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/ io / pytables.py
"""
High level interface to PyTables for reading and writing pandas data structures
to disk
"""
import copy
from datetime import date, datetime
import itertools
import os
import re
import time
from typing import List, Optional, 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.compat._optional import import_optional_dependency
from pandas.errors import PerformanceWarning
from pandas.core.dtypes.common import (
ensure_object,
is_categorical_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_extension_type,
is_list_like,
is_timedelta64_dtype,
)
from pandas.core.dtypes.missing import array_equivalent
from pandas import (
DataFrame,
DatetimeIndex,
Index,
Int64Index,
MultiIndex,
PeriodIndex,
Series,
SparseDataFrame,
SparseSeries,
TimedeltaIndex,
concat,
isna,
to_datetime,
)
from pandas.core.arrays.categorical import Categorical
from pandas.core.arrays.sparse import BlockIndex, IntIndex
import pandas.core.common as com
from pandas.core.computation.pytables import Expr, maybe_expression
from pandas.core.index import ensure_index
from pandas.core.internals import BlockManager, _block_shape, make_block
from pandas.io.common import _stringify_path
from pandas.io.formats.printing import adjoin, pprint_thing
# 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 = Expr
def _ensure_term(where, scope_level):
"""
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"}
format_deprecate_doc = """
the table keyword has been deprecated
use the format='fixed(f)|table(t)' keyword instead
fixed(f) : specifies the Fixed format
and is the default for put operations
table(t) : specifies the Table format
and is the default for append operations
"""
# map object types
_TYPE_MAP = {
Series: "series",
SparseSeries: "sparse_series",
DataFrame: "frame",
SparseDataFrame: "sparse_frame",
}
# storer class map
_STORER_MAP = {
"Series": "LegacySeriesFixed",
"DataFrame": "LegacyFrameFixed",
"DataMatrix": "LegacyFrameFixed",
"series": "SeriesFixed",
"sparse_series": "SparseSeriesFixed",
"frame": "FrameFixed",
"sparse_frame": "SparseFrameFixed",
}
# table class map
_TABLE_MAP = {
"generic_table": "GenericTable",
"appendable_series": "AppendableSeriesTable",
"appendable_multiseries": "AppendableMultiSeriesTable",
"appendable_frame": "AppendableFrameTable",
"appendable_multiframe": "AppendableMultiFrameTable",
"worm": "WORMTable",
}
# 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,
value,
mode=None,
complevel=None,
complib=None,
append=None,
**kwargs
):
""" store this object, close it if we opened it """
if append:
f = lambda store: store.append(key, value, **kwargs)
else:
f = lambda store: store.put(key, value, **kwargs)
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="r", **kwargs):
"""
Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
criteria
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``.
.. versionadded:: 0.19.0 support for pathlib, py.path.
.. versionadded:: 0.21.0 support for __fspath__ protocol.
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'}, optional
Mode to use when opening the file. Ignored if path_or_buf is a
:class:`pandas.HDFStore`. Default is 'r'.
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.
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.
**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(
"mode {0} is not allowed while performing a read. "
"Allowed modes are r, r+ and a.".format(mode)
)
# grab the scope
if "where" in kwargs:
kwargs["where"] = _ensure_term(kwargs["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(
"File {path} does not exist".format(path=path_or_buf)
)
store = HDFStore(path_or_buf, mode=mode, **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("No dataset 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, auto_close=auto_close, **kwargs)
except (ValueError, TypeError, KeyError):
# if there is an error, close the store
try:
store.close()
except AttributeError:
pass
raise
def _is_metadata_of(group, parent_group):
"""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.
Parameters
----------
path : string
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
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()
"""
def __init__(
self, path, mode=None, complevel=None, complib=None, fletcher32=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(
"complib only supports {libs} compression.".format(
libs=tables.filters.all_complibs
)
)
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):
return self.get(key)
def __setitem__(self, key, value):
self.put(key, value)
def __delitem__(self, key):
return self.remove(key)
def __getattr__(self, name):
""" allow attribute access to get stores """
try:
return self.get(name)
except (KeyError, ClosedFileError):
pass
raise AttributeError(
"'{object}' object has no attribute '{name}'".format(
object=type(self).__name__, name=name
)
)
def __contains__(self, key):
""" 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):
return len(self.groups())
def __repr__(self):
return "{type}\nFile path: {path}\n".format(
type=type(self), path=pprint_thing(self._path)
)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def keys(self):
"""
Return a (potentially unordered) list of the keys corresponding to the
objects stored in the HDFStore. These are ABSOLUTE path-names (e.g.
have the leading '/'
Returns
-------
list
"""
return [n._v_pathname for n in self.groups()]
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="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
"""
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(
"Re-opening the file [{0}] with mode [{1}] "
"will delete the current file!".format(self._path, self._mode)
)
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 e: # pragma: no cover
if "can not be written" in str(e):
print("Opening {path} in read-only mode".format(path=self._path))
self._handle = tables.open_file(self._path, "r", **kwargs)
else:
raise
except (ValueError) as e:
# trap PyTables >= 3.1 FILE_OPEN_POLICY exception
# to provide an updated message
if "FILE_OPEN_POLICY" in str(e):
e = ValueError(
"PyTables [{version}] no longer supports opening multiple "
"files\n"
"even in read-only mode on this HDF5 version "
"[{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".format(
version=tables.__version__,
hdf_version=tables.get_hdf5_version(),
)
)
raise e
except (Exception) as e:
# 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(e):
raise IOError(str(e))
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):
"""
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=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):
"""
Retrieve pandas object stored in file
Parameters
----------
key : object
Returns
-------
obj : same type as object stored in file
"""
group = self.get_node(key)
if group is None:
raise KeyError("No object named {key} in the file".format(key=key))
return self._read_group(group)
def select(
self,
key,
where=None,
start=None,
stop=None,
columns=None,
iterator=False,
chunksize=None,
auto_close=False,
**kwargs
):
"""
Retrieve pandas object stored in file, optionally based on where
criteria
Parameters
----------
key : object
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
columns : a list of columns that if not None, will limit the return
columns
iterator : boolean, return an iterator, default False
chunksize : nrows to include in iteration, return an iterator
auto_close : boolean, should automatically close the store when
finished, default is False
Returns
-------
The selected object
"""
group = self.get_node(key)
if group is None:
raise KeyError("No object named {key} in the file".format(key=key))
# 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, where=None, start=None, stop=None, **kwargs):
"""
return the selection as an Index
Parameters
----------
key : object
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)
return self.get_storer(key).read_coordinates(
where=where, start=start, stop=stop, **kwargs
)
def select_column(self, key, column, **kwargs):
"""
return a single column from the table. This is generally only useful to
select an indexable
Parameters
----------
key : object
column: the column of interest
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)
"""
return self.get_storer(key).read_column(column=column, **kwargs)
def select_as_multiple(
self,
keys,
where=None,
selector=None,
columns=None,
start=None,
stop=None,
iterator=False,
chunksize=None,
auto_close=False,
**kwargs
):
""" Retrieve pandas objects from multiple tables
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
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,
**kwargs
)
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("Invalid table [{key}]".format(key=k))
if not t.is_table:
raise TypeError(
"object [{obj}] is not a table, and cannot be used in all "
"select as multiple".format(obj=t.pathname)
)
if nrows is None:
nrows = t.nrows
elif t.nrows != nrows:
raise ValueError("all tables must have exactly the same nrows!")
# 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, **kwargs
)
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, value, format=None, append=False, **kwargs):
"""
Store object in HDFStore
Parameters
----------
key : object
value : {Series, DataFrame}
format : 'fixed(f)|table(t)', default is 'fixed'
fixed(f) : Fixed format
Fast writing/reading. Not-appendable, nor searchable
table(t) : 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 : boolean, default False
This will force Table format, append the input data to the
existing.
data_columns : list of columns to create as data columns, or True to
use all columns. See `here
<http://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
encoding : default None, provide an encoding for strings
dropna : boolean, default False, do not write an ALL nan row to
the store settable by the option 'io.hdf.dropna_table'
"""
if format is None:
format = get_option("io.hdf.default_format") or "fixed"
kwargs = self._validate_format(format, kwargs)
self._write_to_group(key, value, append=append, **kwargs)
def remove(self, key, 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 Exception:
if where is not None:
raise ValueError(
"trying to remove a node with a non-None where clause!"
