Bases: _Tabular
Batch of rows of columns of equal length
Warning
Do not call this classâs constructor directly, use one of the RecordBatch.from_* functions instead.
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"]
Constructing a RecordBatch from arrays:
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede
Constructing a RecordBatch from pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
... 'month': [3, 5, 7, 9],
... 'day': [1, 5, 9, 13],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.RecordBatch.from_pandas(df)
pyarrow.RecordBatch
year: int64
month: int64
day: int64
n_legs: int64
animals: string
----
year: [2020,2022,2021,2022]
month: [3,5,7,9]
day: [1,5,9,13]
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> pa.RecordBatch.from_pandas(df).to_pandas()
year month day n_legs animals
0 2020 3 1 2 Flamingo
1 2022 5 5 4 Horse
2 2021 7 9 5 Brittle stars
3 2022 9 13 100 Centipede
Constructing a RecordBatch from pylist:
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
... {'n_legs': 4, 'animals': 'Dog'}]
>>> pa.RecordBatch.from_pylist(pylist).to_pandas()
n_legs animals
0 2 Flamingo
1 4 Dog
You can also construct a RecordBatch using pyarrow.record_batch():
>>> pa.record_batch([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede
>>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Methods
Attributes
Return the dataframe interchange object implementing the interchange protocol.
False
Whether to tell the DataFrame to overwrite null values in the data with NaN (or NaT).
True
Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail.
DataFrame interchange object
The object which consuming library can use to ingress the dataframe.
Notes
Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html nan_as_null currently has no effect; once support for nullable extension dtypes is added, this value should be propagated to columns.
Add column to RecordBatch at position i.
A new record batch is returned with the column added, the original record batch object is left unchanged.
int
Index to place the column at.
str or Field
If a string is passed then the type is deduced from the column data.
Array or value coercible to array
Column data.
RecordBatch
New record batch with the passed column added.
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
Add column:
>>> year = [2021, 2022, 2019, 2021] >>> batch.add_column(0,"year", year) pyarrow.RecordBatch year: int64 n_legs: int64 animals: string ---- year: [2021,2022,2019,2021] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Original record batch is left unchanged:
>>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Append column at end of columns.
str or Field
If a string is passed then the type is deduced from the column data.
Array or value coercible to array
Column data.
Table or RecordBatch
New table or record batch with the passed column added.
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Append column at the end:
>>> year = [2021, 2022, 2019, 2021]
>>> table.append_column('year', [year])
pyarrow.Table
n_legs: int64
animals: string
year: int64
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
year: [[2021,2022,2019,2021]]
Cast record batch values to another schema.
Schema
Schema to cast to, the names and order of fields must match.
True
Check for overflows or other unsafe conversions.
CastOptions, default None
Additional checks pass by CastOptions
RecordBatch
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> batch.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
Define new schema and cast batch values:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.duration('s')),
... pa.field('animals', pa.string())]
... )
>>> batch.cast(target_schema=my_schema)
pyarrow.RecordBatch
n_legs: duration[s]
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Select single column from Table or RecordBatch.
int or str
The index or name of the column to retrieve.
Array (for RecordBatch) or ChunkedArray (for Table)
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Select a column by numeric index:
>>> table.column(0)
<pyarrow.lib.ChunkedArray object at ...>
[
[
2,
4,
5,
100
]
]
Select a column by its name:
>>> table.column("animals")
<pyarrow.lib.ChunkedArray object at ...>
[
[
"Flamingo",
"Horse",
"Brittle stars",
"Centipede"
]
]
Names of the Table or RecordBatch columns.
list of str
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa >>> table = pa.Table.from_arrays([[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]], ... names=['n_legs', 'animals']) >>> table.column_names ['n_legs', 'animals']
List of all columns in numerical order.
list of Array (for RecordBatch) or list of ChunkedArray (for Table)
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.columns
[<pyarrow.lib.ChunkedArray object at ...>
[
[
null,
4,
5,
null
]
], <pyarrow.lib.ChunkedArray object at ...>
[
[
"Flamingo",
"Horse",
null,
"Centipede"
]
]]
Copy the entire RecordBatch to destination device.
