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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/venv/lib/python3.9/site-packages/pandas/io/pytables.py

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Refactor on docs; Add performance comparison
4 years ago
"""
High level interface to PyTables for reading and writing pandas data structures
to disk
"""
import copy
from datetime import date, tzinfo
import itertools
import os
import re
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
import warnings
import numpy as np
from pandas._config import config, get_option
from pandas._libs import lib, writers as libwriters
from pandas._libs.tslibs import timezones
from pandas._typing import ArrayLike, FrameOrSeries, Label
from pandas.compat._optional import import_optional_dependency
from pandas.compat.pickle_compat import patch_pickle
from pandas.errors import PerformanceWarning
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
ensure_object,
is_categorical_dtype,
is_complex_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_extension_array_dtype,
is_list_like,
is_string_dtype,
is_timedelta64_dtype,
)
from pandas.core.dtypes.generic import ABCExtensionArray
from pandas.core.dtypes.missing import array_equivalent
from pandas import (
DataFrame,
DatetimeIndex,
Index,
Int64Index,
MultiIndex,
PeriodIndex,
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.indexes.api import ensure_index
from pandas.io.common import stringify_path
from pandas.io.formats.printing import adjoin, pprint_thing
if TYPE_CHECKING:
from tables import Col, File, Node # noqa:F401
# 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):
# 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)):
wlist = []
for w in filter(lambda x: x is not None, where):
if not maybe_expression(w):
wlist.append(w)
else:
wlist.append(Term(w, scope_level=level))
where = wlist
elif maybe_expression(where):
where = Term(where, scope_level=level)
return where if where is None or len(where) else None
class PossibleDataLossError(Exception):
pass
class ClosedFileError(Exception):
pass
class IncompatibilityWarning(Warning):
pass
incompatibility_doc = """
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)
"""
class AttributeConflictWarning(Warning):
pass
attribute_conflict_doc = """
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
"""
class DuplicateWarning(Warning):
pass
duplicate_doc = """
duplicate entries in table, taking most recently appended
"""
performance_doc = """
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 = """
: boolean
drop ALL nan rows when appending to a table
"""
format_doc = """
: 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
try:
_table_file_open_policy_is_strict = (
tables.file._FILE_OPEN_POLICY == "strict"
)
except AttributeError:
pass
return _table_mod
# interface to/from ###
def to_hdf(
path_or_buf,
key: str,
value: FrameOrSeries,
mode: str = "a",
complevel: Optional[int] = None,
complib: Optional[str] = None,
append: bool = False,
format: Optional[str] = None,
index: bool = True,
min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
nan_rep=None,
dropna: Optional[bool] = None,
data_columns: Optional[Union[bool, List[str]]] = None,
errors: str = "strict",
encoding: str = "UTF-8",
):
""" 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,
)
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,
key=None,
mode: str = "r",
errors: str = "strict",
where=None,
start: Optional[int] = None,
stop: Optional[int] = None,
columns=None,
iterator=False,
chunksize: Optional[int] = 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 or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: ``file://localhost/path/to/table.h5``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handler (e.g. via builtin ``open`` function)
or ``StringIO``.
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
-------
item : 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'])
>>> df.to_hdf('./store.h5', 'data')
>>> reread = pd.read_hdf('./store.h5')
"""
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 IOError("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, KeyError):
if not isinstance(path_or_buf, HDFStore):
# if there is an error, close the store if we opened it.
try:
store.close()
except AttributeError:
pass
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.
As of v0.20.2 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: Optional["File"]
_mode: str
_complevel: int
_fletcher32: bool
def __init__(
self,
path,
mode: str = "a",
complevel: Optional[int] = None,
complib=None,
fletcher32: bool = False,
**kwargs,
):
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):
return self._path
@property
def root(self):
""" return the root node """
self._check_if_open()
return self._handle.root
@property
def filename(self):
return self._path
def __getitem__(self, key: str):
return self.get(key)
def __setitem__(self, key: str, value):
self.put(key, value)
def __delitem__(self, key: str):
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 name == key or name[1:] == key:
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):
return self
def __exit__(self, exc_type, exc_value, traceback):
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
.. versionadded:: 1.1.0
Returns
-------
list
List of ABSOLUTE path-names (e.g. have the leading '/').