)
# we are actually trying to remove a node (with children)
s = self.get_node(key)
if s is not None:
s._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, value, format=None, append=True, columns=None, dropna=None, **kwargs
):
"""
Append to Table in file. Node must already exist and be Table
format.
Parameters
----------
key : object
value : {Series, DataFrame}
format : 'table' is the default
table(t) : 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 : boolean, 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
<http://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
min_itemsize : dict of columns that specify minimum string sizes
nan_rep : string to use as string nan representation
chunksize : size to chunk the writing
expectedrows : expected TOTAL row size of this table
encoding : default None, provide an encoding for strings
dropna : boolean, 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"
kwargs = self._validate_format(format, kwargs)
self._write_to_group(key, value, append=append, dropna=dropna, **kwargs)
def append_to_multiple(
self, d, 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 = []
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]
# append
for k, v in d.items():
dc = data_columns if k == selector else None
# compute the val
val = value.reindex(v, axis=axis)
self.append(k, val, data_columns=dc, **kwargs)
def create_table_index(self, key, **kwargs):
""" Create a pytables index on the table
Parameters
----------
key : object (the node to index)
Raises
------
raises if the node is not a table
"""
# version requirements
_tables()
s = self.get_storer(key)
if s is None:
return
if not s.is_table:
raise TypeError("cannot create table index on a Fixed format store")
s.create_index(**kwargs)
def groups(self):
"""return a list of all the top-level nodes (that are not themselves a
pandas storage object)
Returns
-------
list
"""
_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, optional
Group where to start walking.
If not supplied, the root group is used.
Yields
------
path : str
Full path to a group (without trailing '/')
groups : list of str
names of the groups contained in `path`
leaves : list of str
names 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):
""" return the node with the key or None if it does not exist """
self._check_if_open()
try:
if not key.startswith("/"):
key = "/" + key
return self._handle.get_node(self.root, key)
except _table_mod.exceptions.NoSuchNodeError:
return None
def get_storer(self, key):
""" return the storer object for a key, raise if not in the file """
group = self.get_node(key)
if group is None:
raise KeyError("No object named {key} in the file".format(key=key))
s = self._create_storer(group)
s.infer_axes()
return s
def copy(
self,
file,
mode="w",
propindexes=True,
keys=None,
complib=None,
complevel=None,
fletcher32=False,
overwrite=True,
):
""" copy the existing store to a new file, upgrading in place
Parameters
----------
propindexes: restore indexes in copied file (defaults to True)
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 s.is_table:
index = 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):
"""
Print detailed information on the store.
.. versionadded:: 0.21.0
Returns
-------
str
"""
output = "{type}\nFile path: {path}\n".format(
type=type(self), path=pprint_thing(self._path)
)
if self.is_open:
lkeys = sorted(list(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 Exception as detail:
keys.append(k)
values.append(
"[invalid_HDFStore node: {detail}]".format(
detail=pprint_thing(detail)
)
)
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("{0} file is not open!".format(self._path))
def _validate_format(self, format, kwargs):
""" validate / deprecate formats; return the new kwargs """
kwargs = kwargs.copy()
# validate
try:
kwargs["format"] = _FORMAT_MAP[format.lower()]
except KeyError:
raise TypeError("invalid HDFStore format specified [{0}]".format(format))
return kwargs
def _create_storer(self, group, format=None, value=None, append=False, **kwargs):
""" return a suitable class to operate """
def error(t):
raise TypeError(
"cannot properly create the storer for: [{t}] [group->"
"{group},value->{value},format->{format},append->{append},"
"kwargs->{kwargs}]".format(
t=t,
group=group,
value=type(value),
format=format,
append=append,
kwargs=kwargs,
)
)
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()
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:
try:
pt = _TYPE_MAP[type(value)]
except KeyError:
error("_TYPE_MAP")
# we are actually a table
if format == "table":
pt += "_table"
# a storer node
if "table" not in pt:
try:
return globals()[_STORER_MAP[pt]](self, group, **kwargs)
except KeyError:
error("_STORER_MAP")
# 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"
elif pt == "wide_table":
tt = "appendable_panel"
elif pt == "ndim_table":
tt = "appendable_ndim"
else:
# distinguish between a frame/table
tt = "legacy_panel"
try:
fields = group.table._v_attrs.fields
if len(fields) == 1 and fields[0] == "value":
tt = "legacy_frame"
except IndexError:
pass
try:
return globals()[_TABLE_MAP[tt]](self, group, **kwargs)
except KeyError:
error("_TABLE_MAP")
def _write_to_group(
self,
key,
value,
format,
index=True,
append=False,
complib=None,
encoding=None,
**kwargs
):
group = self.get_node(key)
# 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 are 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, append=append, encoding=encoding, **kwargs
)
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, append=append, complib=complib, **kwargs)
if s.is_table and index:
s.create_index(columns=index)
def _read_group(self, group, **kwargs):
s = self._create_storer(group)
s.infer_axes()
return s.read(**kwargs)
class TableIterator:
""" define the iteration interface on a table
Parameters
----------
store : the reference store
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 : boolean, whether to use the default iterator
chunksize : the passed chunking value (default is 50000)
auto_close : boolean, automatically close the store at the end of
iteration, default is False
kwargs : the passed kwargs
"""
def __init__(
self,
store,
s,
func,
where,
nrows,
start=None,
stop=None,
iterator=False,
chunksize=None,
auto_close=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=False):
# return the actual iterator
if self.chunksize is not None:
if not self.s.is_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:
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"]
def __init__(
self,
values=None,
kind=None,
typ=None,
cname=None,
itemsize=None,
name=None,
axis=None,
kind_attr=None,
pos=None,
freq=None,
tz=None,
index_name=None,
**kwargs
):
self.values = values
self.kind = kind
self.typ = typ
self.itemsize = itemsize
self.name = name
self.cname = cname
self.kind_attr = kind_attr
self.axis = axis
self.pos = pos
self.freq = freq
self.tz = tz
self.index_name = index_name
self.table = None
self.meta = None
self.metadata = None
if name is not None:
self.set_name(name, kind_attr)
if pos is not None:
self.set_pos(pos)
def set_name(self, name, kind_attr=None):
""" set the name of this indexer """
self.name = name
self.kind_attr = kind_attr or "{name}_kind".format(name=name)
if self.cname is None:
self.cname = name
return self
def set_axis(self, axis):
""" set the axis over which I index """
self.axis = axis
return self
def set_pos(self, pos):
""" 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
return self
def set_table(self, table):
self.table = table
return self
def __repr__(self):
temp = tuple(
map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))
)
return ",".join(
(
"{key}->{value}".format(key=key, value=value)
for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp)
)
)
def __eq__(self, other):
""" 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):
return not self.__eq__(other)
@property
def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False
def copy(self):
new_self = copy.copy(self)
return new_self
def infer(self, handler):
"""infer this column from the table: create and return a new object"""
table = handler.table
new_self = self.copy()
new_self.set_table(table)
new_self.get_attr()
new_self.read_metadata(handler)
return new_self
def convert(self, values, nan_rep, encoding, errors, start=None, stop=None):
""" set the values from this selection: take = take ownership """
# values is a recarray
if values.dtype.fields is not None:
values = values[self.cname]
values = _maybe_convert(values, self.kind, encoding, errors)
kwargs = dict()
if self.freq is not None:
kwargs["freq"] = _ensure_decoded(self.freq)
if self.index_name is not None:
kwargs["name"] = _ensure_decoded(self.index_name)
# making an Index instance could throw a number of different errors
try:
self.values = Index(values, **kwargs)
except Exception: # noqa: E722
# 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
self.values = Index(values, **kwargs)
self.values = _set_tz(self.values, self.tz)
return self
def take_data(self):
""" return the values & release the memory """
self.values, values = None, self.values
return 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(self, handler, append):
self.validate_names()
def validate_names(self):
pass
def validate_and_set(self, handler, append):
self.set_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(
"Trying to store a string with len [{itemsize}] in "
"[{cname}] column but\nthis column has a limit of "
"[{c_itemsize}]!\nConsider using min_itemsize to "
"preset the sizes on these columns".format(
itemsize=itemsize, cname=self.cname, c_itemsize=c.itemsize
)
)
return c.itemsize
return None
def validate_attr(self, append):
# 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(
"incompatible kind in col [{existing} - "
"{self_kind}]".format(existing=existing_kind, self_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 = _get_info(info, 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(
"invalid info for [{name}] for [{key}], "
"existing_value [{existing_value}] conflicts with "
"new value [{value}]".format(
name=self.name,
key=key,
existing_value=existing_value,
value=value,
)
)
else:
if value is not None or existing_value is not None:
idx[key] = value
return self
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 get_attr(self):
""" set the kind for this column """
self.kind = getattr(self.attrs, self.kind_attr, None)
def set_attr(self):
""" set the kind for this column """
setattr(self.attrs, self.kind_attr, self.kind)
def read_metadata(self, handler):
""" retrieve the metadata for this columns """
self.metadata = handler.read_metadata(self.cname)
def validate_metadata(self, handler):
""" 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):
""" 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):
return False
def convert(self, values, nan_rep, encoding, errors, start=None, stop=None):
""" set the values from this selection: take = take ownership
Parameters
----------
values : np.ndarray
nan_rep : str
encoding : str
errors : str
start : int, optional
Table row number: the start of the sub-selection.
