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keshe/.venv/Lib/site-packages/pandas/io/pytables.py

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"""
High level interface to PyTables for reading and writing pandas data structures
to disk
"""
from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Literal,
cast,
overload,
)
import warnings
import numpy as np
from pandas._config import (
config,
get_option,
using_copy_on_write,
using_pyarrow_string_dtype,
)
from pandas._libs import (
lib,
writers as libwriters,
)
from pandas._libs.lib import is_string_array
from pandas._libs.tslibs import timezones
from pandas.compat._optional import import_optional_dependency
from pandas.compat.pickle_compat import patch_pickle
from pandas.errors import (
AttributeConflictWarning,
ClosedFileError,
IncompatibilityWarning,
PerformanceWarning,
PossibleDataLossError,
)
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
ensure_object,
is_bool_dtype,
is_complex_dtype,
is_list_like,
is_string_dtype,
needs_i8_conversion,
)
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
ExtensionDtype,
PeriodDtype,
)
from pandas.core.dtypes.missing import array_equivalent
from pandas import (
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
PeriodIndex,
RangeIndex,
Series,
TimedeltaIndex,
concat,
isna,
)
from pandas.core.arrays import (
Categorical,
DatetimeArray,
PeriodArray,
)
import pandas.core.common as com
from pandas.core.computation.pytables import (
PyTablesExpr,
maybe_expression,
)
from pandas.core.construction import extract_array
from pandas.core.indexes.api import ensure_index
from pandas.core.internals import (
ArrayManager,
BlockManager,
)
from pandas.io.common import stringify_path
from pandas.io.formats.printing import (
adjoin,
pprint_thing,
)
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Iterator,
Sequence,
)
from types import TracebackType
from tables import (
Col,
File,
Node,
)
from pandas._typing import (
AnyArrayLike,
ArrayLike,
AxisInt,
DtypeArg,
FilePath,
Self,
Shape,
npt,
)
from pandas.core.internals import Block
# versioning attribute
_version = "0.15.2"
# encoding
_default_encoding = "UTF-8"
def _ensure_decoded(s):
"""if we have bytes, decode them to unicode"""
if isinstance(s, np.bytes_):
s = s.decode("UTF-8")
return s
def _ensure_encoding(encoding: str | None) -> str:
# set the encoding if we need
if encoding is None:
encoding = _default_encoding
return encoding
def _ensure_str(name):
"""
Ensure that an index / column name is a str (python 3); otherwise they
may be np.string dtype. Non-string dtypes are passed through unchanged.
https://github.com/pandas-dev/pandas/issues/13492
"""
if isinstance(name, str):
name = str(name)
return name
Term = PyTablesExpr
def _ensure_term(where, scope_level: int):
"""
Ensure that the where is a Term or a list of Term.
This makes sure that we are capturing the scope of variables that are
passed create the terms here with a frame_level=2 (we are 2 levels down)
"""
# only consider list/tuple here as an ndarray is automatically a coordinate
# list
level = scope_level + 1
if isinstance(where, (list, tuple)):
where = [
Term(term, scope_level=level + 1) if maybe_expression(term) else term
for term in where
if term is not None
]
elif maybe_expression(where):
where = Term(where, scope_level=level)
return where if where is None or len(where) else None
incompatibility_doc: Final = """
where criteria is being ignored as this version [%s] is too old (or
not-defined), read the file in and write it out to a new file to upgrade (with
the copy_to method)
"""
attribute_conflict_doc: Final = """
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
"""
performance_doc: Final = """
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->%s,key->%s] [items->%s]
"""
# formats
_FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"}
# axes map
_AXES_MAP = {DataFrame: [0]}
# register our configuration options
dropna_doc: Final = """
: boolean
drop ALL nan rows when appending to a table
"""
format_doc: Final = """
: format
default format writing format, if None, then
put will default to 'fixed' and append will default to 'table'
"""
with config.config_prefix("io.hdf"):
config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool)
config.register_option(
"default_format",
None,
format_doc,
validator=config.is_one_of_factory(["fixed", "table", None]),
)
# oh the troubles to reduce import time
_table_mod = None
_table_file_open_policy_is_strict = False
def _tables():
global _table_mod
global _table_file_open_policy_is_strict
if _table_mod is None:
import tables
_table_mod = tables
# set the file open policy
# return the file open policy; this changes as of pytables 3.1
# depending on the HDF5 version
with suppress(AttributeError):
_table_file_open_policy_is_strict = (
tables.file._FILE_OPEN_POLICY == "strict"
)
return _table_mod
# interface to/from ###
def to_hdf(
path_or_buf: FilePath | HDFStore,
key: str,
value: DataFrame | Series,
mode: str = "a",
complevel: int | None = None,
complib: str | None = None,
append: bool = False,
format: str | None = None,
index: bool = True,
min_itemsize: int | dict[str, int] | None = None,
nan_rep=None,
dropna: bool | None = None,
data_columns: Literal[True] | list[str] | None = None,
errors: str = "strict",
encoding: str = "UTF-8",
) -> None:
"""store this object, close it if we opened it"""
if append:
f = lambda store: store.append(
key,
value,
format=format,
index=index,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
dropna=dropna,
data_columns=data_columns,
errors=errors,
encoding=encoding,
)
else:
# NB: dropna is not passed to `put`
f = lambda store: store.put(
key,
value,
format=format,
index=index,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
data_columns=data_columns,
errors=errors,
encoding=encoding,
dropna=dropna,
)
path_or_buf = stringify_path(path_or_buf)
if isinstance(path_or_buf, str):
with HDFStore(
path_or_buf, mode=mode, complevel=complevel, complib=complib
) as store:
f(store)
else:
f(path_or_buf)
def read_hdf(
path_or_buf: FilePath | HDFStore,
key=None,
mode: str = "r",
errors: str = "strict",
where: str | list | None = None,
start: int | None = None,
stop: int | None = None,
columns: list[str] | None = None,
iterator: bool = False,
chunksize: int | None = None,
**kwargs,
):
"""
Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
criteria.
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
path_or_buf : str, path object, pandas.HDFStore
Any valid string path is acceptable. Only supports the local file system,
remote URLs and file-like objects are not supported.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
key : object, optional
The group identifier in the store. Can be omitted if the HDF file
contains a single pandas object.
mode : {'r', 'r+', 'a'}, default 'r'
Mode to use when opening the file. Ignored if path_or_buf is a
:class:`pandas.HDFStore`. Default is 'r'.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
where : list, optional
A list of Term (or convertible) objects.
start : int, optional
Row number to start selection.
stop : int, optional
Row number to stop selection.
columns : list, optional
A list of columns names to return.
iterator : bool, optional
Return an iterator object.
chunksize : int, optional
Number of rows to include in an iteration when using an iterator.
**kwargs
Additional keyword arguments passed to HDFStore.
Returns
-------
object
The selected object. Return type depends on the object stored.
See Also
--------
DataFrame.to_hdf : Write a HDF file from a DataFrame.
HDFStore : Low-level access to HDF files.
Examples
--------
>>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) # doctest: +SKIP
>>> df.to_hdf('./store.h5', 'data') # doctest: +SKIP
>>> reread = pd.read_hdf('./store.h5') # doctest: +SKIP
"""
if mode not in ["r", "r+", "a"]:
raise ValueError(
f"mode {mode} is not allowed while performing a read. "
f"Allowed modes are r, r+ and a."
)
# grab the scope
if where is not None:
where = _ensure_term(where, scope_level=1)
if isinstance(path_or_buf, HDFStore):
if not path_or_buf.is_open:
raise OSError("The HDFStore must be open for reading.")
store = path_or_buf
auto_close = False
else:
path_or_buf = stringify_path(path_or_buf)
if not isinstance(path_or_buf, str):
raise NotImplementedError(
"Support for generic buffers has not been implemented."
)
try:
exists = os.path.exists(path_or_buf)
# if filepath is too long
except (TypeError, ValueError):
exists = False
if not exists:
raise FileNotFoundError(f"File {path_or_buf} does not exist")
store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
# can't auto open/close if we are using an iterator
# so delegate to the iterator
auto_close = True
try:
if key is None:
groups = store.groups()
if len(groups) == 0:
raise ValueError(
"Dataset(s) incompatible with Pandas data types, "
"not table, or no datasets found in HDF5 file."
)
candidate_only_group = groups[0]
# For the HDF file to have only one dataset, all other groups
# should then be metadata groups for that candidate group. (This
# assumes that the groups() method enumerates parent groups
# before their children.)
for group_to_check in groups[1:]:
if not _is_metadata_of(group_to_check, candidate_only_group):
raise ValueError(
"key must be provided when HDF5 "
"file contains multiple datasets."
)
key = candidate_only_group._v_pathname
return store.select(
key,
where=where,
start=start,
stop=stop,
columns=columns,
iterator=iterator,
chunksize=chunksize,
auto_close=auto_close,
)
except (ValueError, TypeError, LookupError):
if not isinstance(path_or_buf, HDFStore):
# if there is an error, close the store if we opened it.
with suppress(AttributeError):
store.close()
raise
def _is_metadata_of(group: Node, parent_group: Node) -> bool:
"""Check if a given group is a metadata group for a given parent_group."""
if group._v_depth <= parent_group._v_depth:
return False
current = group
while current._v_depth > 1:
parent = current._v_parent
if parent == parent_group and current._v_name == "meta":
return True
current = current._v_parent
return False
class HDFStore:
"""
Dict-like IO interface for storing pandas objects in PyTables.
Either Fixed or Table format.
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
path : str
File path to HDF5 file.
mode : {'a', 'w', 'r', 'r+'}, default 'a'
``'r'``
Read-only; no data can be modified.
``'w'``
Write; a new file is created (an existing file with the same
name would be deleted).
``'a'``
Append; an existing file is opened for reading and writing,
and if the file does not exist it is created.
``'r+'``
It is similar to ``'a'``, but the file must already exist.
complevel : int, 0-9, default None
Specifies a compression level for data.
A value of 0 or None disables compression.
complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
Specifies the compression library to be used.
These additional compressors for Blosc are supported
(default if no compressor specified: 'blosc:blosclz'):
{'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
'blosc:zlib', 'blosc:zstd'}.
Specifying a compression library which is not available issues
a ValueError.
fletcher32 : bool, default False
If applying compression use the fletcher32 checksum.
**kwargs
These parameters will be passed to the PyTables open_file method.
Examples
--------
>>> bar = pd.DataFrame(np.random.randn(10, 4))
>>> store = pd.HDFStore('test.h5')
>>> store['foo'] = bar # write to HDF5
>>> bar = store['foo'] # retrieve
>>> store.close()
**Create or load HDF5 file in-memory**
When passing the `driver` option to the PyTables open_file method through
**kwargs, the HDF5 file is loaded or created in-memory and will only be
written when closed:
>>> bar = pd.DataFrame(np.random.randn(10, 4))
>>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
>>> store['foo'] = bar
>>> store.close() # only now, data is written to disk
"""
_handle: File | None
_mode: str
def __init__(
self,
path,
mode: str = "a",
complevel: int | None = None,
complib=None,
fletcher32: bool = False,
**kwargs,
) -> None:
if "format" in kwargs:
raise ValueError("format is not a defined argument for HDFStore")
tables = import_optional_dependency("tables")
if complib is not None and complib not in tables.filters.all_complibs:
raise ValueError(
f"complib only supports {tables.filters.all_complibs} compression."
)
if complib is None and complevel is not None:
complib = tables.filters.default_complib
self._path = stringify_path(path)
if mode is None:
mode = "a"
self._mode = mode
self._handle = None
self._complevel = complevel if complevel else 0
self._complib = complib
self._fletcher32 = fletcher32
self._filters = None
self.open(mode=mode, **kwargs)
def __fspath__(self) -> str:
return self._path
@property
def root(self):
"""return the root node"""
self._check_if_open()
assert self._handle is not None # for mypy
return self._handle.root
@property
def filename(self) -> str:
return self._path
def __getitem__(self, key: str):
return self.get(key)
def __setitem__(self, key: str, value) -> None:
self.put(key, value)
def __delitem__(self, key: str) -> None:
return self.remove(key)
def __getattr__(self, name: str):
"""allow attribute access to get stores"""
try:
return self.get(name)
except (KeyError, ClosedFileError):
pass
raise AttributeError(
f"'{type(self).__name__}' object has no attribute '{name}'"
)
def __contains__(self, key: str) -> bool:
"""
check for existence of this key
can match the exact pathname or the pathnm w/o the leading '/'
"""
node = self.get_node(key)
if node is not None:
name = node._v_pathname
if key in (name, name[1:]):
return True
return False
def __len__(self) -> int:
return len(self.groups())
def __repr__(self) -> str:
pstr = pprint_thing(self._path)
return f"{type(self)}\nFile path: {pstr}\n"
def __enter__(self) -> Self:
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.close()
def keys(self, include: str = "pandas") -> list[str]:
"""
Return a list of keys corresponding to objects stored in HDFStore.
Parameters
----------
include : str, default 'pandas'
When kind equals 'pandas' return pandas objects.
When kind equals 'native' return native HDF5 Table objects.
Returns
-------
list
List of ABSOLUTE path-names (e.g. have the leading '/').
Raises
------
raises ValueError if kind has an illegal value
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
>>> store.get('data') # doctest: +SKIP
>>> print(store.keys()) # doctest: +SKIP
['/data1', '/data2']
>>> store.close() # doctest: +SKIP
"""
if include == "pandas":
return [n._v_pathname for n in self.groups()]
elif include == "native":
assert self._handle is not None # mypy
return [
n._v_pathname for n in self._handle.walk_nodes("/", classname="Table")
]
raise ValueError(
f"`include` should be either 'pandas' or 'native' but is '{include}'"
)
def __iter__(self) -> Iterator[str]:
return iter(self.keys())
def items(self) -> Iterator[tuple[str, list]]:
"""
iterate on key->group
"""
for g in self.groups():
yield g._v_pathname, g
def open(self, mode: str = "a", **kwargs) -> None:
"""
Open the file in the specified mode
Parameters
----------
mode : {'a', 'w', 'r', 'r+'}, default 'a'
See HDFStore docstring or tables.open_file for info about modes
**kwargs
These parameters will be passed to the PyTables open_file method.
"""
tables = _tables()
if self._mode != mode:
# if we are changing a write mode to read, ok
if self._mode in ["a", "w"] and mode in ["r", "r+"]:
pass
elif mode in ["w"]:
# this would truncate, raise here
if self.is_open:
raise PossibleDataLossError(
f"Re-opening the file [{self._path}] with mode [{self._mode}] "
"will delete the current file!"
)
self._mode = mode
# close and reopen the handle
if self.is_open:
self.close()
if self._complevel and self._complevel > 0:
self._filters = _tables().Filters(
self._complevel, self._complib, fletcher32=self._fletcher32
)
if _table_file_open_policy_is_strict and self.is_open:
msg = (
"Cannot open HDF5 file, which is already opened, "
"even in read-only mode."
)
raise ValueError(msg)
self._handle = tables.open_file(self._path, self._mode, **kwargs)
def close(self) -> None:
"""
Close the PyTables file handle
"""
if self._handle is not None:
self._handle.close()
self._handle = None
@property
def is_open(self) -> bool:
"""
return a boolean indicating whether the file is open
"""
if self._handle is None:
return False
return bool(self._handle.isopen)
def flush(self, fsync: bool = False) -> None:
"""
Force all buffered modifications to be written to disk.
Parameters
----------
fsync : bool (default False)
call ``os.fsync()`` on the file handle to force writing to disk.
Notes
-----
Without ``fsync=True``, flushing may not guarantee that the OS writes
to disk. With fsync, the operation will block until the OS claims the
file has been written; however, other caching layers may still
interfere.
