repo stringclasses 85
values | path stringlengths 8 121 | func_name stringlengths 1 82 | original_string stringlengths 112 65.5k | language stringclasses 1
value | code stringlengths 112 65.5k | code_tokens listlengths 20 4.09k | docstring stringlengths 3 46.3k | docstring_tokens listlengths 1 564 | sha stringclasses 85
values | url stringlengths 93 218 | partition stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|
pandas-dev/pandas | pandas/core/nanops.py | nanvar | def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the variance along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in... | python | def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the variance along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in... | [
"def",
"nanvar",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"ddof",
"=",
"1",
",",
"mask",
"=",
"None",
")",
":",
"values",
"=",
"com",
".",
"values_from_object",
"(",
"values",
")",
"dtype",
"=",
"values",
".",
"dtyp... | Compute the variance along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of element... | [
"Compute",
"the",
"variance",
"along",
"given",
"axis",
"while",
"ignoring",
"NaNs"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L616-L679 | train |
pandas-dev/pandas | pandas/core/nanops.py | nansem | def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard error in the mean along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. T... | python | def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
"""
Compute the standard error in the mean along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. T... | [
"def",
"nansem",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"ddof",
"=",
"1",
",",
"mask",
"=",
"None",
")",
":",
"# This checks if non-numeric-like data is passed with numeric_only=False",
"# and raises a TypeError otherwise",
"nanvar",... | Compute the standard error in the mean along given axis while ignoring NaNs
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the... | [
"Compute",
"the",
"standard",
"error",
"in",
"the",
"mean",
"along",
"given",
"axis",
"while",
"ignoring",
"NaNs"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L683-L723 | train |
pandas-dev/pandas | pandas/core/nanops.py | nanargmax | def nanargmax(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of max value in specified... | python | def nanargmax(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of max value in specified... | [
"def",
"nanargmax",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"mask",
"=",
"None",
")",
":",
"values",
",",
"mask",
",",
"dtype",
",",
"_",
",",
"_",
"=",
"_get_values",
"(",
"values",
",",
"skipna",
",",
"fill_value... | Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of max value in specified axis or -1 in the NA case
Examples
--------
>>> import p... | [
"Parameters",
"----------",
"values",
":",
"ndarray",
"axis",
":",
"int",
"optional",
"skipna",
":",
"bool",
"default",
"True",
"mask",
":",
"ndarray",
"[",
"bool",
"]",
"optional",
"nan",
"-",
"mask",
"if",
"known"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L756-L782 | train |
pandas-dev/pandas | pandas/core/nanops.py | nanargmin | def nanargmin(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of min value in specified... | python | def nanargmin(values, axis=None, skipna=True, mask=None):
"""
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of min value in specified... | [
"def",
"nanargmin",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"mask",
"=",
"None",
")",
":",
"values",
",",
"mask",
",",
"dtype",
",",
"_",
",",
"_",
"=",
"_get_values",
"(",
"values",
",",
"skipna",
",",
"fill_value... | Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask : ndarray[bool], optional
nan-mask if known
Returns
--------
result : int
The index of min value in specified axis or -1 in the NA case
Examples
--------
>>> import p... | [
"Parameters",
"----------",
"values",
":",
"ndarray",
"axis",
":",
"int",
"optional",
"skipna",
":",
"bool",
"default",
"True",
"mask",
":",
"ndarray",
"[",
"bool",
"]",
"optional",
"nan",
"-",
"mask",
"if",
"known"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L786-L812 | train |
pandas-dev/pandas | pandas/core/nanops.py | nanskew | def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
--------... | python | def nanskew(values, axis=None, skipna=True, mask=None):
""" Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
--------... | [
"def",
"nanskew",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"mask",
"=",
"None",
")",
":",
"values",
"=",
"com",
".",
"values_from_object",
"(",
"values",
")",
"if",
"mask",
"is",
"None",
":",
"mask",
"=",
"isna",
"(... | Compute the sample skewness.
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G1. The algorithm computes this coefficient directly
from the second and third central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : boo... | [
"Compute",
"the",
"sample",
"skewness",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L816-L891 | train |
pandas-dev/pandas | pandas/core/nanops.py | nankurt | def nankurt(values, axis=None, skipna=True, mask=None):
"""
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
Parameters
----------
values : ndar... | python | def nankurt(values, axis=None, skipna=True, mask=None):
"""
Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
Parameters
----------
values : ndar... | [
"def",
"nankurt",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"mask",
"=",
"None",
")",
":",
"values",
"=",
"com",
".",
"values_from_object",
"(",
"values",
")",
"if",
"mask",
"is",
"None",
":",
"mask",
"=",
"isna",
"(... | Compute the sample excess kurtosis
The statistic computed here is the adjusted Fisher-Pearson standardized
moment coefficient G2, computed directly from the second and fourth
central moment.
Parameters
----------
values : ndarray
axis: int, optional
skipna : bool, default True
mask... | [
"Compute",
"the",
"sample",
"excess",
"kurtosis"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L895-L980 | train |
pandas-dev/pandas | pandas/core/nanops.py | nanprod | def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : ... | python | def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
"""
Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : ... | [
"def",
"nanprod",
"(",
"values",
",",
"axis",
"=",
"None",
",",
"skipna",
"=",
"True",
",",
"min_count",
"=",
"0",
",",
"mask",
"=",
"None",
")",
":",
"if",
"mask",
"is",
"None",
":",
"mask",
"=",
"isna",
"(",
"values",
")",
"if",
"skipna",
"and"... | Parameters
----------
values : ndarray[dtype]
axis: int, optional
skipna : bool, default True
min_count: int, default 0
mask : ndarray[bool], optional
nan-mask if known
Returns
-------
result : dtype
Examples
--------
>>> import pandas.core.nanops as nanops
... | [
"Parameters",
"----------",
"values",
":",
"ndarray",
"[",
"dtype",
"]",
"axis",
":",
"int",
"optional",
"skipna",
":",
"bool",
"default",
"True",
"min_count",
":",
"int",
"default",
"0",
"mask",
":",
"ndarray",
"[",
"bool",
"]",
"optional",
"nan",
"-",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L984-L1016 | train |
pandas-dev/pandas | pandas/core/nanops.py | nancorr | def nancorr(a, b, method='pearson', min_periods=None):
"""
a, b: ndarrays
"""
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():
a = a[valid]
... | python | def nancorr(a, b, method='pearson', min_periods=None):
"""
a, b: ndarrays
"""
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():
a = a[valid]
... | [
"def",
"nancorr",
"(",
"a",
",",
"b",
",",
"method",
"=",
"'pearson'",
",",
"min_periods",
"=",
"None",
")",
":",
"if",
"len",
"(",
"a",
")",
"!=",
"len",
"(",
"b",
")",
":",
"raise",
"AssertionError",
"(",
"'Operands to nancorr must have same size'",
")... | a, b: ndarrays | [
"a",
"b",
":",
"ndarrays"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L1083-L1102 | train |
pandas-dev/pandas | pandas/core/nanops.py | _nanpercentile_1d | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be consi... | python | def _nanpercentile_1d(values, mask, q, na_value, interpolation):
"""
Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be consi... | [
"def",
"_nanpercentile_1d",
"(",
"values",
",",
"mask",
",",
"q",
",",
"na_value",
",",
"interpolation",
")",
":",
"# mask is Union[ExtensionArray, ndarray]",
"values",
"=",
"values",
"[",
"~",
"mask",
"]",
"if",
"len",
"(",
"values",
")",
"==",
"0",
":",
... | Wraper for np.percentile that skips missing values, specialized to
1-dimensional case.
Parameters
----------
values : array over which to find quantiles
mask : ndarray[bool]
locations in values that should be considered missing
q : scalar or array of quantile indices to find
na_valu... | [
"Wraper",
"for",
"np",
".",
"percentile",
"that",
"skips",
"missing",
"values",
"specialized",
"to",
"1",
"-",
"dimensional",
"case",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L1203-L1232 | train |
pandas-dev/pandas | pandas/core/nanops.py | nanpercentile | def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
"""
Wraper for np.percentile that skips missing values.