stop : int, optional
Table row number: the end of the sub-selection. Values larger than
the underlying table's row count are normalized to that.
"""
start = start if start is not None else 0
stop = min(stop, self.table.nrows) if stop is not None else self.table.nrows
self.values = Int64Index(np.arange(stop - start))
return self
def get_attr(self):
pass
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"]
@classmethod
def create_for_block(cls, i=None, name=None, cname=None, version=None, **kwargs):
""" return a new datacol with the block i """
if cname is None:
cname = name or "values_block_{idx}".format(idx=i)
if name is None:
name = cname
# prior to 0.10.1, we named values blocks like: values_block_0 an the
# name values_0
try:
if version[0] == 0 and version[1] <= 10 and version[2] == 0:
m = re.search(r"values_block_(\d+)", name)
if m:
name = "values_{group}".format(group=m.groups()[0])
except IndexError:
pass
return cls(name=name, cname=cname, **kwargs)
def __init__(
self,
values=None,
kind=None,
typ=None,
cname=None,
data=None,
meta=None,
metadata=None,
block=None,
**kwargs
):
super().__init__(values=values, kind=kind, typ=typ, cname=cname, **kwargs)
self.dtype = None
self.dtype_attr = "{name}_dtype".format(name=self.name)
self.meta = meta
self.meta_attr = "{name}_meta".format(name=self.name)
self.set_data(data)
self.set_metadata(metadata)
def __repr__(self):
temp = tuple(
map(
pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape)
)
)
return ",".join(
(
"{key}->{value}".format(key=key, value=value)
for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp)
)
)
def __eq__(self, other):
""" 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, dtype=None):
self.data = data
if data is not None:
if dtype is not None:
self.dtype = dtype
self.set_kind()
elif self.dtype is None:
self.dtype = data.dtype.name
self.set_kind()
def take_data(self):
""" return the data & release the memory """
self.data, data = None, self.data
return data
def set_metadata(self, metadata):
""" record the metadata """
if metadata is not None:
metadata = np.array(metadata, copy=False).ravel()
self.metadata = metadata
def set_kind(self):
# set my kind if we can
if self.dtype is not None:
dtype = _ensure_decoded(self.dtype)
if dtype.startswith("string") or dtype.startswith("bytes"):
self.kind = "string"
elif dtype.startswith("float"):
self.kind = "float"
elif dtype.startswith("complex"):
self.kind = "complex"
elif dtype.startswith("int") or dtype.startswith("uint"):
self.kind = "integer"
elif dtype.startswith("date"):
self.kind = "datetime"
elif dtype.startswith("timedelta"):
self.kind = "timedelta"
elif dtype.startswith("bool"):
self.kind = "bool"
else:
raise AssertionError(
"cannot interpret dtype of [{dtype}] in [{obj}]".format(
dtype=dtype, obj=self
)
)
# set my typ if we need
if self.typ is None:
self.typ = getattr(self.description, self.cname, None)
def set_atom(
self,
block,
block_items,
existing_col,
min_itemsize,
nan_rep,
info,
encoding=None,
errors="strict",
):
""" create and setup my atom from the block b """
self.values = list(block_items)
# short-cut certain block types
if block.is_categorical:
return self.set_atom_categorical(block, items=block_items, info=info)
elif block.is_datetimetz:
return self.set_atom_datetime64tz(block, info=info)
elif block.is_datetime:
return self.set_atom_datetime64(block)
elif block.is_timedelta:
return self.set_atom_timedelta64(block)
elif block.is_complex:
return self.set_atom_complex(block)
dtype = 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 8260
# this only would be hit for a mutli-timezone dtype
# which is an error
raise TypeError(
"too many timezones in this block, create separate " "data columns"
)
elif inferred_type == "unicode":
raise TypeError("[unicode] is not implemented as a table column")
# this is basically a catchall; if say a datetime64 has nans then will
# end up here ###
elif inferred_type == "string" or dtype == "object":
self.set_atom_string(
block,
block_items,
existing_col,
min_itemsize,
nan_rep,
encoding,
errors,
)
# set as a data block
else:
self.set_atom_data(block)
def get_atom_string(self, block, itemsize):
return _tables().StringCol(itemsize=itemsize, shape=block.shape[0])
def set_atom_string(
self, block, block_items, existing_col, min_itemsize, nan_rep, encoding, errors
):
# fill nan items with myself, don't disturb the blocks by
# trying to downcast
block = block.fillna(nan_rep, downcast=False)
if isinstance(block, list):
block = block[0]
data = block.values
# see if we have a valid string type
inferred_type = lib.infer_dtype(data.ravel(), skipna=False)
if inferred_type != "string":
# we cannot serialize this data, so report an exception on a column
# by column basis
for i, item in enumerate(block_items):
col = block.iget(i)
inferred_type = lib.infer_dtype(col.ravel(), skipna=False)
if inferred_type != "string":
raise TypeError(
"Cannot serialize the column [{item}] because\n"
"its data contents are [{type}] object dtype".format(
item=item, type=inferred_type
)
)
# itemsize is the maximum length of a string (along any dimension)
data_converted = _convert_string_array(data, encoding, errors)
