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def __post_init__(self):
self.name = str(self.name) # Make sure we convert NamedSplits in strings
NamedSplit(self.name) # check that it's a valid split name
self.split_info = SplitInfo(name=self.name) | 97 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/splits.py |
class IndexedTableMixin:
def __init__(self, table: pa.Table):
self._schema: pa.Schema = table.schema
self._batches: List[pa.RecordBatch] = [
recordbatch for recordbatch in table.to_batches() if len(recordbatch) > 0
]
self._offsets: np.ndarray = np.cumsum([0] + [len(b) for b in self._batches], dtype=np.int64) | 98 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def fast_gather(self, indices: Union[List[int], np.ndarray]) -> pa.Table:
"""
Create a pa.Table by gathering the records at the records at the specified indices. Should be faster
than pa.concat_tables(table.fast_slice(int(i) % table.num_rows, 1) for i in indices) since NumPy can compute
the binary searches in parallel, highly optimized C
"""
if not len(indices):
raise ValueError("Indices must be non-empty")
batch_indices = np.searchsorted(self._offsets, indices, side="right") - 1
return pa.Table.from_batches(
[
self._batches[batch_idx].slice(i - self._offsets[batch_idx], 1)
for batch_idx, i in zip(batch_indices, indices)
],
schema=self._schema,
) | 98 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def fast_slice(self, offset=0, length=None) -> pa.Table:
"""
Slice the Table using interpolation search.
The behavior is the same as `pyarrow.Table.slice` but it's significantly faster. | 98 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Interpolation search is used to find the start and end indexes of the batches we want to keep.
The batches to keep are then concatenated to form the sliced Table.
"""
if offset < 0:
raise IndexError("Offset must be non-negative")
elif offset >= self._offsets[-1] or (length is not None and length <= 0):
return pa.Table.from_batches([], schema=self._schema)
i = _interpolation_search(self._offsets, offset)
if length is None or length + offset >= self._offsets[-1]:
batches = self._batches[i:]
batches[0] = batches[0].slice(offset - self._offsets[i])
else:
j = _interpolation_search(self._offsets, offset + length - 1)
batches = self._batches[i : j + 1]
batches[-1] = batches[-1].slice(0, offset + length - self._offsets[j])
batches[0] = batches[0].slice(offset - self._offsets[i])
return pa.Table.from_batches(batches, schema=self._schema) | 98 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class Table(IndexedTableMixin):
"""
Wraps a pyarrow Table by using composition.
This is the base class for `InMemoryTable`, `MemoryMappedTable` and `ConcatenationTable`.
It implements all the basic attributes/methods of the pyarrow Table class except
the Table transforms: `slice, filter, flatten, combine_chunks, cast, add_column,
append_column, remove_column, set_column, rename_columns` and `drop`.
The implementation of these methods differs for the subclasses.
"""
def __init__(self, table: pa.Table):
super().__init__(table)
self.table = table | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def __deepcopy__(self, memo: dict):
# arrow tables are immutable, so there's no need to copy self.table
# moreover calling deepcopy on a pyarrow table seems to make pa.total_allocated_bytes() decrease for some reason
# by adding it to the memo, self.table won't be copied
memo[id(self.table)] = self.table
# same for the recordbatches used by the index
memo[id(self._batches)] = list(self._batches)
return _deepcopy(self, memo)
def validate(self, *args, **kwargs):
"""
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)`).
Args:
full (`bool`, defaults to `False`):
If `True`, run expensive checks, otherwise cheap checks only. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Raises:
`pa.lib.ArrowInvalid`: if validation fails
"""
return self.table.validate(*args, **kwargs)
def equals(self, *args, **kwargs):
"""
Check if contents of two tables are equal.
Args:
other ([`~datasets.table.Table`]):
Table to compare against.
check_metadata `bool`, defaults to `False`):
Whether schema metadata equality should be checked as well.
Returns:
`bool`
"""
args = tuple(arg.table if isinstance(arg, Table) else arg for arg in args)
kwargs = {k: v.table if isinstance(v, Table) else v for k, v in kwargs}
return self.table.equals(*args, **kwargs)
def to_batches(self, *args, **kwargs):
"""
Convert Table to list of (contiguous) `RecordBatch` objects. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
max_chunksize (`int`, defaults to `None`):
Maximum size for `RecordBatch` chunks. Individual chunks may be
smaller depending on the chunk layout of individual columns.
Returns:
`List[pyarrow.RecordBatch]`
"""
return self.table.to_batches(*args, **kwargs)
def to_pydict(self, *args, **kwargs):
"""
Convert the Table to a `dict` or `OrderedDict`.
Returns:
`dict`
"""
return self.table.to_pydict(*args, **kwargs)
def to_pylist(self, *args, **kwargs):
"""
Convert the Table to a list
Returns:
`list`
"""
return self.table.to_pylist(*args, **kwargs)
def to_pandas(self, *args, **kwargs):
"""
Convert to a pandas-compatible NumPy array or DataFrame, as appropriate. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
memory_pool (`MemoryPool`, defaults to `None`):
Arrow MemoryPool to use for allocations. Uses the default memory
pool is not passed.
strings_to_categorical (`bool`, defaults to `False`):
Encode string (UTF8) and binary types to `pandas.Categorical`.
categories (`list`, defaults to `empty`):
List of fields that should be returned as `pandas.Categorical`. Only
applies to table-like data structures.
zero_copy_only (`bool`, defaults to `False`):
Raise an `ArrowException` if this function call would require copying
the underlying data.
integer_object_nulls (`bool`, defaults to `False`):
Cast integers with nulls to objects.
date_as_object (`bool`, defaults to `True`):
Cast dates to objects. If `False`, convert to `datetime64[ns]` dtype. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
timestamp_as_object (`bool`, defaults to `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`, defaults to `True`):
Whether to parallelize the conversion using multiple threads.
deduplicate_objects (`bool`, defaults to `False`):
Do not create multiple copies Python objects when created, to save
on memory use. Conversion will be slower.
ignore_metadata (`bool`, defaults to `False`):
If `True`, do not use the 'pandas' metadata to reconstruct the
DataFrame index, if present.
safe (`bool`, defaults to `True`):
For certain data types, a cast is needed in order to store the | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
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`, defaults to `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`, defaults to `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`, defaults to `None`): | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
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. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`pandas.Series` or `pandas.DataFrame`: `pandas.Series` or `pandas.DataFrame` depending on type of object
"""
return self.table.to_pandas(*args, **kwargs)
def to_string(self, *args, **kwargs):
return self.table.to_string(*args, **kwargs)
def to_reader(self, max_chunksize: Optional[int] = None):
"""
Convert the Table to a RecordBatchReader.