Raises
------
raises ValueError if kind has an illegal value
"""
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):
return iter(self.keys())
def items(self):
"""
iterate on key->group
"""
for g in self.groups():
yield g._v_pathname, g
iteritems = items
def open(self, mode: str = "a", **kwargs):
"""
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
)
try:
self._handle = tables.open_file(self._path, self._mode, **kwargs)
except IOError as err: # pragma: no cover
if "can not be written" in str(err):
print(f"Opening {self._path} in read-only mode")
self._handle = tables.open_file(self._path, "r", **kwargs)
else:
raise
except ValueError as err:
# trap PyTables >= 3.1 FILE_OPEN_POLICY exception
# to provide an updated message
if "FILE_OPEN_POLICY" in str(err):
hdf_version = tables.get_hdf5_version()
err = ValueError(
f"PyTables [{tables.__version__}] no longer supports "
"opening multiple files\n"
"even in read-only mode on this HDF5 version "
f"[{hdf_version}]. You can accept this\n"
"and not open the same file multiple times at once,\n"
"upgrade the HDF5 version, or downgrade to PyTables 3.0.0 "
"which allows\n"
"files to be opened multiple times at once\n"
)
raise err
except Exception as err:
# trying to read from a non-existent file causes an error which
# is not part of IOError, make it one
if self._mode == "r" and "Unable to open/create file" in str(err):
raise IOError(str(err)) from err
raise
def close(self):
"""
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):
"""
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:
try:
os.fsync(self._handle.fileno())
except OSError:
pass
def get(self, key: str):
"""
Retrieve pandas object stored in file.
Parameters
----------
key : str
Returns
-------
object
Same type as object stored in file.
"""
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=False,
chunksize=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, default None
List of Term (or convertible) objects, optional.
start : int, default None
Row number to start selection.
stop : int, default None
Row number to stop selection.
columns : list, default None
A list of columns that if not None, will limit the return columns.
iterator : bool, default False
Returns an iterator.
chunksize : int, default None
Number or rows to include in iteration, return an iterator.
auto_close : bool, default False
Should automatically close the store when finished.
Returns
-------
object
Retrieved object from file.
"""
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: Optional[int] = None,
stop: Optional[int] = 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: Optional[int] = None,
stop: Optional[int] = 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=False,
chunksize=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 : boolean, 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 = list({t.non_index_axes[0][0] for t in _tbls})[0]
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: FrameOrSeries,
format=None,
index=True,
append=False,
complib=None,
complevel: Optional[int] = None,
min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
nan_rep=None,
data_columns: Optional[List[str]] = None,
encoding=None,
errors: str = "strict",
track_times: bool = True,
):
"""
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.
append : bool, default False
This will force Table format, append the input data to the
existing.
data_columns : list, 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.
dropna : bool, default False, do not write an ALL nan row to
The store settable by the option 'io.hdf.dropna_table'.
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.
.. versionadded:: 1.1.0
"""
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,
)
def remove(self, key: str, where=None, start=None, stop=None):
"""
Remove pandas object partially by specifying the where condition
Parameters
----------
key : string
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: FrameOrSeries,
format=None,
axes=None,
index=True,
append=True,
complib=None,
complevel: Optional[int] = None,
columns=None,
min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
nan_rep=None,
chunksize=None,
expectedrows=None,
dropna: Optional[bool] = None,
data_columns: Optional[List[str]] = None,
encoding=None,
errors: str = "strict",
):
"""
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.
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
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
"""
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=False,
**kwargs,
):
"""
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 = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0]
# 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: Optional[int] = None,
kind: Optional[str] = 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):
"""
Return a list of all the top-level nodes.
Each node returned is not a pandas storage object.
Returns
-------
list
List of objects.
"""
_tables()
self._check_if_open()
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="/"):
"""
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.
.. versionadded:: 0.24.0
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`.
"""
_tables()
self._check_if_open()
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) -> Optional["Node"]:
""" 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) -> Union["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="w",
propindexes: bool = True,
keys=None,
complib=None,
complevel: Optional[int] = None,
fletcher32: bool = False,
overwrite=True,
):
"""
Copy the existing store to a new file, updating in place.