"""
if self._handle is not None:
self._handle.flush()
if fsync:
with suppress(OSError):
os.fsync(self._handle.fileno())
def get(self, key: str):
"""
Retrieve pandas object stored in file.
Parameters
----------
key : str
Returns
-------
object
Same type as object stored in file.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
>>> store.get('data') # doctest: +SKIP
>>> store.close() # doctest: +SKIP
"""
with patch_pickle():
# GH#31167 Without this patch, pickle doesn't know how to unpickle
# old DateOffset objects now that they are cdef classes.
group = self.get_node(key)
if group is None:
raise KeyError(f"No object named {key} in the file")
return self._read_group(group)
def select(
self,
key: str,
where=None,
start=None,
stop=None,
columns=None,
iterator: bool = False,
chunksize: int | None = None,
auto_close: bool = False,
):
"""
Retrieve pandas object stored in file, optionally based on where criteria.
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
key : str
Object being retrieved from file.
where : list or None
List of Term (or convertible) objects, optional.
start : int or None
Row number to start selection.
stop : int, default None
Row number to stop selection.
columns : list or None
A list of columns that if not None, will limit the return columns.
iterator : bool or False
Returns an iterator.
chunksize : int or None
Number or rows to include in iteration, return an iterator.
auto_close : bool or False
Should automatically close the store when finished.
Returns
-------
object
Retrieved object from file.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
>>> store.get('data') # doctest: +SKIP
>>> print(store.keys()) # doctest: +SKIP
['/data1', '/data2']
>>> store.select('/data1') # doctest: +SKIP
A B
0 1 2
1 3 4
>>> store.select('/data1', where='columns == A') # doctest: +SKIP
A
0 1
1 3
>>> store.close() # doctest: +SKIP
"""
group = self.get_node(key)
if group is None:
raise KeyError(f"No object named {key} in the file")
# create the storer and axes
where = _ensure_term(where, scope_level=1)
s = self._create_storer(group)
s.infer_axes()
# function to call on iteration
def func(_start, _stop, _where):
return s.read(start=_start, stop=_stop, where=_where, columns=columns)
# create the iterator
it = TableIterator(
self,
s,
func,
where=where,
nrows=s.nrows,
start=start,
stop=stop,
iterator=iterator,
chunksize=chunksize,
auto_close=auto_close,
)
return it.get_result()
def select_as_coordinates(
self,
key: str,
where=None,
start: int | None = None,
stop: int | None = None,
):
"""
return the selection as an Index
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
key : str
where : list of Term (or convertible) objects, optional
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
"""
where = _ensure_term(where, scope_level=1)
tbl = self.get_storer(key)
if not isinstance(tbl, Table):
raise TypeError("can only read_coordinates with a table")
return tbl.read_coordinates(where=where, start=start, stop=stop)
def select_column(
self,
key: str,
column: str,
start: int | None = None,
stop: int | None = None,
):
"""
return a single column from the table. This is generally only useful to
select an indexable
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
key : str
column : str
The column of interest.
start : int or None, default None
stop : int or None, default None
Raises
------
raises KeyError if the column is not found (or key is not a valid
store)
raises ValueError if the column can not be extracted individually (it
is part of a data block)
"""
tbl = self.get_storer(key)
if not isinstance(tbl, Table):
raise TypeError("can only read_column with a table")
return tbl.read_column(column=column, start=start, stop=stop)
def select_as_multiple(
self,
keys,
where=None,
selector=None,
columns=None,
start=None,
stop=None,
iterator: bool = False,
chunksize: int | None = None,
auto_close: bool = False,
):
"""
Retrieve pandas objects from multiple tables.
.. warning::
Pandas uses PyTables for reading and writing HDF5 files, which allows
serializing object-dtype data with pickle when using the "fixed" format.
Loading pickled data received from untrusted sources can be unsafe.
See: https://docs.python.org/3/library/pickle.html for more.
Parameters
----------
keys : a list of the tables
selector : the table to apply the where criteria (defaults to keys[0]
if not supplied)
columns : the columns I want back
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
iterator : bool, return an iterator, default False
chunksize : nrows to include in iteration, return an iterator
auto_close : bool, default False
Should automatically close the store when finished.
Raises
------
raises KeyError if keys or selector is not found or keys is empty
raises TypeError if keys is not a list or tuple
raises ValueError if the tables are not ALL THE SAME DIMENSIONS
"""
# default to single select
where = _ensure_term(where, scope_level=1)
if isinstance(keys, (list, tuple)) and len(keys) == 1:
keys = keys[0]
if isinstance(keys, str):
return self.select(
key=keys,
where=where,
columns=columns,
start=start,
stop=stop,
iterator=iterator,
chunksize=chunksize,
auto_close=auto_close,
)
if not isinstance(keys, (list, tuple)):
raise TypeError("keys must be a list/tuple")
if not len(keys):
raise ValueError("keys must have a non-zero length")
if selector is None:
selector = keys[0]
# collect the tables
tbls = [self.get_storer(k) for k in keys]
s = self.get_storer(selector)
# validate rows
nrows = None
for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
if t is None:
raise KeyError(f"Invalid table [{k}]")
if not t.is_table:
raise TypeError(
f"object [{t.pathname}] is not a table, and cannot be used in all "
"select as multiple"
)
if nrows is None:
nrows = t.nrows
elif t.nrows != nrows:
raise ValueError("all tables must have exactly the same nrows!")
# The isinstance checks here are redundant with the check above,
# but necessary for mypy; see GH#29757
_tbls = [x for x in tbls if isinstance(x, Table)]
# axis is the concentration axes
axis = {t.non_index_axes[0][0] for t in _tbls}.pop()
def func(_start, _stop, _where):
# retrieve the objs, _where is always passed as a set of
# coordinates here
objs = [
t.read(where=_where, columns=columns, start=_start, stop=_stop)
for t in tbls
]
# concat and return
return concat(objs, axis=axis, verify_integrity=False)._consolidate()
# create the iterator
it = TableIterator(
self,
s,
func,
where=where,
nrows=nrows,
start=start,
stop=stop,
iterator=iterator,
chunksize=chunksize,
auto_close=auto_close,
)
return it.get_result(coordinates=True)
def put(
self,
key: str,
value: DataFrame | Series,
format=None,
index: bool = True,
append: bool = False,
complib=None,
complevel: int | None = None,
min_itemsize: int | dict[str, int] | None = None,
nan_rep=None,
data_columns: Literal[True] | list[str] | None = None,
encoding=None,
errors: str = "strict",
track_times: bool = True,
dropna: bool = False,
) -> None:
"""
Store object in HDFStore.
Parameters
----------
key : str
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
Format to use when storing object in HDFStore. Value can be one of:
``'fixed'``
Fixed format. Fast writing/reading. Not-appendable, nor searchable.
``'table'``
Table format. Write as a PyTables Table structure which may perform
worse but allow more flexible operations like searching / selecting
subsets of the data.
index : bool, default True
Write DataFrame index as a column.
append : bool, default False
This will force Table format, append the input data to the existing.
data_columns : list of columns or True, default None
List of columns to create as data columns, or True to use all columns.
See `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
encoding : str, default None
Provide an encoding for strings.
track_times : bool, default True
Parameter is propagated to 'create_table' method of 'PyTables'.
If set to False it enables to have the same h5 files (same hashes)
independent on creation time.
dropna : bool, default False, optional
Remove missing values.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
"""
if format is None:
format = get_option("io.hdf.default_format") or "fixed"
format = self._validate_format(format)
self._write_to_group(
key,
value,
format=format,
index=index,
append=append,
complib=complib,
complevel=complevel,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
data_columns=data_columns,
encoding=encoding,
errors=errors,
track_times=track_times,
dropna=dropna,
)
def remove(self, key: str, where=None, start=None, stop=None) -> None:
"""
Remove pandas object partially by specifying the where condition
Parameters
----------
key : str
Node to remove or delete rows from
where : list of Term (or convertible) objects, optional
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
Returns
-------
number of rows removed (or None if not a Table)
Raises
------
raises KeyError if key is not a valid store
"""
where = _ensure_term(where, scope_level=1)
try:
s = self.get_storer(key)
except KeyError:
# the key is not a valid store, re-raising KeyError
raise
except AssertionError:
# surface any assertion errors for e.g. debugging
raise
except Exception as err:
# In tests we get here with ClosedFileError, TypeError, and
# _table_mod.NoSuchNodeError. TODO: Catch only these?
if where is not None:
raise ValueError(
"trying to remove a node with a non-None where clause!"
) from err
# we are actually trying to remove a node (with children)
node = self.get_node(key)
if node is not None:
node._f_remove(recursive=True)
return None
# remove the node
if com.all_none(where, start, stop):
s.group._f_remove(recursive=True)
# delete from the table
else:
if not s.is_table:
raise ValueError(
"can only remove with where on objects written as tables"
)
return s.delete(where=where, start=start, stop=stop)
def append(
self,
key: str,
value: DataFrame | Series,
format=None,
axes=None,
index: bool | list[str] = True,
append: bool = True,
complib=None,
complevel: int | None = None,
columns=None,
min_itemsize: int | dict[str, int] | None = None,
nan_rep=None,
chunksize: int | None = None,
expectedrows=None,
dropna: bool | None = None,
data_columns: Literal[True] | list[str] | None = None,
encoding=None,
errors: str = "strict",
) -> None:
"""
Append to Table in file.
Node must already exist and be Table format.
Parameters
----------
key : str
value : {Series, DataFrame}
format : 'table' is the default
Format to use when storing object in HDFStore. Value can be one of:
``'table'``
Table format. Write as a PyTables Table structure which may perform
worse but allow more flexible operations like searching / selecting
subsets of the data.
index : bool, default True
Write DataFrame index as a column.
append : bool, default True
Append the input data to the existing.
data_columns : list of columns, or True, default None
List of columns to create as indexed data columns for on-disk
queries, or True to use all columns. By default only the axes
of the object are indexed. See `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
min_itemsize : dict of columns that specify minimum str sizes
nan_rep : str to use as str nan representation
chunksize : size to chunk the writing
expectedrows : expected TOTAL row size of this table
encoding : default None, provide an encoding for str
dropna : bool, default False, optional
Do not write an ALL nan row to the store settable
by the option 'io.hdf.dropna_table'.
Notes
-----
Does *not* check if data being appended overlaps with existing
data in the table, so be careful
Examples
--------
>>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df1, format='table') # doctest: +SKIP
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
>>> store.append('data', df2) # doctest: +SKIP
>>> store.close() # doctest: +SKIP
A B
0 1 2
1 3 4
0 5 6
1 7 8
"""
if columns is not None:
raise TypeError(
"columns is not a supported keyword in append, try data_columns"
)
if dropna is None:
dropna = get_option("io.hdf.dropna_table")
if format is None:
format = get_option("io.hdf.default_format") or "table"
format = self._validate_format(format)
self._write_to_group(
key,
value,
format=format,
axes=axes,
index=index,
append=append,
complib=complib,
complevel=complevel,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
chunksize=chunksize,
expectedrows=expectedrows,
dropna=dropna,
data_columns=data_columns,
encoding=encoding,
errors=errors,
)
def append_to_multiple(
self,
d: dict,
value,
selector,
data_columns=None,
axes=None,
dropna: bool = False,
**kwargs,
) -> None:
"""
Append to multiple tables
Parameters
----------
d : a dict of table_name to table_columns, None is acceptable as the
values of one node (this will get all the remaining columns)
value : a pandas object
selector : a string that designates the indexable table; all of its
columns will be designed as data_columns, unless data_columns is
passed, in which case these are used
data_columns : list of columns to create as data columns, or True to
use all columns
dropna : if evaluates to True, drop rows from all tables if any single
row in each table has all NaN. Default False.
Notes
-----
axes parameter is currently not accepted
"""
if axes is not None:
raise TypeError(
"axes is currently not accepted as a parameter to append_to_multiple; "
"you can create the tables independently instead"
)
if not isinstance(d, dict):
raise ValueError(
"append_to_multiple must have a dictionary specified as the "
"way to split the value"
)
if selector not in d:
raise ValueError(
"append_to_multiple requires a selector that is in passed dict"
)
# figure out the splitting axis (the non_index_axis)
axis = next(iter(set(range(value.ndim)) - set(_AXES_MAP[type(value)])))
# figure out how to split the value
remain_key = None
remain_values: list = []
for k, v in d.items():
if v is None:
if remain_key is not None:
raise ValueError(
"append_to_multiple can only have one value in d that is None"
)
remain_key = k
else:
remain_values.extend(v)
if remain_key is not None:
ordered = value.axes[axis]
ordd = ordered.difference(Index(remain_values))
ordd = sorted(ordered.get_indexer(ordd))
d[remain_key] = ordered.take(ordd)
# data_columns
if data_columns is None:
data_columns = d[selector]
# ensure rows are synchronized across the tables
if dropna:
idxs = (value[cols].dropna(how="all").index for cols in d.values())
valid_index = next(idxs)
for index in idxs:
valid_index = valid_index.intersection(index)
value = value.loc[valid_index]
min_itemsize = kwargs.pop("min_itemsize", None)
# append
for k, v in d.items():
dc = data_columns if k == selector else None
# compute the val
val = value.reindex(v, axis=axis)
filtered = (
{key: value for (key, value) in min_itemsize.items() if key in v}
if min_itemsize is not None
else None
)
self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs)
def create_table_index(
self,
key: str,
columns=None,
optlevel: int | None = None,
kind: str | None = None,
) -> None:
"""
Create a pytables index on the table.
Parameters
----------
key : str
columns : None, bool, or listlike[str]
Indicate which columns to create an index on.
* False : Do not create any indexes.
* True : Create indexes on all columns.
* None : Create indexes on all columns.
* listlike : Create indexes on the given columns.
optlevel : int or None, default None
Optimization level, if None, pytables defaults to 6.
kind : str or None, default None
Kind of index, if None, pytables defaults to "medium".
Raises
------
TypeError: raises if the node is not a table
"""
# version requirements
_tables()
s = self.get_storer(key)
if s is None:
return
if not isinstance(s, Table):
raise TypeError("cannot create table index on a Fixed format store")
s.create_index(columns=columns, optlevel=optlevel, kind=kind)
def groups(self) -> list:
"""
Return a list of all the top-level nodes.
Each node returned is not a pandas storage object.
Returns
-------
list
List of objects.
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
>>> print(store.groups()) # doctest: +SKIP
>>> store.close() # doctest: +SKIP
[/data (Group) ''
children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
'block0_items' (Array)]]
"""
_tables()
self._check_if_open()
assert self._handle is not None # for mypy
assert _table_mod is not None # for mypy
return [
g
for g in self._handle.walk_groups()
if (
not isinstance(g, _table_mod.link.Link)
and (
getattr(g._v_attrs, "pandas_type", None)
or getattr(g, "table", None)
or (isinstance(g, _table_mod.table.Table) and g._v_name != "table")
)
)
]
def walk(self, where: str = "/") -> Iterator[tuple[str, list[str], list[str]]]:
"""
Walk the pytables group hierarchy for pandas objects.
This generator will yield the group path, subgroups and pandas object
names for each group.
Any non-pandas PyTables objects that are not a group will be ignored.
The `where` group itself is listed first (preorder), then each of its
child groups (following an alphanumerical order) is also traversed,
following the same procedure.
Parameters
----------
where : str, default "/"
Group where to start walking.
Yields
------
path : str
Full path to a group (without trailing '/').
groups : list
Names (strings) of the groups contained in `path`.
leaves : list
Names (strings) of the pandas objects contained in `path`.
