Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def nancorr(a, b, method='pearson', min_periods=None):
if len(a) != len(b):
raise AssertionError('Operands to nancorr must have same size')
if min_periods is None:
min_periods = 1
valid = notna(a) & notna(b)
if not valid.all():
... | [
"\n a, b: ndarrays\n "
] |
Please provide a description of the function:def _nanpercentile_1d(values, mask, q, na_value, interpolation):
# mask is Union[ExtensionArray, ndarray]
values = values[~mask]
if len(values) == 0:
if lib.is_scalar(q):
return na_value
else:
return np.array([na_valu... | [
"\n Wraper for np.percentile that skips missing values, specialized to\n 1-dimensional case.\n\n Parameters\n ----------\n values : array over which to find quantiles\n mask : ndarray[bool]\n locations in values that should be considered missing\n q : scalar or array of quantile indices ... |
Please provide a description of the function:def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
if not lib.is_scalar(mask) and mask.any():
if ndim == 1:
return _nanpercentile_1d(values, mask, q, na_value,
interpolation=interpolation... | [
"\n Wraper for np.percentile that skips missing values.\n\n Parameters\n ----------\n values : array over which to find quantiles\n q : scalar or array of quantile indices to find\n axis : {0, 1}\n na_value : scalar\n value to return for empty or all-null values\n mask : ndarray[bool]... |
Please provide a description of the function:def write_th(self, s, header=False, indent=0, tags=None):
if header and self.fmt.col_space is not None:
tags = (tags or "")
tags += ('style="min-width: {colspace};"'
.format(colspace=self.fmt.col_space))
... | [
"\n Method for writting a formatted <th> cell.\n\n If col_space is set on the formatter then that is used for\n the value of min-width.\n\n Parameters\n ----------\n s : object\n The data to be written inside the cell.\n header : boolean, default False\n ... |
Please provide a description of the function:def read_clipboard(sep=r'\s+', **kwargs): # pragma: no cover
r
encoding = kwargs.pop('encoding', 'utf-8')
# only utf-8 is valid for passed value because that's what clipboard
# supports
if encoding is not None and encoding.lower().replace('-', '') != 'u... | [
"\n Read text from clipboard and pass to read_csv. See read_csv for the\n full argument list\n\n Parameters\n ----------\n sep : str, default '\\s+'\n A string or regex delimiter. The default of '\\s+' denotes\n one or more whitespace characters.\n\n Returns\n -------\n parsed ... |
Please provide a description of the function:def to_clipboard(obj, excel=True, sep=None, **kwargs): # pragma: no cover
encoding = kwargs.pop('encoding', 'utf-8')
# testing if an invalid encoding is passed to clipboard
if encoding is not None and encoding.lower().replace('-', '') != 'utf8':
ra... | [
"\n Attempt to write text representation of object to the system clipboard\n The clipboard can be then pasted into Excel for example.\n\n Parameters\n ----------\n obj : the object to write to the clipboard\n excel : boolean, defaults to True\n if True, use the provided separator, writi... |
Please provide a description of the function:def _get_skiprows(skiprows):
if isinstance(skiprows, slice):
return lrange(skiprows.start or 0, skiprows.stop, skiprows.step or 1)
elif isinstance(skiprows, numbers.Integral) or is_list_like(skiprows):
return skiprows
elif skiprows is None:
... | [
"Get an iterator given an integer, slice or container.\n\n Parameters\n ----------\n skiprows : int, slice, container\n The iterator to use to skip rows; can also be a slice.\n\n Raises\n ------\n TypeError\n * If `skiprows` is not a slice, integer, or Container\n\n Returns\n -... |
Please provide a description of the function:def _read(obj):
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
elif hasattr(obj, 'read'):
text = obj.read()
elif isinstance(obj, (str, bytes)):
text = obj
try:
if os.path.isfile(text):
... | [
"Try to read from a url, file or string.\n\n Parameters\n ----------\n obj : str, unicode, or file-like\n\n Returns\n -------\n raw_text : str\n "
] |
Please provide a description of the function:def _build_xpath_expr(attrs):
# give class attribute as class_ because class is a python keyword
if 'class_' in attrs:
attrs['class'] = attrs.pop('class_')
s = ["@{key}={val!r}".format(key=k, val=v) for k, v in attrs.items()]
return '[{expr}]'.f... | [
"Build an xpath expression to simulate bs4's ability to pass in kwargs to\n search for attributes when using the lxml parser.\n\n Parameters\n ----------\n attrs : dict\n A dict of HTML attributes. These are NOT checked for validity.\n\n Returns\n -------\n expr : unicode\n An XPa... |