Parameters
----------
propindexes: bool, default True
Restore indexes in copied file.
keys : list of keys to include in the copy (defaults to all)
overwrite : overwrite (remove and replace) existing nodes in the
new store (default is True)
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: Union[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
"""
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):
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: Optional[FrameOrSeries] = None,
encoding: str = "UTF-8",
errors: str = "strict",
) -> Union["GenericFixed", "Table"]:
""" return a suitable class to operate """
cls: Union[Type["GenericFixed"], Type["Table"]]
if value is not None and not isinstance(value, (Series, DataFrame)):
raise TypeError("value must be None, Series, or DataFrame")
def error(t):
# return instead of raising so mypy can tell where we are raising
return TypeError(
f"cannot properly create the storer for: [{t}] [group->"
f"{group},value->{type(value)},format->{format}"
)
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:
_TYPE_MAP = {Series: "series", DataFrame: "frame"}
pt = _TYPE_MAP[type(value)]
# 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 error("_STORER_MAP") 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 error("_TABLE_MAP") from err
return cls(self, group, encoding=encoding, errors=errors)
def _write_to_group(
self,
key: str,
value: FrameOrSeries,
format,
axes=None,
index=True,
append=False,
complib=None,
complevel: Optional[int] = None,
fletcher32=None,
min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
chunksize=None,
expectedrows=None,
dropna=False,
nan_rep=None,
data_columns=None,
encoding=None,
errors: str = "strict",
track_times: bool = True,
):
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
# 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
if group is 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
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()
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: Optional[int]
store: HDFStore
s: Union["GenericFixed", "Table"]
def __init__(
self,
store: HDFStore,
s: Union["GenericFixed", "Table"],
func,
where,
nrows,
start=None,
stop=None,
iterator: bool = False,
chunksize: Optional[int] = None,
auto_close: bool = False,
):
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):
# iterate
current = self.start
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):
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 = True
is_data_indexable = True
_info_fields = ["freq", "tz", "index_name"]
name: str
cname: str
def __init__(
self,
name: str,
values=None,
kind=None,
typ=None,
cname: Optional[str] = None,
axis=None,
pos=None,
freq=None,
tz=None,
index_name=None,
ordered=None,
table=None,
meta=None,
metadata=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):
""" 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: Any) -> 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):
"""
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:
values = values[self.cname]
val_kind = _ensure_decoded(self.kind)
values = _maybe_convert(values, val_kind, encoding, errors)
kwargs = dict()
kwargs["name"] = _ensure_decoded(self.index_name)
if self.freq is not None:
kwargs["freq"] = _ensure_decoded(self.freq)
# making an Index instance could throw a number of different errors
try:
new_pd_index = Index(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 = Index(values, **kwargs)
new_pd_index = _set_tz(new_pd_index, self.tz)
return new_pd_index, new_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):
return iter(self.values)
def maybe_set_size(self, min_itemsize=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):
pass
def validate_and_set(self, handler: "AppendableTable", append: bool):
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):
# 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):
"""
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=6)
# 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}]"
)
else:
if value is not None or existing_value is not None:
idx[key] = value
def set_info(self, info):
""" 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):
""" set the kind for this column """
setattr(self.attrs, self.kind_attr, self.kind)
def validate_metadata(self, handler: "AppendableTable"):
""" 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)
):
raise ValueError(
"cannot append a categorical with "
"different categories to the existing"
)
def write_metadata(self, handler: "AppendableTable"):
""" 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):
"""
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)
values = Int64Index(np.arange(len(values)))
return values, values
def set_attr(self):
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=None,
pos=None,
tz=None,
ordered=None,
table=None,
meta=None,
metadata=None,
dtype=None,
data=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: Any) -> 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):
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
itemsize = dtype.itemsize # type: ignore
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 is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
atom = cls.get_atom_datetime64(shape)
elif is_timedelta64_dtype(dtype):
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):
"""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 == "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
)
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):
""" 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):
if not Index(self.values).is_object():
# 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 """
pass
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[Union[DataFrame, Series]]
ndim: int
encoding: str
parent: HDFStore
group: "Node"
errors: str
is_table = False
def __init__(
self,
parent: HDFStore,
group: "Node",
encoding: str = "UTF-8",
errors: str = "strict",
):
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):
""" set my pandas type & version """
self.attrs.pandas_type = str(self.pandas_kind)
self.attrs.pandas_version = str(_version)
def copy(self):
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):
""" set our object attributes """
pass
def get_attrs(self):
""" get our object attributes """
pass
@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):
""" validate against an existing storable """
if other is None:
return
return True
def validate_version(self, where=None):
