) – Cast integers with nulls to objects
date_as_object (bool, default True) – Cast dates to objects. If False, convert to datetime64[ns] dtype.
timestamp_as_object (bool, 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_threads (bool, default True) – Whether to parallelize the conversion using multiple threads.
deduplicate_objects (bool, default False) – Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.
ignore_metadata (bool, default False) – If True, do not use the ‘pandas’ metadata to reconstruct the DataFrame index, if present
safe (bool, 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_blocks (bool, 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_destruct (bool, 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.
types_mapper (function, 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
Noneif the default conversion should be used for that type. If you have a dictionary mapping, you can passdict.getas function.
- Returns
pandas.Series or pandas.DataFrame depending on type of object
-
to_pylist(self)¶ Convert to a list of native Python objects.
- Returns
lst (list)
-
to_string(self, int indent=0, int window=10)¶
-
tolist(self)¶ Alias of to_pylist for compatibility with NumPy.
-
type¶
-
unique(self)¶ Compute distinct elements in 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
-