Note that this method is zero-copy, it merely exposes the same data under a different API.
Args:
max_chunksize (`int`, defaults to `None`)
Maximum size for RecordBatch chunks. Individual chunks may be smaller depending
on the chunk layout of individual columns.
Returns:
`pyarrow.RecordBatchReader`
"""
return self.table.to_reader(max_chunksize=max_chunksize)
def field(self, *args, **kwargs):
"""
Select a schema field by its column name or numeric index. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
i (`Union[int, str]`):
The index or name of the field to retrieve.
Returns:
`pyarrow.Field`
"""
return self.table.field(*args, **kwargs)
def column(self, *args, **kwargs):
"""
Select a column by its column name, or numeric index.
Args:
i (`Union[int, str]`):
The index or name of the column to retrieve.
Returns:
`pyarrow.ChunkedArray`
"""
return self.table.column(*args, **kwargs)
def itercolumns(self, *args, **kwargs):
"""
Iterator over all columns in their numerical order.
Yields:
`pyarrow.ChunkedArray`
"""
return self.table.itercolumns(*args, **kwargs)
@property
def schema(self):
"""
Schema of the table and its columns.
Returns:
`pyarrow.Schema`
"""
return self.table.schema | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@property
def columns(self):
"""
List of all columns in numerical order.
Returns:
`List[pa.ChunkedArray]`
"""
return self.table.columns
@property
def num_columns(self):
"""
Number of columns in this table.
Returns:
int
"""
return self.table.num_columns
@property
def num_rows(self):
"""
Number of rows in this table.
Due to the definition of a table, all columns have the same number of
rows.
Returns:
int
"""
return self.table.num_rows
@property
def shape(self):
"""
Dimensions of the table: (#rows, #columns).
Returns:
`(int, int)`: Number of rows and number of columns.
"""
return self.table.shape | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@property
def nbytes(self):
"""
Total number of bytes consumed by the elements of the table.
"""
return self.table.nbytes
@property
def column_names(self):
"""
Names of the table's columns.
"""
return self.table.column_names
def __eq__(self, other):
return self.equals(other)
def __getitem__(self, i):
return self.table[i]
def __len__(self):
return len(self.table)
def __repr__(self):
return self.table.__repr__().replace("pyarrow.Table", self.__class__.__name__)
def __str__(self):
return self.table.__str__().replace("pyarrow.Table", self.__class__.__name__)
def slice(self, *args, **kwargs):
"""
Compute zero-copy slice of this Table. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
offset (`int`, defaults to `0`):
Offset from start of table to slice.
length (`int`, defaults to `None`):
Length of slice (default is until end of table starting from
offset).
Returns:
`datasets.table.Table`
"""
raise NotImplementedError()
def filter(self, *args, **kwargs):
"""
Select records from a Table. See `pyarrow.compute.filter` for full usage.
"""
raise NotImplementedError()
def flatten(self, *args, **kwargs):
"""
Flatten this Table. Each column with a struct type is flattened
into one column per struct field. Other columns are left unchanged.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
raise NotImplementedError() | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def combine_chunks(self, *args, **kwargs):
"""
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.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
raise NotImplementedError()
def cast(self, *args, **kwargs):
"""
Cast table values to another schema.
Args:
target_schema (`Schema`):
Schema to cast to, the names and order of fields must match.
safe (`bool`, defaults to `True`):
Check for overflows or other unsafe conversions.
Returns:
`datasets.table.Table`
"""
raise NotImplementedError() | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def replace_schema_metadata(self, *args, **kwargs):
"""
EXPERIMENTAL: 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
Args:
metadata (`dict`, defaults to `None`):
Returns:
`datasets.table.Table`: shallow_copy
"""
raise NotImplementedError()
def add_column(self, *args, **kwargs):
"""
Add column to Table at position.
A new table is returned with the column added, the original table
object is left unchanged.
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`: New table with the passed column added.
"""
raise NotImplementedError()
def append_column(self, *args, **kwargs):
"""
Append column at end of columns.
Args:
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`: New table with the passed column added.
"""
raise NotImplementedError()
def remove_column(self, *args, **kwargs):
"""
Create new Table with the indicated column removed.
Args:
i (`int`):
Index of column to remove.
Returns:
`datasets.table.Table`: New table without the column.
"""
raise NotImplementedError() | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def set_column(self, *args, **kwargs):
"""
Replace column in Table at position.
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`: New table with the passed column set.
"""
raise NotImplementedError()
def rename_columns(self, *args, **kwargs):
"""
Create new table with columns renamed to provided names.
"""
raise NotImplementedError()
def drop(self, *args, **kwargs):
"""
Drop one or more columns and return a new table.
Args:
columns (`List[str]`):
List of field names referencing existing columns. | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Raises:
`KeyError` : if any of the passed columns name are not existing.
Returns:
`datasets.table.Table`: New table without the columns.
"""
raise NotImplementedError()
def select(self, *args, **kwargs):
"""
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved.
Args:
columns (:obj:`Union[List[str], List[int]]`):
The column names or integer indices to select.
Returns:
`datasets.table.Table`: table with only a subset of the columns
"""
raise NotImplementedError() | 99 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class TableBlock(Table):
"""
`TableBlock` is the allowed class inside a `ConcanetationTable`.
Only `MemoryMappedTable` and `InMemoryTable` are `TableBlock`.
This is because we don't want a `ConcanetationTable` made out of other `ConcanetationTables`.
"""
pass | 100 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class InMemoryTable(TableBlock):
"""
The table is said in-memory when it is loaded into the user's RAM.
Pickling it does copy all the data using memory.
Its implementation is simple and uses the underlying pyarrow Table methods directly.
This is different from the `MemoryMapped` table, for which pickling doesn't copy all the
data in memory. For a `MemoryMapped`, unpickling instead reloads the table from the disk.
`InMemoryTable` must be used when data fit in memory, while `MemoryMapped` are reserved for
data bigger than memory or when you want the memory footprint of your application to
stay low.