itemsize = data_converted.itemsize
# specified min_itemsize?
if isinstance(min_itemsize, dict):
min_itemsize = int(
min_itemsize.get(self.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
self.itemsize = itemsize
self.kind = "string"
self.typ = self.get_atom_string(block, itemsize)
self.set_data(
data_converted.astype("|S{size}".format(size=itemsize), copy=False)
)
def get_atom_coltype(self, kind=None):
""" return the PyTables column class for this column """
if kind is None:
kind = self.kind
if self.kind.startswith("uint"):
col_name = "UInt{name}Col".format(name=kind[4:])
else:
col_name = "{name}Col".format(name=kind.capitalize())
return getattr(_tables(), col_name)
def get_atom_data(self, block, kind=None):
return self.get_atom_coltype(kind=kind)(shape=block.shape[0])
def set_atom_complex(self, block):
self.kind = block.dtype.name
itemsize = int(self.kind.split("complex")[-1]) // 8
self.typ = _tables().ComplexCol(itemsize=itemsize, shape=block.shape[0])
self.set_data(block.values.astype(self.typ.type, copy=False))
def set_atom_data(self, block):
self.kind = block.dtype.name
self.typ = self.get_atom_data(block)
self.set_data(block.values.astype(self.typ.type, copy=False))
def set_atom_categorical(self, block, items, info=None, values=None):
# currently only supports a 1-D categorical
# in a 1-D block
values = block.values
codes = values.codes
self.kind = "integer"
self.dtype = codes.dtype.name
if values.ndim > 1:
raise NotImplementedError("only support 1-d categoricals")
if len(items) > 1:
raise NotImplementedError("only support single block categoricals")
# write the codes; must be in a block shape
self.ordered = values.ordered
self.typ = self.get_atom_data(block, kind=codes.dtype.name)
self.set_data(_block_shape(codes))
# write the categories
self.meta = "category"
self.set_metadata(block.values.categories)
# update the info
self.update_info(info)
def get_atom_datetime64(self, block):
return _tables().Int64Col(shape=block.shape[0])
def set_atom_datetime64(self, block, values=None):
self.kind = "datetime64"
self.typ = self.get_atom_datetime64(block)
if values is None:
values = block.values.view("i8")
self.set_data(values, "datetime64")
def set_atom_datetime64tz(self, block, info, values=None):
if values is None:
values = block.values
# convert this column to i8 in UTC, and save the tz
values = values.asi8.reshape(block.shape)
# store a converted timezone
self.tz = _get_tz(block.values.tz)
self.update_info(info)
self.kind = "datetime64"
self.typ = self.get_atom_datetime64(block)
self.set_data(values, "datetime64")
def get_atom_timedelta64(self, block):
return _tables().Int64Col(shape=block.shape[0])
def set_atom_timedelta64(self, block, values=None):
self.kind = "timedelta64"
self.typ = self.get_atom_timedelta64(block)
if values is None:
values = block.values.view("i8")
self.set_data(values, "timedelta64")
@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, nan_rep, encoding, errors, start=None, stop=None):
"""set the data from this selection (and convert to the correct dtype
if we can)
"""
# values is a recarray
if values.dtype.fields is not None:
values = values[self.cname]
self.set_data(values)
# use the meta if needed
meta = _ensure_decoded(self.meta)
# convert to the correct dtype
if self.dtype is not None:
dtype = _ensure_decoded(self.dtype)
# reverse converts
if dtype == "datetime64":
# recreate with tz if indicated
self.data = _set_tz(self.data, self.tz, coerce=True)
elif dtype == "timedelta64":
self.data = np.asarray(self.data, dtype="m8[ns]")
elif dtype == "date":
try:
self.data = np.asarray(
[date.fromordinal(v) for v in self.data], dtype=object
)
except ValueError:
self.data = np.asarray(
[date.fromtimestamp(v) for v in self.data], dtype=object
)
elif dtype == "datetime":
self.data = np.asarray(
[datetime.fromtimestamp(v) for v in self.data], dtype=object
)
elif meta == "category":
# we have a categorical
categories = self.metadata
codes = self.data.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
self.data = Categorical.from_codes(
codes, categories=categories, ordered=self.ordered
)
else:
try:
self.data = self.data.astype(dtype, copy=False)
except TypeError:
self.data = self.data.astype("O", copy=False)
# convert nans / decode
if _ensure_decoded(self.kind) == "string":
self.data = _unconvert_string_array(
self.data, nan_rep=nan_rep, encoding=encoding, errors=errors
)
return self
def get_attr(self):
""" get the data for this column """
self.values = getattr(self.attrs, self.kind_attr, None)
self.dtype = getattr(self.attrs, self.dtype_attr, None)
self.meta = getattr(self.attrs, self.meta_attr, None)
self.set_kind()
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)
if 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():
raise ValueError("cannot have non-object label DataIndexableCol")
def get_atom_string(self, block, itemsize):
return _tables().StringCol(itemsize=itemsize)
def get_atom_data(self, block, kind=None):
return self.get_atom_coltype(kind=kind)()
def get_atom_datetime64(self, block):
return _tables().Int64Col()
def get_atom_timedelta64(self, block):
return _tables().Int64Col()
class GenericDataIndexableCol(DataIndexableCol):
""" represent a generic pytables data column """
def get_attr(self):
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 : my parent HDFStore
group : the group node where the table resides
"""
pandas_kind = None # type: str
obj_type = None # type: Type[Union[DataFrame, Series]]
ndim = None # type: int
is_table = False
def __init__(self, parent, group, encoding=None, errors="strict", **kwargs):
self.parent = parent
self.group = group
self.encoding = _ensure_encoding(encoding)
self.errors = errors
self.set_version()
@property
def is_old_version(self):
return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1
def set_version(self):
""" compute and set our version """
version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None))
try:
self.version = tuple(int(x) for x in version.split("."))
if len(self.version) == 2:
self.version = self.version + (0,)
except AttributeError:
self.version = (0, 0, 0)
@property
def pandas_type(self):
return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None))
@property
def format_type(self):
return "fixed"
def __repr__(self):
""" return a pretty representation of myself """
self.infer_axes()
s = self.shape
if s is not None:
if isinstance(s, (list, tuple)):
s = "[{shape}]".format(shape=",".join(pprint_thing(x) for x in s))
return "{type:12.12} (shape->{shape})".format(
type=self.pandas_type, 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)
self.set_version()
def copy(self):
new_self = copy.copy(self)
return new_self
@property
def storage_obj_type(self):
return self.obj_type
@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):
return self.parent._complevel
@property
def _fletcher32(self):
return self.parent._fletcher32
@property
def _complib(self):
return self.parent._complib
@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):
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, **kwargs):
raise NotImplementedError(
"cannot read on an abstract storer: subclasses should implement"
)
def write(self, **kwargs):
raise NotImplementedError(
"cannot write on an abstract storer: sublcasses should implement"
)
def delete(self, where=None, start=None, stop=None, **kwargs):
"""
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 = [] # type: List[str]
# indexer helpders
def _class_to_alias(self, cls):
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
result = DatetimeIndex._simple_new(values.values, name=None, freq=freq)
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):
return PeriodIndex._simple_new(values, name=None, freq=freq)
return f
return klass
def validate_read(self, kwargs):
"""
remove table keywords from kwargs and return
raise if any keywords are passed which are not-None
"""
kwargs = copy.copy(kwargs)
columns = kwargs.pop("columns", 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"
)
where = kwargs.pop("where", None)
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"
)
return kwargs
@property
def is_exists(self):
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, start=None, stop=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 = 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
ret = _set_tz(ret, getattr(attrs, "tz", None), 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, **kwargs):
variety = _ensure_decoded(getattr(self.attrs, "{key}_variety".format(key=key)))
if variety == "multi":
return self.read_multi_index(key, **kwargs)
elif variety == "block":
return self.read_block_index(key, **kwargs)
elif variety == "sparseint":
return self.read_sparse_intindex(key, **kwargs)
elif variety == "regular":
_, index = self.read_index_node(getattr(self.group, key), **kwargs)
return index
else: # pragma: no cover
raise TypeError(
"unrecognized index variety: {variety}".format(variety=variety)
)
def write_index(self, key, index):
if isinstance(index, MultiIndex):
setattr(self.attrs, "{key}_variety".format(key=key), "multi")
self.write_multi_index(key, index)
elif isinstance(index, BlockIndex):
setattr(self.attrs, "{key}_variety".format(key=key), "block")
self.write_block_index(key, index)
elif isinstance(index, IntIndex):
setattr(self.attrs, "{key}_variety".format(key=key), "sparseint")
self.write_sparse_intindex(key, index)
else:
setattr(self.attrs, "{key}_variety".format(key=key), "regular")
converted = _convert_index(
index, self.encoding, self.errors, self.format_type
).set_name("index")
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 hasattr(index, "freq"):
node._v_attrs.freq = index.freq
if hasattr(index, "tz") and index.tz is not None:
node._v_attrs.tz = _get_tz(index.tz)
def write_block_index(self, key, index):
self.write_array("{key}_blocs".format(key=key), index.blocs)
self.write_array("{key}_blengths".format(key=key), index.blengths)
setattr(self.attrs, "{key}_length".format(key=key), index.length)
def read_block_index(self, key, **kwargs):
length = getattr(self.attrs, "{key}_length".format(key=key))
blocs = self.read_array("{key}_blocs".format(key=key), **kwargs)
blengths = self.read_array("{key}_blengths".format(key=key), **kwargs)
return BlockIndex(length, blocs, blengths)
def write_sparse_intindex(self, key, index):
self.write_array("{key}_indices".format(key=key), index.indices)
setattr(self.attrs, "{key}_length".format(key=key), index.length)
def read_sparse_intindex(self, key, **kwargs):
length = getattr(self.attrs, "{key}_length".format(key=key))
indices = self.read_array("{key}_indices".format(key=key), **kwargs)
return IntIndex(length, indices)
def write_multi_index(self, key, index):
setattr(self.attrs, "{key}_nlevels".format(key=key), index.nlevels)
for i, (lev, level_codes, name) in enumerate(
zip(index.levels, index.codes, index.names)
):
# write the level
if is_extension_type(lev):
raise NotImplementedError(
"Saving a MultiIndex with an " "extension dtype is not supported."