Parameters
----------
values : array over which to find quantiles
q : scalar or array of quantile indices to find
axis : {0, 1}
na_value : scalar
valu... | python | def nanpercentile(values, q, axis, na_value, mask, ndim, interpolation):
"""
Wraper for np.percentile that skips missing values.
Parameters
----------
values : array over which to find quantiles
q : scalar or array of quantile indices to find
axis : {0, 1}
na_value : scalar
valu... | [
"def",
"nanpercentile",
"(",
"values",
",",
"q",
",",
"axis",
",",
"na_value",
",",
"mask",
",",
"ndim",
",",
"interpolation",
")",
":",
"if",
"not",
"lib",
".",
"is_scalar",
"(",
"mask",
")",
"and",
"mask",
".",
"any",
"(",
")",
":",
"if",
"ndim",... | Wraper for np.percentile that skips missing values.
Parameters
----------
values : array over which to find quantiles
q : scalar or array of quantile indices to find
axis : {0, 1}
na_value : scalar
value to return for empty or all-null values
mask : ndarray[bool]
locations i... | [
"Wraper",
"for",
"np",
".",
"percentile",
"that",
"skips",
"missing",
"values",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/nanops.py#L1235-L1272 | train |
pandas-dev/pandas | pandas/io/formats/html.py | HTMLFormatter.write_th | def write_th(self, s, header=False, indent=0, tags=None):
"""
Method for writting a formatted <th> cell.
If col_space is set on the formatter then that is used for
the value of min-width.
Parameters
----------
s : object
The data to be written inside... | python | def write_th(self, s, header=False, indent=0, tags=None):
"""
Method for writting a formatted <th> cell.
If col_space is set on the formatter then that is used for
the value of min-width.
Parameters
----------
s : object
The data to be written inside... | [
"def",
"write_th",
"(",
"self",
",",
"s",
",",
"header",
"=",
"False",
",",
"indent",
"=",
"0",
",",
"tags",
"=",
"None",
")",
":",
"if",
"header",
"and",
"self",
".",
"fmt",
".",
"col_space",
"is",
"not",
"None",
":",
"tags",
"=",
"(",
"tags",
... | Method for writting a formatted <th> cell.
If col_space is set on the formatter then that is used for
the value of min-width.
Parameters
----------
s : object
The data to be written inside the cell.
header : boolean, default False
Set to True if ... | [
"Method",
"for",
"writting",
"a",
"formatted",
"<th",
">",
"cell",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/html.py#L90-L118 | train |
pandas-dev/pandas | pandas/io/clipboards.py | read_clipboard | def read_clipboard(sep=r'\s+', **kwargs): # pragma: no cover
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
one or more whitespa... | python | def read_clipboard(sep=r'\s+', **kwargs): # pragma: no cover
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
one or more whitespa... | [
"def",
"read_clipboard",
"(",
"sep",
"=",
"r'\\s+'",
",",
"*",
"*",
"kwargs",
")",
":",
"# pragma: no cover",
"encoding",
"=",
"kwargs",
".",
"pop",
"(",
"'encoding'",
",",
"'utf-8'",
")",
"# only utf-8 is valid for passed value because that's what clipboard",
"# supp... | r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
one or more whitespace characters.
Returns
-------
parsed : DataFrame | [
"r",
"Read",
"text",
"from",
"clipboard",
"and",
"pass",
"to",
"read_csv",
".",
"See",
"read_csv",
"for",
"the",
"full",
"argument",
"list"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/clipboards.py#L10-L72 | train |
pandas-dev/pandas | pandas/io/clipboards.py | to_clipboard | def to_clipboard(obj, excel=True, sep=None, **kwargs): # pragma: no cover
"""
Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
Parameters
----------
obj : the object to write to the clipboard
excel : boolean, de... | python | def to_clipboard(obj, excel=True, sep=None, **kwargs): # pragma: no cover
"""
Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
Parameters
----------
obj : the object to write to the clipboard
excel : boolean, de... | [
"def",
"to_clipboard",
"(",
"obj",
",",
"excel",
"=",
"True",
",",
"sep",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"# pragma: no cover",
"encoding",
"=",
"kwargs",
".",
"pop",
"(",
"'encoding'",
",",
"'utf-8'",
")",
"# testing if an invalid encoding i... | Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
Parameters
----------
obj : the object to write to the clipboard
excel : boolean, defaults to True
if True, use the provided separator, writing in a csv
... | [
"Attempt",
"to",
"write",
"text",
"representation",
"of",
"object",
"to",
"the",
"system",
"clipboard",
"The",
"clipboard",
"can",
"be",
"then",
"pasted",
"into",
"Excel",
"for",
"example",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/clipboards.py#L75-L132 | train |
pandas-dev/pandas | pandas/io/html.py | _get_skiprows | def _get_skiprows(skiprows):
"""Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
... | python | def _get_skiprows(skiprows):
"""Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
... | [
"def",
"_get_skiprows",
"(",
"skiprows",
")",
":",
"if",
"isinstance",
"(",
"skiprows",
",",
"slice",
")",
":",
"return",
"lrange",
"(",
"skiprows",
".",
"start",
"or",
"0",
",",
"skiprows",
".",
"stop",
",",
"skiprows",
".",
"step",
"or",
"1",
")",
... | Get an iterator given an integer, slice or container.
Parameters
----------
skiprows : int, slice, container
The iterator to use to skip rows; can also be a slice.
Raises
------
TypeError
* If `skiprows` is not a slice, integer, or Container
Returns
-------
it : it... | [
"Get",
"an",
"iterator",
"given",
"an",
"integer",
"slice",
"or",
"container",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L85-L110 | train |
pandas-dev/pandas | pandas/io/html.py | _read | def _read(obj):
"""Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str
"""
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
elif hasattr(obj, 'read'):
text = obj.re... | python | def _read(obj):
"""Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str
"""
if _is_url(obj):
with urlopen(obj) as url:
text = url.read()
elif hasattr(obj, 'read'):
text = obj.re... | [
"def",
"_read",
"(",
"obj",
")",
":",
"if",
"_is_url",
"(",
"obj",
")",
":",
"with",
"urlopen",
"(",
"obj",
")",
"as",
"url",
":",
"text",
"=",
"url",
".",
"read",
"(",
")",
"elif",
"hasattr",
"(",
"obj",
",",
"'read'",
")",
":",
"text",
"=",
... | Try to read from a url, file or string.
Parameters
----------
obj : str, unicode, or file-like
Returns
-------
raw_text : str | [
"Try",
"to",
"read",
"from",
"a",
"url",
"file",
"or",
"string",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L113-L139 | train |
pandas-dev/pandas | pandas/io/html.py | _build_xpath_expr | def _build_xpath_expr(attrs):
"""Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
expr : u... | python | def _build_xpath_expr(attrs):
"""Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
expr : u... | [
"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}={... | Build an xpath expression to simulate bs4's ability to pass in kwargs to
search for attributes when using the lxml parser.
Parameters
----------
attrs : dict
A dict of HTML attributes. These are NOT checked for validity.
Returns
-------
expr : unicode
An XPath expression th... | [
"Build",
"an",
"xpath",
"expression",
"to",
"simulate",
"bs4",
"s",
"ability",
"to",
"pass",
"in",
"kwargs",
"to",
"search",
"for",
"attributes",
"when",
"using",
"the",
"lxml",
"parser",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L601-L620 | train |
pandas-dev/pandas | pandas/io/html.py | _parser_dispatch | def _parser_dispatch(flavor):
"""Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
... | python | def _parser_dispatch(flavor):
"""Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
... | [
"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 '",... | Choose the parser based on the input flavor.
Parameters
----------
flavor : str
The type of parser to use. This must be a valid backend.
Returns
-------
cls : _HtmlFrameParser subclass
The parser class based on the requested input flavor.
Raises
------
ValueError
... | [
"Choose",
"the",
"parser",
"based",
"on",
"the",
"input",
"flavor",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L806-L846 | train |
pandas-dev/pandas | pandas/io/html.py | read_html | 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_default_na=True, displayed_only=True):
r"... | python | 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_default_na=True, displayed_only=True):
r"... | [
"def",
"read_html",
"(",
"io",
",",
"match",
"=",
"'.+'",
",",
"flavor",
"=",
"None",
",",
"header",
"=",
"None",
",",
"index_col",
"=",
"None",
",",
"skiprows",
"=",
"None",
",",
"attrs",
"=",
"None",
",",
"parse_dates",
"=",
"False",
",",
"tupleize... | r"""Read HTML tables into a ``list`` of ``DataFrame`` objects.