Examples
--------
>>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df1, format='table') # doctest: +SKIP
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
>>> store.append('data', df2) # doctest: +SKIP
>>> store.close() # doctest: +SKIP
>>> for group in store.walk(): # doctest: +SKIP
... print(group) # doctest: +SKIP
>>> store.close() # doctest: +SKIP
"""
_tables()
self._check_if_open()
assert self._handle is not None # for mypy
assert _table_mod is not None # for mypy
for g in self._handle.walk_groups(where):
if getattr(g._v_attrs, "pandas_type", None) is not None:
continue
groups = []
leaves = []
for child in g._v_children.values():
pandas_type = getattr(child._v_attrs, "pandas_type", None)
if pandas_type is None:
if isinstance(child, _table_mod.group.Group):
groups.append(child._v_name)
else:
leaves.append(child._v_name)
yield (g._v_pathname.rstrip("/"), groups, leaves)
def get_node(self, key: str) -> Node | None:
"""return the node with the key or None if it does not exist"""
self._check_if_open()
if not key.startswith("/"):
key = "/" + key
assert self._handle is not None
assert _table_mod is not None # for mypy
try:
node = self._handle.get_node(self.root, key)
except _table_mod.exceptions.NoSuchNodeError:
return None
assert isinstance(node, _table_mod.Node), type(node)
return node
def get_storer(self, key: str) -> GenericFixed | Table:
"""return the storer object for a key, raise if not in the file"""
group = self.get_node(key)
if group is None:
raise KeyError(f"No object named {key} in the file")
s = self._create_storer(group)
s.infer_axes()
return s
def copy(
self,
file,
mode: str = "w",
propindexes: bool = True,
keys=None,
complib=None,
complevel: int | None = None,
fletcher32: bool = False,
overwrite: bool = True,
) -> HDFStore:
"""
Copy the existing store to a new file, updating in place.
Parameters
----------
propindexes : bool, default True
Restore indexes in copied file.
keys : list, optional
List of keys to include in the copy (defaults to all).
overwrite : bool, default True
Whether to overwrite (remove and replace) existing nodes in the new store.
mode, complib, complevel, fletcher32 same as in HDFStore.__init__
Returns
-------
open file handle of the new store
"""
new_store = HDFStore(
file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32
)
if keys is None:
keys = list(self.keys())
if not isinstance(keys, (tuple, list)):
keys = [keys]
for k in keys:
s = self.get_storer(k)
if s is not None:
if k in new_store:
if overwrite:
new_store.remove(k)
data = self.select(k)
if isinstance(s, Table):
index: bool | list[str] = False
if propindexes:
index = [a.name for a in s.axes if a.is_indexed]
new_store.append(
k,
data,
index=index,
data_columns=getattr(s, "data_columns", None),
encoding=s.encoding,
)
else:
new_store.put(k, data, encoding=s.encoding)
return new_store
def info(self) -> str:
"""
Print detailed information on the store.
Returns
-------
str
Examples
--------
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
>>> store.put('data', df) # doctest: +SKIP
>>> print(store.info()) # doctest: +SKIP
>>> store.close() # doctest: +SKIP
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5
/data frame (shape->[2,2])
"""
path = pprint_thing(self._path)
output = f"{type(self)}\nFile path: {path}\n"
if self.is_open:
lkeys = sorted(self.keys())
if len(lkeys):
keys = []
values = []
for k in lkeys:
try:
s = self.get_storer(k)
if s is not None:
keys.append(pprint_thing(s.pathname or k))
values.append(pprint_thing(s or "invalid_HDFStore node"))
except AssertionError:
# surface any assertion errors for e.g. debugging
raise
except Exception as detail:
keys.append(k)
dstr = pprint_thing(detail)
values.append(f"[invalid_HDFStore node: {dstr}]")
output += adjoin(12, keys, values)
else:
output += "Empty"
else:
output += "File is CLOSED"
return output
# ------------------------------------------------------------------------
# private methods
def _check_if_open(self) -> None:
if not self.is_open:
raise ClosedFileError(f"{self._path} file is not open!")
def _validate_format(self, format: str) -> str:
"""validate / deprecate formats"""
# validate
try:
format = _FORMAT_MAP[format.lower()]
except KeyError as err:
raise TypeError(f"invalid HDFStore format specified [{format}]") from err
return format
def _create_storer(
self,
group,
format=None,
value: DataFrame | Series | None = None,
encoding: str = "UTF-8",
errors: str = "strict",
) -> GenericFixed | Table:
"""return a suitable class to operate"""
cls: type[GenericFixed | Table]
if value is not None and not isinstance(value, (Series, DataFrame)):
raise TypeError("value must be None, Series, or DataFrame")
pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None))
tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None))
# infer the pt from the passed value
if pt is None:
if value is None:
_tables()
assert _table_mod is not None # for mypy
if getattr(group, "table", None) or isinstance(
group, _table_mod.table.Table
):
pt = "frame_table"
tt = "generic_table"
else:
raise TypeError(
"cannot create a storer if the object is not existing "
"nor a value are passed"
)
else:
if isinstance(value, Series):
pt = "series"
else:
pt = "frame"
# we are actually a table
if format == "table":
pt += "_table"
# a storer node
if "table" not in pt:
_STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed}
try:
cls = _STORER_MAP[pt]
except KeyError as err:
raise TypeError(
f"cannot properly create the storer for: [_STORER_MAP] [group->"
f"{group},value->{type(value)},format->{format}"
) from err
return cls(self, group, encoding=encoding, errors=errors)
# existing node (and must be a table)
if tt is None:
# if we are a writer, determine the tt
if value is not None:
if pt == "series_table":
index = getattr(value, "index", None)
if index is not None:
if index.nlevels == 1:
tt = "appendable_series"
elif index.nlevels > 1:
tt = "appendable_multiseries"
elif pt == "frame_table":
index = getattr(value, "index", None)
if index is not None:
if index.nlevels == 1:
tt = "appendable_frame"
elif index.nlevels > 1:
tt = "appendable_multiframe"
_TABLE_MAP = {
"generic_table": GenericTable,
"appendable_series": AppendableSeriesTable,
"appendable_multiseries": AppendableMultiSeriesTable,
"appendable_frame": AppendableFrameTable,
"appendable_multiframe": AppendableMultiFrameTable,
"worm": WORMTable,
}
try:
cls = _TABLE_MAP[tt]
except KeyError as err:
raise TypeError(
f"cannot properly create the storer for: [_TABLE_MAP] [group->"
f"{group},value->{type(value)},format->{format}"
) from err
return cls(self, group, encoding=encoding, errors=errors)
def _write_to_group(
self,
key: str,
value: DataFrame | Series,
format,
axes=None,
index: bool | list[str] = True,
append: bool = False,
complib=None,
complevel: int | None = None,
fletcher32=None,
min_itemsize: int | dict[str, int] | None = None,
chunksize: int | None = None,
expectedrows=None,
dropna: bool = False,
nan_rep=None,
data_columns=None,
encoding=None,
errors: str = "strict",
track_times: bool = True,
) -> None:
# we don't want to store a table node at all if our object is 0-len
# as there are not dtypes
if getattr(value, "empty", None) and (format == "table" or append):
return
group = self._identify_group(key, append)
s = self._create_storer(group, format, value, encoding=encoding, errors=errors)
if append:
# raise if we are trying to append to a Fixed format,
# or a table that exists (and we are putting)
if not s.is_table or (s.is_table and format == "fixed" and s.is_exists):
raise ValueError("Can only append to Tables")
if not s.is_exists:
s.set_object_info()
else:
s.set_object_info()
if not s.is_table and complib:
raise ValueError("Compression not supported on Fixed format stores")
# write the object
s.write(
obj=value,
axes=axes,
append=append,
complib=complib,
complevel=complevel,
fletcher32=fletcher32,
min_itemsize=min_itemsize,
chunksize=chunksize,
expectedrows=expectedrows,
dropna=dropna,
nan_rep=nan_rep,
data_columns=data_columns,
track_times=track_times,
)
if isinstance(s, Table) and index:
s.create_index(columns=index)
def _read_group(self, group: Node):
s = self._create_storer(group)
s.infer_axes()
return s.read()
def _identify_group(self, key: str, append: bool) -> Node:
"""Identify HDF5 group based on key, delete/create group if needed."""
group = self.get_node(key)
# we make this assertion for mypy; the get_node call will already
# have raised if this is incorrect
assert self._handle is not None
# remove the node if we are not appending
if group is not None and not append:
self._handle.remove_node(group, recursive=True)
group = None
if group is None:
group = self._create_nodes_and_group(key)
return group
def _create_nodes_and_group(self, key: str) -> Node:
"""Create nodes from key and return group name."""
# assertion for mypy
assert self._handle is not None
paths = key.split("/")
# recursively create the groups
path = "/"
for p in paths:
if not len(p):
continue
new_path = path
if not path.endswith("/"):
new_path += "/"
new_path += p
group = self.get_node(new_path)
if group is None:
group = self._handle.create_group(path, p)
path = new_path
return group
class TableIterator:
"""
Define the iteration interface on a table
Parameters
----------
store : HDFStore
s : the referred storer
func : the function to execute the query
where : the where of the query
nrows : the rows to iterate on
start : the passed start value (default is None)
stop : the passed stop value (default is None)
iterator : bool, default False
Whether to use the default iterator.
chunksize : the passed chunking value (default is 100000)
auto_close : bool, default False
Whether to automatically close the store at the end of iteration.
"""
chunksize: int | None
store: HDFStore
s: GenericFixed | Table
def __init__(
self,
store: HDFStore,
s: GenericFixed | Table,
func,
where,
nrows,
start=None,
stop=None,
iterator: bool = False,
chunksize: int | None = None,
auto_close: bool = False,
) -> None:
self.store = store
self.s = s
self.func = func
self.where = where
# set start/stop if they are not set if we are a table
if self.s.is_table:
if nrows is None:
nrows = 0
if start is None:
start = 0
if stop is None:
stop = nrows
stop = min(nrows, stop)
self.nrows = nrows
self.start = start
self.stop = stop
self.coordinates = None
if iterator or chunksize is not None:
if chunksize is None:
chunksize = 100000
self.chunksize = int(chunksize)
else:
self.chunksize = None
self.auto_close = auto_close
def __iter__(self) -> Iterator:
# iterate
current = self.start
if self.coordinates is None:
raise ValueError("Cannot iterate until get_result is called.")
while current < self.stop:
stop = min(current + self.chunksize, self.stop)
value = self.func(None, None, self.coordinates[current:stop])
current = stop
if value is None or not len(value):
continue
yield value
self.close()
def close(self) -> None:
if self.auto_close:
self.store.close()
def get_result(self, coordinates: bool = False):
# return the actual iterator
if self.chunksize is not None:
if not isinstance(self.s, Table):
raise TypeError("can only use an iterator or chunksize on a table")
self.coordinates = self.s.read_coordinates(where=self.where)
return self
# if specified read via coordinates (necessary for multiple selections
if coordinates:
if not isinstance(self.s, Table):
raise TypeError("can only read_coordinates on a table")
where = self.s.read_coordinates(
where=self.where, start=self.start, stop=self.stop
)
else:
where = self.where
# directly return the result
results = self.func(self.start, self.stop, where)
self.close()
return results
class IndexCol:
"""
an index column description class
Parameters
----------
axis : axis which I reference
values : the ndarray like converted values
kind : a string description of this type
typ : the pytables type
pos : the position in the pytables
"""
is_an_indexable: bool = True
is_data_indexable: bool = True
_info_fields = ["freq", "tz", "index_name"]
def __init__(
self,
name: str,
values=None,
kind=None,
typ=None,
cname: str | None = None,
axis=None,
pos=None,
freq=None,
tz=None,
index_name=None,
ordered=None,
table=None,
meta=None,
metadata=None,
) -> None:
if not isinstance(name, str):
raise ValueError("`name` must be a str.")
self.values = values
self.kind = kind
self.typ = typ
self.name = name
self.cname = cname or name
self.axis = axis
self.pos = pos
self.freq = freq
self.tz = tz
self.index_name = index_name
self.ordered = ordered
self.table = table
self.meta = meta
self.metadata = metadata
if pos is not None:
self.set_pos(pos)
# These are ensured as long as the passed arguments match the
# constructor annotations.
assert isinstance(self.name, str)
assert isinstance(self.cname, str)
@property
def itemsize(self) -> int:
# Assumes self.typ has already been initialized
return self.typ.itemsize
@property
def kind_attr(self) -> str:
return f"{self.name}_kind"
def set_pos(self, pos: int) -> None:
"""set the position of this column in the Table"""
self.pos = pos
if pos is not None and self.typ is not None:
self.typ._v_pos = pos
def __repr__(self) -> str:
temp = tuple(
map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))
)
return ",".join(
[
f"{key}->{value}"
for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp)
]
)
def __eq__(self, other: object) -> bool:
"""compare 2 col items"""
return all(
getattr(self, a, None) == getattr(other, a, None)
for a in ["name", "cname", "axis", "pos"]
)
def __ne__(self, other) -> bool:
return not self.__eq__(other)
@property
def is_indexed(self) -> bool:
"""return whether I am an indexed column"""
if not hasattr(self.table, "cols"):
# e.g. if infer hasn't been called yet, self.table will be None.
return False
return getattr(self.table.cols, self.cname).is_indexed
def convert(
self, values: np.ndarray, nan_rep, encoding: str, errors: str
) -> tuple[np.ndarray, np.ndarray] | tuple[Index, Index]:
"""
Convert the data from this selection to the appropriate pandas type.