Please provide a description of the function:def _parser_dispatch(flavor):
valid_parsers = list(_valid_parsers.keys())
if flavor not in valid_parsers:
raise ValueError('{invalid!r} is not a valid flavor, valid flavors '
'are {valid}'
.format(invalid... | [
"Choose the parser based on the input flavor.\n\n Parameters\n ----------\n flavor : str\n The type of parser to use. This must be a valid backend.\n\n Returns\n -------\n cls : _HtmlFrameParser subclass\n The parser class based on the requested input flavor.\n\n Raises\n -----... |
Please provide a description of the function:def read_html(io, match='.+', flavor=None, header=None, index_col=None,
skiprows=None, attrs=None, parse_dates=False,
tupleize_cols=None, thousands=',', encoding=None,
decimal='.', converters=None, na_values=None,
keep_... | [
"Read HTML tables into a ``list`` of ``DataFrame`` objects.\n\n Parameters\n ----------\n io : str or file-like\n A URL, a file-like object, or a raw string containing HTML. Note that\n lxml only accepts the http, ftp and file url protocols. If you have a\n URL that starts with ``'http... |
Please provide a description of the function:def parse_tables(self):
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
return (self._parse_thead_tbody_tfoot(table) for table in tables) | [
"\n Parse and return all tables from the DOM.\n\n Returns\n -------\n list of parsed (header, body, footer) tuples from tables.\n "
] |
Please provide a description of the function:def _parse_thead_tbody_tfoot(self, table_html):
header_rows = self._parse_thead_tr(table_html)
body_rows = self._parse_tbody_tr(table_html)
footer_rows = self._parse_tfoot_tr(table_html)
def row_is_all_th(row):
return al... | [
"\n Given a table, return parsed header, body, and foot.\n\n Parameters\n ----------\n table_html : node-like\n\n Returns\n -------\n tuple of (header, body, footer), each a list of list-of-text rows.\n\n Notes\n -----\n Header and body are lists... |
Please provide a description of the function:def _expand_colspan_rowspan(self, rows):
all_texts = [] # list of rows, each a list of str
remainder = [] # list of (index, text, nrows)
for tr in rows:
texts = [] # the output for this row
next_remainder = []
... | [
"\n Given a list of <tr>s, return a list of text rows.\n\n Parameters\n ----------\n rows : list of node-like\n List of <tr>s\n\n Returns\n -------\n list of list\n Each returned row is a list of str text.\n\n Notes\n -----\n ... |
Please provide a description of the function:def _handle_hidden_tables(self, tbl_list, attr_name):
if not self.displayed_only:
return tbl_list
return [x for x in tbl_list if "display:none" not in
getattr(x, attr_name).get('style', '').replace(" ", "")] | [
"\n Return list of tables, potentially removing hidden elements\n\n Parameters\n ----------\n tbl_list : list of node-like\n Type of list elements will vary depending upon parser used\n attr_name : str\n Name of the accessor for retrieving HTML attributes\n\n... |
Please provide a description of the function:def _build_doc(self):
from lxml.html import parse, fromstring, HTMLParser
from lxml.etree import XMLSyntaxError
parser = HTMLParser(recover=True, encoding=self.encoding)
try:
if _is_url(self.io):
with urlo... | [
"\n Raises\n ------\n ValueError\n * If a URL that lxml cannot parse is passed.\n\n Exception\n * Any other ``Exception`` thrown. For example, trying to parse a\n URL that is syntactically correct on a machine with no internet\n connection ... |
Please provide a description of the function:def get_dtype_kinds(l):
typs = set()
for arr in l:
dtype = arr.dtype
if is_categorical_dtype(dtype):
typ = 'category'
elif is_sparse(arr):
typ = 'sparse'
elif isinstance(arr, ABCRangeIndex):
t... | [
"\n Parameters\n ----------\n l : list of arrays\n\n Returns\n -------\n a set of kinds that exist in this list of arrays\n "
] |
Please provide a description of the function:def _get_series_result_type(result, objs=None):
from pandas import SparseSeries, SparseDataFrame, DataFrame
# concat Series with axis 1
if isinstance(result, dict):
# concat Series with axis 1
if all(isinstance(c, (SparseSeries, SparseDataFr... | [
"\n return appropriate class of Series concat\n input is either dict or array-like\n "
] |
Please provide a description of the function:def _get_frame_result_type(result, objs):
if (result.blocks and (
any(isinstance(obj, ABCSparseDataFrame) for obj in objs))):
from pandas.core.sparse.api import SparseDataFrame
return SparseDataFrame
else:
return next(obj for... | [
