Bases: _Tabular
A collection of top-level named, equal length Arrow arrays.
Warning
Do not call this classâs constructor directly, use one of the from_* methods instead.
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"]
Construct a Table from arrays:
>>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a RecordBatch:
>>> batch = pa.record_batch([n_legs, animals], names=names) >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from pandas DataFrame:
>>> import pandas as pd
>>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021],
... 'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.Table.from_pandas(df)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,2019,2021]]
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a dictionary of arrays:
>>> pydict = {'n_legs': n_legs, 'animals': animals}
>>> 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 list of rows:
>>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}]
>>> pa.Table.from_pylist(pylist)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,null]]
animals: [["Flamingo","Centipede"]]
Construct a Table from a list of rows with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('year', pa.int64()),
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"year": "Year of entry"})
>>> pa.Table.from_pylist(pylist, schema=my_schema).schema
year: int64
n_legs: int64
animals: string
-- schema metadata --
year: 'Year of entry'
Construct a Table with pyarrow.table():
>>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- 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 Table at position.
A new table is returned with the column added, the original table 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, list of Array, or values coercible to arrays
Column data.
Table
New table 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)
Add column:
>>> year = [2021, 2022, 2019, 2021] >>> table.add_column(0,"year", [year]) pyarrow.Table 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 table is left unchanged:
>>> table pyarrow.Table 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 table 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
Table
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)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
Define new schema and cast table values:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.duration('s')),
... pa.field('animals', pa.string())]
... )
>>> table.cast(target_schema=my_schema)
pyarrow.Table
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"
]
]]
Make a new table by combining the chunks this table has.
All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk.
MemoryPool, default None
For memory allocations, if required, otherwise use default pool.
Table
Examples
>>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> names = ["n_legs", "animals"] >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] >>> table.combine_chunks() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4,4,5,100]] animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
Drop one or more columns and return a new table.
Alias of Table.drop_columns, but kept for backwards compatibility.
str or list[str]
Field name(s) referencing existing column(s).
Table
New table without the column(s).
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 tables are equal.
pyarrow.Table
Table 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"])
>>> names=["n_legs", "animals"]
>>> table = pa.Table.from_arrays([n_legs, animals], names=names)
>>> table_0 = pa.Table.from_arrays([])
>>> table_1 = pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata={"n_legs": "Number of legs per animal"})
>>> table.equals(table)
True
>>> table.equals(table_0)
False
>>> table.equals(table_1)
True
>>> table.equals(table_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]]
Flatten this Table.
Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged.
MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Table
Examples
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> month = pa.array([4, 6])
>>> table = pa.Table.from_arrays([struct,month],
... names = ["a", "month"])
>>> table
pyarrow.Table
a: struct<animals: string, n_legs: int64, year: int64>
child 0, animals: string
child 1, n_legs: int64
child 2, year: int64
month: int64
----
a: [
-- is_valid: all not null
-- child 0 type: string
["Parrot",null]
-- child 1 type: int64
[2,4]
-- child 2 type: int64
[null,2022]]
month: [[4,6]]
Flatten the columns with struct field:
>>> table.flatten() pyarrow.Table a.animals: string a.n_legs: int64 a.year: int64 month: int64 ---- a.animals: [["Parrot",null]] a.n_legs: [[2,4]] a.year: [[null,2022]] month: [[4,6]]
Construct a Table from Arrow arrays.
list of pyarrow.Array or pyarrow.ChunkedArray
Equal-length arrays that should form the table.
list of str, optional
Names for the table columns. If not passed, schema must be passed.
Schema, default None
Schema for the created table. If not passed, names must be passed.
dict or Mapping, default None
Optional metadata for the schema (if inferred).
Table
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"]
Construct a Table from arrays:
>>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from arrays with metadata:
>>> my_metadata={"n_legs": "Number of legs per animal"}
>>> pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata=my_metadata)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_arrays([n_legs, animals],
... names=names,
... metadata=my_metadata).schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
Construct a Table from arrays with pyarrow schema:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"animals": "Name of the animal species"})
>>> pa.Table.from_arrays([n_legs, animals],
... schema=my_schema)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> pa.Table.from_arrays([n_legs, animals],
... schema=my_schema).schema
n_legs: int64
animals: string
-- schema metadata --
animals: 'Name of the animal species'
Construct a Table from a sequence or iterator of Arrow RecordBatches.
RecordBatch
Sequence of RecordBatch to be converted, all schemas must be equal.
Schema, default None
If not passed, will be inferred from the first RecordBatch.
Table
Examples
>>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] >>> batch = pa.record_batch([n_legs, animals], names=names) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede
Construct a Table from a RecordBatch:
>>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
Construct a Table from a sequence of RecordBatches:
>>> pa.Table.from_batches([batch, batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]]
Convert pandas.DataFrame to an Arrow Table.
The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of object, we need to guess the datatype by looking at the Python objects in this Series.
Be aware that Series of the object dtype donât carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function.
pandas.DataFrame
pyarrow.Schema, optional
The expected schema of the Arrow Table. 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 Table. 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.