This copies each column of the record batch to create a new record batch where all underlying buffers for the columns have been copied to the destination MemoryManager.
pyarrow.MemoryManager or pyarrow.Device
The destination device to copy the array to.
RecordBatch
The device type where the arrays in the RecordBatch reside.
DeviceAllocationType
Drop one or more columns and return a new Table or RecordBatch.
str or list[str]
Field name(s) referencing existing column(s).
Table or RecordBatch
A tabular object without the column(s).
KeyError
If any of the passed column names do not exist.
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
Drop one column:
>>> table.drop_columns("animals")
pyarrow.Table
n_legs: int64
----
n_legs: [[2,4,5,100]]
Drop one or more columns:
>>> table.drop_columns(["n_legs", "animals"]) pyarrow.Table ... ----
Remove rows that contain missing values from a Table or RecordBatch.
See pyarrow.compute.drop_null() for full usage.
Table or RecordBatch
A tabular object with the same schema, with rows containing no missing values.
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.drop_null()
pyarrow.Table
year: double
n_legs: int64
animals: string
----
year: [[2022,2021]]
n_legs: [[4,100]]
animals: [["Horse","Centipede"]]
Check if contents of two record batches are equal.
pyarrow.RecordBatch
RecordBatch to compare against.
False
Whether schema metadata equality should be checked as well.
Examples
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> batch = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"])
>>> batch_0 = pa.record_batch([])
>>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals],
... names=["n_legs", "animals"],
... metadata={"n_legs": "Number of legs per animal"})
>>> batch.equals(batch)
True
>>> batch.equals(batch_0)
False
>>> batch.equals(batch_1)
True
>>> batch.equals(batch_1, check_metadata=True)
False
Select a schema field by its column name or numeric index.
int or str
The index or name of the field to retrieve.
Field
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.field(0)
pyarrow.Field<n_legs: int64>
>>> table.field(1)
pyarrow.Field<animals: string>
Select rows from the table or record batch based on a boolean mask.
The Table can be filtered based on a mask, which will be passed to pyarrow.compute.filter() to perform the filtering, or it can be filtered through a boolean Expression
Array or array-like or Expression
The boolean mask or the Expression to filter the table with.
str, default âdropâ
How nulls in the mask should be handled, does nothing if an Expression is used.
Table or RecordBatch
A tabular object of the same schema, with only the rows selected by applied filtering
Examples
Using a Table (works similarly for RecordBatch):
>>> import pyarrow as pa
>>> table = pa.table({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
Define an expression and select rows:
>>> import pyarrow.compute as pc
>>> expr = pc.field("year") <= 2020
>>> table.filter(expr)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2019]]
n_legs: [[2,5]]
animals: [["Flamingo","Brittle stars"]]
Define a mask and select rows:
>>> mask=[True, True, False, None] >>> table.filter(mask) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022]] n_legs: [[2,4]] animals: [["Flamingo","Horse"]] >>> table.filter(mask, null_selection_behavior='emit_null') pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,null]] n_legs: [[2,4,null]] animals: [["Flamingo","Horse",null]]
Construct a RecordBatch from multiple pyarrow.Arrays
list of pyarrow.Array
One for each field in RecordBatch
list of str, optional
Names for the batch fields. If not passed, schema must be passed
Schema, default None
Schema for the created batch. If not passed, names must be passed
dict or Mapping, default None
Optional metadata for the schema (if inferred).
pyarrow.RecordBatch
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"]
Construct a RecordBatch from pyarrow Arrays using names:
>>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede
Construct a RecordBatch from pyarrow Arrays using schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas()
n_legs animals
0 2 Flamingo
1 2 Parrot
2 4 Dog
3 4 Horse
4 5 Brittle stars
5 100 Centipede
>>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Convert pandas.DataFrame to an Arrow RecordBatch
pandas.DataFrame
pyarrow.Schema, optional
The expected schema of the RecordBatch. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored.