""" are we trying to operate on an old version? """
return True
def infer_axes(self):
"""
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: Optional[int] = None,
stop: Optional[int] = None,
):
raise NotImplementedError(
"cannot read on an abstract storer: subclasses should implement"
)
def write(self, **kwargs):
raise NotImplementedError(
"cannot write on an abstract storer: subclasses should implement"
)
def delete(
self, where=None, start: Optional[int] = None, stop: Optional[int] = 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, klass):
if klass == 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, freq=freq)
result = DatetimeIndex._simple_new(dta, name=None)
if tz is not None:
result = result.tz_localize("UTC").tz_convert(tz)
return result
return f
elif klass == PeriodIndex:
def f(values, freq=None, tz=None):
parr = PeriodArray._simple_new(values, freq=freq)
return PeriodIndex._simple_new(parr, name=None)
return f
return klass
def validate_read(self, columns, where):
"""
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):
""" set our object attributes """
self.attrs.encoding = self.encoding
self.attrs.errors = self.errors
def get_attrs(self):
""" 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):
self.set_attrs()
def read_array(
self, key: str, start: Optional[int] = None, stop: Optional[int] = 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 == "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: Optional[int] = None, stop: Optional[int] = 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):
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):
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 is_extension_array_dtype(lev):
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: Optional[int] = None, stop: Optional[int] = None
) -> MultiIndex:
nlevels = getattr(self.attrs, f"{key}_nlevels")
levels = []
codes = []
names: List[Label] = []
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: Optional[int] = None, stop: Optional[int] = 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)
index_class = self._alias_to_class(
_ensure_decoded(getattr(node._v_attrs, "index_class", ""))
)
factory = self._get_index_factory(index_class)
kwargs = {}
if "freq" in node._v_attrs:
kwargs["freq"] = node._v_attrs["freq"]
if "tz" in node._v_attrs:
if isinstance(node._v_attrs["tz"], bytes):
# created by python2
kwargs["tz"] = node._v_attrs["tz"].decode("utf-8")
else:
# created by python3
kwargs["tz"] = node._v_attrs["tz"]
if kind == "date":
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):
""" 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, value: ArrayLike, items: Optional[Index] = None):
# TODO: we only have one test that gets here, the only EA
# that gets passed is DatetimeArray, and we never have
# both self._filters and EA
assert isinstance(value, (np.ndarray, ABCExtensionArray)), type(value)
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 is_categorical_dtype(value.dtype):
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:
try:
# get the atom for this datatype
atom = _tables().Atom.from_dtype(value.dtype)
except ValueError:
pass
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=7)
vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
vlarr.append(value)
elif empty_array:
self.write_array_empty(key, value)
elif is_datetime64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "datetime64"
elif is_datetime64tz_dtype(value.dtype):
# store as UTC
# with a zone
self._handle.create_array(self.group, key, value.asi8)
node = getattr(self.group, key)
node._v_attrs.tz = _get_tz(value.tz)
node._v_attrs.value_type = "datetime64"
elif is_timedelta64_dtype(value.dtype):
self._handle.create_array(self.group, key, value.view("i8"))
getattr(self.group, key)._v_attrs.value_type = "timedelta64"
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: Label
@property
def shape(self):
try:
return (len(self.group.values),)
except (TypeError, AttributeError):
return None
def read(
self,
where=None,
columns=None,
start: Optional[int] = None,
stop: Optional[int] = None,
):
self.validate_read(columns, where)
index = self.read_index("index", start=start, stop=stop)
values = self.read_array("values", start=start, stop=stop)
return Series(values, index=index, name=self.name)
def write(self, obj, **kwargs):
super().write(obj, **kwargs)
self.write_index("index", obj.index)
self.write_array("values", obj.values)
self.attrs.name = obj.name
class BlockManagerFixed(GenericFixed):
attributes = ["ndim", "nblocks"]
nblocks: int
@property
def shape(self):
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: Optional[int] = None,
stop: Optional[int] = None,
):
# 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])
dfs.append(df)
if len(dfs) > 0:
out = concat(dfs, axis=1)
out = out.reindex(columns=items, copy=False)
return out
return DataFrame(columns=axes[0], index=axes[1])
def write(self, obj, **kwargs):
super().write(obj, **kwargs)
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), or True to force all
columns
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 = 1
is_table = True
index_axes: List[IndexCol]
non_index_axes: List[Tuple[int, Any]]
values_axes: List[DataCol]
data_columns: List
metadata: List
info: Dict
def __init__(
self,
parent: HDFStore,
group: "Node",
encoding=None,
errors: str = "strict",
index_axes=None,
non_index_axes=None,
values_axes=None,
data_columns=None,
info=None,
nan_rep=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 dict()
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):
""" 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
for i, sax in enumerate(sv):
oax = ov[i]
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):
"""
validate that we can store the multi-index; reset and return the
new object
"""
levels = [
l if l is not None else f"level_{i}" for i, l in enumerate(obj.index.names)
]
try:
return obj.reset_index(), levels
except ValueError as err:
raise ValueError(
"duplicate names/columns in the multi-index when storing as a table"
) from err
@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):
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):
"""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) # type: ignore
# error: List comprehension has incompatible type
# List[Tuple[Any, None]]; expected List[Tuple[str, IndexCol]]
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):
"""
Write out a metadata array to the key as a fixed-format Series.