"""
@classmethod
def from_file(cls, filename: str):
table = _in_memory_arrow_table_from_file(filename)
return cls(table)
@classmethod
def from_buffer(cls, buffer: pa.Buffer):
table = _in_memory_arrow_table_from_buffer(buffer)
return cls(table) | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@classmethod
def from_pandas(cls, *args, **kwargs):
"""
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 don't 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. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
df (`pandas.DataFrame`):
schema (`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.
preserve_index (`bool`, *optional*):
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. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
nthreads (`int`, defaults to `None` (may use up to system CPU count threads))
If greater than 1, convert columns to Arrow in parallel using
indicated number of threads.
columns (`List[str]`, *optional*):
List of column to be converted. If `None`, use all columns.
safe (`bool`, defaults to `True`):
Check for overflows or other unsafe conversions, | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
Examples:
```python
>>> import pandas as pd
>>> import pyarrow as pa
>>> df = pd.DataFrame({
... 'int': [1, 2],
... 'str': ['a', 'b']
... })
>>> pa.Table.from_pandas(df)
<pyarrow.lib.Table object at 0x7f05d1fb1b40>
```
"""
return cls(pa.Table.from_pandas(*args, **kwargs))
@classmethod
def from_arrays(cls, *args, **kwargs):
"""
Construct a Table from Arrow arrays. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
arrays (`List[Union[pyarrow.Array, pyarrow.ChunkedArray]]`):
Equal-length arrays that should form the table.
names (`List[str]`, *optional*):
Names for the table columns. If not passed, schema must be passed.
schema (`Schema`, defaults to `None`):
Schema for the created table. If not passed, names must be passed.
metadata (`Union[dict, Mapping]`, defaults to `None`):
Optional metadata for the schema (if inferred).
Returns:
`datasets.table.Table`
"""
return cls(pa.Table.from_arrays(*args, **kwargs))
@classmethod
def from_pydict(cls, *args, **kwargs):
"""
Construct a Table from Arrow arrays or columns. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
mapping (`Union[dict, Mapping]`):
A mapping of strings to Arrays or Python lists.
schema (`Schema`, defaults to `None`):
If not passed, will be inferred from the Mapping values
metadata (`Union[dict, Mapping]`, defaults to `None`):
Optional metadata for the schema (if inferred).
Returns:
`datasets.table.Table`
"""
return cls(pa.Table.from_pydict(*args, **kwargs))
@classmethod
def from_pylist(cls, mapping, *args, **kwargs):
"""
Construct a Table from list of rows / dictionaries.
Args:
mapping (`List[dict]`):
A mapping of strings to row values.
schema (`Schema`, defaults to `None`):
If not passed, will be inferred from the Mapping values
metadata (`Union[dict, Mapping]`, defaults to `None`):
Optional metadata for the schema (if inferred). | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
return cls(pa.Table.from_pylist(mapping, *args, **kwargs))
@classmethod
def from_batches(cls, *args, **kwargs):
"""
Construct a Table from a sequence or iterator of Arrow `RecordBatches`.
Args:
batches (`Union[Sequence[pyarrow.RecordBatch], Iterator[pyarrow.RecordBatch]]`):
Sequence of `RecordBatch` to be converted, all schemas must be equal.
schema (`Schema`, defaults to `None`):
If not passed, will be inferred from the first `RecordBatch`.
Returns:
`datasets.table.Table`:
"""
return cls(pa.Table.from_batches(*args, **kwargs))
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this Table. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
offset (`int`, defaults to `0`):
Offset from start of table to slice.
length (`int`, defaults to `None`):
Length of slice (default is until end of table starting from
offset).
Returns:
`datasets.table.Table`
"""
# Use fast slicing here
return InMemoryTable(self.fast_slice(offset=offset, length=length))
def filter(self, *args, **kwargs):
"""
Select records from a Table. See `pyarrow.compute.filter` for full usage.
"""
return InMemoryTable(self.table.filter(*args, **kwargs))
def flatten(self, *args, **kwargs):
"""
Flatten this Table. Each column with a struct type is flattened
into one column per struct field. Other columns are left unchanged.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
return InMemoryTable(table_flatten(self.table, *args, **kwargs))
def combine_chunks(self, *args, **kwargs):
"""
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.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
return InMemoryTable(self.table.combine_chunks(*args, **kwargs))
def cast(self, *args, **kwargs):
"""
Cast table values to another schema.
Args:
target_schema (`Schema`):
Schema to cast to, the names and order of fields must match.
safe (`bool`, defaults to `True`):
Check for overflows or other unsafe conversions. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
return InMemoryTable(table_cast(self.table, *args, **kwargs))
def replace_schema_metadata(self, *args, **kwargs):
"""
EXPERIMENTAL: 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).
Args:
metadata (`dict`, defaults to `None`):
Returns:
`datasets.table.Table`: shallow_copy
"""
return InMemoryTable(self.table.replace_schema_metadata(*args, **kwargs))
def add_column(self, *args, **kwargs):
"""
Add column to Table at position.
A new table is returned with the column added, the original table
object is left unchanged. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`: New table with the passed column added.
"""
return InMemoryTable(self.table.add_column(*args, **kwargs))
def append_column(self, *args, **kwargs):
"""
Append column at end of columns.
Args:
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
New table with the passed column added.
"""
return InMemoryTable(self.table.append_column(*args, **kwargs))
def remove_column(self, *args, **kwargs):
"""
Create new Table with the indicated column removed.
Args:
i (`int`):
Index of column to remove.
Returns:
`datasets.table.Table`:
New table without the column.
"""
return InMemoryTable(self.table.remove_column(*args, **kwargs))
def set_column(self, *args, **kwargs):
"""
Replace column in Table at position.
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
New table with the passed column set.
"""
return InMemoryTable(self.table.set_column(*args, **kwargs))
def rename_columns(self, *args, **kwargs):
"""
Create new table with columns renamed to provided names.
"""
return InMemoryTable(self.table.rename_columns(*args, **kwargs))
def drop(self, *args, **kwargs):
"""
Drop one or more columns and return a new table.
Args:
columns (`List[str]`):
List of field names referencing existing columns.
Raises:
`KeyError` : if any of the passed columns name are not existing.
Returns:
`datasets.table.Table`:
New table without the columns.
"""
return InMemoryTable(self.table.drop(*args, **kwargs))
def select(self, *args, **kwargs):
"""
Select columns of the table. | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns a new table with the specified columns, and metadata preserved.
Args:
columns (:obj:`Union[List[str], List[int]]`):
The column names or integer indices to select.