"""
assert isinstance(values, np.ndarray), type(values)
# values is a recarray
if values.dtype.fields is not None:
# Copy, otherwise values will be a view
# preventing the original recarry from being free'ed
values = values[self.cname].copy()
val_kind = _ensure_decoded(self.kind)
values = _maybe_convert(values, val_kind, encoding, errors)
kwargs = {}
kwargs["name"] = _ensure_decoded(self.index_name)
if self.freq is not None:
kwargs["freq"] = _ensure_decoded(self.freq)
factory: type[Index | DatetimeIndex] = Index
if lib.is_np_dtype(values.dtype, "M") or isinstance(
values.dtype, DatetimeTZDtype
):
factory = DatetimeIndex
elif values.dtype == "i8" and "freq" in kwargs:
# PeriodIndex data is stored as i8
# error: Incompatible types in assignment (expression has type
# "Callable[[Any, KwArg(Any)], PeriodIndex]", variable has type
# "Union[Type[Index], Type[DatetimeIndex]]")
factory = lambda x, **kwds: PeriodIndex.from_ordinals( # type: ignore[assignment]
x, freq=kwds.get("freq", None)
)._rename(
kwds["name"]
)
# making an Index instance could throw a number of different errors
try:
new_pd_index = factory(values, **kwargs)
except ValueError:
# if the output freq is different that what we recorded,
# it should be None (see also 'doc example part 2')
if "freq" in kwargs:
kwargs["freq"] = None
new_pd_index = factory(values, **kwargs)
final_pd_index = _set_tz(new_pd_index, self.tz)
return final_pd_index, final_pd_index
def take_data(self):
"""return the values"""
return self.values
@property
def attrs(self):
return self.table._v_attrs
@property
def description(self):
return self.table.description
@property
def col(self):
"""return my current col description"""
return getattr(self.description, self.cname, None)
@property
def cvalues(self):
"""return my cython values"""
return self.values
def __iter__(self) -> Iterator:
return iter(self.values)
def maybe_set_size(self, min_itemsize=None) -> None:
"""
maybe set a string col itemsize:
min_itemsize can be an integer or a dict with this columns name
with an integer size
"""
if _ensure_decoded(self.kind) == "string":
if isinstance(min_itemsize, dict):
min_itemsize = min_itemsize.get(self.name)
if min_itemsize is not None and self.typ.itemsize < min_itemsize:
self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos)
def validate_names(self) -> None:
pass
def validate_and_set(self, handler: AppendableTable, append: bool) -> None:
self.table = handler.table
self.validate_col()
self.validate_attr(append)
self.validate_metadata(handler)
self.write_metadata(handler)
self.set_attr()
def validate_col(self, itemsize=None):
"""validate this column: return the compared against itemsize"""
# validate this column for string truncation (or reset to the max size)
if _ensure_decoded(self.kind) == "string":
c = self.col
if c is not None:
if itemsize is None:
itemsize = self.itemsize
if c.itemsize < itemsize:
raise ValueError(
f"Trying to store a string with len [{itemsize}] in "
f"[{self.cname}] column but\nthis column has a limit of "
f"[{c.itemsize}]!\nConsider using min_itemsize to "
"preset the sizes on these columns"
)
return c.itemsize
return None
def validate_attr(self, append: bool) -> None:
# check for backwards incompatibility
if append:
existing_kind = getattr(self.attrs, self.kind_attr, None)
if existing_kind is not None and existing_kind != self.kind:
raise TypeError(
f"incompatible kind in col [{existing_kind} - {self.kind}]"
)
def update_info(self, info) -> None:
"""
set/update the info for this indexable with the key/value
if there is a conflict raise/warn as needed
"""
for key in self._info_fields:
value = getattr(self, key, None)
idx = info.setdefault(self.name, {})
existing_value = idx.get(key)
if key in idx and value is not None and existing_value != value:
# frequency/name just warn
if key in ["freq", "index_name"]:
ws = attribute_conflict_doc % (key, existing_value, value)
warnings.warn(
ws, AttributeConflictWarning, stacklevel=find_stack_level()
)
# reset
idx[key] = None
setattr(self, key, None)
else:
raise ValueError(
f"invalid info for [{self.name}] for [{key}], "
f"existing_value [{existing_value}] conflicts with "
f"new value [{value}]"
)
elif value is not None or existing_value is not None:
idx[key] = value
def set_info(self, info) -> None:
"""set my state from the passed info"""
idx = info.get(self.name)
if idx is not None:
self.__dict__.update(idx)
def set_attr(self) -> None:
"""set the kind for this column"""
setattr(self.attrs, self.kind_attr, self.kind)
def validate_metadata(self, handler: AppendableTable) -> None:
"""validate that kind=category does not change the categories"""
if self.meta == "category":
new_metadata = self.metadata
cur_metadata = handler.read_metadata(self.cname)
if (
new_metadata is not None
and cur_metadata is not None
and not array_equivalent(
new_metadata, cur_metadata, strict_nan=True, dtype_equal=True
)
):
raise ValueError(
"cannot append a categorical with "
"different categories to the existing"
)
def write_metadata(self, handler: AppendableTable) -> None:
"""set the meta data"""
if self.metadata is not None:
handler.write_metadata(self.cname, self.metadata)
class GenericIndexCol(IndexCol):
"""an index which is not represented in the data of the table"""
@property
def is_indexed(self) -> bool:
return False
def convert(
self, values: np.ndarray, nan_rep, encoding: str, errors: str
) -> tuple[Index, Index]:
"""
Convert the data from this selection to the appropriate pandas type.
Parameters
----------
values : np.ndarray
nan_rep : str
encoding : str
errors : str
"""
assert isinstance(values, np.ndarray), type(values)
index = RangeIndex(len(values))
return index, index
def set_attr(self) -> None:
pass
class DataCol(IndexCol):
"""
a data holding column, by definition this is not indexable
Parameters
----------
data : the actual data
cname : the column name in the table to hold the data (typically
values)
meta : a string description of the metadata
metadata : the actual metadata
"""
is_an_indexable = False
is_data_indexable = False
_info_fields = ["tz", "ordered"]
def __init__(
self,
name: str,
values=None,
kind=None,
typ=None,
cname: str | None = None,
pos=None,
tz=None,
ordered=None,
table=None,
meta=None,
metadata=None,
dtype: DtypeArg | None = None,
data=None,
) -> None:
super().__init__(
name=name,
values=values,
kind=kind,
typ=typ,
pos=pos,
cname=cname,
tz=tz,
ordered=ordered,
table=table,
meta=meta,
metadata=metadata,
)
self.dtype = dtype
self.data = data
@property
def dtype_attr(self) -> str:
return f"{self.name}_dtype"
@property
def meta_attr(self) -> str:
return f"{self.name}_meta"
def __repr__(self) -> str:
temp = tuple(
map(
pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape)
)
)
return ",".join(
[
f"{key}->{value}"
for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp)
]
)
def __eq__(self, other: object) -> bool:
"""compare 2 col items"""
return all(
getattr(self, a, None) == getattr(other, a, None)
for a in ["name", "cname", "dtype", "pos"]
)
def set_data(self, data: ArrayLike) -> None:
assert data is not None
assert self.dtype is None
data, dtype_name = _get_data_and_dtype_name(data)
self.data = data
self.dtype = dtype_name
self.kind = _dtype_to_kind(dtype_name)
def take_data(self):
"""return the data"""
return self.data
@classmethod
def _get_atom(cls, values: ArrayLike) -> Col:
"""
Get an appropriately typed and shaped pytables.Col object for values.
"""
dtype = values.dtype
# error: Item "ExtensionDtype" of "Union[ExtensionDtype, dtype[Any]]" has no
# attribute "itemsize"
itemsize = dtype.itemsize # type: ignore[union-attr]
shape = values.shape
if values.ndim == 1:
# EA, use block shape pretending it is 2D
# TODO(EA2D): not necessary with 2D EAs
shape = (1, values.size)
if isinstance(values, Categorical):
codes = values.codes
atom = cls.get_atom_data(shape, kind=codes.dtype.name)
elif lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype):
atom = cls.get_atom_datetime64(shape)
elif lib.is_np_dtype(dtype, "m"):
atom = cls.get_atom_timedelta64(shape)
elif is_complex_dtype(dtype):
atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0])
elif is_string_dtype(dtype):
atom = cls.get_atom_string(shape, itemsize)
else:
atom = cls.get_atom_data(shape, kind=dtype.name)
return atom
@classmethod
def get_atom_string(cls, shape, itemsize):
return _tables().StringCol(itemsize=itemsize, shape=shape[0])
@classmethod
def get_atom_coltype(cls, kind: str) -> type[Col]:
"""return the PyTables column class for this column"""
if kind.startswith("uint"):
k4 = kind[4:]
col_name = f"UInt{k4}Col"
elif kind.startswith("period"):
# we store as integer
col_name = "Int64Col"
else:
kcap = kind.capitalize()
col_name = f"{kcap}Col"
return getattr(_tables(), col_name)
@classmethod
def get_atom_data(cls, shape, kind: str) -> Col:
return cls.get_atom_coltype(kind=kind)(shape=shape[0])
@classmethod
def get_atom_datetime64(cls, shape):
return _tables().Int64Col(shape=shape[0])
@classmethod
def get_atom_timedelta64(cls, shape):
return _tables().Int64Col(shape=shape[0])
@property
def shape(self):
return getattr(self.data, "shape", None)
@property
def cvalues(self):
"""return my cython values"""
return self.data
def validate_attr(self, append) -> None:
"""validate that we have the same order as the existing & same dtype"""
if append:
existing_fields = getattr(self.attrs, self.kind_attr, None)
if existing_fields is not None and existing_fields != list(self.values):
raise ValueError("appended items do not match existing items in table!")
existing_dtype = getattr(self.attrs, self.dtype_attr, None)
if existing_dtype is not None and existing_dtype != self.dtype:
raise ValueError(
"appended items dtype do not match existing items dtype in table!"
)
def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
"""
Convert the data from this selection to the appropriate pandas type.
Parameters
----------
values : np.ndarray
nan_rep :
encoding : str
errors : str
Returns
-------
index : listlike to become an Index
data : ndarraylike to become a column
"""
assert isinstance(values, np.ndarray), type(values)
# values is a recarray
if values.dtype.fields is not None:
values = values[self.cname]
assert self.typ is not None
if self.dtype is None:
# Note: in tests we never have timedelta64 or datetime64,
# so the _get_data_and_dtype_name may be unnecessary
converted, dtype_name = _get_data_and_dtype_name(values)
kind = _dtype_to_kind(dtype_name)
else:
converted = values
dtype_name = self.dtype
kind = self.kind
assert isinstance(converted, np.ndarray) # for mypy
# use the meta if needed
meta = _ensure_decoded(self.meta)
metadata = self.metadata
ordered = self.ordered
tz = self.tz
assert dtype_name is not None
# convert to the correct dtype
dtype = _ensure_decoded(dtype_name)
# reverse converts
if dtype.startswith("datetime64"):
# recreate with tz if indicated
converted = _set_tz(converted, tz, coerce=True)
elif dtype == "timedelta64":
converted = np.asarray(converted, dtype="m8[ns]")
elif dtype == "date":
try:
converted = np.asarray(
[date.fromordinal(v) for v in converted], dtype=object
)
except ValueError:
converted = np.asarray(
[date.fromtimestamp(v) for v in converted], dtype=object
)
elif meta == "category":
# we have a categorical
categories = metadata
codes = converted.ravel()
# if we have stored a NaN in the categories
# then strip it; in theory we could have BOTH
# -1s in the codes and nulls :<
if categories is None:
# Handle case of NaN-only categorical columns in which case
# the categories are an empty array; when this is stored,
# pytables cannot write a zero-len array, so on readback
# the categories would be None and `read_hdf()` would fail.
categories = Index([], dtype=np.float64)
else:
mask = isna(categories)
if mask.any():
categories = categories[~mask]
codes[codes != -1] -= mask.astype(int).cumsum()._values
converted = Categorical.from_codes(
codes, categories=categories, ordered=ordered, validate=False
)
else:
try:
converted = converted.astype(dtype, copy=False)
except TypeError:
converted = converted.astype("O", copy=False)
# convert nans / decode
if _ensure_decoded(kind) == "string":
converted = _unconvert_string_array(
converted, nan_rep=nan_rep, encoding=encoding, errors=errors
)
return self.values, converted
def set_attr(self) -> None:
"""set the data for this column"""
setattr(self.attrs, self.kind_attr, self.values)
setattr(self.attrs, self.meta_attr, self.meta)
assert self.dtype is not None
setattr(self.attrs, self.dtype_attr, self.dtype)
class DataIndexableCol(DataCol):
"""represent a data column that can be indexed"""
is_data_indexable = True
def validate_names(self) -> None:
if not is_string_dtype(Index(self.values).dtype):
# TODO: should the message here be more specifically non-str?
raise ValueError("cannot have non-object label DataIndexableCol")
@classmethod
def get_atom_string(cls, shape, itemsize):
return _tables().StringCol(itemsize=itemsize)
@classmethod
def get_atom_data(cls, shape, kind: str) -> Col:
return cls.get_atom_coltype(kind=kind)()
@classmethod
def get_atom_datetime64(cls, shape):
return _tables().Int64Col()
@classmethod
def get_atom_timedelta64(cls, shape):
return _tables().Int64Col()
class GenericDataIndexableCol(DataIndexableCol):
"""represent a generic pytables data column"""
class Fixed:
"""
represent an object in my store
facilitate read/write of various types of objects
this is an abstract base class
Parameters
----------
parent : HDFStore
group : Node
The group node where the table resides.
"""
pandas_kind: str
format_type: str = "fixed" # GH#30962 needed by dask
obj_type: type[DataFrame | Series]
ndim: int
parent: HDFStore
is_table: bool = False
def __init__(
self,
parent: HDFStore,
group: Node,
encoding: str | None = "UTF-8",
errors: str = "strict",
) -> None:
assert isinstance(parent, HDFStore), type(parent)
assert _table_mod is not None # needed for mypy
assert isinstance(group, _table_mod.Node), type(group)
self.parent = parent
self.group = group
self.encoding = _ensure_encoding(encoding)
self.errors = errors
@property
def is_old_version(self) -> bool:
return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1
@property
def version(self) -> tuple[int, int, int]:
"""compute and set our version"""
version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None))
try:
version = tuple(int(x) for x in version.split("."))
if len(version) == 2:
version = version + (0,)
except AttributeError:
version = (0, 0, 0)
return version
@property
def pandas_type(self):
return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None))
def __repr__(self) -> str:
"""return a pretty representation of myself"""
self.infer_axes()
s = self.shape
if s is not None:
if isinstance(s, (list, tuple)):
jshape = ",".join([pprint_thing(x) for x in s])
s = f"[{jshape}]"
return f"{self.pandas_type:12.12} (shape->{s})"
return self.pandas_type
def set_object_info(self) -> None:
"""set my pandas type & version"""
self.attrs.pandas_type = str(self.pandas_kind)
self.attrs.pandas_version = str(_version)
def copy(self) -> Fixed:
new_self = copy.copy(self)
return new_self
@property
def shape(self):
return self.nrows
@property
def pathname(self):
return self.group._v_pathname
@property
def _handle(self):
return self.parent._handle
@property
def _filters(self):
return self.parent._filters
@property
def _complevel(self) -> int:
return self.parent._complevel
@property
def _fletcher32(self) -> bool:
return self.parent._fletcher32
@property
def attrs(self):
return self.group._v_attrs
def set_attrs(self) -> None:
"""set our object attributes"""
def get_attrs(self) -> None:
"""get our object attributes"""
@property
def storable(self):
"""return my storable"""
return self.group
@property
def is_exists(self) -> bool:
return False
@property
def nrows(self):
return getattr(self.storable, "nrows", None)
def validate(self, other) -> Literal[True] | None:
"""validate against an existing storable"""
if other is None:
return None
return True
def validate_version(self, where=None) -> None:
"""are we trying to operate on an old version?"""