True
Check for overflows or other unsafe conversions.
Table
Examples
>>> import pyarrow as pa
>>> import pandas as pd
>>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100],
... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
>>> pa.Table.from_pandas(df)
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
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 Table from a StructArray.
Each field in the StructArray will become a column in the resulting Table.
StructArray or ChunkedArray
Array to construct the table from.
pyarrow.Table
Examples
>>> import pyarrow as pa
>>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'},
... {'year': 2022, 'n_legs': 4}])
>>> pa.Table.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 table.
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
>>> 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.get_total_buffer_size()
76
Declare a grouping over the columns of the table.
Resulting grouping can then be used to perform aggregations with a subsequent aggregate() method.
str or list[str]
Name of the columns that should be used as the grouping key.
True
Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed.
TableGroupBy
Examples
>>> 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.group_by('year').aggregate([('n_legs', 'sum')])
pyarrow.Table
year: int64
n_legs_sum: int64
----
year: [[2020,2022,2021,2019]]
n_legs_sum: [[2,6,104,5]]
Whether all ChunkedArrays 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
Perform a join between this table and another one.
Result of the join will be a new Table, where further operations can be applied.
Table
The table to join to the current one, acting as the right table in the join operation.
str or list[str]
The columns from current table that should be used as keys of the join operation left side.
str or list[str], default None
The columns from the right_table that should be used as keys on the join operation right side. When None use the same key names as the left table.
str, default âleft outerâ
The kind of join that should be performed, one of (âleft semiâ, âright semiâ, âleft antiâ, âright antiâ, âinnerâ, âleft outerâ, âright outerâ, âfull outerâ)
str, default None
Which suffix to add to left column names. This prevents confusion when the columns in left and right tables have colliding names.
str, default None
Which suffix to add to the right column names. This prevents confusion when the columns in left and right tables have colliding names.
True
If the duplicated keys should be omitted from one of the sides in the join result.
True
Whether to use multithreading or not.
Table
Examples
>>> import pandas as pd
>>> import pyarrow as pa
>>> df1 = pd.DataFrame({'id': [1, 2, 3],
... 'year': [2020, 2022, 2019]})
>>> df2 = pd.DataFrame({'id': [3, 4],
... 'n_legs': [5, 100],
... 'animal': ["Brittle stars", "Centipede"]})
>>> t1 = pa.Table.from_pandas(df1)
>>> t2 = pa.Table.from_pandas(df2)
Left outer join:
>>> t1.join(t2, 'id').combine_chunks().sort_by('year')
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2]]
year: [[2019,2020,2022]]
n_legs: [[5,null,null]]
animal: [["Brittle stars",null,null]]
Full outer join:
>>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year')
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2,4]]
year: [[2019,2020,2022,null]]
n_legs: [[5,null,null,100]]
animal: [["Brittle stars",null,null,"Centipede"]]
Right outer join:
>>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year')
pyarrow.Table
year: int64
id: int64
n_legs: int64
animal: string
----
year: [[2019,null]]
id: [[3,4]]
n_legs: [[5,100]]
animal: [["Brittle stars","Centipede"]]
Right anti join
>>> t1.join(t2, 'id', join_type="right anti") pyarrow.Table id: int64 n_legs: int64 animal: string ---- id: [[4]] n_legs: [[100]] animal: [["Centipede"]]
Perform an asof join between this table and another one.
This is similar to a left-join except that we match on nearest key rather than equal keys. Both tables must be sorted by the key. This type of join is most useful for time series data that are not perfectly aligned.
Optionally match on equivalent keys with âbyâ before searching with âonâ.
Result of the join will be a new Table, where further operations can be applied.
Table
The table to join to the current one, acting as the right table in the join operation.
str
The column from current table that should be used as the âonâ key of the join operation left side.
An inexact match is used on the âonâ key, i.e. a row is considered a match if and only if left_on - tolerance <= right_on <= left_on.
The input dataset must be sorted by the âonâ key. Must be a single field of a common type.
Currently, the âonâ key must be an integer, date, or timestamp type.
str or list[str]
The columns from current table that should be used as the keys of the join operation left side. The join operation is then done only for the matches in these columns.
int
The tolerance for inexact âonâ key matching. A right row is considered a match with the left row right.on - left.on <= tolerance. The tolerance may be:
negative, in which case a past-as-of-join occurs;
or positive, in which case a future-as-of-join occurs;
or zero, in which case an exact-as-of-join occurs.
The tolerance is interpreted in the same units as the âonâ key.
str or list[str], default None
The columns from the right_table that should be used as the on key on the join operation right side. When None use the same key name as the left table.
str or list[str], default None
The columns from the right_table that should be used as keys on the join operation right side. When None use the same key names as the left table.
Table
Total number of bytes consumed by the elements of the table.
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
>>> 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.nbytes
72
Number of columns in this table.
int
Examples
>>> 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.num_columns
2
Number of rows in this table.
Due to the definition of a table, all columns have the same number of rows.
int
Examples
>>> 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.num_rows
4
Create new Table with the indicated column removed.
int
Index of column to remove.