Whether to store the index as an additional column in the resulting RecordBatch. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use preserve_index=True to force it to be stored as a column.
int, default None
If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows pyarrow.cpu_count() (may use up to system CPU count threads).
list, optional
List of column to be converted. If None, use all columns.
pyarrow.RecordBatch
Examples
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022],
... 'month': [3, 5, 7, 9],
... 'day': [1, 5, 9, 13],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
Convert pandas DataFrame to RecordBatch:
>>> import pyarrow as pa >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Convert pandas DataFrame to RecordBatch using schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.RecordBatch.from_pandas(df, schema=my_schema)
pyarrow.RecordBatch
n_legs: int64
animals: string
----
n_legs: [2,4,5,100]
animals: ["Flamingo","Horse","Brittle stars","Centipede"]
Convert pandas DataFrame to RecordBatch specifying columns:
>>> pa.RecordBatch.from_pandas(df, columns=["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100]
Construct a Table or RecordBatch from Arrow arrays or columns.
dict or Mapping
A mapping of strings to Arrays or Python lists.
Schema, default None
If not passed, will be inferred from the Mapping values.
dict or Mapping, default None
Optional metadata for the schema (if inferred).
Table or RecordBatch
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"])
>>> pydict = {'n_legs': n_legs, 'animals': animals}
Construct a Table from a dictionary of arrays:
>>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string
Construct a Table from a dictionary of arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from a dictionary of arrays with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.Table.from_pydict(pydict, schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table or RecordBatch from list of rows / dictionaries.
list of dicts of rows
A mapping of strings to row values.
Schema, default None
If not passed, will be inferred from the first row of the mapping values.
dict or Mapping, default None
Optional metadata for the schema (if inferred).
Table or RecordBatch
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'},
... {'n_legs': 4, 'animals': 'Dog'}]
Construct a Table from a list of rows:
>>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4]] animals: [["Flamingo","Dog"]]
Construct a Table from a list of rows with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from a list of rows with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> pa.Table.from_pylist(pylist, schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a RecordBatch from a StructArray.
Each field in the StructArray will become a column in the resulting RecordBatch.
StructArray
Array to construct the record batch from.
pyarrow.RecordBatch
Examples
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> pa.RecordBatch.from_struct_array(struct).to_pandas()
animals n_legs year
0 Parrot 2 NaN
1 None 4 2022.0
The sum of bytes in each buffer referenced by the record batch
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.
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.get_total_buffer_size() 120
Whether the RecordBatchâs arrays are CPU-accessible.
Iterator over all columns in their numerical order.
Array (for RecordBatch) or ChunkedArray (for Table)
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> for i in table.itercolumns():
... print(i.null_count)
...
2
1
Total number of bytes consumed by the elements of the record batch.
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.
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.nbytes 116
Number of columns
int
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_columns 2
Number of rows
Due to the definition of a RecordBatch, all columns have the same number of rows.
int
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_rows 6
Create new RecordBatch with the indicated column removed.
int
Index of column to remove.
Table
New record batch without the column.
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> batch.remove_column(1)
pyarrow.RecordBatch
n_legs: int64
----
n_legs: [2,4,5,100]
Create new record batch with columns renamed to provided names.
list[str] or dict[str, str]
List of new column names or mapping of old column names to new column names.
If a mapping of old to new column names is passed, then all columns which are found to match a provided old column name will be renamed to the new column name. If any column names are not found in the mapping, a KeyError will be raised.
RecordBatch
KeyError
If any of the column names passed in the names mapping do not exist.