Parameters
----------
io : str or file-like
A URL, a file-like object, or a raw string containing HTML. Note that
lxml only accepts the http, ftp and file url protocols. If you have a
URL that starts with ``'https'`` you... | [
"r",
"Read",
"HTML",
"tables",
"into",
"a",
"list",
"of",
"DataFrame",
"objects",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L921-L1088 | train |
pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser.parse_tables | def parse_tables(self):
"""
Parse and return all tables from the DOM.
Returns
-------
list of parsed (header, body, footer) tuples from tables.
"""
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
return (self._parse_thead_tbody_tfoo... | python | def parse_tables(self):
"""
Parse and return all tables from the DOM.
Returns
-------
list of parsed (header, body, footer) tuples from tables.
"""
tables = self._parse_tables(self._build_doc(), self.match, self.attrs)
return (self._parse_thead_tbody_tfoo... | [
"def",
"parse_tables",
"(",
"self",
")",
":",
"tables",
"=",
"self",
".",
"_parse_tables",
"(",
"self",
".",
"_build_doc",
"(",
")",
",",
"self",
".",
"match",
",",
"self",
".",
"attrs",
")",
"return",
"(",
"self",
".",
"_parse_thead_tbody_tfoot",
"(",
... | Parse and return all tables from the DOM.
Returns
-------
list of parsed (header, body, footer) tuples from tables. | [
"Parse",
"and",
"return",
"all",
"tables",
"from",
"the",
"DOM",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L208-L217 | train |
pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser._parse_thead_tbody_tfoot | def _parse_thead_tbody_tfoot(self, table_html):
"""
Given a table, return parsed header, body, and foot.
Parameters
----------
table_html : node-like
Returns
-------
tuple of (header, body, footer), each a list of list-of-text rows.
Notes
... | python | def _parse_thead_tbody_tfoot(self, table_html):
"""
Given a table, return parsed header, body, and foot.
Parameters
----------
table_html : node-like
Returns
-------
tuple of (header, body, footer), each a list of list-of-text rows.
Notes
... | [
"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",
".",
... | Given a table, return parsed header, body, and foot.
Parameters
----------
table_html : node-like
Returns
-------
tuple of (header, body, footer), each a list of list-of-text rows.
Notes
-----
Header and body are lists-of-lists. Top level list i... | [
"Given",
"a",
"table",
"return",
"parsed",
"header",
"body",
"and",
"foot",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L375-L420 | train |
pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser._expand_colspan_rowspan | def _expand_colspan_rowspan(self, rows):
"""
Given a list of <tr>s, return a list of text rows.
Parameters
----------
rows : list of node-like
List of <tr>s
Returns
-------
list of list
Each returned row is a list of str text.
... | python | def _expand_colspan_rowspan(self, rows):
"""
Given a list of <tr>s, return a list of text rows.
Parameters
----------
rows : list of node-like
List of <tr>s
Returns
-------
list of list
Each returned row is a list of str text.
... | [
"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 o... | Given a list of <tr>s, return a list of text rows.
Parameters
----------
rows : list of node-like
List of <tr>s
Returns
-------
list of list
Each returned row is a list of str text.
Notes
-----
Any cell with ``rowspan`` o... | [
"Given",
"a",
"list",
"of",
"<tr",
">",
"s",
"return",
"a",
"list",
"of",
"text",
"rows",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L422-L496 | train |
pandas-dev/pandas | pandas/io/html.py | _HtmlFrameParser._handle_hidden_tables | def _handle_hidden_tables(self, tbl_list, attr_name):
"""
Return list of tables, potentially removing hidden elements
Parameters
----------
tbl_list : list of node-like
Type of list elements will vary depending upon parser used
attr_name : str
Nam... | python | def _handle_hidden_tables(self, tbl_list, attr_name):
"""
Return list of tables, potentially removing hidden elements
Parameters
----------
tbl_list : list of node-like
Type of list elements will vary depending upon parser used
attr_name : str
Nam... | [
"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... | Return list of tables, potentially removing hidden elements
Parameters
----------
tbl_list : list of node-like
Type of list elements will vary depending upon parser used
attr_name : str
Name of the accessor for retrieving HTML attributes
Returns
... | [
"Return",
"list",
"of",
"tables",
"potentially",
"removing",
"hidden",
"elements"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L498-L518 | train |
pandas-dev/pandas | pandas/io/html.py | _LxmlFrameParser._build_doc | def _build_doc(self):
"""
Raises
------
ValueError
* If a URL that lxml cannot parse is passed.
Exception
* Any other ``Exception`` thrown. For example, trying to parse a
URL that is syntactically correct on a machine with no internet
... | python | def _build_doc(self):
"""
Raises
------
ValueError
* If a URL that lxml cannot parse is passed.
Exception
* Any other ``Exception`` thrown. For example, trying to parse a
URL that is syntactically correct on a machine with no internet
... | [
"def",
"_build_doc",
"(",
"self",
")",
":",
"from",
"lxml",
".",
"html",
"import",
"parse",
",",
"fromstring",
",",
"HTMLParser",
"from",
"lxml",
".",
"etree",
"import",
"XMLSyntaxError",
"parser",
"=",
"HTMLParser",
"(",
"recover",
"=",
"True",
",",
"enco... | Raises
------
ValueError
* If a URL that lxml cannot parse is passed.
Exception
* Any other ``Exception`` thrown. For example, trying to parse a
URL that is syntactically correct on a machine with no internet
connection will fail.
See... | [
"Raises",
"------",
"ValueError",
"*",
"If",
"a",
"URL",
"that",
"lxml",
"cannot",
"parse",
"is",
"passed",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/html.py#L690-L735 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | get_dtype_kinds | def get_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays
"""
typs = set()
for arr in l:
dtype = arr.dtype
if is_categorical_dtype(dtype):
typ = 'category'
elif is_s... | python | def get_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays
"""
typs = set()
for arr in l:
dtype = arr.dtype
if is_categorical_dtype(dtype):
typ = 'category'
elif is_s... | [
"def",
"get_dtype_kinds",
"(",
"l",
")",
":",
"typs",
"=",
"set",
"(",
")",
"for",
"arr",
"in",
"l",
":",
"dtype",
"=",
"arr",
".",
"dtype",
"if",
"is_categorical_dtype",
"(",
"dtype",
")",
":",
"typ",
"=",
"'category'",
"elif",
"is_sparse",
"(",
"ar... | Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that exist in this list of arrays | [
"Parameters",
"----------",
"l",
":",
"list",
"of",
"arrays"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L18-L56 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _get_series_result_type | 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
... | python | 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
... | [
"def",
"_get_series_result_type",
"(",
"result",
",",
"objs",
"=",
"None",
")",
":",
"from",
"pandas",
"import",
"SparseSeries",
",",
"SparseDataFrame",
",",
"DataFrame",
"# concat Series with axis 1",
"if",
"isinstance",
"(",
"result",
",",
"dict",
")",
":",
"#... | return appropriate class of Series concat
input is either dict or array-like | [
"return",
"appropriate",
"class",
"of",
"Series",
"concat",
"input",
"is",
"either",
"dict",
"or",
"array",
"-",
"like"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L59-L79 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _get_frame_result_type | 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... | python | 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... | [
"def",
"_get_frame_result_type",
"(",
"result",
",",
"objs",
")",
":",
"if",
"(",
"result",
".",
"blocks",
"and",
"(",
"any",
"(",
"isinstance",
"(",
"obj",
",",
"ABCSparseDataFrame",
")",
"for",
"obj",
"in",
"objs",
")",
")",
")",
":",
"from",
"pandas... | return appropriate class of DataFrame-like concat
if all blocks are sparse, return SparseDataFrame
otherwise, return 1st obj | [
"return",
"appropriate",
"class",
"of",
"DataFrame",
"-",
"like",
"concat",
"if",
"all",
"blocks",
"are",
"sparse",
"return",
"SparseDataFrame",
"otherwise",
"return",
"1st",
"obj"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L82-L95 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_compat | 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)
... | python | 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)
... | [
"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",
"exce... | 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)
Parameters
----------
to_concat : arra... | [
"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... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L98-L165 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_categorical | 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... | python | 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... | [
"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",
... | 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 only have 1D categoricals
Returns
-------
... | [
"Concatenate",
"an",
"object",
"/",
"categorical",
"array",
"of",
"arrays",
"each",
"of",
"which",
"is",
"a",
"single",