Table
New table 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"]})
>>> table = pa.Table.from_pandas(df)
>>> table.remove_column(1)
pyarrow.Table
n_legs: int64
----
n_legs: [[2,4,5,100]]
Create new table 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.
Table
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"]})
>>> table = pa.Table.from_pandas(df)
>>> new_names = ["n", "name"]
>>> table.rename_columns(new_names)
pyarrow.Table
n: int64
name: string
----
n: [[2,4,5,100]]
name: [["Flamingo","Horse","Brittle stars","Centipede"]]
>>> new_names = {"n_legs": "n", "animals": "name"}
>>> table.rename_columns(new_names)
pyarrow.Table
n: int64
name: string
----
n: [[2,4,5,100]]
name: [["Flamingo","Horse","Brittle stars","Centipede"]]
Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata.
dict, default None
Table
Examples
>>> 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)
Constructing a Table with pyarrow schema and metadata:
>>> my_schema = pa.schema([
... pa.field('n_legs', pa.int64()),
... pa.field('animals', pa.string())],
... metadata={"n_legs": "Number of legs per animal"})
>>> table= pa.table(df, my_schema)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
n_legs: 'Number of legs per animal'
pandas: ...
Create a shallow copy of a Table with deleted schema metadata:
>>> table.replace_schema_metadata().schema n_legs: int64 animals: string
Create a shallow copy of a Table with new schema metadata:
>>> metadata={"animals": "Which animal"}
>>> table.replace_schema_metadata(metadata = metadata).schema
n_legs: int64
animals: string
-- schema metadata --
animals: 'Which animal'
Schema of the table and its columns.
Schema
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)
>>> table.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ...
Select columns of the Table.
Returns a new Table with the specified columns, and metadata preserved.
The column names or integer indices to select.
Table
Examples
>>> 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.select([0,1])
pyarrow.Table
year: int64
n_legs: int64
----
year: [[2020,2022,2019,2021]]
n_legs: [[2,4,5,100]]
>>> table.select(["year"])
pyarrow.Table
year: int64
----
year: [[2020,2022,2019,2021]]
Replace column in Table 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, list of Array, or values coercible to arrays
Column data.
Table
New table 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"]})
>>> table = pa.Table.from_pandas(df)
Replace a column:
>>> year = [2021, 2022, 2019, 2021] >>> table.set_column(1,'year', [year]) pyarrow.Table 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 Table.
int, default 0
Offset from start of table to slice.
int, default None
Length of slice (default is until end of table starting from offset).
Table
Examples
>>> 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.slice(length=3)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2020,2022,2019]]
n_legs: [[2,4,5]]
animals: [["Flamingo","Horse","Brittle stars"]]
>>> table.slice(offset=2)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2019,2021]]
n_legs: [[5,100]]
animals: [["Brittle stars","Centipede"]]
>>> table.slice(offset=2, length=1)
pyarrow.Table
year: int64
n_legs: int64
animals: string
----
year: [[2019]]
n_legs: [[5]]
animals: [["Brittle stars"]]
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 Table to a list of RecordBatch objects.
Note that this method is zero-copy, it merely exposes the same data under a different API.
int, default None
Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns.
list[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"]})
>>> table = pa.Table.from_pandas(df)
Convert a Table to a RecordBatch:
>>> table.to_batches()[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede
Convert a Table to a list of RecordBatches:
>>> table.to_batches(max_chunksize=2)[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse >>> table.to_batches(max_chunksize=2)[1].to_pandas() n_legs animals 0 5 Brittle stars 1 100 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'}, ...
Convert the Table to a RecordBatchReader.
Note that this method is zero-copy, it merely exposes the same data under a different API.
int, default None
Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns.
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)
Convert a Table to a RecordBatchReader:
>>> table.to_reader() <pyarrow.lib.RecordBatchReader object at ...>
>>> reader = table.to_reader()
>>> reader.schema
n_legs: int64
animals: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ...
>>> reader.read_all()
pyarrow.Table
n_legs: int64
animals: string
----
n_legs: [[2,4,5,100]]
animals: [["Flamingo","Horse","Brittle stars","Centipede"]]
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 chunked array of struct type.
int, default None
Maximum number of rows for ChunkedArray chunks. Individual chunks may be smaller depending on the chunk layout of individual columns.
ChunkedArray
Unify dictionaries across all chunks.
This method returns an equivalent table, but where all chunks of each column share the same dictionary values. Dictionary indices are transposed accordingly.
Columns without dictionaries are returned unchanged.
MemoryPool, default None
For memory allocations, if required, otherwise use default pool
Table
Examples
>>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> table = pa.table([c_arr], names=["animals"]) >>> table pyarrow.Table animals: dictionary<values=string, indices=int32, ordered=0> ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog"] -- indices: [0,1,2], -- dictionary: ["Horse","Brittle stars","Centipede"] -- indices: [0,1,2]]
Unify dictionaries across both chunks:
>>> table.unify_dictionaries() pyarrow.Table animals: dictionary<values=string, indices=int32, ordered=0> ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [0,1,2], -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [3,4,5]]
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
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4