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
>>> new_names = ["n", "name"]
>>> batch.rename_columns(new_names)
pyarrow.RecordBatch
n: int64
name: string
----
n: [2,4,5,100]
name: ["Flamingo","Horse","Brittle stars","Centipede"]
>>> new_names = {"n_legs": "n", "animals": "name"}
>>> batch.rename_columns(new_names)
pyarrow.RecordBatch
n: int64
name: string
----
n: [2,4,5,100]
name: ["Flamingo","Horse","Brittle stars","Centipede"]
Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata
dict, default None
RecordBatch
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
Constructing a RecordBatch with schema and metadata:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64())],
... metadata={"n_legs": "Number of legs per animal"})
>>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema)
>>> batch.schema
n_legs: int64
-- schema metadata --
n_legs: 'Number of legs per animal'
Shallow copy of a RecordBatch with deleted schema metadata:
>>> batch.replace_schema_metadata().schema n_legs: int64
Schema of the RecordBatch and its columns
pyarrow.Schema
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.schema n_legs: int64 animals: string
Select columns of the RecordBatch.
Returns a new RecordBatch with the specified columns, and metadata preserved.
The column names or integer indices to select.
RecordBatch
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"])
Select columns my indices:
>>> batch.select([1]) pyarrow.RecordBatch animals: string ---- animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
Select columns by names:
>>> batch.select(["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,2,4,4,5,100]
Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema.
To reconstruct a RecordBatch from the encapsulated IPC message Buffer returned by this function, a Schema must be passed separately. See Examples.
MemoryPool, default None
Uses default memory pool if not specified
Buffer
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> buf = batch.serialize() >>> buf <pyarrow.Buffer address=0x... size=... is_cpu=True is_mutable=True>
Reconstruct RecordBatch from IPC message Buffer and original Schema
>>> pa.ipc.read_record_batch(buf, batch.schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]
Replace column in RecordBatch at position.
int
Index to place the column at.
str or Field
If a string is passed then the type is deduced from the column data.
Array or value coercible to array
Column data.
RecordBatch
New record batch with the passed column set.
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> batch = pa.RecordBatch.from_pandas(df)
Replace a column:
>>> year = [2021, 2022, 2019, 2021] >>> batch.set_column(1,'year', year) pyarrow.RecordBatch n_legs: int64 year: int64 ---- n_legs: [2,4,5,100] year: [2021,2022,2019,2021]
Dimensions of the table or record batch: (#rows, #columns).
int, int)
Number of rows and number of columns.
Examples
>>> import pyarrow as pa
>>> table = pa.table({'n_legs': [None, 4, 5, None],
... 'animals': ["Flamingo", "Horse", None, "Centipede"]})
>>> table.shape
(4, 2)
Compute zero-copy slice of this RecordBatch
int, default 0
Offset from start of record batch to slice
int, default None
Length of slice (default is until end of batch starting from offset)
RecordBatch
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> batch.slice(offset=3).to_pandas() n_legs animals 0 4 Horse 1 5 Brittle stars 2 100 Centipede >>> batch.slice(length=2).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot >>> batch.slice(offset=3, length=1).to_pandas() n_legs animals 0 4 Horse
Sort the Table or RecordBatch by one or multiple columns.
str or list[tuple(name, order)]
Name of the column to use to sort (ascending), or a list of multiple sorting conditions where each entry is a tuple with column name and sorting order (âascendingâ or âdescendingâ)
dict, optional
Additional sorting options. As allowed by SortOptions
Table or RecordBatch
A new tabular object sorted according to the sort keys.
Examples
Table (works similarly for RecordBatch)
>>> import pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021],
... 'n_legs': [2, 2, 4, 4, 5, 100],
... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",
... "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.sort_by('animal')
pyarrow.Table
year: int64
n_legs: int64
animal: string
----
year: [[2019,2021,2021,2020,2022,2022]]
n_legs: [[5,100,4,2,4,2]]
animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]]
Select rows from a Table or RecordBatch.
See pyarrow.compute.take() for full usage.
Array or array-like
The indices in the tabular object whose rows will be returned.
Table or RecordBatch
A tabular object with the same schema, containing the taken rows.
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> table = pa.Table.from_pandas(df)
>>> table.take([1,3])
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2022,2021]]
n_legs: [[4,100]]
animals: [["Horse","Centipede"]]
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate
MemoryPool, default None
Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed.
list, default empty
List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures.
False
Encode string (UTF8) and binary types to pandas.Categorical.