Returns:
:class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
"""
return InMemoryTable(self.table.select(*args, **kwargs)) | 101 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class MemoryMappedTable(TableBlock):
"""
The table is said memory mapped when it doesn't use the user's RAM but loads the data
from the disk instead.
Pickling it doesn't copy the data into memory.
Instead, only the path to the memory mapped arrow file is pickled, as well as the list
of transforms to "replay" when reloading the table from the disk.
Its implementation requires to store an history of all the transforms that were applied
to the underlying pyarrow Table, so that they can be "replayed" when reloading the Table
from the disk.
This is different from the `InMemoryTable` table, for which pickling does copy all the
data in memory.
`InMemoryTable` must be used when data fit in memory, while `MemoryMapped` are reserved for
data bigger than memory or when you want the memory footprint of your application to
stay low.
""" | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def __init__(self, table: pa.Table, path: str, replays: Optional[List[Replay]] = None):
super().__init__(table)
self.path = os.path.abspath(path)
self.replays: List[Replay] = replays if replays is not None else []
@classmethod
def from_file(cls, filename: str, replays=None):
table = _memory_mapped_arrow_table_from_file(filename)
table = cls._apply_replays(table, replays)
return cls(table, filename, replays)
def __getstate__(self):
return {"path": self.path, "replays": self.replays}
def __setstate__(self, state):
path = state["path"]
replays = state["replays"]
table = _memory_mapped_arrow_table_from_file(path)
table = self._apply_replays(table, replays)
MemoryMappedTable.__init__(self, table, path=path, replays=replays) | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@staticmethod
def _apply_replays(table: pa.Table, replays: Optional[List[Replay]] = None) -> pa.Table:
if replays is not None:
for name, args, kwargs in replays:
if name == "cast":
table = table_cast(table, *args, **kwargs)
elif name == "flatten":
table = table_flatten(table, *args, **kwargs)
else:
table = getattr(table, name)(*args, **kwargs)
return table
def _append_replay(self, replay: Replay) -> List[Replay]:
replays = copy.deepcopy(self.replays)
replays.append(replay)
return replays
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this Table. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
offset (`int`, defaults to `0`):
Offset from start of table to slice.
length (`int`, defaults to `None`):
Length of slice (default is until end of table starting from
offset).
Returns:
`datasets.table.Table`
"""
replay = ("slice", (offset, length), {})
replays = self._append_replay(replay)
# Use fast slicing here
return MemoryMappedTable(self.fast_slice(offset=offset, length=length), self.path, replays)
def filter(self, *args, **kwargs):
"""
Select records from a Table. See `pyarrow.compute.filter` for full usage.
"""
replay = ("filter", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.filter(*args, **kwargs), self.path, replays) | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def flatten(self, *args, **kwargs):
"""
Flatten this Table. Each column with a struct type is flattened
into one column per struct field. Other columns are left unchanged.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
replay = ("flatten", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(table_flatten(self.table, *args, **kwargs), self.path, replays)
def combine_chunks(self, *args, **kwargs):
"""
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. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
replay = ("combine_chunks", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.combine_chunks(*args, **kwargs), self.path, replays)
def cast(self, *args, **kwargs):
"""
Cast table values to another schema
Args:
target_schema (`Schema`):
Schema to cast to, the names and order of fields must match.
safe (`bool`, defaults to `True`):
Check for overflows or other unsafe conversions. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
replay = ("cast", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(table_cast(self.table, *args, **kwargs), self.path, replays)
def replace_schema_metadata(self, *args, **kwargs):
"""
EXPERIMENTAL: 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.
Args:
metadata (`dict`, defaults to `None`):
Returns:
`datasets.table.Table`: shallow_copy
"""
replay = ("replace_schema_metadata", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.replace_schema_metadata(*args, **kwargs), self.path, replays)
def add_column(self, *args, **kwargs):
"""
Add column to Table at position. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
A new table is returned with the column added, the original table
object is left unchanged.
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`: New table with the passed column added.
"""
replay = ("add_column", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.add_column(*args, **kwargs), self.path, replays)
def append_column(self, *args, **kwargs):
"""
Append column at end of columns. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`:
New table with the passed column added.
"""
replay = ("append_column", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.append_column(*args, **kwargs), self.path, replays)
def remove_column(self, *args, **kwargs):
"""
Create new Table with the indicated column removed.
Args:
i (`int`):
Index of column to remove. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
New table without the column.
"""
replay = ("remove_column", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.remove_column(*args, **kwargs), self.path, replays)
def set_column(self, *args, **kwargs):
"""
Replace column in Table at position.
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
New table with the passed column set.
"""
replay = ("set_column", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.set_column(*args, **kwargs), self.path, replays)
def rename_columns(self, *args, **kwargs):
"""
Create new table with columns renamed to provided names.
"""
replay = ("rename_columns", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.rename_columns(*args, **kwargs), self.path, replays)
def drop(self, *args, **kwargs):
"""
Drop one or more columns and return a new table.
Args:
columns (`List[str]`):
List of field names referencing existing columns.
Raises:
`KeyError` : if any of the passed columns name are not existing. | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`:
New table without the columns.
"""
replay = ("drop", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.drop(*args, **kwargs), self.path, replays)
def select(self, *args, **kwargs):
"""
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved.
Args:
columns (:obj:`Union[List[str], List[int]]`):
The column names or integer indices to select.
Returns:
:class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
"""
replay = ("select", copy.deepcopy(args), copy.deepcopy(kwargs))
replays = self._append_replay(replay)
return MemoryMappedTable(self.table.select(*args, **kwargs), self.path, replays) | 102 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class ConcatenationTable(Table):
"""
The table comes from the concatenation of several tables called blocks.
It enables concatenation on both axis 0 (append rows) and axis 1 (append columns).
The underlying tables are called "blocks" and can be either `InMemoryTable`
or `MemoryMappedTable` objects.
This allows to combine tables that come from memory or that are memory mapped.
When a `ConcatenationTable` is pickled, then each block is pickled:
- the `InMemoryTable` objects are pickled by copying all the data in memory.
- the MemoryMappedTable objects are pickled without copying the data into memory.
Instead, only the path to the memory mapped arrow file is pickled, as well as the list
of transforms to "replays" when reloading the table from the disk. | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Its implementation requires to store each block separately.
The `blocks` attributes stores a list of list of blocks.
The first axis concatenates the tables along the axis 0 (it appends rows),
while the second axis concatenates tables along the axis 1 (it appends columns).