"\n return appropriate class of DataFrame-like concat\n if all blocks are sparse, return SparseDataFrame\n otherwise, return 1st obj\n "
] |
Please provide a description of the function:def _concat_compat(to_concat, axis=0):
# filter empty arrays
# 1-d dtypes always are included here
def is_nonempty(x):
try:
return x.shape[axis] > 0
except Exception:
return True
# If all arrays are empty, there'... | [
"\n provide concatenation of an array of arrays each of which is a single\n 'normalized' dtypes (in that for example, if it's object, then it is a\n non-datetimelike and provide a combined dtype for the resulting array that\n preserves the overall dtype if possible)\n\n Parameters\n ----------\n ... |
Please provide a description of the function:def _concat_categorical(to_concat, axis=0):
# we could have object blocks and categoricals here
# if we only have a single categoricals then combine everything
# else its a non-compat categorical
categoricals = [x for x in to_concat if is_categorical_dt... | [
"Concatenate an object/categorical array of arrays, each of which is a\n single dtype\n\n Parameters\n ----------\n to_concat : array of arrays\n axis : int\n Axis to provide concatenation in the current implementation this is\n always 0, e.g. we only have 1D categoricals\n\n Returns... |
Please provide a description of the function:def union_categoricals(to_union, sort_categories=False, ignore_order=False):
from pandas import Index, Categorical, CategoricalIndex, Series
from pandas.core.arrays.categorical import _recode_for_categories
if len(to_union) == 0:
raise ValueError('N... | [
"\n Combine list-like of Categorical-like, unioning categories. All\n categories must have the same dtype.\n\n .. versionadded:: 0.19.0\n\n Parameters\n ----------\n to_union : list-like of Categorical, CategoricalIndex,\n or Series with dtype='category'\n sort_categories : boolea... |
Please provide a description of the function:def _concat_datetime(to_concat, axis=0, typs=None):
if typs is None:
typs = get_dtype_kinds(to_concat)
# multiple types, need to coerce to object
if len(typs) != 1:
return _concatenate_2d([_convert_datetimelike_to_object(x)
... | [
"\n provide concatenation of an datetimelike array of arrays each of which is a\n single M8[ns], datetimet64[ns, tz] or m8[ns] dtype\n\n Parameters\n ----------\n to_concat : array of arrays\n axis : axis to provide concatenation\n typs : set of to_concat dtypes\n\n Returns\n -------\n ... |
Please provide a description of the function:def _concat_datetimetz(to_concat, name=None):
# Right now, internals will pass a List[DatetimeArray] here
# for reductions like quantile. I would like to disentangle
# all this before we get here.
sample = to_concat[0]
if isinstance(sample, ABCIndex... | [
"\n concat DatetimeIndex with the same tz\n all inputs must be DatetimeIndex\n it is used in DatetimeIndex.append also\n "
] |
Please provide a description of the function:def _concat_index_asobject(to_concat, name=None):
from pandas import Index
from pandas.core.arrays import ExtensionArray
klasses = (ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex,
ExtensionArray)
to_concat = [x.astype(object) if isin... | [
"\n concat all inputs as object. DatetimeIndex, TimedeltaIndex and\n PeriodIndex are converted to object dtype before concatenation\n "
] |
Please provide a description of the function:def _concat_sparse(to_concat, axis=0, typs=None):
from pandas.core.arrays import SparseArray
fill_values = [x.fill_value for x in to_concat
if isinstance(x, SparseArray)]
fill_value = fill_values[0]
# TODO: Fix join unit generation ... | [
"\n provide concatenation of an sparse/dense array of arrays each of which is a\n single dtype\n\n Parameters\n ----------\n to_concat : array of arrays\n axis : axis to provide concatenation\n typs : set of to_concat dtypes\n\n Returns\n -------\n a single array, preserving the combin... |
Please provide a description of the function:def _concat_rangeindex_same_dtype(indexes):
from pandas import Int64Index, RangeIndex
start = step = next = None
# Filter the empty indexes
non_empty_indexes = [obj for obj in indexes if len(obj)]
for obj in non_empty_indexes:
if start is... | [
"\n Concatenates multiple RangeIndex instances. All members of \"indexes\" must\n be of type RangeIndex; result will be RangeIndex if possible, Int64Index\n otherwise. E.g.:\n indexes = [RangeIndex(3), RangeIndex(3, 6)] -> RangeIndex(6)\n indexes = [RangeIndex(3), RangeIndex(4, 6)] -> Int64Index([0,1... |
Please provide a description of the function:def rewrite_exception(old_name, new_name):
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 = arg... | [
"Rewrite the message of an exception."