Parameters
----------
key : str
values : ndarray
"""
values = Series(values)
self.parent.put(
self._get_metadata_path(key),
values,
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):
""" 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):
""" 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 dict()
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 = 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):
""" are we trying to operate on an old version? """
if where is not None:
if self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1:
ws = incompatibility_doc % ".".join([str(x) for x in self.version])
warnings.warn(ws, IncompatibilityWarning)
def validate_min_itemsize(self, min_itemsize):
"""
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, v in min_itemsize.items():
# 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)
kind = _dtype_to_kind(dtype)
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: Optional[str] = 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 = dict()
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: Optional[int] = None, stop: Optional[int] = None
) -> List[Tuple[ArrayLike, ArrayLike]]:
"""
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, dict())
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 = [x for x in [0, 1] if x not in axes][0]
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)):
# ahah! -> reindex
if array_equivalent(
np.array(sorted(append_axis)), np.array(sorted(exist_axis))
):
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])
def get_blk_items(mgr, blocks):
return [mgr.items.take(blk.mgr_locs) for blk in blocks]
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
)
block_obj = self.get_object(obj, transposed)._consolidate()
blocks, blk_items = self._get_blocks_and_items(
block_obj, table_exists, new_non_index_axes, self.values_axes, data_columns
)
# add my values
vaxes = []
for i, (b, 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: Optional[DataCol]
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,
b,
existing_col=existing_col,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
encoding=self.encoding,
errors=self.errors,
)
adj_name = _maybe_adjust_name(new_name, self.version)
typ = klass._get_atom(data_converted)
kind = _dtype_to_kind(data_converted.dtype.name)
tz = _get_tz(data_converted.tz) if hasattr(data_converted, "tz") else None
meta = metadata = ordered = None
if is_categorical_dtype(data_converted.dtype):
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(
block_obj, table_exists, new_non_index_axes, values_axes, data_columns
):
# Helper to clarify non-state-altering parts of _create_axes
def get_blk_items(mgr, blocks):
return [mgr.items.take(blk.mgr_locs) for blk in blocks]
blocks = block_obj._mgr.blocks
blk_items = get_blk_items(block_obj._mgr, blocks)
if len(data_columns):
axis, axis_labels = new_non_index_axes[0]
new_labels = Index(axis_labels).difference(Index(data_columns))
mgr = block_obj.reindex(new_labels, axis=axis)._mgr
blocks = list(mgr.blocks)
blk_items = get_blk_items(mgr, blocks)
for c in data_columns:
mgr = block_obj.reindex([c], axis=axis)._mgr
blocks.extend(mgr.blocks)
blk_items.extend(get_blk_items(mgr, mgr.blocks))
# 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 = []
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):
""" 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)
# apply the selection filters (but keep in the same order)
if selection.filter is not None:
for field, op, filt in selection.filter.format():
def process_filter(field, filt):
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!")