If some columns are missing when concatenating on axis 0, they are filled with null values.
This is done using `pyarrow.concat_tables(tables, promote=True)`.
You can access the fully combined table by accessing the `ConcatenationTable.table` attribute,
and the blocks by accessing the `ConcatenationTable.blocks` attribute.
""" | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def __init__(self, table: pa.Table, blocks: List[List[TableBlock]]):
super().__init__(table)
self.blocks = blocks
# Check that all the blocks have the right type.
# Only InMemoryTable and MemoryMappedTable are allowed.
for subtables in blocks:
for subtable in subtables:
if not isinstance(subtable, TableBlock):
raise TypeError(
"The blocks of a ConcatenationTable must be InMemoryTable or MemoryMappedTable objects"
f", but got {_short_str(subtable)}."
)
def __getstate__(self):
return {"blocks": self.blocks, "schema": self.table.schema} | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def __setstate__(self, state):
blocks = state["blocks"]
schema = state["schema"]
table = self._concat_blocks_horizontally_and_vertically(blocks)
if schema is not None and table.schema != schema:
# We fix the columns by concatenating with an empty table with the right columns
empty_table = pa.Table.from_batches([], schema=schema)
# We set promote_options="default" to fill missing columns with null values
table = pa.concat_tables([table, empty_table], promote_options="default")
ConcatenationTable.__init__(self, table, blocks=blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@staticmethod
def _concat_blocks(blocks: List[Union[TableBlock, pa.Table]], axis: int = 0) -> pa.Table:
pa_tables = [table.table if hasattr(table, "table") else table for table in blocks]
if axis == 0:
# We set promote_options="default" to fill missing columns with null values
return pa.concat_tables(pa_tables, promote_options="default")
elif axis == 1:
for i, table in enumerate(pa_tables):
if i == 0:
pa_table = table
else:
for name, col in zip(table.column_names, table.columns):
pa_table = pa_table.append_column(name, col)
return pa_table
else:
raise ValueError("'axis' must be either 0 or 1") | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@classmethod
def _concat_blocks_horizontally_and_vertically(cls, blocks: List[List[TableBlock]]) -> pa.Table:
pa_tables_to_concat_vertically = []
for i, tables in enumerate(blocks):
if not tables:
continue
pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)
pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)
return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@classmethod
def _merge_blocks(cls, blocks: TableBlockContainer, axis: Optional[int] = None) -> TableBlockContainer:
if axis is not None:
merged_blocks = []
for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)):
if is_in_memory:
block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]
merged_blocks += list(block_group)
else: # both
merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
if all(len(row_block) == 1 for row_block in merged_blocks):
merged_blocks = cls._merge_blocks(
[block for row_block in merged_blocks for block in row_block], axis=0
)
return merged_blocks | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@classmethod
def _consolidate_blocks(cls, blocks: TableBlockContainer) -> TableBlockContainer:
if isinstance(blocks, TableBlock):
return blocks
elif isinstance(blocks[0], TableBlock):
return cls._merge_blocks(blocks, axis=0)
else:
return cls._merge_blocks(blocks)
@classmethod
def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable":
blocks = cls._consolidate_blocks(blocks)
if isinstance(blocks, TableBlock):
table = blocks
return cls(table.table, [[table]])
elif isinstance(blocks[0], TableBlock):
table = cls._concat_blocks(blocks, axis=0)
blocks = [[t] for t in blocks]
return cls(table, blocks)
else:
table = cls._concat_blocks_horizontally_and_vertically(blocks)
return cls(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
@classmethod
def from_tables(cls, tables: List[Union[pa.Table, Table]], axis: int = 0) -> "ConcatenationTable":
"""Create `ConcatenationTable` from list of tables.
Args:
tables (list of `Table` or list of `pyarrow.Table`):
List of tables.
axis (`{0, 1}`, defaults to `0`, meaning over rows):
Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
(horizontally).
<Added version="1.6.0"/>
"""
def to_blocks(table: Union[pa.Table, Table]) -> List[List[TableBlock]]:
if isinstance(table, pa.Table):
return [[InMemoryTable(table)]]
elif isinstance(table, ConcatenationTable):
return copy.deepcopy(table.blocks)
else:
return [[table]] | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def _slice_row_block(row_block: List[TableBlock], length: int) -> Tuple[List[TableBlock], List[TableBlock]]:
sliced = [table.slice(0, length) for table in row_block]
remainder = [table.slice(length, len(row_block[0]) - length) for table in row_block]
return sliced, remainder
def _split_both_like(
result: List[List[TableBlock]], blocks: List[List[TableBlock]]
) -> Tuple[List[List[TableBlock]], List[List[TableBlock]]]:
"""
Make sure each row_block contain the same num_rows to be able to concatenate them on axis=1.
To do so, we modify both blocks sets to have the same row_blocks boundaries.
For example, if `result` has 2 row_blocks of 3 rows and `blocks` has 3 row_blocks of 2 rows,
we modify both to have 4 row_blocks of size 2, 1, 1 and 2: | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
[ x x x | x x x ]
+ [ y y | y y | y y ]
-----------------------------
= [ x x | x | x | x x ]
[ y y | y | y | y y ] | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
"""
result, blocks = list(result), list(blocks)
new_result, new_blocks = [], []
while result and blocks:
# we slice the longest row block to save two row blocks of same length
# and we replace the long row block by its remainder if necessary
if len(result[0][0]) > len(blocks[0][0]):
new_blocks.append(blocks[0])
sliced, result[0] = _slice_row_block(result[0], len(blocks.pop(0)[0]))
new_result.append(sliced)
elif len(result[0][0]) < len(blocks[0][0]):
new_result.append(result[0])
sliced, blocks[0] = _slice_row_block(blocks[0], len(result.pop(0)[0]))
new_blocks.append(sliced)
else:
new_result.append(result.pop(0))
new_blocks.append(blocks.pop(0))
if result or blocks: | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
raise ValueError("Failed to concatenate on axis=1 because tables don't have the same number of rows")
return new_result, new_blocks | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def _extend_blocks(
result: List[List[TableBlock]], blocks: List[List[TableBlock]], axis: int = 0
) -> List[List[TableBlock]]:
if axis == 0:
result.extend(blocks)
elif axis == 1:
# We make sure each row_block have the same num_rows
result, blocks = _split_both_like(result, blocks)
for i, row_block in enumerate(blocks):
result[i].extend(row_block)
return result
blocks = to_blocks(tables[0])
for table in tables[1:]:
table_blocks = to_blocks(table)
blocks = _extend_blocks(blocks, table_blocks, axis=axis)
return cls.from_blocks(blocks)
@property
def _slices(self):
offset = 0
for tables in self.blocks:
length = len(tables[0])
yield (offset, length)
offset += length | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def slice(self, offset=0, length=None):
"""
Compute zero-copy slice of this Table.