] |
Please provide a description of the function:def _get_level_lengths(index, hidden_elements=None):
sentinel = object()
levels = index.format(sparsify=sentinel, adjoin=False, names=False)
if hidden_elements is None:
hidden_elements = []
lengths = {}
if index.nlevels == 1:
for i,... | [
"\n Given an index, find the level length for each element.\n\n Optional argument is a list of index positions which\n should not be visible.\n\n Result is a dictionary of (level, inital_position): span\n "
] |
Please provide a description of the function:def _translate(self):
table_styles = self.table_styles or []
caption = self.caption
ctx = self.ctx
precision = self.precision
hidden_index = self.hidden_index
hidden_columns = self.hidden_columns
uuid = self.uu... | [
"\n Convert the DataFrame in `self.data` and the attrs from `_build_styles`\n into a dictionary of {head, body, uuid, cellstyle}.\n "
] |
Please provide a description of the function:def format(self, formatter, subset=None):
if subset is None:
row_locs = range(len(self.data))
col_locs = range(len(self.data.columns))
else:
subset = _non_reducing_slice(subset)
if len(subset) == 1:
... | [
"\n Format the text display value of cells.\n\n .. versionadded:: 0.18.0\n\n Parameters\n ----------\n formatter : str, callable, or dict\n subset : IndexSlice\n An argument to ``DataFrame.loc`` that restricts which elements\n ``formatter`` is applied ... |
Please provide a description of the function:def render(self, **kwargs):
self._compute()
# TODO: namespace all the pandas keys
d = self._translate()
# filter out empty styles, every cell will have a class
# but the list of props may just be [['', '']].
# so we ha... | [
"\n Render the built up styles to HTML.\n\n Parameters\n ----------\n **kwargs\n Any additional keyword arguments are passed\n through to ``self.template.render``.\n This is useful when you need to provide\n additional variables for a custom te... |
Please provide a description of the function:def _update_ctx(self, attrs):
for row_label, v in attrs.iterrows():
for col_label, col in v.iteritems():
i = self.index.get_indexer([row_label])[0]
j = self.columns.get_indexer([col_label])[0]
for p... | [
"\n Update the state of the Styler.\n\n Collects a mapping of {index_label: ['<property>: <value>']}.\n\n attrs : Series or DataFrame\n should contain strings of '<property>: <value>;<prop2>: <val2>'\n Whitespace shouldn't matter and the final trailing ';' shouldn't\n matte... |
Please provide a description of the function:def _compute(self):
r = self
for func, args, kwargs in self._todo:
r = func(self)(*args, **kwargs)
return r | [
"\n Execute the style functions built up in `self._todo`.\n\n Relies on the conventions that all style functions go through\n .apply or .applymap. The append styles to apply as tuples of\n\n (application method, *args, **kwargs)\n "
] |
Please provide a description of the function:def apply(self, func, axis=0, subset=None, **kwargs):
self._todo.append((lambda instance: getattr(instance, '_apply'),
(func, axis, subset), kwargs))
return self | [
"\n Apply a function column-wise, row-wise, or table-wise,\n updating the HTML representation with the result.\n\n Parameters\n ----------\n func : function\n ``func`` should take a Series or DataFrame (depending\n on ``axis``), and return an object with the ... |
Please provide a description of the function:def applymap(self, func, subset=None, **kwargs):
self._todo.append((lambda instance: getattr(instance, '_applymap'),
(func, subset), kwargs))
return self | [
"\n Apply a function elementwise, updating the HTML\n representation with the result.\n\n Parameters\n ----------\n func : function\n ``func`` should take a scalar and return a scalar\n subset : IndexSlice\n a valid indexer to limit ``data`` to *before... |
Please provide a description of the function:def where(self, cond, value, other=None, subset=None, **kwargs):
if other is None:
other = ''
return self.applymap(lambda val: value if cond(val) else other,
subset=subset, **kwargs) | [
"\n Apply a function elementwise, updating the HTML\n representation with a style which is selected in\n accordance with the return value of a function.\n\n .. versionadded:: 0.21.0\n\n Parameters\n ----------\n cond : callable\n ``cond`` should take a sca... |
Please provide a description of the function:def hide_columns(self, subset):
subset = _non_reducing_slice(subset)
hidden_df = self.data.loc[subset]
self.hidden_columns = self.columns.get_indexer_for(hidden_df.columns)
return self | [
"\n Hide columns from rendering.\n\n .. versionadded:: 0.23.0\n\n Parameters\n ----------\n subset : IndexSlice\n An argument to ``DataFrame.loc`` that identifies which columns\n are hidden.\n\n Returns\n -------\n self : Styler\n ... |
Please provide a description of the function:def highlight_null(self, null_color='red'):
self.applymap(self._highlight_null, null_color=null_color)
return self | [
"\n Shade the background ``null_color`` for missing values.\n\n Parameters\n ----------\n null_color : str\n\n Returns\n -------\n self : Styler\n "
] |
Please provide a description of the function:def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
subset=None, text_color_threshold=0.408):