)
level_key = "{key}_level{idx}".format(key=key, idx=i)
conv_level = _convert_index(
lev, self.encoding, self.errors, self.format_type
).set_name(level_key)
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, "{key}_name{name}".format(key=key, name=name), name)
# write the labels
label_key = "{key}_label{idx}".format(key=key, idx=i)
self.write_array(label_key, level_codes)
def read_multi_index(self, key, **kwargs):
nlevels = getattr(self.attrs, "{key}_nlevels".format(key=key))
levels = []
codes = []
names = []
for i in range(nlevels):
level_key = "{key}_level{idx}".format(key=key, idx=i)
name, lev = self.read_index_node(getattr(self.group, level_key), **kwargs)
levels.append(lev)
names.append(name)
label_key = "{key}_label{idx}".format(key=key, idx=i)
level_codes = self.read_array(label_key, **kwargs)
codes.append(level_codes)
return MultiIndex(
levels=levels, codes=codes, names=names, verify_integrity=True
)
def read_index_node(self, node, start=None, stop=None):
data = node[start:stop]
# If the index was an empty array write_array_empty() will
# have written a sentinel. Here we relace it with the original.
if "shape" in node._v_attrs and self._is_empty_array(
getattr(node._v_attrs, "shape")
):
data = np.empty(
getattr(node._v_attrs, "shape"),
dtype=getattr(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:
kwargs["tz"] = node._v_attrs["tz"]
if kind in ("date", "datetime"):
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 name, index
def write_array_empty(self, key, value):
""" 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)
getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
getattr(self.group, key)._v_attrs.shape = value.shape
def _is_empty_array(self, shape):
"""Returns true if any axis is zero length."""
return any(x == 0 for x in shape)
def write_array(self, key, value, items=None):
if key in self.group:
self._handle.remove_node(self.group, key)
# Transform needed to interface with pytables row/col notation
empty_array = self._is_empty_array(value.shape)
transposed = False
if is_categorical_dtype(value):
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
if self._filters is not None:
atom = None
try:
# get the atom for this datatype
atom = _tables().Atom.from_dtype(value.dtype)
except ValueError:
pass
if atom is not None:
# 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
getattr(self.group, key)._v_attrs.transposed = transposed
else:
self.write_array_empty(key, value)
return
if 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.ravel(), skipna=False)
if empty_array:
pass
elif inferred_type == "string":
pass
else:
try:
items = list(items)
except TypeError:
pass
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)
else:
if empty_array:
self.write_array_empty(key, value)
else:
if 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 LegacyFixed(GenericFixed):
def read_index_legacy(self, key, start=None, stop=None):
node = getattr(self.group, key)
data = node[start:stop]
kind = node._v_attrs.kind
return _unconvert_index_legacy(
data, kind, encoding=self.encoding, errors=self.errors
)
class LegacySeriesFixed(LegacyFixed):
def read(self, **kwargs):
kwargs = self.validate_read(kwargs)
index = self.read_index_legacy("index")
values = self.read_array("values")
return Series(values, index=index)
class LegacyFrameFixed(LegacyFixed):
def read(self, **kwargs):
kwargs = self.validate_read(kwargs)
index = self.read_index_legacy("index")
columns = self.read_index_legacy("columns")
values = self.read_array("values")
return DataFrame(values, index=index, columns=columns)
class SeriesFixed(GenericFixed):
pandas_kind = "series"
attributes = ["name"]
@property
def shape(self):
try:
return (len(getattr(self.group, "values")),)
except (TypeError, AttributeError):
return None
def read(self, **kwargs):
kwargs = self.validate_read(kwargs)
index = self.read_index("index", **kwargs)
values = self.read_array("values", **kwargs)
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 SparseFixed(GenericFixed):
def validate_read(self, kwargs):
"""
we don't support start, stop kwds in Sparse
"""
kwargs = super().validate_read(kwargs)
if "start" in kwargs or "stop" in kwargs:
raise NotImplementedError(
"start and/or stop are not supported " "in fixed Sparse reading"
)
return kwargs
class SparseSeriesFixed(SparseFixed):
pandas_kind = "sparse_series"
attributes = ["name", "fill_value", "kind"]
def read(self, **kwargs):
kwargs = self.validate_read(kwargs)
index = self.read_index("index")
sp_values = self.read_array("sp_values")
sp_index = self.read_index("sp_index")
return SparseSeries(
sp_values,
index=index,
sparse_index=sp_index,
kind=self.kind or "block",
fill_value=self.fill_value,
name=self.name,
)
def write(self, obj, **kwargs):
super().write(obj, **kwargs)
self.write_index("index", obj.index)
self.write_index("sp_index", obj.sp_index)
self.write_array("sp_values", obj.sp_values)
self.attrs.name = obj.name
self.attrs.fill_value = obj.fill_value
self.attrs.kind = obj.kind
class SparseFrameFixed(SparseFixed):
pandas_kind = "sparse_frame"
attributes = ["default_kind", "default_fill_value"]
def read(self, **kwargs):
kwargs = self.validate_read(kwargs)
columns = self.read_index("columns")
sdict = {}
for c in columns:
key = "sparse_series_{columns}".format(columns=c)
s = SparseSeriesFixed(self.parent, getattr(self.group, key))
s.infer_axes()
sdict[c] = s.read()
return SparseDataFrame(
sdict,
columns=columns,
default_kind=self.default_kind,
default_fill_value=self.default_fill_value,
)
def write(self, obj, **kwargs):
""" write it as a collection of individual sparse series """
super().write(obj, **kwargs)
for name, ss in obj.items():
key = "sparse_series_{name}".format(name=name)
if key not in self.group._v_children:
node = self._handle.create_group(self.group, key)
else:
node = getattr(self.group, key)
s = SparseSeriesFixed(self.parent, node)
s.write(ss)
self.attrs.default_fill_value = obj.default_fill_value
self.attrs.default_kind = obj.default_kind
self.write_index("columns", obj.columns)
class BlockManagerFixed(GenericFixed):
attributes = ["ndim", "nblocks"]
is_shape_reversed = False
@property
def shape(self):
try:
ndim = self.ndim
# items
items = 0
for i in range(self.nblocks):
node = getattr(self.group, "block{idx}_items".format(idx=i))
shape = getattr(node, "shape", None)
if shape is not None:
items += shape[0]
# data shape
node = getattr(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)
# hacky - this works for frames, but is reversed for panels
if self.is_shape_reversed:
shape = shape[::-1]
return shape
except AttributeError:
return None
def read(self, start=None, stop=None, **kwargs):
# start, stop applied to rows, so 0th axis only
kwargs = self.validate_read(kwargs)
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("axis{idx}".format(idx=i), start=_start, stop=_stop)
axes.append(ax)
items = axes[0]
blocks = []
for i in range(self.nblocks):
blk_items = self.read_index("block{idx}_items".format(idx=i))
values = self.read_array(
"block{idx}_values".format(idx=i), start=_start, stop=_stop
)
blk = make_block(values, placement=items.get_indexer(blk_items))
blocks.append(blk)
return self.obj_type(BlockManager(blocks, axes))
def write(self, obj, **kwargs):
super().write(obj, **kwargs)
data = obj._data
if not data.is_consolidated():
data = data.consolidate()
self.attrs.ndim = data.ndim
for i, ax in enumerate(data.axes):
if i == 0:
if not ax.is_unique:
raise ValueError("Columns index has to be unique for fixed format")
self.write_index("axis{idx}".format(idx=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(
"block{idx}_values".format(idx=i), blk.values, items=blk_items
)
self.write_index("block{idx}_items".format(idx=i), 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"
table_type = None # type: str
levels = 1
is_table = True
is_shape_reversed = False
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.index_axes = []
self.non_index_axes = []
self.values_axes = []
self.data_columns = []
self.metadata = []
self.info = dict()
self.nan_rep = None
self.selection = None
@property
def table_type_short(self):
return self.table_type.split("_")[0]
@property
def format_type(self):
return "table"
def __repr__(self):
""" return a pretty representation of myself """
self.infer_axes()
dc = ",dc->[{columns}]".format(
columns=(",".join(self.data_columns) if len(self.data_columns) else "")
)
ver = ""
if self.is_old_version:
ver = "[{version}]".format(version=".".join(str(x) for x in self.version))
return (
"{pandas_type:12.12}{ver} (typ->{table_type},nrows->{nrows},"