"dtype"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L168-L206 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | union_categoricals | 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,
... | python | 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,
... | [
"def",
"union_categoricals",
"(",
"to_union",
",",
"sort_categories",
"=",
"False",
",",
"ignore_order",
"=",
"False",
")",
":",
"from",
"pandas",
"import",
"Index",
",",
"Categorical",
",",
"CategoricalIndex",
",",
"Series",
"from",
"pandas",
".",
"core",
"."... | 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,
or Series with dtype='category'
sort_categories : boolean, default False
... | [
"Combine",
"list",
"-",
"like",
"of",
"Categorical",
"-",
"like",
"unioning",
"categories",
".",
"All",
"categories",
"must",
"have",
"the",
"same",
"dtype",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L209-L381 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_datetime | 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_... | python | 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_... | [
"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",
"(",
"t... | 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 dtypes
Returns
-------
a single array, prese... | [
"provide",
"concatenation",
"of",
"an",
"datetimelike",
"array",
"of",
"arrays",
"each",
"of",
"which",
"is",
"a",
"single",
"M8",
"[",
"ns",
"]",
"datetimet64",
"[",
"ns",
"tz",
"]",
"or",
"m8",
"[",
"ns",
"]",
"dtype"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L391-L435 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_datetimetz | 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... | python | 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... | [
"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",
... | concat DatetimeIndex with the same tz
all inputs must be DatetimeIndex
it is used in DatetimeIndex.append also | [
"concat",
"DatetimeIndex",
"with",
"the",
"same",
"tz",
"all",
"inputs",
"must",
"be",
"DatetimeIndex",
"it",
"is",
"used",
"in",
"DatetimeIndex",
".",
"append",
"also"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L459-L473 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_index_asobject | 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... | python | 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... | [
"def",
"_concat_index_asobject",
"(",
"to_concat",
",",
"name",
"=",
"None",
")",
":",
"from",
"pandas",
"import",
"Index",
"from",
"pandas",
".",
"core",
".",
"arrays",
"import",
"ExtensionArray",
"klasses",
"=",
"(",
"ABCDatetimeIndex",
",",
"ABCTimedeltaIndex... | concat all inputs as object. DatetimeIndex, TimedeltaIndex and
PeriodIndex are converted to object dtype before concatenation | [
"concat",
"all",
"inputs",
"as",
"object",
".",
"DatetimeIndex",
"TimedeltaIndex",
"and",
"PeriodIndex",
"are",
"converted",
"to",
"object",
"dtype",
"before",
"concatenation"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L481-L501 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_sparse | 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
-------
... | python | 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
-------
... | [
"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",
... | 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
-------
a single array, preserving the combined dtypes | [
"provide",
"concatenation",
"of",
"an",
"sparse",
"/",
"dense",
"array",
"of",
"arrays",
"each",
"of",
"which",
"is",
"a",
"single",
"dtype"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L504-L531 | train |
pandas-dev/pandas | pandas/core/dtypes/concat.py | _concat_rangeindex_same_dtype | 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... | python | 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... | [
"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",
... | 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]) | [
"Concatenates",
"multiple",
"RangeIndex",
"instances",
".",
"All",
"members",
"of",
"indexes",
"must",
"be",
"of",
"type",
"RangeIndex",
";",
"result",
"will",
"be",
"RangeIndex",
"if",
"possible",
"Int64Index",
"otherwise",
".",
"E",
".",
"g",
".",
":",
"in... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/concat.py#L534-L578 | train |
pandas-dev/pandas | pandas/util/_exceptions.py | rewrite_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
... | python | 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
... | [
"def",
"rewrite_exception",
"(",
"old_name",
",",
"new_name",
")",
":",
"try",
":",
"yield",
"except",
"Exception",
"as",
"e",
":",
"msg",
"=",
"e",
".",
"args",
"[",
"0",
"]",
"msg",
"=",
"msg",
".",
"replace",
"(",
"old_name",
",",
"new_name",
")",... | Rewrite the message of an exception. | [
"Rewrite",
"the",
"message",
"of",
"an",
"exception",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/util/_exceptions.py#L5-L16 | train |
pandas-dev/pandas | pandas/io/formats/style.py | _get_level_lengths | 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... | python | 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... | [
"def",
"_get_level_lengths",
"(",
"index",
",",
"hidden_elements",
"=",
"None",
")",
":",
"sentinel",
"=",
"object",
"(",
")",
"levels",
"=",
"index",
".",
"format",
"(",
"sparsify",
"=",
"sentinel",
",",
"adjoin",
"=",
"False",
",",
"names",
"=",
"False... | 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 | [
"Given",
"an",
"index",
"find",
"the",
"level",
"length",
"for",
"each",
"element",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1322-L1362 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._translate | 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... | python | 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... | [
"def",
"_translate",
"(",
"self",
")",
":",
"table_styles",
"=",
"self",
".",
"table_styles",
"or",
"[",
"]",
"caption",
"=",
"self",
".",
"caption",
"ctx",
"=",
"self",
".",
"ctx",
"precision",
"=",
"self",
".",
"precision",
"hidden_index",
"=",
"self",... | Convert the DataFrame in `self.data` and the attrs from `_build_styles`
into a dictionary of {head, body, uuid, cellstyle}. | [
"Convert",
"the",
"DataFrame",
"in",
"self",
".",
"data",
"and",
"the",
"attrs",
"from",
"_build_styles",
"into",
"a",
"dictionary",
"of",
"{",
"head",
"body",
"uuid",
"cellstyle",
"}",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L189-L354 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.format | 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... | python | 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... | [
"def",
"format",
"(",
"self",
",",
"formatter",
",",
"subset",
"=",
"None",
")",
":",
"if",
"subset",
"is",
"None",
":",
"row_locs",
"=",
"range",
"(",
"len",
"(",
"self",
".",
"data",
")",
")",
"col_locs",
"=",
"range",
"(",
"len",
"(",
"self",
... | 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
``formatter`` is applied to.
Returns
... | [
"Format",
"the",
"text",
"display",
"value",
"of",
"cells",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L356-L419 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.render | 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 ... | python | 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 ... | [
"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 pr... | 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 variables for a custom template.
..... | [
"Render",
"the",
"built",
"up",
"styles",
"to",
"HTML",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L421-L471 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._update_ctx | 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... | python | 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... | [
"def",
"_update_ctx",
"(",
"self",
",",
"attrs",
")",
":",
"for",
"row_label",
",",
"v",
"in",
"attrs",
".",
"iterrows",
"(",
")",
":",
"for",
"col_label",
",",
"col",
"in",
"v",
".",
"iteritems",
"(",
")",
":",
"i",
"=",
"self",
".",
"index",
".... | 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 ';' shouldn't
matter. | [
"Update",
"the",
"state",
"of",
"the",
"Styler",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L473-L489 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._compute | 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... | python | 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... | [
"def",
"_compute",
"(",
"self",
")",
":",
"r",
"=",
"self",
"for",
"func",
",",
"args",
",",
"kwargs",
"in",
"self",
".",
"_todo",
":",
"r",
"=",
"func",
"(",
"self",
")",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
"return",
"r"
] | 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) | [
"Execute",
"the",
"style",
"functions",
"built",
"up",
"in",
"self",
".",
"_todo",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L520-L532 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.apply | 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
... | python | 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
... | [
"def",
"apply",
"(",
"self",
",",
"func",
",",
"axis",
"=",
"0",
",",
"subset",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"self",
".",
"_todo",
".",
"append",
"(",
"(",
"lambda",
"instance",
":",
"getattr",
"(",
"instance",
",",
"'_apply'",
... | 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
on ``axis``), and return an object with the same shape.
