INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
return appropriate class of Series concat input is either dict or array - like | def _get_series_result_type(result, objs=None):
"""
return appropriate class of Series concat
input is either dict or array-like
"""
from pandas import SparseSeries, SparseDataFrame, DataFrame
# concat Series with axis 1
if isinstance(result, dict):
# concat Series with axis 1
... |
return appropriate class of DataFrame - like concat if all blocks are sparse return SparseDataFrame otherwise return 1st obj | def _get_frame_result_type(result, objs):
"""
return appropriate class of DataFrame-like concat
if all blocks are sparse, return SparseDataFrame
otherwise, return 1st obj
"""
if (result.blocks and (
any(isinstance(obj, ABCSparseDataFrame) for obj in objs))):
from pandas.core... |
provide concatenation of an array of arrays each of which is a single normalized dtypes ( in that for example if it s object then it is a non - datetimelike and provide a combined dtype for the resulting array that preserves the overall dtype if possible ) | def _concat_compat(to_concat, axis=0):
"""
provide concatenation of an array of arrays each of which is a single
'normalized' dtypes (in that for example, if it's object, then it is a
non-datetimelike and provide a combined dtype for the resulting array that
preserves the overall dtype if possible)
... |
Concatenate an object/ categorical array of arrays each of which is a single dtype | def _concat_categorical(to_concat, axis=0):
"""Concatenate an object/categorical array of arrays, each of which is a
single dtype
Parameters
----------
to_concat : array of arrays
axis : int
Axis to provide concatenation in the current implementation this is
always 0, e.g. we on... |
Combine list - like of Categorical - like unioning categories. All categories must have the same dtype. | def union_categoricals(to_union, sort_categories=False, ignore_order=False):
"""
Combine list-like of Categorical-like, unioning categories. All
categories must have the same dtype.
.. versionadded:: 0.19.0
Parameters
----------
to_union : list-like of Categorical, CategoricalIndex,
... |
provide concatenation of an datetimelike array of arrays each of which is a single M8 [ ns ] datetimet64 [ ns tz ] or m8 [ ns ] dtype | def _concat_datetime(to_concat, axis=0, typs=None):
"""
provide concatenation of an datetimelike array of arrays each of which is a
single M8[ns], datetimet64[ns, tz] or m8[ns] dtype
Parameters
----------
to_concat : array of arrays
axis : axis to provide concatenation
typs : set of to_... |
concat DatetimeIndex with the same tz all inputs must be DatetimeIndex it is used in DatetimeIndex. append also | def _concat_datetimetz(to_concat, name=None):
"""
concat DatetimeIndex with the same tz
all inputs must be DatetimeIndex
it is used in DatetimeIndex.append also
"""
# Right now, internals will pass a List[DatetimeArray] here
# for reductions like quantile. I would like to disentangle
# a... |
concat all inputs as object. DatetimeIndex TimedeltaIndex and PeriodIndex are converted to object dtype before concatenation | def _concat_index_asobject(to_concat, name=None):
"""
concat all inputs as object. DatetimeIndex, TimedeltaIndex and
PeriodIndex are converted to object dtype before concatenation
"""
from pandas import Index
from pandas.core.arrays import ExtensionArray
klasses = (ABCDatetimeIndex, ABCTime... |
provide concatenation of an sparse/ dense array of arrays each of which is a single dtype | def _concat_sparse(to_concat, axis=0, typs=None):
"""
provide concatenation of an sparse/dense array of arrays each of which is a
single dtype
Parameters
----------
to_concat : array of arrays
axis : axis to provide concatenation
typs : set of to_concat dtypes
Returns
-------
... |
Concatenates multiple RangeIndex instances. All members of indexes must be of type RangeIndex ; result will be RangeIndex if possible Int64Index otherwise. E. g.: indexes = [ RangeIndex ( 3 ) RangeIndex ( 3 6 ) ] - > RangeIndex ( 6 ) indexes = [ RangeIndex ( 3 ) RangeIndex ( 4 6 ) ] - > Int64Index ( [ 0 1 2 4 5 ] ) | def _concat_rangeindex_same_dtype(indexes):
"""
Concatenates multiple RangeIndex instances. All members of "indexes" must
be of type RangeIndex; result will be RangeIndex if possible, Int64Index
otherwise. E.g.:
indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)
indexes = [RangeIndex(3... |
Rewrite the message of an exception. | def rewrite_exception(old_name, new_name):
"""Rewrite the message of an exception."""
try:
yield
except Exception as e:
msg = e.args[0]
msg = msg.replace(old_name, new_name)
args = (msg,)
if len(e.args) > 1:
args = args + e.args[1:]
e.args = args
... |
Given an index find the level length for each element. | def _get_level_lengths(index, hidden_elements=None):
"""
Given an index, find the level length for each element.