subset = _maybe_numeric_slice(self.data, subset)
subset = _non_reducing_slice(subset)
self.apply(self._bac... | [
"\n Color the background in a gradient according to\n the data in each column (optionally row).\n\n Requires matplotlib.\n\n Parameters\n ----------\n cmap : str or colormap\n matplotlib colormap\n low, high : float\n compress the range by these... |
Please provide a description of the function:def _background_gradient(s, cmap='PuBu', low=0, high=0,
text_color_threshold=0.408):
if (not isinstance(text_color_threshold, (float, int)) or
not 0 <= text_color_threshold <= 1):
msg = "`text_color_th... | [
"\n Color background in a range according to the data.\n ",
"\n Calculate relative luminance of a color.\n\n The calculation adheres to the W3C standards\n (https://www.w3.org/WAI/GL/wiki/Relative_luminance)\n\n Parameters\n ... |
Please provide a description of the function:def set_properties(self, subset=None, **kwargs):
values = ';'.join('{p}: {v}'.format(p=p, v=v)
for p, v in kwargs.items())
f = lambda x: values
return self.applymap(f, subset=subset) | [
"\n Convenience method for setting one or more non-data dependent\n properties or each cell.\n\n Parameters\n ----------\n subset : IndexSlice\n a valid slice for ``data`` to limit the style application to\n kwargs : dict\n property: value pairs to be ... |
Please provide a description of the function:def _bar(s, align, colors, width=100, vmin=None, vmax=None):
# 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 else vmax
... | [
"\n Draw bar chart in dataframe cells.\n ",
"\n Generate CSS code to draw a bar from start to end.\n "
] |
Please provide a description of the function:def bar(self, subset=None, axis=0, color='#d65f5f', width=100,
align='left', vmin=None, vmax=None):
if align not in ('left', 'zero', 'mid'):
raise ValueError("`align` must be one of {'left', 'zero',' mid'}")
if not (is_list_l... | [
"\n Draw bar chart in the cell backgrounds.\n\n Parameters\n ----------\n subset : IndexSlice, optional\n A valid slice for `data` to limit the style application to.\n axis : {0 or 'index', 1 or 'columns', None}, default 0\n apply to each column (``axis=0`` o... |
Please provide a description of the function:def highlight_max(self, subset=None, color='yellow', axis=0):
return self._highlight_handler(subset=subset, color=color, axis=axis,
max_=True) | [
"\n Highlight the maximum by shading the background.\n\n Parameters\n ----------\n subset : IndexSlice, default None\n a valid slice for ``data`` to limit the style application to.\n color : str, default 'yellow'\n axis : {0 or 'index', 1 or 'columns', None}, def... |
Please provide a description of the function:def highlight_min(self, subset=None, color='yellow', axis=0):
return self._highlight_handler(subset=subset, color=color, axis=axis,
max_=False) | [
"\n Highlight the minimum by shading the background.\n\n Parameters\n ----------\n subset : IndexSlice, default None\n a valid slice for ``data`` to limit the style application to.\n color : str, default 'yellow'\n axis : {0 or 'index', 1 or 'columns', None}, def... |
Please provide a description of the function:def _highlight_extrema(data, color='yellow', max_=True):
attr = 'background-color: {0}'.format(color)
if data.ndim == 1: # Series from .apply
if max_:
extrema = data == data.max()
else:
extrema... | [
"\n Highlight the min or max in a Series or DataFrame.\n "
] |
Please provide a description of the function:def from_custom_template(cls, searchpath, name):
loader = ChoiceLoader([
FileSystemLoader(searchpath),
cls.loader,
])
class MyStyler(cls):
env = Environment(loader=loader)
template = env.get_te... | [
"\n Factory function for creating a subclass of ``Styler``\n with a custom template and Jinja environment.\n\n Parameters\n ----------\n searchpath : str or list\n Path or paths of directories containing the templates\n name : str\n Name of your custom... |
Please provide a description of the function:def register(self, dtype):
if not issubclass(dtype, (PandasExtensionDtype, ExtensionDtype)):
raise ValueError("can only register pandas extension dtypes")
self.dtypes.append(dtype) | [
"\n Parameters\n ----------\n dtype : ExtensionDtype\n "
] |
Please provide a description of the function:def find(self, dtype):
if not isinstance(dtype, str):
dtype_type = dtype
if not isinstance(dtype, type):
dtype_type = type(dtype)
if issubclass(dtype_type, ExtensionDtype):
return dtype
... | [
"\n Parameters\n ----------\n dtype : PandasExtensionDtype or string\n\n Returns\n -------\n return the first matching dtype, otherwise return None\n "
] |
Please provide a description of the function:def np_datetime64_compat(s, *args, **kwargs):
s = tz_replacer(s)
return np.datetime64(s, *args, **kwargs) | [
"\n provide compat for construction of strings to numpy datetime64's with\n tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation\n warning, when need to pass '2015-01-01 09:00:00'\n "
] |
Please provide a description of the function:def np_array_datetime64_compat(arr, *args, **kwargs):
# is_list_like
if (hasattr(arr, '__iter__') and not isinstance(arr, (str, bytes))):
arr = [tz_replacer(s) for s in arr]
else:
arr = tz_replacer(arr)
return np.array(arr, *args, **kwar... | [