def infer_axes(self) -> bool:
"""
infer the axes of my storer
return a boolean indicating if we have a valid storer or not
"""
s = self.storable
if s is None:
return False
self.get_attrs()
return True
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
):
raise NotImplementedError(
"cannot read on an abstract storer: subclasses should implement"
)
def write(self, obj, **kwargs) -> None:
raise NotImplementedError(
"cannot write on an abstract storer: subclasses should implement"
)
def delete(
self, where=None, start: int | None = None, stop: int | None = None
) -> None:
"""
support fully deleting the node in its entirety (only) - where
specification must be None
"""
if com.all_none(where, start, stop):
self._handle.remove_node(self.group, recursive=True)
return None
raise TypeError("cannot delete on an abstract storer")
class GenericFixed(Fixed):
"""a generified fixed version"""
_index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"}
_reverse_index_map = {v: k for k, v in _index_type_map.items()}
attributes: list[str] = []
# indexer helpers
def _class_to_alias(self, cls) -> str:
return self._index_type_map.get(cls, "")
def _alias_to_class(self, alias):
if isinstance(alias, type): # pragma: no cover
# compat: for a short period of time master stored types
return alias
return self._reverse_index_map.get(alias, Index)
def _get_index_factory(self, attrs):
index_class = self._alias_to_class(
_ensure_decoded(getattr(attrs, "index_class", ""))
)
factory: Callable
if index_class == DatetimeIndex:
def f(values, freq=None, tz=None):
# data are already in UTC, localize and convert if tz present
dta = DatetimeArray._simple_new(
values.values, dtype=values.dtype, freq=freq
)
result = DatetimeIndex._simple_new(dta, name=None)
if tz is not None:
result = result.tz_localize("UTC").tz_convert(tz)
return result
factory = f
elif index_class == PeriodIndex:
def f(values, freq=None, tz=None):
dtype = PeriodDtype(freq)
parr = PeriodArray._simple_new(values, dtype=dtype)
return PeriodIndex._simple_new(parr, name=None)
factory = f
else:
factory = index_class
kwargs = {}
if "freq" in attrs:
kwargs["freq"] = attrs["freq"]
if index_class is Index:
# DTI/PI would be gotten by _alias_to_class
factory = TimedeltaIndex
if "tz" in attrs:
if isinstance(attrs["tz"], bytes):
# created by python2
kwargs["tz"] = attrs["tz"].decode("utf-8")
else:
# created by python3
kwargs["tz"] = attrs["tz"]
assert index_class is DatetimeIndex # just checking
return factory, kwargs
def validate_read(self, columns, where) -> None:
"""
raise if any keywords are passed which are not-None
"""
if columns is not None:
raise TypeError(
"cannot pass a column specification when reading "
"a Fixed format store. this store must be selected in its entirety"
)
if where is not None:
raise TypeError(
"cannot pass a where specification when reading "
"from a Fixed format store. this store must be selected in its entirety"
)
@property
def is_exists(self) -> bool:
return True
def set_attrs(self) -> None:
"""set our object attributes"""
self.attrs.encoding = self.encoding
self.attrs.errors = self.errors
def get_attrs(self) -> None:
"""retrieve our attributes"""
self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
for n in self.attributes:
setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None)))
def write(self, obj, **kwargs) -> None:
self.set_attrs()
def read_array(self, key: str, start: int | None = None, stop: int | None = None):
"""read an array for the specified node (off of group"""
import tables
node = getattr(self.group, key)
attrs = node._v_attrs
transposed = getattr(attrs, "transposed", False)
if isinstance(node, tables.VLArray):
ret = node[0][start:stop]
else:
dtype = _ensure_decoded(getattr(attrs, "value_type", None))
shape = getattr(attrs, "shape", None)
if shape is not None:
# length 0 axis
ret = np.empty(shape, dtype=dtype)
else:
ret = node[start:stop]
if dtype and dtype.startswith("datetime64"):
# reconstruct a timezone if indicated
tz = getattr(attrs, "tz", None)
ret = _set_tz(ret, tz, coerce=True)
elif dtype == "timedelta64":
ret = np.asarray(ret, dtype="m8[ns]")
if transposed:
return ret.T
else:
return ret
def read_index(
self, key: str, start: int | None = None, stop: int | None = None
) -> Index:
variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety"))
if variety == "multi":
return self.read_multi_index(key, start=start, stop=stop)
elif variety == "regular":
node = getattr(self.group, key)
index = self.read_index_node(node, start=start, stop=stop)
return index
else: # pragma: no cover
raise TypeError(f"unrecognized index variety: {variety}")
def write_index(self, key: str, index: Index) -> None:
if isinstance(index, MultiIndex):
setattr(self.attrs, f"{key}_variety", "multi")
self.write_multi_index(key, index)
else:
setattr(self.attrs, f"{key}_variety", "regular")
converted = _convert_index("index", index, self.encoding, self.errors)
self.write_array(key, converted.values)
node = getattr(self.group, key)
node._v_attrs.kind = converted.kind
node._v_attrs.name = index.name
if isinstance(index, (DatetimeIndex, PeriodIndex)):
node._v_attrs.index_class = self._class_to_alias(type(index))
if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
node._v_attrs.freq = index.freq
if isinstance(index, DatetimeIndex) and index.tz is not None:
node._v_attrs.tz = _get_tz(index.tz)
def write_multi_index(self, key: str, index: MultiIndex) -> None:
setattr(self.attrs, f"{key}_nlevels", index.nlevels)
for i, (lev, level_codes, name) in enumerate(
zip(index.levels, index.codes, index.names)
):
# write the level
if isinstance(lev.dtype, ExtensionDtype):
raise NotImplementedError(
"Saving a MultiIndex with an extension dtype is not supported."
)
level_key = f"{key}_level{i}"
conv_level = _convert_index(level_key, lev, self.encoding, self.errors)
self.write_array(level_key, conv_level.values)
node = getattr(self.group, level_key)
node._v_attrs.kind = conv_level.kind
node._v_attrs.name = name
# write the name
setattr(node._v_attrs, f"{key}_name{name}", name)
# write the labels
label_key = f"{key}_label{i}"
self.write_array(label_key, level_codes)
def read_multi_index(
self, key: str, start: int | None = None, stop: int | None = None
) -> MultiIndex:
nlevels = getattr(self.attrs, f"{key}_nlevels")
levels = []
codes = []
names: list[Hashable] = []
for i in range(nlevels):
level_key = f"{key}_level{i}"
node = getattr(self.group, level_key)
lev = self.read_index_node(node, start=start, stop=stop)
levels.append(lev)
names.append(lev.name)
label_key = f"{key}_label{i}"
level_codes = self.read_array(label_key, start=start, stop=stop)
codes.append(level_codes)
return MultiIndex(
levels=levels, codes=codes, names=names, verify_integrity=True
)
def read_index_node(
self, node: Node, start: int | None = None, stop: int | None = None
) -> Index:
data = node[start:stop]
# If the index was an empty array write_array_empty() will
# have written a sentinel. Here we replace it with the original.
if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type)
kind = _ensure_decoded(node._v_attrs.kind)
name = None
if "name" in node._v_attrs:
name = _ensure_str(node._v_attrs.name)
name = _ensure_decoded(name)
attrs = node._v_attrs
factory, kwargs = self._get_index_factory(attrs)
if kind in ("date", "object"):
index = factory(
_unconvert_index(
data, kind, encoding=self.encoding, errors=self.errors
),
dtype=object,
**kwargs,
)
else:
index = factory(
_unconvert_index(
data, kind, encoding=self.encoding, errors=self.errors
),
**kwargs,
)
index.name = name
return index
def write_array_empty(self, key: str, value: ArrayLike) -> None:
"""write a 0-len array"""
# ugly hack for length 0 axes
arr = np.empty((1,) * value.ndim)
self._handle.create_array(self.group, key, arr)
node = getattr(self.group, key)
node._v_attrs.value_type = str(value.dtype)
node._v_attrs.shape = value.shape
def write_array(
self, key: str, obj: AnyArrayLike, items: Index | None = None
) -> None:
# TODO: we only have a few tests that get here, the only EA
# that gets passed is DatetimeArray, and we never have
# both self._filters and EA
value = extract_array(obj, extract_numpy=True)
if key in self.group:
self._handle.remove_node(self.group, key)
# Transform needed to interface with pytables row/col notation
empty_array = value.size == 0
transposed = False
if isinstance(value.dtype, CategoricalDtype):
raise NotImplementedError(
"Cannot store a category dtype in a HDF5 dataset that uses format="
'"fixed". Use format="table".'
)
if not empty_array:
if hasattr(value, "T"):
# ExtensionArrays (1d) may not have transpose.
value = value.T
transposed = True
atom = None
if self._filters is not None:
with suppress(ValueError):
# get the atom for this datatype
atom = _tables().Atom.from_dtype(value.dtype)
if atom is not None:
# We only get here if self._filters is non-None and
# the Atom.from_dtype call succeeded
# create an empty chunked array and fill it from value
if not empty_array:
ca = self._handle.create_carray(
self.group, key, atom, value.shape, filters=self._filters
)
ca[:] = value
else:
self.write_array_empty(key, value)
elif value.dtype.type == np.object_:
# infer the type, warn if we have a non-string type here (for
# performance)
inferred_type = lib.infer_dtype(value, skipna=False)
if empty_array:
pass
elif inferred_type == "string":
pass
else:
ws = performance_doc % (inferred_type, key, items)
warnings.warn(ws, PerformanceWarning, stacklevel=find_stack_level())
vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
vlarr.append(value)
elif lib.is_np_dtype(value.dtype, "M"):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
elif isinstance(value.dtype, DatetimeTZDtype):
# store as UTC
# with a zone
# error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
# attribute "asi8"
self._handle.create_array(
self.group, key, value.asi8 # type: ignore[union-attr]
)
node = getattr(self.group, key)
# error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
# attribute "tz"
node._v_attrs.tz = _get_tz(value.tz) # type: ignore[union-attr]
node._v_attrs.value_type = f"datetime64[{value.dtype.unit}]"
elif lib.is_np_dtype(value.dtype, "m"):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "timedelta64"
elif empty_array:
self.write_array_empty(key, value)
else:
self._handle.create_array(self.group, key, value)
getattr(self.group, key)._v_attrs.transposed = transposed
class SeriesFixed(GenericFixed):
pandas_kind = "series"
attributes = ["name"]
name: Hashable
@property
def shape(self):
try:
return (len(self.group.values),)
except (TypeError, AttributeError):
return None
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
) -> Series:
self.validate_read(columns, where)
index = self.read_index("index", start=start, stop=stop)
values = self.read_array("values", start=start, stop=stop)
result = Series(values, index=index, name=self.name, copy=False)
if using_pyarrow_string_dtype() and is_string_array(values, skipna=True):
result = result.astype("string[pyarrow_numpy]")
return result
def write(self, obj, **kwargs) -> None:
super().write(obj, **kwargs)
self.write_index("index", obj.index)
self.write_array("values", obj)
self.attrs.name = obj.name
class BlockManagerFixed(GenericFixed):
attributes = ["ndim", "nblocks"]
nblocks: int
@property
def shape(self) -> Shape | None:
try:
ndim = self.ndim
# items
items = 0
for i in range(self.nblocks):
node = getattr(self.group, f"block{i}_items")
shape = getattr(node, "shape", None)
if shape is not None:
items += shape[0]
# data shape
node = self.group.block0_values
shape = getattr(node, "shape", None)
if shape is not None:
shape = list(shape[0 : (ndim - 1)])
else:
shape = []
shape.append(items)
return shape
except AttributeError:
return None
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
) -> DataFrame:
# start, stop applied to rows, so 0th axis only
self.validate_read(columns, where)
select_axis = self.obj_type()._get_block_manager_axis(0)
axes = []
for i in range(self.ndim):
_start, _stop = (start, stop) if i == select_axis else (None, None)
ax = self.read_index(f"axis{i}", start=_start, stop=_stop)
axes.append(ax)
items = axes[0]
dfs = []
for i in range(self.nblocks):
blk_items = self.read_index(f"block{i}_items")
values = self.read_array(f"block{i}_values", start=_start, stop=_stop)
columns = items[items.get_indexer(blk_items)]
df = DataFrame(values.T, columns=columns, index=axes[1], copy=False)
if using_pyarrow_string_dtype() and is_string_array(values, skipna=True):
df = df.astype("string[pyarrow_numpy]")
dfs.append(df)
if len(dfs) > 0:
out = concat(dfs, axis=1, copy=True)
if using_copy_on_write():
# with CoW, concat ignores the copy keyword. Here, we still want
# to copy to enforce optimized column-major layout
out = out.copy()
out = out.reindex(columns=items, copy=False)
return out
return DataFrame(columns=axes[0], index=axes[1])
def write(self, obj, **kwargs) -> None:
super().write(obj, **kwargs)
# TODO(ArrayManager) HDFStore relies on accessing the blocks
if isinstance(obj._mgr, ArrayManager):
obj = obj._as_manager("block")
data = obj._mgr
if not data.is_consolidated():
data = data.consolidate()
self.attrs.ndim = data.ndim
for i, ax in enumerate(data.axes):
if i == 0 and (not ax.is_unique):
raise ValueError("Columns index has to be unique for fixed format")
self.write_index(f"axis{i}", ax)
# Supporting mixed-type DataFrame objects...nontrivial
self.attrs.nblocks = len(data.blocks)
for i, blk in enumerate(data.blocks):
# I have no idea why, but writing values before items fixed #2299
blk_items = data.items.take(blk.mgr_locs)
self.write_array(f"block{i}_values", blk.values, items=blk_items)
self.write_index(f"block{i}_items", blk_items)
class FrameFixed(BlockManagerFixed):
pandas_kind = "frame"
obj_type = DataFrame
class Table(Fixed):
"""
represent a table:
facilitate read/write of various types of tables
Attrs in Table Node
-------------------
These are attributes that are store in the main table node, they are
necessary to recreate these tables when read back in.
index_axes : a list of tuples of the (original indexing axis and
index column)
non_index_axes: a list of tuples of the (original index axis and
columns on a non-indexing axis)
values_axes : a list of the columns which comprise the data of this
table
data_columns : a list of the columns that we are allowing indexing
(these become single columns in values_axes)
nan_rep : the string to use for nan representations for string
objects
levels : the names of levels
metadata : the names of the metadata columns
"""
pandas_kind = "wide_table"
format_type: str = "table" # GH#30962 needed by dask
table_type: str
levels: int | list[Hashable] = 1
is_table = True
metadata: list
def __init__(
self,
parent: HDFStore,
group: Node,
encoding: str | None = None,
errors: str = "strict",
index_axes: list[IndexCol] | None = None,
non_index_axes: list[tuple[AxisInt, Any]] | None = None,
values_axes: list[DataCol] | None = None,
data_columns: list | None = None,
info: dict | None = None,
nan_rep=None,
) -> None:
super().__init__(parent, group, encoding=encoding, errors=errors)
self.index_axes = index_axes or []
self.non_index_axes = non_index_axes or []
self.values_axes = values_axes or []
self.data_columns = data_columns or []
self.info = info or {}
self.nan_rep = nan_rep
@property
def table_type_short(self) -> str:
return self.table_type.split("_")[0]
def __repr__(self) -> str:
"""return a pretty representation of myself"""
self.infer_axes()
jdc = ",".join(self.data_columns) if len(self.data_columns) else ""
dc = f",dc->[{jdc}]"
ver = ""
if self.is_old_version:
jver = ".".join([str(x) for x in self.version])
ver = f"[{jver}]"
jindex_axes = ",".join([a.name for a in self.index_axes])
return (
f"{self.pandas_type:12.12}{ver} "
f"(typ->{self.table_type_short},nrows->{self.nrows},"
f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})"
)
def __getitem__(self, c: str):
"""return the axis for c"""
for a in self.axes:
if c == a.name:
return a
return None
def validate(self, other) -> None:
"""validate against an existing table"""
if other is None:
return
if other.table_type != self.table_type:
raise TypeError(
"incompatible table_type with existing "
f"[{other.table_type} - {self.table_type}]"
)
for c in ["index_axes", "non_index_axes", "values_axes"]:
sv = getattr(self, c, None)
ov = getattr(other, c, None)
if sv != ov:
# show the error for the specific axes
# Argument 1 to "enumerate" has incompatible type
# "Optional[Any]"; expected "Iterable[Any]" [arg-type]
for i, sax in enumerate(sv): # type: ignore[arg-type]
# Value of type "Optional[Any]" is not indexable [index]
oax = ov[i] # type: ignore[index]
if sax != oax:
raise ValueError(
f"invalid combination of [{c}] on appending data "
f"[{sax}] vs current table [{oax}]"
)
# should never get here
raise Exception(
f"invalid combination of [{c}] on appending data [{sv}] vs "
f"current table [{ov}]"
)
@property
def is_multi_index(self) -> bool:
"""the levels attribute is 1 or a list in the case of a multi-index"""
return isinstance(self.levels, list)
def validate_multiindex(
self, obj: DataFrame | Series
) -> tuple[DataFrame, list[Hashable]]:
"""
validate that we can store the multi-index; reset and return the
new object
"""
levels = com.fill_missing_names(obj.index.names)
try:
reset_obj = obj.reset_index()
except ValueError as err:
raise ValueError(
"duplicate names/columns in the multi-index when storing as a table"
) from err
assert isinstance(reset_obj, DataFrame) # for mypy
return reset_obj, levels
@property
def nrows_expected(self) -> int:
"""based on our axes, compute the expected nrows"""
return np.prod([i.cvalues.shape[0] for i in self.index_axes])
@property
def is_exists(self) -> bool:
"""has this table been created"""
return "table" in self.group
@property
def storable(self):
return getattr(self.group, "table", None)
@property
def table(self):
"""return the table group (this is my storable)"""
return self.storable
@property
def dtype(self):
return self.table.dtype
@property
def description(self):
return self.table.description
@property
def axes(self) -> itertools.chain[IndexCol]:
return itertools.chain(self.index_axes, self.values_axes)
@property
def ncols(self) -> int:
"""the number of total columns in the values axes"""
return sum(len(a.values) for a in self.values_axes)
@property
def is_transposed(self) -> bool:
return False
@property
def data_orientation(self) -> tuple[int, ...]:
"""return a tuple of my permutated axes, non_indexable at the front"""
return tuple(
itertools.chain(
[int(a[0]) for a in self.non_index_axes],
[int(a.axis) for a in self.index_axes],
)
)
def queryables(self) -> dict[str, Any]:
"""return a dict of the kinds allowable columns for this object"""
# mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here
axis_names = {0: "index", 1: "columns"}
# compute the values_axes queryables
d1 = [(a.cname, a) for a in self.index_axes]
d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes]
d3 = [
(v.cname, v) for v in self.values_axes if v.name in set(self.data_columns)
]
return dict(d1 + d2 + d3)
def index_cols(self):
"""return a list of my index cols"""
# Note: each `i.cname` below is assured to be a str.
return [(i.axis, i.cname) for i in self.index_axes]
def values_cols(self) -> list[str]:
"""return a list of my values cols"""
return [i.cname for i in self.values_axes]
def _get_metadata_path(self, key: str) -> str:
"""return the metadata pathname for this key"""
group = self.group._v_pathname
return f"{group}/meta/{key}/meta"
def write_metadata(self, key: str, values: np.ndarray) -> None:
"""
Write out a metadata array to the key as a fixed-format Series.