Optional argument is a list of index positions which
should not be visible.
Result is a dictionary of (level, inital_position): span
"""
sentinel = object()
levels = index.f... |
Convert the DataFrame in self. data and the attrs from _build_styles into a dictionary of { head body uuid cellstyle }. | def _translate(self):
"""
Convert the DataFrame in `self.data` and the attrs from `_build_styles`
into a dictionary of {head, body, uuid, cellstyle}.
"""
table_styles = self.table_styles or []
caption = self.caption
ctx = self.ctx
precision = self.precisio... |
Format the text display value of cells. | def format(self, formatter, subset=None):
"""
Format the text display value of cells.
.. versionadded:: 0.18.0
Parameters
----------
formatter : str, callable, or dict
subset : IndexSlice
An argument to ``DataFrame.loc`` that restricts which elements... |
Render the built up styles to HTML. | def render(self, **kwargs):
"""
Render the built up styles to HTML.
Parameters
----------
**kwargs
Any additional keyword arguments are passed
through to ``self.template.render``.
This is useful when you need to provide
additional ... |
Update the state of the Styler. | def _update_ctx(self, attrs):
"""
Update the state of the Styler.
Collects a mapping of {index_label: ['<property>: <value>']}.
attrs : Series or DataFrame
should contain strings of '<property>: <value>;<prop2>: <val2>'
Whitespace shouldn't matter and the final trailing... |
Execute the style functions built up in self. _todo. | def _compute(self):
"""
Execute the style functions built up in `self._todo`.
Relies on the conventions that all style functions go through
.apply or .applymap. The append styles to apply as tuples of
(application method, *args, **kwargs)
"""
r = self
fo... |
Apply a function column - wise row - wise or table - wise updating the HTML representation with the result. | def apply(self, func, axis=0, subset=None, **kwargs):
"""
Apply a function column-wise, row-wise, or table-wise,
updating the HTML representation with the result.
Parameters
----------
func : function
``func`` should take a Series or DataFrame (depending
... |
Apply a function elementwise updating the HTML representation with the result. | def applymap(self, func, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with the result.
Parameters
----------
func : function
``func`` should take a scalar and return a scalar
subset : IndexSlice
... |
Apply a function elementwise updating the HTML representation with a style which is selected in accordance with the return value of a function. | def where(self, cond, value, other=None, subset=None, **kwargs):
"""
Apply a function elementwise, updating the HTML
representation with a style which is selected in
accordance with the return value of a function.
.. versionadded:: 0.21.0
Parameters
----------
... |
Hide columns from rendering. | def hide_columns(self, subset):
"""
Hide columns from rendering.
.. versionadded:: 0.23.0
Parameters
----------
subset : IndexSlice
An argument to ``DataFrame.loc`` that identifies which columns
are hidden.
Returns
-------
... |
Shade the background null_color for missing values. | def highlight_null(self, null_color='red'):
"""
Shade the background ``null_color`` for missing values.
Parameters
----------
null_color : str
Returns
-------
self : Styler
"""
self.applymap(self._highlight_null, null_color=null_color)
... |
Color the background in a gradient according to the data in each column ( optionally row ). | def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
subset=None, text_color_threshold=0.408):
"""
Color the background in a gradient according to
the data in each column (optionally row).
Requires matplotlib.
Parameters
--------... |
Color background in a range according to the data. | def _background_gradient(s, cmap='PuBu', low=0, high=0,
text_color_threshold=0.408):
"""
Color background in a range according to the data.
"""
if (not isinstance(text_color_threshold, (float, int)) or
not 0 <= text_color_threshold <= 1):
... |
Convenience method for setting one or more non - data dependent properties or each cell. | def set_properties(self, subset=None, **kwargs):
"""
Convenience method for setting one or more non-data dependent
properties or each cell.
Parameters
----------
subset : IndexSlice
a valid slice for ``data`` to limit the style application to
kwargs :... |
Draw bar chart in dataframe cells. | def _bar(s, align, colors, width=100, vmin=None, vmax=None):
"""
Draw bar chart in dataframe cells.