"ncols->{ncols},indexers->[{index_axes}]{dc})".format(
pandas_type=self.pandas_type,
ver=ver,
table_type=self.table_type_short,
nrows=self.nrows,
ncols=self.ncols,
index_axes=(",".join(a.name for a in self.index_axes)),
dc=dc,
)
)
def __getitem__(self, c):
""" 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 "
"[{other} - {self}]".format(
other=other.table_type, self=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(
"invalid combinate of [{c}] on appending data "
"[{sax}] vs current table [{oax}]".format(
c=c, sax=sax, oax=oax
)
)
# should never get here
raise Exception(
"invalid combinate of [{c}] on appending data [{sv}] vs "
"current table [{ov}]".format(c=c, sv=sv, ov=ov)
)
@property
def is_multi_index(self):
"""the levels attribute is 1 or a list in the case of a multi-index"""
return isinstance(self.levels, list)
def validate_metadata(self, existing):
""" create / validate metadata """
self.metadata = [c.name for c in self.values_axes if c.metadata is not None]
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 "level_{0}".format(i)
for i, l in enumerate(obj.index.names)
]
try:
return obj.reset_index(), levels
except ValueError:
raise ValueError(
"duplicate names/columns in the multi-index when " "storing as a table"
)
@property
def nrows_expected(self):
""" 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):
""" 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):
""" 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):
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):
""" return a dict of the kinds allowable columns for this object """
# compute the values_axes queryables
return dict(
[(a.cname, a) for a in self.index_axes]
+ [
(self.storage_obj_type._AXIS_NAMES[axis], None)
for axis, values in self.non_index_axes
]
+ [
(v.cname, v)
for v in self.values_axes
if v.name in set(self.data_columns)
]
)
def index_cols(self):
""" return a list of my index cols """
return [(i.axis, i.cname) for i in self.index_axes]
def values_cols(self):
""" return a list of my values cols """
return [i.cname for i in self.values_axes]
def _get_metadata_path(self, key):
""" return the metadata pathname for this key """
return "{group}/meta/{key}/meta".format(group=self.group._v_pathname, key=key)
def write_metadata(self, key, values):
"""
write out a meta data array to the key as a fixed-format Series
Parameters
----------
key : string
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):
""" 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_info(self):
""" update our table index info """
self.attrs.info = self.info
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.metadata = self.metadata
self.set_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.infer(self) for a in self.indexables if a.is_an_indexable]
self.values_axes = [
a.infer(self) for a in self.indexables if not a.is_an_indexable
]
self.metadata = getattr(self.attrs, "metadata", None) or []
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(
"min_itemsize has the key [{key}] which is not an axis or "
"data_column".format(key=k)
)
@property
def indexables(self):
""" create/cache the indexables if they don't exist """
if self._indexables is None:
self._indexables = []
# index columns
self._indexables.extend(
[
IndexCol(name=name, axis=axis, pos=i)
for i, (axis, name) in enumerate(self.attrs.index_cols)
]
)
# values columns
dc = set(self.data_columns)
base_pos = len(self._indexables)
def f(i, c):
klass = DataCol
if c in dc:
klass = DataIndexableCol
return klass.create_for_block(
i=i, name=c, pos=base_pos + i, version=self.version
)
self._indexables.extend(
[f(i, c) for i, c in enumerate(self.attrs.values_cols)]
)
return self._indexables
def create_index(self, columns=None, optlevel=None, kind=None):
"""
Create a pytables index on the specified columns
note: cannot index Time64Col() or ComplexCol currently;
PyTables must be >= 3.0
Parameters
----------
columns : False (don't create an index), True (create all columns
index), None or list_like (the indexers to index)
optlevel: optimization level (defaults to 6)
kind : kind of index (defaults to 'medium')
Raises
------
raises if the node is not a table
"""
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)
def read_axes(self, where, **kwargs):
"""create and return the axes sniffed from the table: return boolean
for success
"""
# validate the version
self.validate_version(where)
# infer the data kind
if not self.infer_axes():
return False
# create the selection
self.selection = Selection(self, where=where, **kwargs)
values = self.selection.select()
# convert the data
for a in self.axes:
a.set_info(self.info)
# `kwargs` may contain `start` and `stop` arguments if passed to
# `store.select()`. If set they determine the index size.
a.convert(
values,
nan_rep=self.nan_rep,
encoding=self.encoding,
errors=self.errors,
start=kwargs.get("start"),
stop=kwargs.get("stop"),
)
return True
def get_object(self, obj):
""" return the data for this obj """
return obj
def validate_data_columns(self, data_columns, min_itemsize):
"""take the input data_columns and min_itemize and create a data
columns spec
"""
if not len(self.non_index_axes):
return []
axis, axis_labels = self.non_index_axes[0]
info = self.info.get(axis, dict())
if info.get("type") == "MultiIndex" and data_columns:
raise ValueError(
"cannot use a multi-index on axis [{0}] with "
"data_columns {1}".format(axis, data_columns)
)
# evaluate the passed data_columns, True == use all columns
# take only valide 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.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,
validate=True,
nan_rep=None,
data_columns=None,
min_itemsize=None,
**kwargs
):
""" create and return the axes
legacy tables create an indexable column, indexable index,
non-indexable fields
Parameters
----------
axes: a list of the axes in order to create (names or numbers of
the axes)
obj : the object to create axes on
validate: validate the obj against an existing object already
written
min_itemsize: a dict of the min size for a column in bytes
nan_rep : a values to use for string column nan_rep
encoding : the encoding for string values
data_columns : a list of columns that we want to create separate to
allow indexing (or True will force all columns)
"""
# set the default axes if needed
if axes is None:
try:
axes = _AXES_MAP[type(obj)]
except KeyError:
raise TypeError(
"cannot properly create the storer for: [group->{group},"
"value->{value}]".format(group=self.group._v_name, value=type(obj))
)
# 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():
existing_table = self.copy()
existing_table.infer_axes()
axes = [a.axis for a in existing_table.index_axes]
data_columns = existing_table.data_columns
nan_rep = existing_table.nan_rep
self.encoding = existing_table.encoding
self.errors = existing_table.errors
self.info = copy.copy(existing_table.info)
else:
existing_table = None
# 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
self.non_index_axes = []
self.data_columns = []
# nan_representation
if nan_rep is None:
nan_rep = "nan"
self.nan_rep = nan_rep
# create axes to index and non_index
index_axes_map = dict()
for i, a in enumerate(obj.axes):
if i in axes:
name = obj._AXIS_NAMES[i]
index_axes_map[i] = (
_convert_index(a, self.encoding, self.errors, self.format_type)
.set_name(name)
.set_axis(i)
)
else:
# we might be able to change the axes on the appending data if
# necessary
append_axis = list(a)
if existing_table is not None:
indexer = len(self.non_index_axes)
exist_axis = existing_table.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 = _get_info(self.info, i)
info["names"] = list(a.names)
info["type"] = a.__class__.__name__
self.non_index_axes.append((i, append_axis))
# set axis positions (based on the axes)
self.index_axes = [
index_axes_map[a].set_pos(j).update_info(self.info)
for j, a in enumerate(axes)
]
j = len(self.index_axes)
# check for column conflicts
for a in self.axes:
a.maybe_set_size(min_itemsize=min_itemsize)
# reindex by our non_index_axes & compute data_columns
for a in self.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]
# figure out data_columns and get out blocks
block_obj = self.get_object(obj)._consolidate()
blocks = block_obj._data.blocks
blk_items = get_blk_items(block_obj._data, blocks)
if len(self.non_index_axes):
axis, axis_labels = self.non_index_axes[0]
data_columns = self.validate_data_columns(data_columns, min_itemsize)
if len(data_columns):
mgr = block_obj.reindex(
Index(axis_labels).difference(Index(data_columns)), axis=axis
)._data