... | [
"Apply",
"a",
"function",
"column",
"-",
"wise",
"row",
"-",
"wise",
"or",
"table",
"-",
"wise",
"updating",
"the",
"HTML",
"representation",
"with",
"the",
"result",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L567-L614 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.applymap | 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
... | python | 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
... | [
"def",
"applymap",
"(",
"self",
",",
"func",
",",
"subset",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"self",
".",
"_todo",
".",
"append",
"(",
"(",
"lambda",
"instance",
":",
"getattr",
"(",
"instance",
",",
"'_applymap'",
")",
",",
"(",
"fu... | 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
a valid indexer to limit ``data`` to *before* applying the
... | [
"Apply",
"a",
"function",
"elementwise",
"updating",
"the",
"HTML",
"representation",
"with",
"the",
"result",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L625-L650 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.where | 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
----------
... | python | 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
----------
... | [
"def",
"where",
"(",
"self",
",",
"cond",
",",
"value",
",",
"other",
"=",
"None",
",",
"subset",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"other",
"is",
"None",
":",
"other",
"=",
"''",
"return",
"self",
".",
"applymap",
"(",
"lambd... | 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
----------
cond : callable
``cond`` should take a scalar and return a boolean... | [
"Apply",
"a",
"function",
"elementwise",
"updating",
"the",
"HTML",
"representation",
"with",
"a",
"style",
"which",
"is",
"selected",
"in",
"accordance",
"with",
"the",
"return",
"value",
"of",
"a",
"function",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L652-L687 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.hide_columns | 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
-------
... | python | 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
-------
... | [
"def",
"hide_columns",
"(",
"self",
",",
"subset",
")",
":",
"subset",
"=",
"_non_reducing_slice",
"(",
"subset",
")",
"hidden_df",
"=",
"self",
".",
"data",
".",
"loc",
"[",
"subset",
"]",
"self",
".",
"hidden_columns",
"=",
"self",
".",
"columns",
".",... | Hide columns from rendering.
.. versionadded:: 0.23.0
Parameters
----------
subset : IndexSlice
An argument to ``DataFrame.loc`` that identifies which columns
are hidden.
Returns
-------
self : Styler | [
"Hide",
"columns",
"from",
"rendering",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L838-L857 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_null | 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)
... | python | 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)
... | [
"def",
"highlight_null",
"(",
"self",
",",
"null_color",
"=",
"'red'",
")",
":",
"self",
".",
"applymap",
"(",
"self",
".",
"_highlight_null",
",",
"null_color",
"=",
"null_color",
")",
"return",
"self"
] | Shade the background ``null_color`` for missing values.
Parameters
----------
null_color : str
Returns
-------
self : Styler | [
"Shade",
"the",
"background",
"null_color",
"for",
"missing",
"values",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L868-L881 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.background_gradient | 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
--------... | python | 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
--------... | [
"def",
"background_gradient",
"(",
"self",
",",
"cmap",
"=",
"'PuBu'",
",",
"low",
"=",
"0",
",",
"high",
"=",
"0",
",",
"axis",
"=",
"0",
",",
"subset",
"=",
"None",
",",
"text_color_threshold",
"=",
"0.408",
")",
":",
"subset",
"=",
"_maybe_numeric_s... | Color the background in a gradient according to
the data in each column (optionally row).
Requires matplotlib.
Parameters
----------
cmap : str or colormap
matplotlib colormap
low, high : float
compress the range by these values.
axis : {... | [
"Color",
"the",
"background",
"in",
"a",
"gradient",
"according",
"to",
"the",
"data",
"in",
"each",
"column",
"(",
"optionally",
"row",
")",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L883-L931 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._background_gradient | 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):
... | python | 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):
... | [
"def",
"_background_gradient",
"(",
"s",
",",
"cmap",
"=",
"'PuBu'",
",",
"low",
"=",
"0",
",",
"high",
"=",
"0",
",",
"text_color_threshold",
"=",
"0.408",
")",
":",
"if",
"(",
"not",
"isinstance",
"(",
"text_color_threshold",
",",
"(",
"float",
",",
... | Color background in a range according to the data. | [
"Color",
"background",
"in",
"a",
"range",
"according",
"to",
"the",
"data",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L934-L989 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.set_properties | 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 :... | python | 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 :... | [
"def",
"set_properties",
"(",
"self",
",",
"subset",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"values",
"=",
"';'",
".",
"join",
"(",
"'{p}: {v}'",
".",
"format",
"(",
"p",
"=",
"p",
",",
"v",
"=",
"v",
")",
"for",
"p",
",",
"v",
"in",
... | 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 : dict
property: value pairs to be set for each cell
... | [
"Convenience",
"method",
"for",
"setting",
"one",
"or",
"more",
"non",
"-",
"data",
"dependent",
"properties",
"or",
"each",
"cell",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L991-L1016 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._bar | 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... | python | 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... | [
"def",
"_bar",
"(",
"s",
",",
"align",
",",
"colors",
",",
"width",
"=",
"100",
",",
"vmin",
"=",
"None",
",",
"vmax",
"=",
"None",
")",
":",
"# Get input value range.",
"smin",
"=",
"s",
".",
"min",
"(",
")",
"if",
"vmin",
"is",
"None",
"else",
... | Draw bar chart in dataframe cells. | [
"Draw",
"bar",
"chart",
"in",
"dataframe",
"cells",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1019-L1075 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.bar | 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.... | python | 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.... | [
"def",
"bar",
"(",
"self",
",",
"subset",
"=",
"None",
",",
"axis",
"=",
"0",
",",
"color",
"=",
"'#d65f5f'",
",",
"width",
"=",
"100",
",",
"align",
"=",
"'left'",
",",
"vmin",
"=",
"None",
",",
"vmax",
"=",
"None",
")",
":",
"if",
"align",
"n... | Draw bar chart in the cell backgrounds.
Parameters
----------
subset : IndexSlice, optional
A valid slice for `data` to limit the style application to.