"""
# Get input value range.
smin = s.min() if vmin is None else vmin
if isinstance(smin, ABCSeries):
smin = smin.min()
smax = s.max() if vmax is None e... |
Draw bar chart in the cell backgrounds. | def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
align='left', vmin=None, vmax=None):
"""
Draw bar chart in the cell backgrounds.
Parameters
----------
subset : IndexSlice, optional
A valid slice for `data` to limit the style application to.... |
Highlight the maximum by shading the background. | def highlight_max(self, subset=None, color='yellow', axis=0):
"""
Highlight the maximum by shading the background.
Parameters
----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
color : str, default 'yell... |
Highlight the minimum by shading the background. | def highlight_min(self, subset=None, color='yellow', axis=0):
"""
Highlight the minimum by shading the background.
Parameters
----------
subset : IndexSlice, default None
a valid slice for ``data`` to limit the style application to.
color : str, default 'yell... |
Highlight the min or max in a Series or DataFrame. | def _highlight_extrema(data, color='yellow', max_=True):
"""
Highlight the min or max in a Series or DataFrame.
"""
attr = 'background-color: {0}'.format(color)
if data.ndim == 1: # Series from .apply
if max_:
extrema = data == data.max()
... |
Factory function for creating a subclass of Styler with a custom template and Jinja environment. | def from_custom_template(cls, searchpath, name):
"""
Factory function for creating a subclass of ``Styler``
with a custom template and Jinja environment.
Parameters
----------
searchpath : str or list
Path or paths of directories containing the templates
... |
Parameters ---------- dtype: ExtensionDtype | def register(self, dtype):
"""
Parameters
----------
dtype : ExtensionDtype
"""
if not issubclass(dtype, (PandasExtensionDtype, ExtensionDtype)):
raise ValueError("can only register pandas extension dtypes")
self.dtypes.append(dtype) |
Parameters ---------- dtype: PandasExtensionDtype or string | def find(self, dtype):
"""
Parameters
----------
dtype : PandasExtensionDtype or string
Returns
-------
return the first matching dtype, otherwise return None
"""
if not isinstance(dtype, str):
dtype_type = dtype
if not isi... |
provide compat for construction of strings to numpy datetime64 s with tz - changes in 1. 11 that make 2015 - 01 - 01 09: 00: 00Z show a deprecation warning when need to pass 2015 - 01 - 01 09: 00: 00 | def np_datetime64_compat(s, *args, **kwargs):
"""
provide compat for construction of strings to numpy datetime64's with
tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation
warning, when need to pass '2015-01-01 09:00:00'
"""
s = tz_replacer(s)
return np.datetime64(s, *args... |
provide compat for construction of an array of strings to a np. array (... dtype = np. datetime64 (.. )) tz - changes in 1. 11 that make 2015 - 01 - 01 09: 00: 00Z show a deprecation warning when need to pass 2015 - 01 - 01 09: 00: 00 | def np_array_datetime64_compat(arr, *args, **kwargs):
"""
provide compat for construction of an array of strings to a
np.array(..., dtype=np.datetime64(..))
tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation
warning, when need to pass '2015-01-01 09:00:00'
"""
# is_list_l... |
Ensure incoming data can be represented as ints. | def _assert_safe_casting(cls, data, subarr):
"""
Ensure incoming data can be represented as ints.