Parameters
----------
key : str
values : ndarray
"""
self.parent.put(
self._get_metadata_path(key),
Series(values, copy=False),
format="table",
encoding=self.encoding,
errors=self.errors,
nan_rep=self.nan_rep,
)
def read_metadata(self, key: str):
"""return the meta data array for this key"""
if getattr(getattr(self.group, "meta", None), key, None) is not None:
return self.parent.select(self._get_metadata_path(key))
return None
def set_attrs(self) -> None:
"""set our table type & indexables"""
self.attrs.table_type = str(self.table_type)
self.attrs.index_cols = self.index_cols()
self.attrs.values_cols = self.values_cols()
self.attrs.non_index_axes = self.non_index_axes
self.attrs.data_columns = self.data_columns
self.attrs.nan_rep = self.nan_rep
self.attrs.encoding = self.encoding
self.attrs.errors = self.errors
self.attrs.levels = self.levels
self.attrs.info = self.info
def get_attrs(self) -> None:
"""retrieve our attributes"""
self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or []
self.data_columns = getattr(self.attrs, "data_columns", None) or []
self.info = getattr(self.attrs, "info", None) or {}
self.nan_rep = getattr(self.attrs, "nan_rep", None)
self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
self.levels: list[Hashable] = getattr(self.attrs, "levels", None) or []
self.index_axes = [a for a in self.indexables if a.is_an_indexable]
self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
def validate_version(self, where=None) -> None:
"""are we trying to operate on an old version?"""
if where is not None:
if self.is_old_version:
ws = incompatibility_doc % ".".join([str(x) for x in self.version])
warnings.warn(
ws,
IncompatibilityWarning,
stacklevel=find_stack_level(),
)
def validate_min_itemsize(self, min_itemsize) -> None:
"""
validate the min_itemsize doesn't contain items that are not in the
axes this needs data_columns to be defined
"""
if min_itemsize is None:
return
if not isinstance(min_itemsize, dict):
return
q = self.queryables()
for k in min_itemsize:
# ok, apply generally
if k == "values":
continue
if k not in q:
raise ValueError(
f"min_itemsize has the key [{k}] which is not an axis or "
"data_column"
)
@cache_readonly
def indexables(self):
"""create/cache the indexables if they don't exist"""
_indexables = []
desc = self.description
table_attrs = self.table.attrs
# Note: each of the `name` kwargs below are str, ensured
# by the definition in index_cols.
# index columns
for i, (axis, name) in enumerate(self.attrs.index_cols):
atom = getattr(desc, name)
md = self.read_metadata(name)
meta = "category" if md is not None else None
kind_attr = f"{name}_kind"
kind = getattr(table_attrs, kind_attr, None)
index_col = IndexCol(
name=name,
axis=axis,
pos=i,
kind=kind,
typ=atom,
table=self.table,
meta=meta,
metadata=md,
)
_indexables.append(index_col)
# values columns
dc = set(self.data_columns)
base_pos = len(_indexables)
def f(i, c):
assert isinstance(c, str)
klass = DataCol
if c in dc:
klass = DataIndexableCol
atom = getattr(desc, c)
adj_name = _maybe_adjust_name(c, self.version)
# TODO: why kind_attr here?
values = getattr(table_attrs, f"{adj_name}_kind", None)
dtype = getattr(table_attrs, f"{adj_name}_dtype", None)
# Argument 1 to "_dtype_to_kind" has incompatible type
# "Optional[Any]"; expected "str" [arg-type]
kind = _dtype_to_kind(dtype) # type: ignore[arg-type]
md = self.read_metadata(c)
# TODO: figure out why these two versions of `meta` dont always match.
# meta = "category" if md is not None else None
meta = getattr(table_attrs, f"{adj_name}_meta", None)
obj = klass(
name=adj_name,
cname=c,
values=values,
kind=kind,
pos=base_pos + i,
typ=atom,
table=self.table,
meta=meta,
metadata=md,
dtype=dtype,
)
return obj
# Note: the definition of `values_cols` ensures that each
# `c` below is a str.
_indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)])
return _indexables
def create_index(
self, columns=None, optlevel=None, kind: str | None = None
) -> None:
"""
Create a pytables index on the specified columns.
Parameters
----------
columns : None, bool, or listlike[str]
Indicate which columns to create an index on.
* False : Do not create any indexes.
* True : Create indexes on all columns.
* None : Create indexes on all columns.
* listlike : Create indexes on the given columns.
optlevel : int or None, default None
Optimization level, if None, pytables defaults to 6.
kind : str or None, default None
Kind of index, if None, pytables defaults to "medium".
Raises
------
TypeError if trying to create an index on a complex-type column.
Notes
-----
Cannot index Time64Col or ComplexCol.
Pytables must be >= 3.0.
"""
if not self.infer_axes():
return
if columns is False:
return
# index all indexables and data_columns
if columns is None or columns is True:
columns = [a.cname for a in self.axes if a.is_data_indexable]
if not isinstance(columns, (tuple, list)):
columns = [columns]
kw = {}
if optlevel is not None:
kw["optlevel"] = optlevel
if kind is not None:
kw["kind"] = kind
table = self.table
for c in columns:
v = getattr(table.cols, c, None)
if v is not None:
# remove the index if the kind/optlevel have changed
if v.is_indexed:
index = v.index
cur_optlevel = index.optlevel
cur_kind = index.kind
if kind is not None and cur_kind != kind:
v.remove_index()
else:
kw["kind"] = cur_kind
if optlevel is not None and cur_optlevel != optlevel:
v.remove_index()
else:
kw["optlevel"] = cur_optlevel
# create the index
if not v.is_indexed:
if v.type.startswith("complex"):
raise TypeError(
"Columns containing complex values can be stored but "
"cannot be indexed when using table format. Either use "
"fixed format, set index=False, or do not include "
"the columns containing complex values to "
"data_columns when initializing the table."
)
v.create_index(**kw)
elif c in self.non_index_axes[0][1]:
# GH 28156
raise AttributeError(
f"column {c} is not a data_column.\n"
f"In order to read column {c} you must reload the dataframe \n"
f"into HDFStore and include {c} with the data_columns argument."
)
def _read_axes(
self, where, start: int | None = None, stop: int | None = None
) -> list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]:
"""
Create the axes sniffed from the table.
Parameters
----------
where : ???
start : int or None, default None
stop : int or None, default None
Returns
-------
List[Tuple[index_values, column_values]]
"""
# create the selection
selection = Selection(self, where=where, start=start, stop=stop)
values = selection.select()
results = []
# convert the data
for a in self.axes:
a.set_info(self.info)
res = a.convert(
values,
nan_rep=self.nan_rep,
encoding=self.encoding,
errors=self.errors,
)
results.append(res)
return results
@classmethod
def get_object(cls, obj, transposed: bool):
"""return the data for this obj"""
return obj
def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):
"""
take the input data_columns and min_itemize and create a data
columns spec
"""
if not len(non_index_axes):
return []
axis, axis_labels = non_index_axes[0]
info = self.info.get(axis, {})
if info.get("type") == "MultiIndex" and data_columns:
raise ValueError(
f"cannot use a multi-index on axis [{axis}] with "
f"data_columns {data_columns}"
)
# evaluate the passed data_columns, True == use all columns
# take only valid axis labels
if data_columns is True:
data_columns = list(axis_labels)
elif data_columns is None:
data_columns = []
# if min_itemsize is a dict, add the keys (exclude 'values')
if isinstance(min_itemsize, dict):
existing_data_columns = set(data_columns)
data_columns = list(data_columns) # ensure we do not modify
data_columns.extend(
[
k
for k in min_itemsize.keys()
if k != "values" and k not in existing_data_columns
]
)
# return valid columns in the order of our axis
return [c for c in data_columns if c in axis_labels]
def _create_axes(
self,
axes,
obj: DataFrame,
validate: bool = True,
nan_rep=None,
data_columns=None,
min_itemsize=None,
):
"""
Create and return the axes.
Parameters
----------
axes: list or None
The names or numbers of the axes to create.
obj : DataFrame
The object to create axes on.
validate: bool, default True
Whether to validate the obj against an existing object already written.
nan_rep :
A value to use for string column nan_rep.
data_columns : List[str], True, or None, default None
Specify the columns that we want to create to allow indexing on.
* True : Use all available columns.
* None : Use no columns.
* List[str] : Use the specified columns.
min_itemsize: Dict[str, int] or None, default None
The min itemsize for a column in bytes.
"""
if not isinstance(obj, DataFrame):
group = self.group._v_name
raise TypeError(
f"cannot properly create the storer for: [group->{group},"
f"value->{type(obj)}]"
)
# set the default axes if needed
if axes is None:
axes = [0]
# map axes to numbers
axes = [obj._get_axis_number(a) for a in axes]
# do we have an existing table (if so, use its axes & data_columns)
if self.infer_axes():
table_exists = True
axes = [a.axis for a in self.index_axes]
data_columns = list(self.data_columns)
nan_rep = self.nan_rep
# TODO: do we always have validate=True here?
else:
table_exists = False
new_info = self.info
assert self.ndim == 2 # with next check, we must have len(axes) == 1
# currently support on ndim-1 axes
if len(axes) != self.ndim - 1:
raise ValueError(
"currently only support ndim-1 indexers in an AppendableTable"
)
# create according to the new data
new_non_index_axes: list = []
# nan_representation
if nan_rep is None:
nan_rep = "nan"
# We construct the non-index-axis first, since that alters new_info
idx = next(x for x in [0, 1] if x not in axes)
a = obj.axes[idx]
# we might be able to change the axes on the appending data if necessary
append_axis = list(a)
if table_exists:
indexer = len(new_non_index_axes) # i.e. 0
exist_axis = self.non_index_axes[indexer][1]
if not array_equivalent(
np.array(append_axis),
np.array(exist_axis),
strict_nan=True,
dtype_equal=True,
):
# ahah! -> reindex
if array_equivalent(
np.array(sorted(append_axis)),
np.array(sorted(exist_axis)),
strict_nan=True,
dtype_equal=True,
):
append_axis = exist_axis
# the non_index_axes info
info = new_info.setdefault(idx, {})
info["names"] = list(a.names)
info["type"] = type(a).__name__
new_non_index_axes.append((idx, append_axis))
# Now we can construct our new index axis
idx = axes[0]
a = obj.axes[idx]
axis_name = obj._get_axis_name(idx)
new_index = _convert_index(axis_name, a, self.encoding, self.errors)
new_index.axis = idx
# Because we are always 2D, there is only one new_index, so
# we know it will have pos=0
new_index.set_pos(0)
new_index.update_info(new_info)
new_index.maybe_set_size(min_itemsize) # check for column conflicts
new_index_axes = [new_index]
j = len(new_index_axes) # i.e. 1
assert j == 1
# reindex by our non_index_axes & compute data_columns
assert len(new_non_index_axes) == 1
for a in new_non_index_axes:
obj = _reindex_axis(obj, a[0], a[1])
transposed = new_index.axis == 1
# figure out data_columns and get out blocks
data_columns = self.validate_data_columns(
data_columns, min_itemsize, new_non_index_axes
)
frame = self.get_object(obj, transposed)._consolidate()
blocks, blk_items = self._get_blocks_and_items(
frame, table_exists, new_non_index_axes, self.values_axes, data_columns
)
# add my values
vaxes = []
for i, (blk, b_items) in enumerate(zip(blocks, blk_items)):
# shape of the data column are the indexable axes
klass = DataCol
name = None
# we have a data_column
if data_columns and len(b_items) == 1 and b_items[0] in data_columns:
klass = DataIndexableCol
name = b_items[0]
if not (name is None or isinstance(name, str)):
# TODO: should the message here be more specifically non-str?
raise ValueError("cannot have non-object label DataIndexableCol")
# make sure that we match up the existing columns
# if we have an existing table
existing_col: DataCol | None
if table_exists and validate:
try:
existing_col = self.values_axes[i]
except (IndexError, KeyError) as err:
raise ValueError(
f"Incompatible appended table [{blocks}]"
f"with existing table [{self.values_axes}]"
) from err
else:
existing_col = None
new_name = name or f"values_block_{i}"
data_converted = _maybe_convert_for_string_atom(
new_name,
blk.values,
existing_col=existing_col,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
encoding=self.encoding,
errors=self.errors,
columns=b_items,
)
adj_name = _maybe_adjust_name(new_name, self.version)
typ = klass._get_atom(data_converted)
kind = _dtype_to_kind(data_converted.dtype.name)
tz = None
if getattr(data_converted, "tz", None) is not None:
tz = _get_tz(data_converted.tz)
meta = metadata = ordered = None
if isinstance(data_converted.dtype, CategoricalDtype):
ordered = data_converted.ordered
meta = "category"
metadata = np.array(data_converted.categories, copy=False).ravel()
data, dtype_name = _get_data_and_dtype_name(data_converted)
col = klass(
name=adj_name,
cname=new_name,
values=list(b_items),
typ=typ,
pos=j,
kind=kind,
tz=tz,
ordered=ordered,
meta=meta,
metadata=metadata,
dtype=dtype_name,
data=data,
)
col.update_info(new_info)
vaxes.append(col)
j += 1
dcs = [col.name for col in vaxes if col.is_data_indexable]
new_table = type(self)(
parent=self.parent,
group=self.group,
encoding=self.encoding,
errors=self.errors,
index_axes=new_index_axes,
non_index_axes=new_non_index_axes,
values_axes=vaxes,
data_columns=dcs,
info=new_info,
nan_rep=nan_rep,
)
if hasattr(self, "levels"):
# TODO: get this into constructor, only for appropriate subclass
new_table.levels = self.levels
new_table.validate_min_itemsize(min_itemsize)
if validate and table_exists:
new_table.validate(self)
return new_table
@staticmethod
def _get_blocks_and_items(
frame: DataFrame,
table_exists: bool,
new_non_index_axes,
values_axes,
data_columns,
):
# Helper to clarify non-state-altering parts of _create_axes
# TODO(ArrayManager) HDFStore relies on accessing the blocks
if isinstance(frame._mgr, ArrayManager):
frame = frame._as_manager("block")
def get_blk_items(mgr):
return [mgr.items.take(blk.mgr_locs) for blk in mgr.blocks]
mgr = frame._mgr
mgr = cast(BlockManager, mgr)
blocks: list[Block] = list(mgr.blocks)
blk_items: list[Index] = get_blk_items(mgr)
if len(data_columns):