"\n provide compat for construction of an array of strings to a\n np.array(..., dtype=np.datetime64(..))\n tz-changes in 1.11 that make '2015-01-01 09:00:00Z' show a deprecation\n warning, when need to pass '2015-01-01 09:00:00'\n "
] |
Please provide a description of the function:def _assert_safe_casting(cls, data, subarr):
if not issubclass(data.dtype.type, np.signedinteger):
if not np.array_equal(data, subarr):
raise TypeError('Unsafe NumPy casting, you must '
'explicitly ... | [
"\n Ensure incoming data can be represented as ints.\n "
] |
Please provide a description of the function:def get_value(self, series, key):
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_values | [
" we always want to get an index value, never a value "
] |
Please provide a description of the function:def equals(self, other):
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
# since nans don't compare equal this... | [
"\n Determines if two Index objects contain the same elements.\n "
] |
Please provide a description of the function:def _ensure_decoded(s):
if isinstance(s, np.bytes_):
s = s.decode('UTF-8')
return s | [
" if we have bytes, decode them to unicode "
] |
Please provide a description of the function:def _ensure_term(where, scope_level):
# 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, whe... | [
"\n ensure that the where is a Term or a list of Term\n this makes sure that we are capturing the scope of variables\n that are passed\n create the terms here with a frame_level=2 (we are 2 levels down)\n "
] |
Please provide a description of the function:def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None,
append=None, **kwargs):
if append:
f = lambda store: store.append(key, value, **kwargs)
else:
f = lambda store: store.put(key, value, **kwargs)
path_or_... | [
" store this object, close it if we opened it "
] |
Please provide a description of the function:def read_hdf(path_or_buf, key=None, mode='r', **kwargs):
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
i... | [
"\n Read from the store, close it if we opened it.\n\n Retrieve pandas object stored in file, optionally based on where\n criteria\n\n Parameters\n ----------\n path_or_buf : string, buffer or path object\n Path to the file to open, or an open :class:`pandas.HDFStore` object.\n Suppo... |
Please provide a description of the function:def _is_metadata_of(group, 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':
... | [
"Check if a given group is a metadata group for a given parent_group."
] |
Please provide a description of the function:def _get_info(info, name):
try:
idx = info[name]
except KeyError:
idx = info[name] = dict()
return idx | [
" get/create the info for this name "
] |
Please provide a description of the function:def _get_tz(tz):
zone = timezones.get_timezone(tz)
if zone is None:
zone = tz.utcoffset().total_seconds()
return zone | [
" for a tz-aware type, return an encoded zone "
] |
Please provide a description of the function:def _set_tz(values, tz, preserve_UTC=False, coerce=False):
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)
... | [
"\n coerce the values to a DatetimeIndex if tz is set\n preserve the input shape if possible\n\n Parameters\n ----------\n values : ndarray\n tz : string/pickled tz object\n preserve_UTC : boolean,\n preserve the UTC of the result\n coerce : if we do not have a passed timezone, coerce... |
Please provide a description of the function:def _convert_string_array(data, encoding, errors, itemsize=None):
# 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
... | [
"\n we take a string-like that is object dtype and coerce to a fixed size\n string type\n\n Parameters\n ----------\n data : a numpy array of object dtype\n encoding : None or string-encoding\n errors : handler for encoding errors\n itemsize : integer, optional, defaults to the max length of... |
Please provide a description of the function:def _unconvert_string_array(data, nan_rep=None, encoding=None,
errors='strict'):
shape = data.shape
data = np.asarray(data.ravel(), dtype=object)
# guard against a None encoding (because of a legacy
# where the passed encodin... | [
"\n inverse of _convert_string_array\n\n Parameters\n ----------\n data : fixed length string dtyped array\n nan_rep : the storage repr of NaN, optional\n encoding : the encoding of the data, optional\n errors : handler for encoding errors, default 'strict'\n\n Returns\n -------\n an o... |
Please provide a description of the function:def open(self, mode='a', **kwargs):
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 [... | [
"\n Open the file in the specified mode\n\n Parameters\n ----------\n mode : {'a', 'w', 'r', 'r+'}, default 'a'\n See HDFStore docstring or tables.open_file for info about modes\n "
] |
Please provide a description of the function:def flush(self, fsync=False):
if self._handle is not None:
self._handle.flush()
if fsync:
try:
os.fsync(self._handle.fileno())
except OSError:
pass | [
"\n Force all buffered modifications to be written to disk.\n\n Parameters\n ----------\n fsync : bool (default False)\n call ``os.fsync()`` on the file handle to force writing to disk.\n\n Notes\n -----\n Without ``fsync=True``, flushing may not guarantee t... |
Please provide a description of the function:def get(self, key):