blocks = list(mgr.blocks)
blk_items = get_blk_items(mgr, blocks)
for c in data_columns:
mgr = block_obj.reindex([c], axis=axis)._data
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 existing_table is not None:
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 existing_table.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):
raise ValueError(
"cannot match existing table structure for [{items}] "
"on appending data".format(
items=(",".join(pprint_thing(item) for item in items))
)
)
blocks = new_blocks
blk_items = new_blk_items
# add my values
self.values_axes = []
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]
self.data_columns.append(name)
# make sure that we match up the existing columns
# if we have an existing table
if existing_table is not None and validate:
try:
existing_col = existing_table.values_axes[i]
except (IndexError, KeyError):
raise ValueError(
"Incompatible appended table [{blocks}]"
"with existing table [{table}]".format(
blocks=blocks, table=existing_table.values_axes
)
)
else:
existing_col = None
try:
col = klass.create_for_block(i=i, name=name, version=self.version)
col.set_atom(
block=b,
block_items=b_items,
existing_col=existing_col,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
encoding=self.encoding,
errors=self.errors,
info=self.info,
)
col.set_pos(j)
self.values_axes.append(col)
except (NotImplementedError, ValueError, TypeError) as e:
raise e
except Exception as detail:
raise Exception(
"cannot find the correct atom type -> "
"[dtype->{name},items->{items}] {detail!s}".format(
name=b.dtype.name, items=b_items, detail=detail
)
)
j += 1
# validate our min_itemsize
self.validate_min_itemsize(min_itemsize)
# validate our metadata
self.validate_metadata(existing_table)
# validate the axes if we have an existing table
if validate:
self.validate(existing_table)
def process_axes(self, obj, 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:
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 self.selection.filter is not None:
for field, op, filt in self.selection.filter.format():
def process_filter(field, filt):
for axis_name in obj._AXIS_NAMES.values():
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._getitem_axis(takers, axis=axis_number)
# 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._getitem_axis(takers, axis=axis_number)
raise ValueError(
"cannot find the field [{field}] for "
"filtering!".format(field=field)
)
obj = process_filter(field, filt)
return obj
def create_description(
self, complib=None, complevel=None, fletcher32=False, expectedrows=None
):
""" 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=None, stop=None, **kwargs):
"""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
self.selection = Selection(self, where=where, start=start, stop=stop, **kwargs)
coords = self.selection.select_coords()
if self.selection.filter is not None:
for field, op, filt in self.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, where=None, start=None, stop=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(
"column [{column}] can not be extracted individually; "
"it is not data indexable".format(column=column)
)
# column must be an indexable or a data column
c = getattr(self.table.cols, column)
a.set_info(self.info)
return Series(
_set_tz(
a.convert(
c[start:stop],
nan_rep=self.nan_rep,
encoding=self.encoding,
errors=self.errors,
).take_data(),
a.tz,
True,
),
name=column,
)
raise KeyError("column [{column}] not found in the table".format(column=column))
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, **kwargs):
""" 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("WORKTable needs to implement write")
class LegacyTable(Table):
""" an appendable table: allow append/query/delete operations to a
(possibly) already existing appendable table this table ALLOWS
append (but doesn't require them), and stores the data in a format
that can be easily searched
"""
_indexables = [
IndexCol(name="index", axis=1, pos=0),
IndexCol(name="column", axis=2, pos=1, index_kind="columns_kind"),
DataCol(name="fields", cname="values", kind_attr="fields", pos=2),
] # type: Optional[List[IndexCol]]
table_type = "legacy"
ndim = 3
def write(self, **kwargs):
raise TypeError("write operations are not allowed on legacy tables!")
def read(self, where=None, columns=None, **kwargs):
"""we have n indexable columns, with an arbitrary number of data
axes
"""
if not self.read_axes(where=where, **kwargs):
return None
class AppendableTable(LegacyTable):
""" support the new appendable table formats """
_indexables = None
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,
**kwargs
):
if not append and self.is_exists:
self._handle.remove_node(self.group, "table")
# create the axes
self.create_axes(
axes=axes, obj=obj, validate=append, min_itemsize=min_itemsize, **kwargs
)
for a in self.axes:
a.validate(self, append)
if not self.is_exists:
# create the table
options = self.create_description(
complib=complib,
complevel=complevel,
fletcher32=fletcher32,
expectedrows=expectedrows,
)
# set the table attributes
self.set_attrs()
# create the table
self._handle.create_table(self.group, **options)
else:
pass
# table = self.table
# update my info
self.set_info()
# validate the axes and set the kinds
for a in self.axes:
a.validate_and_set(self, append)
# add the rows
self.write_data(chunksize, dropna=dropna)
def write_data(self, chunksize, dropna=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)
bindexes = []
for i, idx in enumerate(indexes):
# broadcast to all other indexes except myself
if i > 0 and i < nindexes:
repeater = np.prod([indexes[bi].shape[0] for bi in range(0, i)])
idx = np.tile(idx, repeater)
if i < nindexes - 1:
repeater = np.prod(
[indexes[bi].shape[0] for bi in range(i + 1, nindexes)]
)
idx = np.repeat(idx, repeater)
bindexes.append(idx)
# 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 = int(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 bindexes],
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, indexes, mask, values):
"""
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
try:
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]
except Exception as detail:
raise Exception("cannot create row-data -> {detail}".format(detail=detail))
try:
if len(rows):
self.table.append(rows)
self.table.flush()
except Exception as detail:
raise TypeError(
"tables cannot write this data -> {detail}".format(detail=detail)
)
def delete(self, where=None, start=None, stop=None, **kwargs):
# 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
self.selection = Selection(self, where, start=start, stop=stop, **kwargs)
values = self.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 = DataFrame # type: Type[Union[DataFrame, Series]]
@property
def is_transposed(self):
return self.index_axes[0].axis == 1
def get_object(self, obj):
""" these are written transposed """
if self.is_transposed:
obj = obj.T
return obj
def read(self, where=None, columns=None, **kwargs):
if not self.read_axes(where=where, **kwargs):
return None
info = (
self.info.get(self.non_index_axes[0][0], dict())
if len(self.non_index_axes)
else dict()
)
index = self.index_axes[0].values
frames = []
for a in self.values_axes:
# 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(a.values)
else:
cols = Index(a.values)
names = info.get("names")
if names is not None:
cols.set_names(names, inplace=True)
if self.is_transposed:
values = a.cvalues
index_ = cols
cols_ = Index(index, name=getattr(index, "name", None))
else:
values = a.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]))
block = make_block(values, placement=np.arange(len(cols_)))
mgr = BlockManager([block], [cols_, index_])
frames.append(DataFrame(mgr))
if len(frames) == 1:
df = frames[0]
else:
df = concat(frames, axis=1)
# apply the selection filters & axis orderings
df = self.process_axes(df, 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
storage_obj_type = DataFrame
@property
def is_transposed(self):
return False
def get_object(self, obj):
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 = DataFrame({name: obj}, index=obj.index)
obj.columns = [name]
return super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
def read(self, columns=None, **kwargs):
is_multi_index = self.is_multi_index
if columns is not None and is_multi_index:
for n in self.levels:
if n not in columns:
columns.insert(0, n)
s = super().read(columns=columns, **kwargs)
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):
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.infer(self) for a in self.indexables if a.is_an_indexable]