axis : {0 or 'index', 1 or 'columns', None}, default 0
apply to each column (``axis=0`` or ``'index'``), to eac... | [
"Draw",
"bar",
"chart",
"in",
"the",
"cell",
"backgrounds",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1077-L1145 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_max | 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... | python | 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... | [
"def",
"highlight_max",
"(",
"self",
",",
"subset",
"=",
"None",
",",
"color",
"=",
"'yellow'",
",",
"axis",
"=",
"0",
")",
":",
"return",
"self",
".",
"_highlight_handler",
"(",
"subset",
"=",
"subset",
",",
"color",
"=",
"color",
",",
"axis",
"=",
... | 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 'yellow'
axis : {0 or 'index', 1 or 'columns', None}, default 0
app... | [
"Highlight",
"the",
"maximum",
"by",
"shading",
"the",
"background",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1147-L1166 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.highlight_min | 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... | python | 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... | [
"def",
"highlight_min",
"(",
"self",
",",
"subset",
"=",
"None",
",",
"color",
"=",
"'yellow'",
",",
"axis",
"=",
"0",
")",
":",
"return",
"self",
".",
"_highlight_handler",
"(",
"subset",
"=",
"subset",
",",
"color",
"=",
"color",
",",
"axis",
"=",
... | 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 'yellow'
axis : {0 or 'index', 1 or 'columns', None}, default 0
app... | [
"Highlight",
"the",
"minimum",
"by",
"shading",
"the",
"background",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1168-L1187 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler._highlight_extrema | 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()
... | python | 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()
... | [
"def",
"_highlight_extrema",
"(",
"data",
",",
"color",
"=",
"'yellow'",
",",
"max_",
"=",
"True",
")",
":",
"attr",
"=",
"'background-color: {0}'",
".",
"format",
"(",
"color",
")",
"if",
"data",
".",
"ndim",
"==",
"1",
":",
"# Series from .apply",
"if",
... | Highlight the min or max in a Series or DataFrame. | [
"Highlight",
"the",
"min",
"or",
"max",
"in",
"a",
"Series",
"or",
"DataFrame",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1197-L1214 | train |
pandas-dev/pandas | pandas/io/formats/style.py | Styler.from_custom_template | 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
... | python | 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
... | [
"def",
"from_custom_template",
"(",
"cls",
",",
"searchpath",
",",
"name",
")",
":",
"loader",
"=",
"ChoiceLoader",
"(",
"[",
"FileSystemLoader",
"(",
"searchpath",
")",
",",
"cls",
".",
"loader",
",",
"]",
")",
"class",
"MyStyler",
"(",
"cls",
")",
":",... | 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
name : str
Name of your custom template to use for re... | [
"Factory",
"function",
"for",
"creating",
"a",
"subclass",
"of",
"Styler",
"with",
"a",
"custom",
"template",
"and",
"Jinja",
"environment",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/formats/style.py#L1217-L1243 | train |
pandas-dev/pandas | pandas/core/dtypes/dtypes.py | Registry.register | 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) | python | 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) | [
"def",
"register",
"(",
"self",
",",
"dtype",
")",
":",
"if",
"not",
"issubclass",
"(",
"dtype",
",",
"(",
"PandasExtensionDtype",
",",
"ExtensionDtype",
")",
")",
":",
"raise",
"ValueError",
"(",
"\"can only register pandas extension dtypes\"",
")",
"self",
"."... | Parameters
----------
dtype : ExtensionDtype | [
"Parameters",
"----------",
"dtype",
":",
"ExtensionDtype"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/dtypes.py#L60-L69 | train |
pandas-dev/pandas | pandas/core/dtypes/dtypes.py | Registry.find | 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... | python | 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... | [
"def",
"find",
"(",
"self",
",",
"dtype",
")",
":",
"if",
"not",
"isinstance",
"(",
"dtype",
",",
"str",
")",
":",
"dtype_type",
"=",
"dtype",
"if",
"not",
"isinstance",
"(",
"dtype",
",",
"type",
")",
":",
"dtype_type",
"=",
"type",
"(",
"dtype",
... | Parameters
----------
dtype : PandasExtensionDtype or string
Returns
-------
return the first matching dtype, otherwise return None | [
"Parameters",
"----------",
"dtype",
":",
"PandasExtensionDtype",
"or",
"string"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/dtypes/dtypes.py#L71-L96 | train |
pandas-dev/pandas | pandas/compat/numpy/__init__.py | np_datetime64_compat | 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... | python | 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... | [
"def",
"np_datetime64_compat",
"(",
"s",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"s",
"=",
"tz_replacer",
"(",
"s",
")",
"return",
"np",
".",
"datetime64",
"(",
"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' | [
"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",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/compat/numpy/__init__.py#L37-L44 | train |
pandas-dev/pandas | pandas/compat/numpy/__init__.py | np_array_datetime64_compat | 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... | python | 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... | [
"def",
"np_array_datetime64_compat",
"(",
"arr",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"# is_list_like",
"if",
"(",
"hasattr",
"(",
"arr",
",",
"'__iter__'",
")",
"and",
"not",
"isinstance",
"(",
"arr",
",",
"(",
"str",
",",
"bytes",
")"... | 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' | [
"provide",
"compat",
"for",
"construction",
"of",
"an",
"array",
"of",
"strings",
"to",
"a",
"np",
".",
"array",
"(",
"...",
"dtype",
"=",
"np",
".",
"datetime64",
"(",
"..",
"))",
"tz",
"-",
"changes",
"in",
"1",
".",
"11",
"that",
"make",
"2015",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/compat/numpy/__init__.py#L47-L60 | train |
pandas-dev/pandas | pandas/core/indexes/numeric.py | Int64Index._assert_safe_casting | 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 '
... | python | 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 '
... | [
"def",
"_assert_safe_casting",
"(",
"cls",
",",
"data",
",",
"subarr",
")",
":",
"if",
"not",
"issubclass",
"(",
"data",
".",
"dtype",
".",
"type",
",",
"np",
".",
"signedinteger",
")",
":",
"if",
"not",
"np",
".",
"array_equal",
"(",
"data",
",",
"s... | Ensure incoming data can be represented as ints. | [
"Ensure",
"incoming",
"data",
"can",
"be",
"represented",
"as",
"ints",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/indexes/numeric.py#L215-L222 | train |
pandas-dev/pandas | pandas/core/indexes/numeric.py | Float64Index.get_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... | python | 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... | [
"def",
"get_value",
"(",
"self",
",",
"series",
",",
"key",
")",
":",
"if",
"not",
"is_scalar",
"(",
"key",
")",
":",
"raise",
"InvalidIndexError",
"k",
"=",
"com",
".",
"values_from_object",
"(",
"key",
")",
"loc",
"=",
"self",
".",
"get_loc",
"(",
... | we always want to get an index value, never a value | [
"we",
"always",
"want",
"to",
"get",
"an",
"index",
"value",
"never",
"a",
"value"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/indexes/numeric.py#L364-L373 | train |
pandas-dev/pandas | pandas/core/indexes/numeric.py | Float64Index.equals | 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
... | python | 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
... | [
"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",
... | Determines if two Index objects contain the same elements. | [
"Determines",
"if",
"two",
"Index",
"objects",
"contain",
"the",
"same",
"elements",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/core/indexes/numeric.py#L375-L396 | train |
pandas-dev/pandas | pandas/io/pytables.py | _ensure_decoded | def _ensure_decoded(s):
""" if we have bytes, decode them to unicode """
if isinstance(s, np.bytes_):
s = s.decode('UTF-8')
return s | python | 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_decoded",
"(",
"s",
")",
":",
"if",
"isinstance",
"(",
"s",
",",
"np",
".",
"bytes_",
")",
":",
"s",
"=",
"s",
".",
"decode",
"(",
"'UTF-8'",
")",
"return",
"s"
] | if we have bytes, decode them to unicode | [
"if",
"we",
"have",
"bytes",
"decode",
"them",
"to",
"unicode"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L51-L55 | train |
pandas-dev/pandas | pandas/io/pytables.py | _ensure_term | 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... | python | 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... | [
"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",
")",
... | 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) | [
"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",
... | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L81-L102 | train |
pandas-dev/pandas | pandas/io/pytables.py | to_hdf | 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... | python | 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... | [
"def",
"to_hdf",
"(",
"path_or_buf",
",",
"key",
",",
"value",
",",
"mode",
"=",
"None",
",",
"complevel",
"=",
"None",
",",
"complib",
"=",
"None",
",",
"append",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"append",
":",
"f",
"=",
"la... | store this object, close it if we opened it | [
"store",
"this",
"object",
"close",
"it",
"if",
"we",
"opened",
"it"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L248-L263 | train |
pandas-dev/pandas | pandas/io/pytables.py | read_hdf | 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... | python | 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... | [
"def",
"read_hdf",
"(",
"path_or_buf",
",",
"key",
"=",
"None",
",",
"mode",
"=",
"'r'",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"mode",
"not",
"in",
"[",
"'r'",
",",
"'r+'",
",",
"'a'",
"]",
":",
"raise",
"ValueError",
"(",
"'mode {0} is not allowe... | 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 open :class:`pandas.HDFStore` object.