"""
if not issubclass(data.dtype.type, np.signedinteger):
if not np.array_equal(data, subarr):
raise TypeError('Unsafe NumPy casting, you must '
... |
we always want to get an index value never a value | def get_value(self, series, key):
""" we always want to get an index value, never a value """
if not is_scalar(key):
raise InvalidIndexError
k = com.values_from_object(key)
loc = self.get_loc(k)
new_values = com.values_from_object(series)[loc]
return new_val... |
Determines if two Index objects contain the same elements. | def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if self is other:
return True
if not isinstance(other, Index):
return False
# need to compare nans locations and make sure that they are the same
... |
if we have bytes decode them to unicode | def _ensure_decoded(s):
""" if we have bytes, decode them to unicode """
if isinstance(s, np.bytes_):
s = s.decode('UTF-8')
return s |
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 ) | 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 aut... |
store this object close it if we opened it | 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)
pa... |
Read from the store close it if we opened it. | 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 : string, buffer or path object
Path to the file to open, or an op... |
Check if a given group is a metadata group for a given parent_group. | 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 curr... |
get/ create the info for this name | def _get_info(info, name):
""" get/create the info for this name """
try:
idx = info[name]
except KeyError:
idx = info[name] = dict()
return idx |
for a tz - aware type return an encoded zone | 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 |
coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible | 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
c... |
we take a string - like that is object dtype and coerce to a fixed size string type | 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
... |
inverse of _convert_string_array | 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, op... |
Open the file in the specified mode | 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 !... |
Force all buffered modifications to be written to disk. | 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``, fl... |
Retrieve pandas object stored in file | 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(... |
Retrieve pandas object stored in file optionally based on where criteria | 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
whe... |
return the selection as an Index | 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... |
return a single column from the table. This is generally only useful to select an indexable | 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
Exceptions
----------
raises... |
Retrieve pandas objects from multiple tables | 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
----------
ke... |
Store object in HDFStore | 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
... |
Remove pandas object partially by specifying the where condition | 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
... |
Append to Table in file. Node must already exist and be Table format. | 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 : '... |
Append to multiple tables | 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 n... |
Create a pytables index on the table Parameters ---------- key: object ( the node to index ) | def create_table_index(self, key, **kwargs):
""" Create a pytables index on the table
Parameters
----------
key : object (the node to index)
Exceptions
----------
raises if the node is not a table
"""
# version requirements
_tables()
... |
return a list of all the top - level nodes ( that are not themselves a pandas storage object ) | def groups(self):
"""return a list of all the top-level nodes (that are not themselves a
pandas storage object)
"""
_tables()
self._check_if_open()
return [
g for g in self._handle.walk_groups()
if (not isinstance(g, _table_mod.link.Link) and
... |
Walk the pytables group hierarchy for pandas objects | 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 list... |
return the node with the key or None if it does not exist | 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.NoSuchNodeEr... |
return the storer object for a key raise if not in the file | 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()
retu... |
copy the existing store to a new file upgrading in place | 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... |
Print detailed information on the store. | def info(self):
"""
Print detailed information on the store.
.. versionadded:: 0.21.0
"""
output = '{type}\nFile path: {path}\n'.format(
type=type(self), path=pprint_thing(self._path))
if self.is_open:
lkeys = sorted(list(self.keys()))
... |
validate/ deprecate formats ; return the new kwargs | 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 specifi... |
return a suitable class to operate | 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},... |
set the name of this indexer | 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 |
set the position of this column in the Table | 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 |
return whether I am an indexed column | def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False |
infer this column from the table: create and return a new object | 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 |
set the values from this selection: take = take ownership | def convert(self, values, nan_rep, encoding, errors):
""" 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)
... |
maybe set a string col itemsize: min_itemsize can be an integer or a dict with this columns name with an integer size | 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):
... |
validate this column: return the compared against itemsize | 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 ... |
set/ update the info for this indexable with the key/ value if there is a conflict raise/ warn as needed | 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 = i... |
set my state from the passed info | 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) |
validate that kind = category does not change the categories | 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... |
set the meta data | def write_metadata(self, handler):
""" set the meta data """
if self.metadata is not None:
handler.write_metadata(self.cname, self.metadata) |
set the values from this selection: take = take ownership | def convert(self, values, nan_rep, encoding, errors):
""" set the values from this selection: take = take ownership """
self.values = Int64Index(np.arange(self.table.nrows))
return self |
return a new datacol with the block i | 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 ... |
record the metadata | def set_metadata(self, metadata):
""" record the metadata """
if metadata is not None:
metadata = np.array(metadata, copy=False).ravel()
self.metadata = metadata |
create and setup my atom from the block b | 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:
ret... |
return the PyTables column class for this column | 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... |
validate that we have the same order as the existing & same dtype | 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)):
... |
set the data from this selection ( and convert to the correct dtype if we can ) | def convert(self, values, nan_rep, encoding, errors):
"""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)
#... |
get the data for this column | 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() |
set the data for this column | 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) |
compute and set our version | 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 = ... |
set my pandas type & version | 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() |
infer the axes of my storer return a boolean indicating if we have a valid storer or not | 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 |
support fully deleting the node in its entirety ( only ) - where specification must be None | 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)
retu... |
remove table keywords from kwargs and return raise if any keywords are passed which are not - None | 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 ... |
set our object attributes | def set_attrs(self):
""" set our object attributes """
self.attrs.encoding = self.encoding
self.attrs.errors = self.errors |
retrieve our attributes | 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... |
read an array for the specified node ( off of group | 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):
... |
write a 0 - len array | 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)._... |
we don t support start stop kwds in Sparse | 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 "
... |
write it as a collection of individual sparse series | 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.... |
validate against an existing table | 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(
... |
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