# TODO: prove that we only get here with axis == 1?
# It is the case in all extant tests, but NOT the case
# outside this `if len(data_columns)` check.
axis, axis_labels = new_non_index_axes[0]
new_labels = Index(axis_labels).difference(Index(data_columns))
mgr = frame.reindex(new_labels, axis=axis)._mgr
mgr = cast(BlockManager, mgr)
blocks = list(mgr.blocks)
blk_items = get_blk_items(mgr)
for c in data_columns:
# This reindex would raise ValueError if we had a duplicate
# index, so we can infer that (as long as axis==1) we
# get a single column back, so a single block.
mgr = frame.reindex([c], axis=axis)._mgr
mgr = cast(BlockManager, mgr)
blocks.extend(mgr.blocks)
blk_items.extend(get_blk_items(mgr))
# reorder the blocks in the same order as the existing table if we can
if table_exists:
by_items = {
tuple(b_items.tolist()): (b, b_items)
for b, b_items in zip(blocks, blk_items)
}
new_blocks: list[Block] = []
new_blk_items = []
for ea in values_axes:
items = tuple(ea.values)
try:
b, b_items = by_items.pop(items)
new_blocks.append(b)
new_blk_items.append(b_items)
except (IndexError, KeyError) as err:
jitems = ",".join([pprint_thing(item) for item in items])
raise ValueError(
f"cannot match existing table structure for [{jitems}] "
"on appending data"
) from err
blocks = new_blocks
blk_items = new_blk_items
return blocks, blk_items
def process_axes(self, obj, selection: Selection, columns=None) -> DataFrame:
"""process axes filters"""
# make a copy to avoid side effects
if columns is not None:
columns = list(columns)
# make sure to include levels if we have them
if columns is not None and self.is_multi_index:
assert isinstance(self.levels, list) # assured by is_multi_index
for n in self.levels:
if n not in columns:
columns.insert(0, n)
# reorder by any non_index_axes & limit to the select columns
for axis, labels in self.non_index_axes:
obj = _reindex_axis(obj, axis, labels, columns)
def process_filter(field, filt, op):
for axis_name in obj._AXIS_ORDERS:
axis_number = obj._get_axis_number(axis_name)
axis_values = obj._get_axis(axis_name)
assert axis_number is not None
# see if the field is the name of an axis
if field == axis_name:
# if we have a multi-index, then need to include
# the levels
if self.is_multi_index:
filt = filt.union(Index(self.levels))
takers = op(axis_values, filt)
return obj.loc(axis=axis_number)[takers]
# this might be the name of a file IN an axis
elif field in axis_values:
# we need to filter on this dimension
values = ensure_index(getattr(obj, field).values)
filt = ensure_index(filt)
# hack until we support reversed dim flags
if isinstance(obj, DataFrame):
axis_number = 1 - axis_number
takers = op(values, filt)
return obj.loc(axis=axis_number)[takers]
raise ValueError(f"cannot find the field [{field}] for filtering!")
# apply the selection filters (but keep in the same order)
if selection.filter is not None:
for field, op, filt in selection.filter.format():
obj = process_filter(field, filt, op)
return obj
def create_description(
self,
complib,
complevel: int | None,
fletcher32: bool,
expectedrows: int | None,
) -> dict[str, Any]:
"""create the description of the table from the axes & values"""
# provided expected rows if its passed
if expectedrows is None:
expectedrows = max(self.nrows_expected, 10000)
d = {"name": "table", "expectedrows": expectedrows}
# description from the axes & values
d["description"] = {a.cname: a.typ for a in self.axes}
if complib:
if complevel is None:
complevel = self._complevel or 9
filters = _tables().Filters(
complevel=complevel,
complib=complib,
fletcher32=fletcher32 or self._fletcher32,
)
d["filters"] = filters
elif self._filters is not None:
d["filters"] = self._filters
return d
def read_coordinates(
self, where=None, start: int | None = None, stop: int | None = None
):
"""
select coordinates (row numbers) from a table; return the
coordinates object
"""
# validate the version
self.validate_version(where)
# infer the data kind
if not self.infer_axes():
return False
# create the selection
selection = Selection(self, where=where, start=start, stop=stop)
coords = selection.select_coords()
if selection.filter is not None:
for field, op, filt in selection.filter.format():
data = self.read_column(
field, start=coords.min(), stop=coords.max() + 1
)
coords = coords[op(data.iloc[coords - coords.min()], filt).values]
return Index(coords)
def read_column(
self,
column: str,
where=None,
start: int | None = None,
stop: int | None = None,
):
"""
return a single column from the table, generally only indexables
are interesting
"""
# validate the version
self.validate_version()
# infer the data kind
if not self.infer_axes():
return False
if where is not None:
raise TypeError("read_column does not currently accept a where clause")
# find the axes
for a in self.axes:
if column == a.name:
if not a.is_data_indexable:
raise ValueError(
f"column [{column}] can not be extracted individually; "
"it is not data indexable"
)
# column must be an indexable or a data column
c = getattr(self.table.cols, column)
a.set_info(self.info)
col_values = a.convert(
c[start:stop],
nan_rep=self.nan_rep,
encoding=self.encoding,
errors=self.errors,
)
return Series(_set_tz(col_values[1], a.tz), name=column, copy=False)
raise KeyError(f"column [{column}] not found in the table")
class WORMTable(Table):
"""
a write-once read-many table: this format DOES NOT ALLOW appending to a
table. writing is a one-time operation the data are stored in a format
that allows for searching the data on disk
"""
table_type = "worm"
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
):
"""
read the indices and the indexing array, calculate offset rows and return
"""
raise NotImplementedError("WORMTable needs to implement read")
def write(self, obj, **kwargs) -> None:
"""
write in a format that we can search later on (but cannot append
to): write out the indices and the values using _write_array
(e.g. a CArray) create an indexing table so that we can search
"""
raise NotImplementedError("WORMTable needs to implement write")
class AppendableTable(Table):
"""support the new appendable table formats"""
table_type = "appendable"
# error: Signature of "write" incompatible with supertype "Fixed"
def write( # type: ignore[override]
self,
obj,
axes=None,
append: bool = False,
complib=None,
complevel=None,
fletcher32=None,
min_itemsize=None,
chunksize: int | None = None,
expectedrows=None,
dropna: bool = False,
nan_rep=None,
data_columns=None,
track_times: bool = True,
) -> None:
if not append and self.is_exists:
self._handle.remove_node(self.group, "table")
# create the axes
table = self._create_axes(
axes=axes,
obj=obj,
validate=append,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
data_columns=data_columns,
)
for a in table.axes:
a.validate_names()
if not table.is_exists:
# create the table
options = table.create_description(
complib=complib,
complevel=complevel,
fletcher32=fletcher32,
expectedrows=expectedrows,
)
# set the table attributes
table.set_attrs()
options["track_times"] = track_times
# create the table
table._handle.create_table(table.group, **options)
# update my info
table.attrs.info = table.info
# validate the axes and set the kinds
for a in table.axes:
a.validate_and_set(table, append)
# add the rows
table.write_data(chunksize, dropna=dropna)
def write_data(self, chunksize: int | None, dropna: bool = False) -> None:
"""
we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
"""
names = self.dtype.names
nrows = self.nrows_expected
# if dropna==True, then drop ALL nan rows
masks = []
if dropna:
for a in self.values_axes:
# figure the mask: only do if we can successfully process this
# column, otherwise ignore the mask
mask = isna(a.data).all(axis=0)
if isinstance(mask, np.ndarray):
masks.append(mask.astype("u1", copy=False))
# consolidate masks
if len(masks):
mask = masks[0]
for m in masks[1:]:
mask = mask & m
mask = mask.ravel()
else:
mask = None
# broadcast the indexes if needed
indexes = [a.cvalues for a in self.index_axes]
nindexes = len(indexes)
assert nindexes == 1, nindexes # ensures we dont need to broadcast
# transpose the values so first dimension is last
# reshape the values if needed
values = [a.take_data() for a in self.values_axes]
values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values]
bvalues = []
for i, v in enumerate(values):
new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
bvalues.append(v.reshape(new_shape))
# write the chunks
if chunksize is None:
chunksize = 100000
rows = np.empty(min(chunksize, nrows), dtype=self.dtype)
chunks = nrows // chunksize + 1
for i in range(chunks):
start_i = i * chunksize
end_i = min((i + 1) * chunksize, nrows)
if start_i >= end_i:
break
self.write_data_chunk(
rows,
indexes=[a[start_i:end_i] for a in indexes],
mask=mask[start_i:end_i] if mask is not None else None,
values=[v[start_i:end_i] for v in bvalues],
)
def write_data_chunk(
self,
rows: np.ndarray,
indexes: list[np.ndarray],
mask: npt.NDArray[np.bool_] | None,
values: list[np.ndarray],
) -> None:
"""
Parameters
----------
rows : an empty memory space where we are putting the chunk
indexes : an array of the indexes
mask : an array of the masks
values : an array of the values
"""
# 0 len
for v in values:
if not np.prod(v.shape):
return
nrows = indexes[0].shape[0]
if nrows != len(rows):
rows = np.empty(nrows, dtype=self.dtype)
names = self.dtype.names
nindexes = len(indexes)
# indexes
for i, idx in enumerate(indexes):
rows[names[i]] = idx
# values
for i, v in enumerate(values):
rows[names[i + nindexes]] = v
# mask
if mask is not None:
m = ~mask.ravel().astype(bool, copy=False)
if not m.all():
rows = rows[m]
if len(rows):
self.table.append(rows)
self.table.flush()
def delete(self, where=None, start: int | None = None, stop: int | None = None):
# delete all rows (and return the nrows)
if where is None or not len(where):
if start is None and stop is None:
nrows = self.nrows
self._handle.remove_node(self.group, recursive=True)
else:
# pytables<3.0 would remove a single row with stop=None
if stop is None:
stop = self.nrows
nrows = self.table.remove_rows(start=start, stop=stop)
self.table.flush()
return nrows
# infer the data kind
if not self.infer_axes():
return None
# create the selection
table = self.table
selection = Selection(self, where, start=start, stop=stop)
values = selection.select_coords()
# delete the rows in reverse order
sorted_series = Series(values, copy=False).sort_values()
ln = len(sorted_series)
if ln:
# construct groups of consecutive rows
diff = sorted_series.diff()
groups = list(diff[diff > 1].index)
# 1 group
if not len(groups):
groups = [0]
# final element
if groups[-1] != ln:
groups.append(ln)
# initial element
if groups[0] != 0:
groups.insert(0, 0)
# we must remove in reverse order!
pg = groups.pop()
for g in reversed(groups):
rows = sorted_series.take(range(g, pg))
table.remove_rows(
start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1
)
pg = g
self.table.flush()
# return the number of rows removed
return ln
class AppendableFrameTable(AppendableTable):
"""support the new appendable table formats"""
pandas_kind = "frame_table"
table_type = "appendable_frame"
ndim = 2
obj_type: type[DataFrame | Series] = DataFrame
@property
def is_transposed(self) -> bool:
return self.index_axes[0].axis == 1
@classmethod
def get_object(cls, obj, transposed: bool):
"""these are written transposed"""
if transposed:
obj = obj.T
return obj
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
):
# validate the version
self.validate_version(where)
# infer the data kind
if not self.infer_axes():
return None
result = self._read_axes(where=where, start=start, stop=stop)
info = (
self.info.get(self.non_index_axes[0][0], {})
if len(self.non_index_axes)
else {}
)
inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]]
assert len(inds) == 1
ind = inds[0]
index = result[ind][0]
frames = []
for i, a in enumerate(self.axes):
if a not in self.values_axes:
continue
index_vals, cvalues = result[i]
# we could have a multi-index constructor here
# ensure_index doesn't recognized our list-of-tuples here
if info.get("type") != "MultiIndex":
cols = Index(index_vals)
else:
cols = MultiIndex.from_tuples(index_vals)
names = info.get("names")
if names is not None:
cols.set_names(names, inplace=True)
if self.is_transposed:
values = cvalues
index_ = cols
cols_ = Index(index, name=getattr(index, "name", None))
else:
values = cvalues.T
index_ = Index(index, name=getattr(index, "name", None))
cols_ = cols
# if we have a DataIndexableCol, its shape will only be 1 dim
if values.ndim == 1 and isinstance(values, np.ndarray):
values = values.reshape((1, values.shape[0]))
if isinstance(values, np.ndarray):
df = DataFrame(values.T, columns=cols_, index=index_, copy=False)
elif isinstance(values, Index):
df = DataFrame(values, columns=cols_, index=index_)
else:
# Categorical
df = DataFrame._from_arrays([values], columns=cols_, index=index_)
if not (using_pyarrow_string_dtype() and values.dtype.kind == "O"):
assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
if using_pyarrow_string_dtype() and is_string_array(
values, # type: ignore[arg-type]
skipna=True,
):
df = df.astype("string[pyarrow_numpy]")
frames.append(df)
if len(frames) == 1:
df = frames[0]
else:
df = concat(frames, axis=1)
selection = Selection(self, where=where, start=start, stop=stop)
# apply the selection filters & axis orderings
df = self.process_axes(df, selection=selection, columns=columns)
return df
class AppendableSeriesTable(AppendableFrameTable):
"""support the new appendable table formats"""
pandas_kind = "series_table"
table_type = "appendable_series"
ndim = 2
obj_type = Series
@property
def is_transposed(self) -> bool:
return False
@classmethod
def get_object(cls, obj, transposed: bool):
return obj
# error: Signature of "write" incompatible with supertype "Fixed"
def write(self, obj, data_columns=None, **kwargs) -> None: # type: ignore[override]
"""we are going to write this as a frame table"""
if not isinstance(obj, DataFrame):
name = obj.name or "values"
obj = obj.to_frame(name)
super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
) -> Series:
is_multi_index = self.is_multi_index
if columns is not None and is_multi_index:
assert isinstance(self.levels, list) # needed for mypy
for n in self.levels:
if n not in columns:
columns.insert(0, n)
s = super().read(where=where, columns=columns, start=start, stop=stop)
if is_multi_index:
s.set_index(self.levels, inplace=True)
s = s.iloc[:, 0]
# remove the default name
if s.name == "values":
s.name = None
return s
class AppendableMultiSeriesTable(AppendableSeriesTable):
"""support the new appendable table formats"""
pandas_kind = "series_table"
table_type = "appendable_multiseries"
# error: Signature of "write" incompatible with supertype "Fixed"
def write(self, obj, **kwargs) -> None: # type: ignore[override]
"""we are going to write this as a frame table"""
name = obj.name or "values"
newobj, self.levels = self.validate_multiindex(obj)
assert isinstance(self.levels, list) # for mypy
cols = list(self.levels)
cols.append(name)
newobj.columns = Index(cols)
super().write(obj=newobj, **kwargs)
class GenericTable(AppendableFrameTable):
"""a table that read/writes the generic pytables table format"""
pandas_kind = "frame_table"
table_type = "generic_table"
ndim = 2
obj_type = DataFrame
levels: list[Hashable]
@property
def pandas_type(self) -> str:
return self.pandas_kind
@property
def storable(self):
return getattr(self.group, "table", None) or self.group
def get_attrs(self) -> None:
"""retrieve our attributes"""
self.non_index_axes = []
self.nan_rep = None
self.levels = []
self.index_axes = [a for a in self.indexables if a.is_an_indexable]
self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
self.data_columns = [a.name for a in self.values_axes]
@cache_readonly
def indexables(self):
"""create the indexables from the table description"""
d = self.description
# TODO: can we get a typ for this? AFAICT it is the only place
# where we aren't passing one
# the index columns is just a simple index
md = self.read_metadata("index")
meta = "category" if md is not None else None
index_col = GenericIndexCol(
name="index", axis=0, table=self.table, meta=meta, metadata=md
)
_indexables: list[GenericIndexCol | GenericDataIndexableCol] = [index_col]
for i, n in enumerate(d._v_names):
assert isinstance(n, str)
atom = getattr(d, n)
md = self.read_metadata(n)
meta = "category" if md is not None else None
dc = GenericDataIndexableCol(
name=n,
pos=i,
values=[n],
typ=atom,
table=self.table,
meta=meta,
metadata=md,
)
_indexables.append(dc)
return _indexables
# error: Signature of "write" incompatible with supertype "AppendableTable"
def write(self, **kwargs) -> None: # type: ignore[override]
raise NotImplementedError("cannot write on an generic table")
class AppendableMultiFrameTable(AppendableFrameTable):
"""a frame with a multi-index"""
table_type = "appendable_multiframe"
obj_type = DataFrame
ndim = 2
_re_levels = re.compile(r"^level_\d+$")
@property
def table_type_short(self) -> str:
return "appendable_multi"
# error: Signature of "write" incompatible with supertype "Fixed"
def write(self, obj, data_columns=None, **kwargs) -> None: # type: ignore[override]
if data_columns is None:
data_columns = []
elif data_columns is True:
data_columns = obj.columns.tolist()
obj, self.levels = self.validate_multiindex(obj)
assert isinstance(self.levels, list) # for mypy
for n in self.levels:
if n not in data_columns:
data_columns.insert(0, n)
super().write(obj=obj, data_columns=data_columns, **kwargs)
def read(
self,
where=None,
columns=None,
start: int | None = None,
stop: int | None = None,
):
df = super().read(where=where, columns=columns, start=start, stop=stop)
df = df.set_index(self.levels)
# remove names for 'level_%d'
df.index = df.index.set_names(
[None if self._re_levels.search(name) else name for name in df.index.names]
)
return df
def _reindex_axis(
obj: DataFrame, axis: AxisInt, labels: Index, other=None
) -> DataFrame:
ax = obj._get_axis(axis)
labels = ensure_index(labels)
# try not to reindex even if other is provided
# if it equals our current index
if other is not None:
other = ensure_index(other)
if (other is None or labels.equals(other)) and labels.equals(ax):
return obj
labels = ensure_index(labels.unique())
if other is not None:
labels = ensure_index(other.unique()).intersection(labels, sort=False)
if not labels.equals(ax):
slicer: list[slice | Index] = [slice(None, None)] * obj.ndim
slicer[axis] = labels
obj = obj.loc[tuple(slicer)]
return obj
# tz to/from coercion
def _get_tz(tz: tzinfo) -> str | tzinfo:
"""for a tz-aware type, return an encoded zone"""
zone = timezones.get_timezone(tz)
return zone
@overload
def _set_tz(
values: np.ndarray | Index, tz: str | tzinfo, coerce: bool = False
) -> DatetimeIndex:
...