Args:
offset (`int`, defaults to `0`):
Offset from start of table to slice.
length (`int`, defaults to `None`):
Length of slice (default is until end of table starting from
offset). | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
table = self.table.slice(offset, length=length)
length = length if length is not None else self.num_rows - offset
blocks = []
for tables in self.blocks:
n_rows = len(tables[0])
if length == 0:
break
elif n_rows <= offset:
offset = offset - n_rows
elif n_rows <= offset + length:
blocks.append([t.slice(offset) for t in tables])
length, offset = length + offset - n_rows, 0
else:
blocks.append([t.slice(offset, length) for t in tables])
length, offset = 0, 0
return ConcatenationTable(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def filter(self, mask, *args, **kwargs):
"""
Select records from a Table. See `pyarrow.compute.filter` for full usage.
"""
table = self.table.filter(mask, *args, **kwargs)
blocks = []
for (offset, length), tables in zip(self._slices, self.blocks):
submask = mask.slice(offset, length)
blocks.append([t.filter(submask, *args, **kwargs) for t in tables])
return ConcatenationTable(table, blocks)
def flatten(self, *args, **kwargs):
"""
Flatten this Table. Each column with a struct type is flattened
into one column per struct field. Other columns are left unchanged.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool. | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Returns:
`datasets.table.Table`
"""
table = table_flatten(self.table, *args, **kwargs)
blocks = []
for tables in self.blocks:
blocks.append([t.flatten(*args, **kwargs) for t in tables])
return ConcatenationTable(table, blocks)
def combine_chunks(self, *args, **kwargs):
"""
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.
Args:
memory_pool (`MemoryPool`, defaults to `None`):
For memory allocations, if required, otherwise use default pool.
Returns:
`datasets.table.Table`
"""
table = self.table.combine_chunks(*args, **kwargs)
blocks = []
for tables in self.blocks:
blocks.append([t.combine_chunks(*args, **kwargs) for t in tables])
return ConcatenationTable(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def cast(self, target_schema, *args, **kwargs):
"""
Cast table values to another schema.
Args:
target_schema (`Schema`):
Schema to cast to, the names and order of fields must match.
safe (`bool`, defaults to `True`):
Check for overflows or other unsafe conversions.
Returns:
`datasets.table.Table`
"""
from .features import Features | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
table = table_cast(self.table, target_schema, *args, **kwargs)
target_features = Features.from_arrow_schema(target_schema)
blocks = []
for subtables in self.blocks:
new_tables = []
fields = list(target_schema)
for subtable in subtables:
subfields = []
for name in subtable.column_names:
subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name)))
subfeatures = Features({subfield.name: target_features[subfield.name] for subfield in subfields})
subschema = subfeatures.arrow_schema
new_tables.append(subtable.cast(subschema, *args, **kwargs))
blocks.append(new_tables)
return ConcatenationTable(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def replace_schema_metadata(self, *args, **kwargs):
"""
EXPERIMENTAL: 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).
Args:
metadata (`dict`, defaults to `None`):
Returns:
`datasets.table.Table`: shallow_copy
"""
table = self.table.replace_schema_metadata(*args, **kwargs)
blocks = []
for tables in self.blocks:
blocks.append([t.replace_schema_metadata(*args, **kwargs) for t in tables])
return ConcatenationTable(table, self.blocks)
def add_column(self, *args, **kwargs):
"""
Add column to Table at position.
A new table is returned with the column added, the original table
object is left unchanged. | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`: New table with the passed column added.
"""
raise NotImplementedError()
def append_column(self, *args, **kwargs):
"""
Append column at end of columns.
Args:
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`:
New table with the passed column added.
"""
raise NotImplementedError() | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def remove_column(self, i, *args, **kwargs):
"""
Create new Table with the indicated column removed.
Args:
i (`int`):
Index of column to remove.
Returns:
`datasets.table.Table`:
New table without the column.
"""
table = self.table.remove_column(i, *args, **kwargs)
name = self.table.column_names[i]
blocks = []
for tables in self.blocks:
blocks.append(
[
t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t
for t in tables
]
)
return ConcatenationTable(table, blocks)
def set_column(self, *args, **kwargs):
"""
Replace column in Table at position. | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
i (`int`):
Index to place the column at.
field_ (`Union[str, pyarrow.Field]`):
If a string is passed then the type is deduced from the column
data.
column (`Union[pyarrow.Array, List[pyarrow.Array]]`):
Column data.
Returns:
`datasets.table.Table`:
New table with the passed column set.
"""
raise NotImplementedError()
def rename_columns(self, names, *args, **kwargs):
"""
Create new table with columns renamed to provided names.
"""
table = self.table.rename_columns(names, *args, **kwargs)
names = dict(zip(self.table.column_names, names))
blocks = []
for tables in self.blocks:
blocks.append(
[t.rename_columns([names[name] for name in t.column_names], *args, **kwargs) for t in tables]
)
return ConcatenationTable(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def drop(self, columns, *args, **kwargs):
"""
Drop one or more columns and return a new table.
Args:
columns (`List[str]`):
List of field names referencing existing columns.
Raises:
`KeyError` : if any of the passed columns name are not existing.
Returns:
`datasets.table.Table`:
New table without the columns.
"""
table = self.table.drop(columns, *args, **kwargs)
blocks = []
for tables in self.blocks:
blocks.append([t.drop([c for c in columns if c in t.column_names], *args, **kwargs) for t in tables])
return ConcatenationTable(table, blocks)
def select(self, columns, *args, **kwargs):
"""
Select columns of the table.
Returns a new table with the specified columns, and metadata preserved. | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
Args:
columns (:obj:`Union[List[str], List[int]]`):
The column names or integer indices to select.
Returns:
:class:`datasets.table.Table`: New table with the specified columns, and metadata preserved.