obj = process_filter(field, filt)
return obj
def create_description(
self,
complib,
complevel: Optional[int],
fletcher32: bool,
expectedrows: Optional[int],
) -> 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 = dict(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: Optional[int] = None, stop: Optional[int] = 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: Optional[int] = None,
stop: Optional[int] = 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)
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: Optional[int] = None,
stop: Optional[int] = None,
):
"""
read the indices and the indexing array, calculate offset rows and return
"""
raise NotImplementedError("WORMTable needs to implement read")
def write(self, **kwargs):
"""
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"
def write(
self,
obj,
axes=None,
append=False,
complib=None,
complevel=None,
fletcher32=None,
min_itemsize=None,
chunksize=None,
expectedrows=None,
dropna=False,
nan_rep=None,
data_columns=None,
track_times=True,
):
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: Optional[int], dropna: bool = False):
"""
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(values[i].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: Optional[np.ndarray],
values: List[np.ndarray],
):
"""
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: Optional[int] = None, stop: Optional[int] = 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).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[Union[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: Optional[int] = None,
stop: Optional[int] = 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], dict())
if len(self.non_index_axes)
else dict()
)
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 = MultiIndex.from_tuples(index_vals)
else:
cols = Index(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_)
elif isinstance(values, Index):
df = DataFrame(values, columns=cols_, index=index_)
else:
# Categorical
df = DataFrame([values], columns=cols_, index=index_)
assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
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
def write(self, obj, data_columns=None, **kwargs):
""" 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)
return super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
def read(
self,
where=None,
columns=None,
start: Optional[int] = None,
stop: Optional[int] = 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"
def write(self, obj, **kwargs):
""" we are going to write this as a frame table """
name = obj.name or "values"
obj, self.levels = self.validate_multiindex(obj)
cols = list(self.levels)
cols.append(name)
obj.columns = cols
return super().write(obj=obj, **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
@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):
""" 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 = [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
def write(self, **kwargs):
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"
def write(self, obj, data_columns=None, **kwargs):
if data_columns is None:
data_columns = []
elif data_columns is True:
data_columns = obj.columns.tolist()
obj, self.levels = self.validate_multiindex(obj)
for n in self.levels:
if n not in data_columns:
data_columns.insert(0, n)
return super().write(obj=obj, data_columns=data_columns, **kwargs)
def read(
self,
where=None,
columns=None,
start: Optional[int] = None,
stop: Optional[int] = 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(l) else l for l in df.index.names]
)
return df
def _reindex_axis(obj: DataFrame, axis: int, 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[Union[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) -> Union[str, tzinfo]:
""" for a tz-aware type, return an encoded zone """
zone = timezones.get_timezone(tz)
return zone
def _set_tz(
values: Union[np.ndarray, Index],
tz: Optional[Union[str, tzinfo]],
coerce: bool = False,
) -> Union[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 tz is not None:
name = getattr(values, "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]")
return values
def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
assert isinstance(name, str)
index_name = index.name
converted, dtype_name = _get_data_and_dtype_name(index)
kind = _dtype_to_kind(dtype_name)
atom = DataIndexableCol._get_atom(converted)
if isinstance(index, Int64Index):
# Includes Int64Index, 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
) -> Union[np.ndarray, Index]:
index: Union[Index, np.ndarray]
if kind == "datetime64":
index = DatetimeIndex(data)
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"):
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, block, existing_col, min_itemsize, nan_rep, encoding, errors
):
if not block.is_object:
return block.values
dtype_name = block.dtype.name
inferred_type = lib.infer_dtype(block.values, skipna=False)
if inferred_type == "date":
raise TypeError("[date] is not implemented as a table column")
elif 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"
)
elif not (inferred_type == "string" or dtype_name == "object"):
return block.values
block = block.fillna(nan_rep, downcast=False)
if isinstance(block, list):
# Note: because block is always object dtype, fillna goes
# through a path such that the result is always a 1-element list
block = block[0]
data = block.values
# 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
for i in range(len(block.shape[0])):
col = block.iget(i)
inferred_type = lib.infer_dtype(col, skipna=False)
if inferred_type != "string":
iloc = block.mgr_locs.indexer[i]
raise TypeError(
f"Cannot serialize the column [{iloc}] because\n"
f"its data contents are [{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)
assert data_converted.shape == block.shape, (data_converted.shape, block.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 > 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())
.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).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"
data = 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 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"):
return True
return False
def _maybe_adjust_name(name: str, version) -> 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
"""
try:
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}"
except IndexError:
pass
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") or dtype_str.startswith("bytes"):
kind = "string"
elif dtype_str.startswith("float"):
kind = "float"
elif dtype_str.startswith("complex"):
kind = "complex"
elif dtype_str.startswith("int") or dtype_str.startswith("uint"):
kind = "integer"
elif dtype_str.startswith("datetime64"):
kind = "datetime64"
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
# For datetime64tz we need to drop the TZ in tests TODO: why?
dtype_name = data.dtype.name.split("[")[0]
if data.dtype.kind in ["m", "M"]:
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: Optional[int] = None,
stop: Optional[int] = 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
try:
inferred = lib.infer_dtype(where, skipna=False)
if inferred == "integer" or inferred == "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
except ValueError:
pass
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())
raise ValueError(
f"The passed where expression: {where}\n"
" contains an invalid variable reference\n"
" all of the variable references must be a "
"reference to\n"
" an axis (e.g. 'index' or 'columns'), or a "
"data_column\n"
f" The currently defined references are: {qkeys}\n"
) 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 self.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)