@overload
def _set_tz(values: np.ndarray | Index, tz: None, coerce: bool = False) -> np.ndarray:
...
def _set_tz(
values: np.ndarray | Index, tz: str | tzinfo | None, coerce: bool = False
) -> np.ndarray | DatetimeIndex:
"""
coerce the values to a DatetimeIndex if tz is set
preserve the input shape if possible
Parameters
----------
values : ndarray or Index
tz : str or tzinfo
coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
"""
if isinstance(values, DatetimeIndex):
# If values is tzaware, the tz gets dropped in the values.ravel()
# call below (which returns an ndarray). So we are only non-lossy
# if `tz` matches `values.tz`.
assert values.tz is None or values.tz == tz
if values.tz is not None:
return values
if tz is not None:
if isinstance(values, DatetimeIndex):
name = values.name
else:
name = None
values = values.ravel()
tz = _ensure_decoded(tz)
values = DatetimeIndex(values, name=name)
values = values.tz_localize("UTC").tz_convert(tz)
elif coerce:
values = np.asarray(values, dtype="M8[ns]")
# error: Incompatible return value type (got "Union[ndarray, Index]",
# expected "Union[ndarray, DatetimeIndex]")
return values # type: ignore[return-value]
def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
assert isinstance(name, str)
index_name = index.name
# error: Argument 1 to "_get_data_and_dtype_name" has incompatible type "Index";
# expected "Union[ExtensionArray, ndarray]"
converted, dtype_name = _get_data_and_dtype_name(index) # type: ignore[arg-type]
kind = _dtype_to_kind(dtype_name)
atom = DataIndexableCol._get_atom(converted)
if (
lib.is_np_dtype(index.dtype, "iu")
or needs_i8_conversion(index.dtype)
or is_bool_dtype(index.dtype)
):
# Includes Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex,
# in which case "kind" is "integer", "integer", "datetime64",
# "timedelta64", and "integer", respectively.
return IndexCol(
name,
values=converted,
kind=kind,
typ=atom,
freq=getattr(index, "freq", None),
tz=getattr(index, "tz", None),
index_name=index_name,
)
if isinstance(index, MultiIndex):
raise TypeError("MultiIndex not supported here!")
inferred_type = lib.infer_dtype(index, skipna=False)
# we won't get inferred_type of "datetime64" or "timedelta64" as these
# would go through the DatetimeIndex/TimedeltaIndex paths above
values = np.asarray(index)
if inferred_type == "date":
converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
return IndexCol(
name, converted, "date", _tables().Time32Col(), index_name=index_name
)
elif inferred_type == "string":
converted = _convert_string_array(values, encoding, errors)
itemsize = converted.dtype.itemsize
return IndexCol(
name,
converted,
"string",
_tables().StringCol(itemsize),
index_name=index_name,
)
elif inferred_type in ["integer", "floating"]:
return IndexCol(
name, values=converted, kind=kind, typ=atom, index_name=index_name
)
else:
assert isinstance(converted, np.ndarray) and converted.dtype == object
assert kind == "object", kind
atom = _tables().ObjectAtom()
return IndexCol(name, converted, kind, atom, index_name=index_name)
def _unconvert_index(data, kind: str, encoding: str, errors: str) -> np.ndarray | Index:
index: Index | np.ndarray
if kind.startswith("datetime64"):
if kind == "datetime64":
# created before we stored resolution information
index = DatetimeIndex(data)
else:
index = DatetimeIndex(data.view(kind))
elif kind == "timedelta64":
index = TimedeltaIndex(data)
elif kind == "date":
try:
index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
except ValueError:
index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object)
elif kind in ("integer", "float", "bool"):
index = np.asarray(data)
elif kind in ("string"):
index = _unconvert_string_array(
data, nan_rep=None, encoding=encoding, errors=errors
)
elif kind == "object":
index = np.asarray(data[0])
else: # pragma: no cover
raise ValueError(f"unrecognized index type {kind}")
return index
def _maybe_convert_for_string_atom(
name: str,
bvalues: ArrayLike,
existing_col,
min_itemsize,
nan_rep,
encoding,
errors,
columns: list[str],
):
if bvalues.dtype != object:
return bvalues
bvalues = cast(np.ndarray, bvalues)
dtype_name = bvalues.dtype.name
inferred_type = lib.infer_dtype(bvalues, skipna=False)
if inferred_type == "date":
raise TypeError("[date] is not implemented as a table column")
if inferred_type == "datetime":
# after GH#8260
# this only would be hit for a multi-timezone dtype which is an error
raise TypeError(
"too many timezones in this block, create separate data columns"
)
if not (inferred_type == "string" or dtype_name == "object"):
return bvalues
mask = isna(bvalues)
data = bvalues.copy()
data[mask] = nan_rep
# see if we have a valid string type
inferred_type = lib.infer_dtype(data, skipna=False)
if inferred_type != "string":
# we cannot serialize this data, so report an exception on a column
# by column basis
# expected behaviour:
# search block for a non-string object column by column
for i in range(data.shape[0]):
col = data[i]
inferred_type = lib.infer_dtype(col, skipna=False)
if inferred_type != "string":
error_column_label = columns[i] if len(columns) > i else f"No.{i}"
raise TypeError(
f"Cannot serialize the column [{error_column_label}]\n"
f"because its data contents are not [string] but "
f"[{inferred_type}] object dtype"
)
# itemsize is the maximum length of a string (along any dimension)
data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape)
itemsize = data_converted.itemsize
# specified min_itemsize?
if isinstance(min_itemsize, dict):
min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0)
itemsize = max(min_itemsize or 0, itemsize)
# check for column in the values conflicts
if existing_col is not None:
eci = existing_col.validate_col(itemsize)
if eci is not None and eci > itemsize:
itemsize = eci
data_converted = data_converted.astype(f"|S{itemsize}", copy=False)
return data_converted
def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray:
"""
Take a string-like that is object dtype and coerce to a fixed size string type.
Parameters
----------
data : np.ndarray[object]
encoding : str
errors : str
Handler for encoding errors.
Returns
-------
np.ndarray[fixed-length-string]
"""
# encode if needed
if len(data):
data = (
Series(data.ravel(), copy=False)
.str.encode(encoding, errors)
._values.reshape(data.shape)
)
# create the sized dtype
ensured = ensure_object(data.ravel())
itemsize = max(1, libwriters.max_len_string_array(ensured))
data = np.asarray(data, dtype=f"S{itemsize}")
return data
def _unconvert_string_array(
data: np.ndarray, nan_rep, encoding: str, errors: str
) -> np.ndarray:
"""
Inverse of _convert_string_array.
Parameters
----------
data : np.ndarray[fixed-length-string]
nan_rep : the storage repr of NaN
encoding : str
errors : str
Handler for encoding errors.
Returns
-------
np.ndarray[object]
Decoded data.
"""
shape = data.shape
data = np.asarray(data.ravel(), dtype=object)
if len(data):
itemsize = libwriters.max_len_string_array(ensure_object(data))
dtype = f"U{itemsize}"
if isinstance(data[0], bytes):
data = Series(data, copy=False).str.decode(encoding, errors=errors)._values
else:
data = data.astype(dtype, copy=False).astype(object, copy=False)
if nan_rep is None:
nan_rep = "nan"
libwriters.string_array_replace_from_nan_rep(data, nan_rep)
return data.reshape(shape)
def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str):
assert isinstance(val_kind, str), type(val_kind)
if _need_convert(val_kind):
conv = _get_converter(val_kind, encoding, errors)
values = conv(values)
return values
def _get_converter(kind: str, encoding: str, errors: str):
if kind == "datetime64":
return lambda x: np.asarray(x, dtype="M8[ns]")
elif "datetime64" in kind:
return lambda x: np.asarray(x, dtype=kind)
elif kind == "string":
return lambda x: _unconvert_string_array(
x, nan_rep=None, encoding=encoding, errors=errors
)
else: # pragma: no cover
raise ValueError(f"invalid kind {kind}")
def _need_convert(kind: str) -> bool:
if kind in ("datetime64", "string") or "datetime64" in kind:
return True
return False
def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
"""
Prior to 0.10.1, we named values blocks like: values_block_0 an the
name values_0, adjust the given name if necessary.
Parameters
----------
name : str
version : Tuple[int, int, int]
Returns
-------
str
"""
if isinstance(version, str) or len(version) < 3:
raise ValueError("Version is incorrect, expected sequence of 3 integers.")
if version[0] == 0 and version[1] <= 10 and version[2] == 0:
m = re.search(r"values_block_(\d+)", name)
if m:
grp = m.groups()[0]
name = f"values_{grp}"
return name
def _dtype_to_kind(dtype_str: str) -> str:
"""
Find the "kind" string describing the given dtype name.
"""
dtype_str = _ensure_decoded(dtype_str)
if dtype_str.startswith(("string", "bytes")):
kind = "string"
elif dtype_str.startswith("float"):
kind = "float"
elif dtype_str.startswith("complex"):
kind = "complex"
elif dtype_str.startswith(("int", "uint")):
kind = "integer"
elif dtype_str.startswith("datetime64"):
kind = dtype_str
elif dtype_str.startswith("timedelta"):
kind = "timedelta64"
elif dtype_str.startswith("bool"):
kind = "bool"
elif dtype_str.startswith("category"):
kind = "category"
elif dtype_str.startswith("period"):
# We store the `freq` attr so we can restore from integers
kind = "integer"
elif dtype_str == "object":
kind = "object"
else:
raise ValueError(f"cannot interpret dtype of [{dtype_str}]")
return kind
def _get_data_and_dtype_name(data: ArrayLike):
"""
Convert the passed data into a storable form and a dtype string.
"""
if isinstance(data, Categorical):
data = data.codes
if isinstance(data.dtype, DatetimeTZDtype):
# For datetime64tz we need to drop the TZ in tests TODO: why?
dtype_name = f"datetime64[{data.dtype.unit}]"
else:
dtype_name = data.dtype.name
if data.dtype.kind in "mM":
data = np.asarray(data.view("i8"))
# TODO: we used to reshape for the dt64tz case, but no longer
# doing that doesn't seem to break anything. why?
elif isinstance(data, PeriodIndex):
data = data.asi8
data = np.asarray(data)
return data, dtype_name
class Selection:
"""
Carries out a selection operation on a tables.Table object.
Parameters
----------
table : a Table object
where : list of Terms (or convertible to)
start, stop: indices to start and/or stop selection
"""
def __init__(
self,
table: Table,
where=None,
start: int | None = None,
stop: int | None = None,
) -> None:
self.table = table
self.where = where
self.start = start
self.stop = stop
self.condition = None
self.filter = None
self.terms = None
self.coordinates = None
if is_list_like(where):
# see if we have a passed coordinate like
with suppress(ValueError):
inferred = lib.infer_dtype(where, skipna=False)
if inferred in ("integer", "boolean"):
where = np.asarray(where)
if where.dtype == np.bool_:
start, stop = self.start, self.stop
if start is None:
start = 0
if stop is None:
stop = self.table.nrows
self.coordinates = np.arange(start, stop)[where]
elif issubclass(where.dtype.type, np.integer):
if (self.start is not None and (where < self.start).any()) or (
self.stop is not None and (where >= self.stop).any()
):
raise ValueError(
"where must have index locations >= start and < stop"
)
self.coordinates = where
if self.coordinates is None:
self.terms = self.generate(where)
# create the numexpr & the filter
if self.terms is not None:
self.condition, self.filter = self.terms.evaluate()
def generate(self, where):
"""where can be a : dict,list,tuple,string"""
if where is None:
return None
q = self.table.queryables()
try:
return PyTablesExpr(where, queryables=q, encoding=self.table.encoding)
except NameError as err:
# raise a nice message, suggesting that the user should use
# data_columns
qkeys = ",".join(q.keys())
msg = dedent(
f"""\
The passed where expression: {where}
contains an invalid variable reference
all of the variable references must be a reference to
an axis (e.g. 'index' or 'columns'), or a data_column
The currently defined references are: {qkeys}
"""
)
raise ValueError(msg) from err
def select(self):
"""
generate the selection
"""
if self.condition is not None:
return self.table.table.read_where(
self.condition.format(), start=self.start, stop=self.stop
)
elif self.coordinates is not None:
return self.table.table.read_coordinates(self.coordinates)
return self.table.table.read(start=self.start, stop=self.stop)
def select_coords(self):
"""
generate the selection
"""
start, stop = self.start, self.stop
nrows = self.table.nrows
if start is None:
start = 0
elif start < 0:
start += nrows
if stop is None:
stop = nrows
elif stop < 0:
stop += nrows
if self.condition is not None:
return self.table.table.get_where_list(
self.condition.format(), start=start, stop=stop, sort=True
)
elif self.coordinates is not None:
return self.coordinates
return np.arange(start, stop)
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