self.values_axes = [
a.infer(self) for a in self.indexables if not a.is_an_indexable
]
self.data_columns = [a.name for a in self.values_axes]
@property
def indexables(self):
""" create the indexables from the table description """
if self._indexables is None:
d = self.description
# the index columns is just a simple index
self._indexables = [GenericIndexCol(name="index", axis=0)]
for i, n in enumerate(d._v_names):
dc = GenericDataIndexableCol(
name=n, pos=i, values=[n], version=self.version
)
self._indexables.append(dc)
return self._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):
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, **kwargs):
df = super().read(**kwargs)
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, axis, labels, other=None):
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 = [slice(None, None)] * obj.ndim
slicer[axis] = labels
obj = obj.loc[tuple(slicer)]
return obj
def _get_info(info, name):
""" get/create the info for this name """
try:
idx = info[name]
except KeyError:
idx = info[name] = dict()
return idx
# tz to/from coercion
def _get_tz(tz):
""" for a tz-aware type, return an encoded zone """
zone = timezones.get_timezone(tz)
if zone is None:
zone = tz.utcoffset().total_seconds()
return zone
def _set_tz(values, tz, preserve_UTC=False, coerce=False):
"""
coerce the values to a DatetimeIndex if tz is set
preserve the input shape if possible
Parameters
----------
values : ndarray
tz : string/pickled tz object
preserve_UTC : boolean,
preserve the UTC of the result
coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
"""
if tz is not None:
name = getattr(values, "name", None)
values = values.ravel()
tz = timezones.get_timezone(_ensure_decoded(tz))
values = DatetimeIndex(values, name=name)
if values.tz is None:
values = values.tz_localize("UTC").tz_convert(tz)
if preserve_UTC:
if tz == "UTC":
values = list(values)
elif coerce:
values = np.asarray(values, dtype="M8[ns]")
return values
def _convert_index(index, encoding=None, errors="strict", format_type=None):
index_name = getattr(index, "name", None)
if isinstance(index, DatetimeIndex):
converted = index.asi8
return IndexCol(
converted,
"datetime64",
_tables().Int64Col(),
freq=getattr(index, "freq", None),
tz=getattr(index, "tz", None),
index_name=index_name,
)
elif isinstance(index, TimedeltaIndex):
converted = index.asi8
return IndexCol(
converted,
"timedelta64",
_tables().Int64Col(),
freq=getattr(index, "freq", None),
index_name=index_name,
)
elif isinstance(index, (Int64Index, PeriodIndex)):
atom = _tables().Int64Col()
# avoid to store ndarray of Period objects
return IndexCol(
index._ndarray_values,
"integer",
atom,
freq=getattr(index, "freq", None),
index_name=index_name,
)
if isinstance(index, MultiIndex):
raise TypeError("MultiIndex not supported here!")
inferred_type = lib.infer_dtype(index, skipna=False)
values = np.asarray(index)
if inferred_type == "datetime64":
converted = values.view("i8")
return IndexCol(
converted,
"datetime64",
_tables().Int64Col(),
freq=getattr(index, "freq", None),
tz=getattr(index, "tz", None),
index_name=index_name,
)
elif inferred_type == "timedelta64":
converted = values.view("i8")
return IndexCol(
converted,
"timedelta64",
_tables().Int64Col(),
freq=getattr(index, "freq", None),
index_name=index_name,
)
elif inferred_type == "datetime":
converted = np.asarray(
[(time.mktime(v.timetuple()) + v.microsecond / 1e6) for v in values],
dtype=np.float64,
)
return IndexCol(
converted, "datetime", _tables().Time64Col(), index_name=index_name
)
elif inferred_type == "date":
converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
return IndexCol(converted, "date", _tables().Time32Col(), index_name=index_name)
elif inferred_type == "string":
# atom = _tables().ObjectAtom()
# return np.asarray(values, dtype='O'), 'object', atom
converted = _convert_string_array(values, encoding, errors)
itemsize = converted.dtype.itemsize
return IndexCol(
converted,
"string",
_tables().StringCol(itemsize),
itemsize=itemsize,
index_name=index_name,
)
elif inferred_type == "unicode":
if format_type == "fixed":
atom = _tables().ObjectAtom()
return IndexCol(
np.asarray(values, dtype="O"), "object", atom, index_name=index_name
)
raise TypeError(
"[unicode] is not supported as a in index type for [{0}] formats".format(
format_type
)
)
elif inferred_type == "integer":
# take a guess for now, hope the values fit
atom = _tables().Int64Col()
return IndexCol(
np.asarray(values, dtype=np.int64), "integer", atom, index_name=index_name
)
elif inferred_type == "floating":
atom = _tables().Float64Col()
return IndexCol(
np.asarray(values, dtype=np.float64), "float", atom, index_name=index_name
)
else: # pragma: no cover
atom = _tables().ObjectAtom()
return IndexCol(
np.asarray(values, dtype="O"), "object", atom, index_name=index_name
)
def _unconvert_index(data, kind, encoding=None, errors="strict"):
kind = _ensure_decoded(kind)
if kind == "datetime64":
index = DatetimeIndex(data)
elif kind == "timedelta64":
index = TimedeltaIndex(data)
elif kind == "datetime":
index = np.asarray([datetime.fromtimestamp(v) for v in data], dtype=object)
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("unrecognized index type {kind}".format(kind=kind))
return index
def _unconvert_index_legacy(data, kind, legacy=False, encoding=None, errors="strict"):
kind = _ensure_decoded(kind)
if kind == "datetime":
index = to_datetime(data)
elif kind in ("integer"):
index = np.asarray(data, dtype=object)
elif kind in ("string"):
index = _unconvert_string_array(
data, nan_rep=None, encoding=encoding, errors=errors
)
else: # pragma: no cover
raise ValueError("unrecognized index type {kind}".format(kind=kind))
return index
def _convert_string_array(data, encoding, errors, itemsize=None):
"""
we take a string-like that is object dtype and coerce to a fixed size
string type
Parameters
----------
data : a numpy array of object dtype
encoding : None or string-encoding
errors : handler for encoding errors
itemsize : integer, optional, defaults to the max length of the strings
Returns
-------
data in a fixed-length string dtype, encoded to bytes if needed
"""
# encode if needed
if encoding is not None and len(data):
data = (
Series(data.ravel()).str.encode(encoding, errors).values.reshape(data.shape)
)
# create the sized dtype
if itemsize is None:
ensured = ensure_object(data.ravel())
itemsize = max(1, libwriters.max_len_string_array(ensured))
data = np.asarray(data, dtype="S{size}".format(size=itemsize))
return data
def _unconvert_string_array(data, nan_rep=None, encoding=None, errors="strict"):
"""
inverse of _convert_string_array
Parameters
----------
data : fixed length string dtyped array
nan_rep : the storage repr of NaN, optional
encoding : the encoding of the data, optional
errors : handler for encoding errors, default 'strict'
Returns
-------
an object array of the decoded data
"""
shape = data.shape
data = np.asarray(data.ravel(), dtype=object)
# guard against a None encoding (because of a legacy
# where the passed encoding is actually None)
encoding = _ensure_encoding(encoding)
if encoding is not None and len(data):
itemsize = libwriters.max_len_string_array(ensure_object(data))
dtype = "U{0}".format(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, val_kind, encoding, errors):
if _need_convert(val_kind):
conv = _get_converter(val_kind, encoding, errors)
# conv = np.frompyfunc(conv, 1, 1)
values = conv(values)
return values
def _get_converter(kind, encoding, errors):
kind = _ensure_decoded(kind)
if kind == "datetime64":
return lambda x: np.asarray(x, dtype="M8[ns]")
elif kind == "datetime":
return lambda x: to_datetime(x, cache=True).to_pydatetime()
elif kind == "string":
return lambda x: _unconvert_string_array(x, encoding=encoding, errors=errors)
else: # pragma: no cover
raise ValueError("invalid kind {kind}".format(kind=kind))
def _need_convert(kind):
kind = _ensure_decoded(kind)
if kind in ("datetime", "datetime64", "string"):
return True
return False
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, where=None, start=None, stop=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 Expr(where, queryables=q, encoding=self.table.encoding)
except NameError:
# raise a nice message, suggesting that the user should use
# data_columns
raise ValueError(
"The passed where expression: {0}\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"
" The currently defined references are: {1}\n".format(
where, ",".join(q.keys())
)
)
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)