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) | [
"\n Retrieve pandas object stored in file\n\n Parameters\n ----------\n key : object\n\n Returns\n -------\n obj : same type as object stored in file\n "
] |
Please provide a description of the function:def select(self, key, where=None, start=None, stop=None, columns=None,
iterator=False, chunksize=None, auto_close=False, **kwargs):
group = self.get_node(key)
if group is None:
raise KeyError('No object named {key} in the f... | [
"\n Retrieve pandas object stored in file, optionally based on where\n criteria\n\n Parameters\n ----------\n key : object\n where : list of Term (or convertible) objects, optional\n start : integer (defaults to None), row number to start selection\n stop : i... |
Please provide a description of the function:def select_as_coordinates(
self, key, where=None, start=None, stop=None, **kwargs):
where = _ensure_term(where, scope_level=1)
return self.get_storer(key).read_coordinates(where=where, start=start,
... | [
"\n return the selection as an Index\n\n Parameters\n ----------\n key : object\n where : list of Term (or convertible) objects, optional\n start : integer (defaults to None), row number to start selection\n stop : integer (defaults to None), row number to stop sele... |
Please provide a description of the function:def select_column(self, key, column, **kwargs):
return self.get_storer(key).read_column(column=column, **kwargs) | [
"\n return a single column from the table. This is generally only useful to\n select an indexable\n\n Parameters\n ----------\n key : object\n column: the column of interest\n\n Exceptions\n ----------\n raises KeyError if the column is not found (or ke... |
Please provide a description of the function:def select_as_multiple(self, keys, where=None, selector=None, columns=None,
start=None, stop=None, iterator=False,
chunksize=None, auto_close=False, **kwargs):
# default to single select
where = ... | [
" Retrieve pandas objects from multiple tables\n\n Parameters\n ----------\n keys : a list of the tables\n selector : the table to apply the where criteria (defaults to keys[0]\n if not supplied)\n columns : the columns I want back\n start : integer (defaults to ... |
Please provide a description of the function:def put(self, key, value, format=None, append=False, **kwargs):
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=appe... | [
"\n Store object in HDFStore\n\n Parameters\n ----------\n key : object\n value : {Series, DataFrame}\n format : 'fixed(f)|table(t)', default is 'fixed'\n fixed(f) : Fixed format\n Fast writing/reading. Not-appendable, nor searchab... |
Please provide a description of the function:def remove(self, key, where=None, start=None, stop=None):
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
... | [
"\n Remove pandas object partially by specifying the where condition\n\n Parameters\n ----------\n key : string\n Node to remove or delete rows from\n where : list of Term (or convertible) objects, optional\n start : integer (defaults to None), row number to star... |
Please provide a description of the function:def append(self, key, value, format=None, append=True, columns=None,
dropna=None, **kwargs):
if columns is not None:
raise TypeError("columns is not a supported keyword in append, "
"try data_columns")
... | [
"\n Append to Table in file. Node must already exist and be Table\n format.\n\n Parameters\n ----------\n key : object\n value : {Series, DataFrame}\n format : 'table' is the default\n table(t) : table format\n Write as a PyTables Tab... |
Please provide a description of the function:def append_to_multiple(self, d, value, selector, data_columns=None,
axes=None, dropna=False, **kwargs):
if axes is not None:
raise TypeError("axes is currently not accepted as a parameter to"
... | [
"\n Append to multiple tables\n\n Parameters\n ----------\n d : a dict of table_name to table_columns, None is acceptable as the\n values of one node (this will get all the remaining columns)\n value : a pandas object\n selector : a string that designates the ind... |
Please provide a description of the function:def create_table_index(self, key, **kwargs):
# version requirements
_tables()
s = self.get_storer(key)
if s is None:
return
if not s.is_table:
raise TypeError(
"cannot create table ind... | [
" Create a pytables index on the table\n Parameters\n ----------\n key : object (the node to index)\n\n Exceptions\n ----------\n raises if the node is not a table\n\n "
] |
Please provide a description of the function:def groups(self):
_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
... | [
"return a list of all the top-level nodes (that are not themselves a\n pandas storage object)\n "
] |
Please provide a description of the function:def walk(self, where="/"):
_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 = [... | [
" Walk the pytables group hierarchy for pandas objects\n\n This generator will yield the group path, subgroups and pandas object\n names for each group.\n Any non-pandas PyTables objects that are not a group will be ignored.\n\n The `where` group itself is listed first (preorder), then e... |
Please provide a description of the function:def get_node(self, key):