Supports any object imple... | [
"Read",
"from",
"the",
"store",
"close",
"it",
"if",
"we",
"opened",
"it",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L266-L384 | train |
pandas-dev/pandas | pandas/io/pytables.py | _is_metadata_of | 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... | python | 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... | [
"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",
"="... | Check if a given group is a metadata group for a given parent_group. | [
"Check",
"if",
"a",
"given",
"group",
"is",
"a",
"metadata",
"group",
"for",
"a",
"given",
"parent_group",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L387-L398 | train |
pandas-dev/pandas | pandas/io/pytables.py | _get_info | def _get_info(info, name):
""" get/create the info for this name """
try:
idx = info[name]
except KeyError:
idx = info[name] = dict()
return idx | python | def _get_info(info, name):
""" get/create the info for this name """
try:
idx = info[name]
except KeyError:
idx = info[name] = dict()
return idx | [
"def",
"_get_info",
"(",
"info",
",",
"name",
")",
":",
"try",
":",
"idx",
"=",
"info",
"[",
"name",
"]",
"except",
"KeyError",
":",
"idx",
"=",
"info",
"[",
"name",
"]",
"=",
"dict",
"(",
")",
"return",
"idx"
] | get/create the info for this name | [
"get",
"/",
"create",
"the",
"info",
"for",
"this",
"name"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L4345-L4351 | train |
pandas-dev/pandas | pandas/io/pytables.py | _get_tz | 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 | python | 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",
"_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 | [
"for",
"a",
"tz",
"-",
"aware",
"type",
"return",
"an",
"encoded",
"zone"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L4356-L4361 | train |
pandas-dev/pandas | pandas/io/pytables.py | _set_tz | 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... | python | 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... | [
"def",
"_set_tz",
"(",
"values",
",",
"tz",
",",
"preserve_UTC",
"=",
"False",
",",
"coerce",
"=",
"False",
")",
":",
"if",
"tz",
"is",
"not",
"None",
":",
"name",
"=",
"getattr",
"(",
"values",
",",
"'name'",
",",
"None",
")",
"values",
"=",
"valu... | 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 | [
"coerce",
"the",
"values",
"to",
"a",
"DatetimeIndex",
"if",
"tz",
"is",
"set",
"preserve",
"the",
"input",
"shape",
"if",
"possible"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L4364-L4390 | train |
pandas-dev/pandas | pandas/io/pytables.py | _convert_string_array | 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
... | python | 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
... | [
"def",
"_convert_string_array",
"(",
"data",
",",
"encoding",
",",
"errors",
",",
"itemsize",
"=",
"None",
")",
":",
"# encode if needed",
"if",
"encoding",
"is",
"not",
"None",
"and",
"len",
"(",
"data",
")",
":",
"data",
"=",
"Series",
"(",
"data",
"."... | 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
R... | [
"we",
"take",
"a",
"string",
"-",
"like",
"that",
"is",
"object",
"dtype",
"and",
"coerce",
"to",
"a",
"fixed",
"size",
"string",
"type"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L4521-L4549 | train |
pandas-dev/pandas | pandas/io/pytables.py | _unconvert_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... | python | 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... | [
"def",
"_unconvert_string_array",
"(",
"data",
",",
"nan_rep",
"=",
"None",
",",
"encoding",
"=",
"None",
",",
"errors",
"=",
"'strict'",
")",
":",
"shape",
"=",
"data",
".",
"shape",
"data",
"=",
"np",
".",
"asarray",
"(",
"data",
".",
"ravel",
"(",
... | 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 dec... | [
"inverse",
"of",
"_convert_string_array"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L4552-L4589 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.open | 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 !... | python | 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 !... | [
"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",
... | 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 | [
"Open",
"the",
"file",
"in",
"the",
"specified",
"mode"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L553-L624 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.flush | 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... | python | 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... | [
"def",
"flush",
"(",
"self",
",",
"fsync",
"=",
"False",
")",
":",
"if",
"self",
".",
"_handle",
"is",
"not",
"None",
":",
"self",
".",
"_handle",
".",
"flush",
"(",
")",
"if",
"fsync",
":",
"try",
":",
"os",
".",
"fsync",
"(",
"self",
".",
"_h... | 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
... | [
"Force",
"all",
"buffered",
"modifications",
"to",
"be",
"written",
"to",
"disk",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L643-L665 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get | 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(... | python | 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(... | [
"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",
")",
")",... | Retrieve pandas object stored in file
Parameters
----------
key : object
Returns
-------
obj : same type as object stored in file | [
"Retrieve",
"pandas",
"object",
"stored",
"in",
"file"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L667-L682 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select | 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... | python | 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... | [
"def",
"select",
"(",
"self",
",",
"key",
",",
"where",
"=",
"None",
",",
"start",
"=",
"None",
",",
"stop",
"=",
"None",
",",
"columns",
"=",
"None",
",",
"iterator",
"=",
"False",
",",
"chunksize",
"=",
"None",
",",
"auto_close",
"=",
"False",
",... | 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 Non... | [
"Retrieve",
"pandas",
"object",
"stored",
"in",
"file",
"optionally",
"based",
"on",
"where",
"criteria"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L684-L727 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_as_coordinates | 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... | python | 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... | [
"def",
"select_as_coordinates",
"(",
"self",
",",
"key",
",",
"where",
"=",
"None",
",",
"start",
"=",
"None",
",",
"stop",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"where",
"=",
"_ensure_term",
"(",
"where",
",",
"scope_level",
"=",
"1",
")",... | 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 | [
"return",
"the",
"selection",
"as",
"an",
"Index"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L729-L743 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_column | 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... | python | 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... | [
"def",
"select_column",
"(",
"self",
",",
"key",
",",
"column",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"get_storer",
"(",
"key",
")",
".",
"read_column",
"(",
"column",
"=",
"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 KeyError if the column is not found (or key is not a valid
... | [
"return",
"a",
"single",
"column",
"from",
"the",
"table",
".",
"This",
"is",
"generally",
"only",
"useful",
"to",
"select",
"an",
"indexable"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L745-L763 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.select_as_multiple | 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... | python | 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... | [
"def",
"select_as_multiple",
"(",
"self",
",",
"keys",
",",
"where",
"=",
"None",
",",
"selector",
"=",
"None",
",",
"columns",
"=",
"None",
",",
"start",
"=",
"None",
",",
"stop",
"=",
"None",
",",
"iterator",
"=",
"False",
",",
"chunksize",
"=",
"N... | 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 num... | [
"Retrieve",
"pandas",
"objects",
"from",
"multiple",
"tables"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L765-L846 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.put | 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
... | python | 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
... | [
"def",
"put",
"(",
"self",
",",
"key",
",",
"value",
",",
"format",
"=",
"None",
",",
"append",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"format",
"is",
"None",
":",
"format",
"=",
"get_option",
"(",
"\"io.hdf.default_format\"",
")",
"o... | 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)... | [
"Store",
"object",
"in",
"HDFStore"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L848-L876 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.remove | 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
... | python | 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
... | [
"def",
"remove",
"(",
"self",
",",
"key",
",",
"where",
"=",
"None",
",",
"start",
"=",
"None",
",",
"stop",
"=",
"None",
")",
":",
"where",
"=",
"_ensure_term",
"(",
"where",
",",
"scope_level",
"=",
"1",
")",
"try",
":",
"s",
"=",
"self",
".",
... | 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
st... | [
"Remove",
"pandas",
"object",
"partially",
"by",
"specifying",
"the",
"where",
"condition"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L878-L926 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.append | 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 : '... | python | 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 : '... | [
"def",
"append",
"(",
"self",
",",
"key",
",",
"value",
",",
"format",
"=",
"None",
",",
"append",
"=",
"True",
",",
"columns",
"=",
"None",
",",
"dropna",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"columns",
"is",
"not",
"None",
":",... | 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 p... | [
"Append",
"to",
"Table",
"in",
"file",
".",
"Node",
"must",
"already",
"exist",
"and",
"be",
"Table",
"format",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L928-L973 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.append_to_multiple | 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... | python | 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... | [
"def",
"append_to_multiple",
"(",
"self",
",",
"d",
",",
"value",
",",
"selector",
",",
"data_columns",
"=",
"None",
",",
"axes",
"=",
"None",
",",
"dropna",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"axes",
"is",
"not",
"None",
":",
"... | Append to multiple tables
Parameters
----------
d : a dict of table_name to table_columns, None is acceptable as the
values of one node (this will get all the remaining columns)
value : a pandas object
selector : a string that designates the indexable table; all of i... | [
"Append",
"to",
"multiple",