"""
table = self.table.select(columns, *args, **kwargs)
blocks = []
for tables in self.blocks:
blocks.append([t.select([c for c in columns if c in t.column_names], *args, **kwargs) for t in tables])
return ConcatenationTable(table, blocks) | 103 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class CastError(ValueError):
"""When it's not possible to cast an Arrow table to a specific schema or set of features"""
def __init__(self, *args, table_column_names: List[str], requested_column_names: List[str]) -> None:
super().__init__(*args)
self.table_column_names = table_column_names
self.requested_column_names = requested_column_names
def __reduce__(self):
# Fix unpickling: TypeError: __init__() missing 2 required keyword-only arguments: 'table_column_names' and 'requested_column_names'
return partial(
CastError, table_column_names=self.table_column_names, requested_column_names=self.requested_column_names
), () | 104 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
def details(self):
new_columns = set(self.table_column_names) - set(self.requested_column_names)
missing_columns = set(self.requested_column_names) - set(self.table_column_names)
if new_columns and missing_columns:
return f"there are {len(new_columns)} new columns ({_short_str(new_columns)}) and {len(missing_columns)} missing columns ({_short_str(missing_columns)})."
elif new_columns:
return f"there are {len(new_columns)} new columns ({_short_str(new_columns)})"
else:
return f"there are {len(missing_columns)} missing columns ({_short_str(missing_columns)})" | 104 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/table.py |
class _InitializeConfiguredDatasetBuilder:
"""
From https://stackoverflow.com/questions/4647566/pickle-a-dynamically-parameterized-sub-class
See also ConfiguredDatasetBuilder.__reduce__
When called with the param value as the only argument, returns an
un-initialized instance of the parameterized class. Subsequent __setstate__
will be called by pickle.
"""
def __call__(self, builder_cls, metadata_configs, default_config_name, name):
# make a simple object which has no complex __init__ (this one will do)
obj = _InitializeConfiguredDatasetBuilder()
obj.__class__ = configure_builder_class(
builder_cls, metadata_configs, default_config_name=default_config_name, dataset_name=name
)
return obj | 105 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class ConfiguredDatasetBuilder(builder_cls):
BUILDER_CONFIGS = builder_configs
DEFAULT_CONFIG_NAME = default_config_name
__module__ = builder_cls.__module__ # so that the actual packaged builder can be imported
def __reduce__(self): # to make dynamically created class pickable, see _InitializeParameterizedDatasetBuilder
parent_builder_cls = self.__class__.__mro__[1]
return (
_InitializeConfiguredDatasetBuilder(),
(
parent_builder_cls,
self.BUILDER_CONFIGS,
self.DEFAULT_CONFIG_NAME,
self.dataset_name,
),
self.__dict__.copy(),
) | 106 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class BuilderConfigsParameters:
"""Dataclass containing objects related to creation of builder configurations from yaml's metadata content.
Attributes:
metadata_configs (`MetadataConfigs`, *optional*):
Configs parsed from yaml's metadata.
builder_configs (`list[BuilderConfig]`, *optional*):
List of BuilderConfig objects created from metadata_configs above.
default_config_name (`str`):
Name of default config taken from yaml's metadata.
"""
metadata_configs: Optional[MetadataConfigs] = None
builder_configs: Optional[List[BuilderConfig]] = None
default_config_name: Optional[str] = None | 107 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class DatasetModule:
module_path: str
hash: str
builder_kwargs: dict
builder_configs_parameters: BuilderConfigsParameters = field(default_factory=BuilderConfigsParameters)
dataset_infos: Optional[DatasetInfosDict] = None
importable_file_path: Optional[str] = None | 108 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class _DatasetModuleFactory:
def get_module(self) -> DatasetModule:
raise NotImplementedError | 109 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class LocalDatasetModuleFactoryWithScript(_DatasetModuleFactory):
"""Get the module of a local dataset. The dataset script is loaded from a local script."""
def __init__(
self,
path: str,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
dynamic_modules_path: Optional[str] = None,
trust_remote_code: Optional[bool] = None,
):
self.path = path
self.name = Path(path).stem
self.download_config = download_config or DownloadConfig()
self.download_mode = download_mode
self.dynamic_modules_path = dynamic_modules_path
self.trust_remote_code = trust_remote_code | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
def get_module(self) -> DatasetModule:
if config.HF_DATASETS_TRUST_REMOTE_CODE and self.trust_remote_code is None:
warnings.warn(
f"The repository for {self.name} contains custom code which must be executed to correctly "
f"load the dataset. You can inspect the repository content at {self.path}\n"
f"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n"
f"Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.",
FutureWarning,
)
# get script and other files
dataset_infos_path = Path(self.path).parent / config.DATASETDICT_INFOS_FILENAME
dataset_readme_path = Path(self.path).parent / config.REPOCARD_FILENAME
imports = get_imports(self.path)
local_imports, library_imports = _download_additional_modules(
name=self.name, | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
base_path=str(Path(self.path).parent),
imports=imports,
download_config=self.download_config,
)
additional_files = []
if dataset_infos_path.is_file():
additional_files.append((config.DATASETDICT_INFOS_FILENAME, str(dataset_infos_path)))
if dataset_readme_path.is_file():
additional_files.append((config.REPOCARD_FILENAME, dataset_readme_path))
# copy the script and the files in an importable directory
dynamic_modules_path = self.dynamic_modules_path if self.dynamic_modules_path else init_dynamic_modules()
hash = files_to_hash([self.path] + [loc[1] for loc in local_imports])
importable_file_path = _get_importable_file_path(
dynamic_modules_path=dynamic_modules_path,
module_namespace="datasets",
subdirectory_name=hash,
name=self.name,
)
if not os.path.exists(importable_file_path): | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
trust_remote_code = resolve_trust_remote_code(self.trust_remote_code, self.name)
if trust_remote_code:
_create_importable_file(
local_path=self.path,
local_imports=local_imports,
additional_files=additional_files,
dynamic_modules_path=dynamic_modules_path,
module_namespace="datasets",
subdirectory_name=hash,
name=self.name,
download_mode=self.download_mode,
)
else:
raise ValueError(
f"Loading {self.name} requires you to execute the dataset script in that"
" repo on your local machine. Make sure you have read the code there to avoid malicious use, then"
" set the option `trust_remote_code=True` to remove this error."
) | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
_check_library_imports(name=self.name, library_imports=library_imports)
module_path, hash = _load_importable_file(
dynamic_modules_path=dynamic_modules_path,
module_namespace="datasets",
subdirectory_name=hash,
name=self.name,
) | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
# make the new module to be noticed by the import system
importlib.invalidate_caches()
builder_kwargs = {"base_path": str(Path(self.path).parent)}
return DatasetModule(module_path, hash, builder_kwargs, importable_file_path=importable_file_path) | 110 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class LocalDatasetModuleFactoryWithoutScript(_DatasetModuleFactory):
"""Get the module of a dataset loaded from the user's data files. The dataset builder module to use is inferred
from the data files extensions."""
def __init__(
self,
path: str,
data_dir: Optional[str] = None,
data_files: Optional[Union[str, List, Dict]] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
):
if data_dir and os.path.isabs(data_dir):
raise ValueError(f"`data_dir` must be relative to a dataset directory's root: {path}")
self.path = Path(path).as_posix()
self.name = Path(path).stem
self.data_files = data_files
self.data_dir = data_dir
self.download_mode = download_mode | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
def get_module(self) -> DatasetModule:
readme_path = os.path.join(self.path, config.REPOCARD_FILENAME)
standalone_yaml_path = os.path.join(self.path, config.REPOYAML_FILENAME)
dataset_card_data = DatasetCard.load(readme_path).data if os.path.isfile(readme_path) else DatasetCardData()
if os.path.exists(standalone_yaml_path):
with open(standalone_yaml_path, "r", encoding="utf-8") as f:
standalone_yaml_data = yaml.safe_load(f.read())
if standalone_yaml_data:
_dataset_card_data_dict = dataset_card_data.to_dict()
_dataset_card_data_dict.update(standalone_yaml_data)
dataset_card_data = DatasetCardData(**_dataset_card_data_dict)
metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data)
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
# we need a set of data files to find which dataset builder to use | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
# because we need to infer module name by files extensions
base_path = Path(self.path, self.data_dir or "").expanduser().resolve().as_posix()
if self.data_files is not None:
patterns = sanitize_patterns(self.data_files)
elif metadata_configs and not self.data_dir and "data_files" in next(iter(metadata_configs.values())):
patterns = sanitize_patterns(next(iter(metadata_configs.values()))["data_files"])
else:
patterns = get_data_patterns(base_path)
data_files = DataFilesDict.from_patterns(
patterns,
base_path=base_path,
allowed_extensions=ALL_ALLOWED_EXTENSIONS,
)
module_name, default_builder_kwargs = infer_module_for_data_files(
data_files=data_files,
path=self.path,
)
data_files = data_files.filter_extensions(_MODULE_TO_EXTENSIONS[module_name])
# Collect metadata files if the module supports them | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
supports_metadata = module_name in _MODULE_SUPPORTS_METADATA
if self.data_files is None and supports_metadata:
try:
metadata_patterns = get_metadata_patterns(base_path)
except FileNotFoundError:
metadata_patterns = None
if metadata_patterns is not None:
metadata_data_files_list = DataFilesList.from_patterns(metadata_patterns, base_path=base_path)
if metadata_data_files_list:
data_files = DataFilesDict(
{
split: data_files_list + metadata_data_files_list
for split, data_files_list in data_files.items()
}
) | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
module_path, _ = _PACKAGED_DATASETS_MODULES[module_name]
if metadata_configs:
builder_configs, default_config_name = create_builder_configs_from_metadata_configs(
module_path,
metadata_configs,
base_path=base_path,
supports_metadata=supports_metadata,
default_builder_kwargs=default_builder_kwargs,
)
else:
builder_configs: List[BuilderConfig] = [
import_main_class(module_path).BUILDER_CONFIG_CLASS(
data_files=data_files,
**default_builder_kwargs,
)
]
default_config_name = None
builder_kwargs = {
"base_path": self.path,
"dataset_name": camelcase_to_snakecase(Path(self.path).name),
}
if self.data_dir:
builder_kwargs["data_files"] = data_files | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
# this file is deprecated and was created automatically in old versions of push_to_hub
if os.path.isfile(os.path.join(self.path, config.DATASETDICT_INFOS_FILENAME)):
with open(os.path.join(self.path, config.DATASETDICT_INFOS_FILENAME), encoding="utf-8") as f:
legacy_dataset_infos = DatasetInfosDict(
{
config_name: DatasetInfo.from_dict(dataset_info_dict)
for config_name, dataset_info_dict in json.load(f).items()
}
)
if len(legacy_dataset_infos) == 1:
# old config e.g. named "username--dataset_name"
legacy_config_name = next(iter(legacy_dataset_infos))
legacy_dataset_infos["default"] = legacy_dataset_infos.pop(legacy_config_name)
legacy_dataset_infos.update(dataset_infos)
dataset_infos = legacy_dataset_infos | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
if default_config_name is None and len(dataset_infos) == 1:
default_config_name = next(iter(dataset_infos)) | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
hash = Hasher.hash({"dataset_infos": dataset_infos, "builder_configs": builder_configs})
return DatasetModule(
module_path,
hash,
builder_kwargs,
dataset_infos=dataset_infos,
builder_configs_parameters=BuilderConfigsParameters(
metadata_configs=metadata_configs,
builder_configs=builder_configs,
default_config_name=default_config_name,
),
) | 111 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
class PackagedDatasetModuleFactory(_DatasetModuleFactory):
"""Get the dataset builder module from the ones that are packaged with the library: csv, json, etc."""
def __init__(
self,
name: str,
data_dir: Optional[str] = None,
data_files: Optional[Union[str, List, Dict]] = None,
download_config: Optional[DownloadConfig] = None,
download_mode: Optional[Union[DownloadMode, str]] = None,
):
self.name = name
self.data_files = data_files
self.data_dir = data_dir
self.download_config = download_config
self.download_mode = download_mode
increase_load_count(name) | 112 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
def get_module(self) -> DatasetModule:
base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
patterns = (
sanitize_patterns(self.data_files)
if self.data_files is not None
else get_data_patterns(base_path, download_config=self.download_config)
)
data_files = DataFilesDict.from_patterns(
patterns,
download_config=self.download_config,
base_path=base_path,
)
supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
if self.data_files is None and supports_metadata and patterns != DEFAULT_PATTERNS_ALL:
try:
metadata_patterns = get_metadata_patterns(base_path, download_config=self.download_config)
except FileNotFoundError:
metadata_patterns = None
if metadata_patterns is not None:
metadata_data_files_list = DataFilesList.from_patterns( | 112 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/load.py |
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