self._check_if_open()
try:
if not key.startswith('/'):
key = '/' + key
return self._handle.get_node(self.root, key)
except _table_mod.exceptions.NoSuchNodeError:
ret... | [
" return the node with the key or None if it does not exist "
] |
Please provide a description of the function:def get_storer(self, key):
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 | [
" return the storer object for a key, raise if not in the file "
] |
Please provide a description of the function:def copy(self, file, mode='w', propindexes=True, keys=None, complib=None,
complevel=None, fletcher32=False, overwrite=True):
new_store = HDFStore(
file,
mode=mode,
complib=complib,
complevel=comple... | [
" copy the existing store to a new file, upgrading in place\n\n Parameters\n ----------\n propindexes: restore indexes in copied file (defaults to True)\n keys : list of keys to include in the copy (defaults to all)\n overwrite : overwrite (remove and re... |
Please provide a description of the function:def info(self):
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 = []
... | [
"\n Print detailed information on the store.\n\n .. versionadded:: 0.21.0\n "
] |
Please provide a description of the function:def _validate_format(self, format, kwargs):
kwargs = kwargs.copy()
# validate
try:
kwargs['format'] = _FORMAT_MAP[format.lower()]
except KeyError:
raise TypeError("invalid HDFStore format specified [{0}]"
... | [
" validate / deprecate formats; return the new kwargs "
] |
Please provide a description of the function:def _create_storer(self, group, format=None, value=None, append=False,
**kwargs):
def error(t):
raise TypeError(
"cannot properly create the storer for: [{t}] [group->"
"{group},value->{valu... | [
" return a suitable class to operate "
] |
Please provide a description of the function:def set_name(self, name, kind_attr=None):
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 name of this indexer "
] |
Please provide a description of the function:def set_pos(self, pos):
self.pos = pos
if pos is not None and self.typ is not None:
self.typ._v_pos = pos
return self | [
" set the position of this column in the Table "
] |
Please provide a description of the function:def is_indexed(self):
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False | [
" return whether I am an indexed column "
] |
Please provide a description of the function:def infer(self, handler):
table = handler.table
new_self = self.copy()
new_self.set_table(table)
new_self.get_attr()
new_self.read_metadata(handler)
return new_self | [
"infer this column from the table: create and return a new object"
] |
Please provide a description of the function:def convert(self, values, nan_rep, encoding, errors):
# 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()
... | [
" set the values from this selection: take = take ownership "
] |
Please provide a description of the function:def maybe_set_size(self, min_itemsize=None):
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 ... | [
" maybe set a string col itemsize:\n min_itemsize can be an integer or a dict with this columns name\n with an integer size "
] |
Please provide a description of the function:def validate_col(self, itemsize=None):
# 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:
... | [
" validate this column: return the compared against itemsize "
] |
Please provide a description of the function:def update_info(self, info):
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_val... | [
" set/update the info for this indexable with the key/value\n if there is a conflict raise/warn as needed "
] |
Please provide a description of the function:def set_info(self, info):
idx = info.get(self.name)
if idx is not None:
self.__dict__.update(idx) | [
" set my state from the passed info "
] |
Please provide a description of the function:def validate_metadata(self, handler):
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
... | [
" validate that kind=category does not change the categories "
] |
Please provide a description of the function:def write_metadata(self, handler):
if self.metadata is not None:
handler.write_metadata(self.cname, self.metadata) | [
" set the meta data "
] |
Please provide a description of the function:def convert(self, values, nan_rep, encoding, errors):
self.values = Int64Index(np.arange(self.table.nrows))
return self | [
" set the values from this selection: take = take ownership "
] |
Please provide a description of the function:def create_for_block(
cls, i=None, name=None, cname=None, version=None, **kwargs):
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 ... | [
" return a new datacol with the block i "
] |
Please provide a description of the function:def set_metadata(self, metadata):
if metadata is not None:
metadata = np.array(metadata, copy=False).ravel()
self.metadata = metadata | [
" record the metadata "
] |
Please provide a description of the function:def set_atom(self, block, block_items, existing_col, min_itemsize,
nan_rep, info, encoding=None, errors='strict'):
self.values = list(block_items)
# short-cut certain block types
if block.is_categorical:
return ... | [
" create and setup my atom from the block b "
] |
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