"tables"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L975-L1055 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.create_table_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()
... | python | 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()
... | [
"def",
"create_table_index",
"(",
"self",
",",
"key",
",",
"*",
"*",
"kwargs",
")",
":",
"# version requirements",
"_tables",
"(",
")",
"s",
"=",
"self",
".",
"get_storer",
"(",
"key",
")",
"if",
"s",
"is",
"None",
":",
"return",
"if",
"not",
"s",
".... | Create a pytables index on the table
Parameters
----------
key : object (the node to index)
Exceptions
----------
raises if the node is not a table | [
"Create",
"a",
"pytables",
"index",
"on",
"the",
"table",
"Parameters",
"----------",
"key",
":",
"object",
"(",
"the",
"node",
"to",
"index",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1057-L1078 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.groups | 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
... | python | 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
... | [
"def",
"groups",
"(",
"self",
")",
":",
"_tables",
"(",
")",
"self",
".",
"_check_if_open",
"(",
")",
"return",
"[",
"g",
"for",
"g",
"in",
"self",
".",
"_handle",
".",
"walk_groups",
"(",
")",
"if",
"(",
"not",
"isinstance",
"(",
"g",
",",
"_table... | return a list of all the top-level nodes (that are not themselves a
pandas storage object) | [
"return",
"a",
"list",
"of",
"all",
"the",
"top",
"-",
"level",
"nodes",
"(",
"that",
"are",
"not",
"themselves",
"a",
"pandas",
"storage",
"object",
")"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1080-L1093 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.walk | 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... | python | 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... | [
"def",
"walk",
"(",
"self",
",",
"where",
"=",
"\"/\"",
")",
":",
"_tables",
"(",
")",
"self",
".",
"_check_if_open",
"(",
")",
"for",
"g",
"in",
"self",
".",
"_handle",
".",
"walk_groups",
"(",
"where",
")",
":",
"if",
"getattr",
"(",
"g",
".",
... | 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
... | [
"Walk",
"the",
"pytables",
"group",
"hierarchy",
"for",
"pandas",
"objects"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1095-L1139 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get_node | 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... | python | 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... | [
"def",
"get_node",
"(",
"self",
",",
"key",
")",
":",
"self",
".",
"_check_if_open",
"(",
")",
"try",
":",
"if",
"not",
"key",
".",
"startswith",
"(",
"'/'",
")",
":",
"key",
"=",
"'/'",
"+",
"key",
"return",
"self",
".",
"_handle",
".",
"get_node"... | return the node with the key or None if it does not exist | [
"return",
"the",
"node",
"with",
"the",
"key",
"or",
"None",
"if",
"it",
"does",
"not",
"exist"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1141-L1149 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.get_storer | 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... | python | 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... | [
"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",
")",... | return the storer object for a key, raise if not in the file | [
"return",
"the",
"storer",
"object",
"for",
"a",
"key",
"raise",
"if",
"not",
"in",
"the",
"file"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1151-L1159 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.copy | 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... | python | 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... | [
"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) exist... | [
"copy",
"the",
"existing",
"store",
"to",
"a",
"new",
"file",
"upgrading",
"in",
"place"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1161-L1210 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore.info | 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()))
... | python | 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()))
... | [
"def",
"info",
"(",
"self",
")",
":",
"output",
"=",
"'{type}\\nFile path: {path}\\n'",
".",
"format",
"(",
"type",
"=",
"type",
"(",
"self",
")",
",",
"path",
"=",
"pprint_thing",
"(",
"self",
".",
"_path",
")",
")",
"if",
"self",
".",
"is_open",
":",... | Print detailed information on the store.
.. versionadded:: 0.21.0 | [
"Print",
"detailed",
"information",
"on",
"the",
"store",
"."
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1212-L1245 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore._validate_format | 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... | python | 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... | [
"def",
"_validate_format",
"(",
"self",
",",
"format",
",",
"kwargs",
")",
":",
"kwargs",
"=",
"kwargs",
".",
"copy",
"(",
")",
"# validate",
"try",
":",
"kwargs",
"[",
"'format'",
"]",
"=",
"_FORMAT_MAP",
"[",
"format",
".",
"lower",
"(",
")",
"]",
... | validate / deprecate formats; return the new kwargs | [
"validate",
"/",
"deprecate",
"formats",
";",
"return",
"the",
"new",
"kwargs"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1252-L1263 | train |
pandas-dev/pandas | pandas/io/pytables.py | HDFStore._create_storer | 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},... | python | 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},... | [
"def",
"_create_storer",
"(",
"self",
",",
"group",
",",
"format",
"=",
"None",
",",
"value",
"=",
"None",
",",
"append",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",
"def",
"error",
"(",
"t",
")",
":",
"raise",
"TypeError",
"(",
"\"cannot proper... | return a suitable class to operate | [
"return",
"a",
"suitable",
"class",
"to",
"operate"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1265-L1350 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.set_name | 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 | python | 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_name",
"(",
"self",
",",
"name",
",",
"kind_attr",
"=",
"None",
")",
":",
"self",
".",
"name",
"=",
"name",
"self",
".",
"kind_attr",
"=",
"kind_attr",
"or",
"\"{name}_kind\"",
".",
"format",
"(",
"name",
"=",
"name",
")",
"if",
"self",
"... | set the name of this indexer | [
"set",
"the",
"name",
"of",
"this",
"indexer"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1552-L1559 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.set_pos | 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 | python | 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_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 | [
"set",
"the",
"position",
"of",
"this",
"column",
"in",
"the",
"Table"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1567-L1572 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.is_indexed | def is_indexed(self):
""" return whether I am an indexed column """
try:
return getattr(self.table.cols, self.cname).is_indexed
except AttributeError:
False | python | 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",
"is_indexed",
"(",
"self",
")",
":",
"try",
":",
"return",
"getattr",
"(",
"self",
".",
"table",
".",
"cols",
",",
"self",
".",
"cname",
")",
".",
"is_indexed",
"except",
"AttributeError",
":",
"False"
] | return whether I am an indexed column | [
"return",
"whether",
"I",
"am",
"an",
"indexed",
"column"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1599-L1604 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.infer | 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 | python | 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",
"infer",
"(",
"self",
",",
"handler",
")",
":",
"table",
"=",
"handler",
".",
"table",
"new_self",
"=",
"self",
".",
"copy",
"(",
")",
"new_self",
".",
"set_table",
"(",
"table",
")",
"new_self",
".",
"get_attr",
"(",
")",
"new_self",
".",
"re... | infer this column from the table: create and return a new object | [
"infer",
"this",
"column",
"from",
"the",
"table",
":",
"create",
"and",
"return",
"a",
"new",
"object"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1610-L1617 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.convert | 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)
... | python | 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)
... | [
"def",
"convert",
"(",
"self",
",",
"values",
",",
"nan_rep",
",",
"encoding",
",",
"errors",
")",
":",
"# values is a recarray",
"if",
"values",
".",
"dtype",
".",
"fields",
"is",
"not",
"None",
":",
"values",
"=",
"values",
"[",
"self",
".",
"cname",
... | set the values from this selection: take = take ownership | [
"set",
"the",
"values",
"from",
"this",
"selection",
":",
"take",
"=",
"take",
"ownership"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1619-L1646 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.maybe_set_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):
... | python | 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):
... | [
"def",
"maybe_set_size",
"(",
"self",
",",
"min_itemsize",
"=",
"None",
")",
":",
"if",
"_ensure_decoded",
"(",
"self",
".",
"kind",
")",
"==",
"'string'",
":",
"if",
"isinstance",
"(",
"min_itemsize",
",",
"dict",
")",
":",
"min_itemsize",
"=",
"min_items... | maybe set a string col itemsize:
min_itemsize can be an integer or a dict with this columns name
with an integer size | [
"maybe",
"set",
"a",
"string",
"col",
"itemsize",
":",
"min_itemsize",
"can",
"be",
"an",
"integer",
"or",
"a",
"dict",
"with",
"this",
"columns",
"name",
"with",
"an",
"integer",
"size"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1674-L1685 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.validate_col | 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 ... | python | 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 ... | [
"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"... | validate this column: return the compared against itemsize | [
"validate",
"this",
"column",
":",
"return",
"the",
"compared",
"against",
"itemsize"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1701-L1720 | train |
pandas-dev/pandas | pandas/io/pytables.py | IndexCol.update_info | 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... | python | 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... | [
"def",
"update_info",
"(",
"self",
",",
"info",
")",
":",
"for",
"key",
"in",
"self",
".",
"_info_fields",
":",
"value",
"=",
"getattr",
"(",
"self",
",",
"key",
",",
"None",
")",
"idx",
"=",
"_get_info",
"(",
"info",
",",
"self",
".",
"name",
")",... | set/update the info for this indexable with the key/value
if there is a conflict raise/warn as needed | [
"set",
"/",
"update",
"the",
"info",
"for",
"this",
"indexable",
"with",
"the",
"key",
"/",
"value",
"if",
"there",
"is",
"a",
"conflict",
"raise",
"/",
"warn",
"as",
"needed"
] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/io/pytables.py#L1732-L1764 | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.