False
Raise an ArrowException if this function call would require copying the underlying data.
False
Cast integers with nulls to objects
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.
False
Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that donât 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.
True
Whether to parallelize the conversion using multiple threads.
True
Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower.
False
If True, do not use the âpandasâ metadata to reconstruct the DataFrame index, if present
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.
False
If True, generate one internal âblockâ 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 âconsolidationâ which may balloon memory use.
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 canât be freed until all columns are converted.
str, optional, default None
Valid values are None, âlossyâ, or âstrictâ. 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), â¦].
If âlossyâ or âstrictâ, 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 âlossyâ, this key deduplication results in a warning printed when detected. If âstrictâ, this instead results in an exception being raised when detected.
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.
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 youâd like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise).
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
Convert the Table or RecordBatch to a dict or OrderedDict.
str, optional, default None
Valid values are None, âlossyâ, or âstrictâ. 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), â¦].
If âlossyâ or âstrictâ, convert Arrow Map arrays to native Python dicts.
If âlossyâ, whenever duplicate keys are detected, a warning will be printed. The last seen value of a duplicate key will be in the Python dictionary. If âstrictâ, this instead results in an exception being raised when detected.
dict
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> n_legs = pa.array([2, 2, 4, 4, 5, 100])
>>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"])
>>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"])
>>> table.to_pydict()
{'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']}
Convert the Table or RecordBatch to a list of rows / dictionaries.
str, optional, default None
Valid values are None, âlossyâ, or âstrictâ. 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), â¦].
If âlossyâ or âstrictâ, convert Arrow Map arrays to native Python dicts.
If âlossyâ, whenever duplicate keys are detected, a warning will be printed. The last seen value of a duplicate key will be in the Python dictionary. If âstrictâ, this instead results in an exception being raised when detected.
list
Examples
Table (works similarly for RecordBatch)
>>> import pyarrow as pa
>>> data = [[2, 4, 5, 100],
... ["Flamingo", "Horse", "Brittle stars", "Centipede"]]
>>> table = pa.table(data, names=["n_legs", "animals"])
>>> table.to_pylist()
[{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ...
Return human-readable string representation of Table or RecordBatch.
False
Display Field-level and Schema-level KeyValueMetadata.
int, default 0
Display values of the columns for the first N columns.
str
Convert to a struct array.
Convert to a Tensor.
RecordBatches that can be converted have fields of type signed or unsigned integer or float, including all bit-widths.
null_to_nan is False by default and this method will raise an error in case any nulls are present. RecordBatches with nulls can be converted with null_to_nan set to True. In this case null values are converted to NaN and integer type arrays are promoted to the appropriate float type.
False
Whether to write null values in the result as NaN.
True
Whether resulting Tensor is row-major or column-major
MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Examples
>>> import pyarrow as pa >>> batch = pa.record_batch( ... [ ... pa.array([1, 2, 3, 4, None], type=pa.int32()), ... pa.array([10, 20, 30, 40, None], type=pa.float32()), ... ], names = ["a", "b"] ... )
>>> batch pyarrow.RecordBatch a: int32 b: float ---- a: [1,2,3,4,null] b: [10,20,30,40,null]
Convert a RecordBatch to row-major Tensor with null values written as ``NaN``s
>>> batch.to_tensor(null_to_nan=True)
<pyarrow.Tensor>
type: double
shape: (5, 2)
strides: (16, 8)
>>> batch.to_tensor(null_to_nan=True).to_numpy()
array([[ 1., 10.],
[ 2., 20.],
[ 3., 30.],
[ 4., 40.],
[nan, nan]])
Convert a RecordBatch to column-major Tensor
>>> batch.to_tensor(null_to_nan=True, row_major=False)
<pyarrow.Tensor>
type: double
shape: (5, 2)
strides: (8, 40)
>>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy()
array([[ 1., 10.],
[ 2., 20.],
[ 3., 30.],
[ 4., 40.],
[nan, nan]])
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)).
False
If True, run expensive checks, otherwise cheap checks only.
ArrowInvalid
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