body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
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def drop_duplicates(self, subset: Optional[Union[(Name, List[Name])]]=None, keep: Union[(bool, str)]='first', inplace: bool=False) -> Optional['DataFrame']:
"\n Return DataFrame with duplicate rows removed, optionally only\n considering certain columns.\n\n Parameters\n ----------\n ... | -3,917,699,198,602,438,700 | Return DataFrame with duplicate rows removed, optionally only
considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns.
keep : {'first', 'last', False}, default 'first'
... | python/pyspark/pandas/frame.py | drop_duplicates | Flyangz/spark | python | def drop_duplicates(self, subset: Optional[Union[(Name, List[Name])]]=None, keep: Union[(bool, str)]='first', inplace: bool=False) -> Optional['DataFrame']:
"\n Return DataFrame with duplicate rows removed, optionally only\n considering certain columns.\n\n Parameters\n ----------\n ... |
def reindex(self, labels: Optional[Sequence[Any]]=None, index: Optional[Union[('Index', Sequence[Any])]]=None, columns: Optional[Union[(pd.Index, Sequence[Any])]]=None, axis: Optional[Axis]=None, copy: Optional[bool]=True, fill_value: Optional[Any]=None) -> 'DataFrame':
'\n Conform DataFrame to new index wit... | 285,927,188,171,719,780 | Conform DataFrame to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
``copy=False``.
Parameters
----------
labels: array-like, optional
New labels / index to conform the axis... | python/pyspark/pandas/frame.py | reindex | Flyangz/spark | python | def reindex(self, labels: Optional[Sequence[Any]]=None, index: Optional[Union[('Index', Sequence[Any])]]=None, columns: Optional[Union[(pd.Index, Sequence[Any])]]=None, axis: Optional[Axis]=None, copy: Optional[bool]=True, fill_value: Optional[Any]=None) -> 'DataFrame':
'\n Conform DataFrame to new index wit... |
def reindex_like(self, other: 'DataFrame', copy: bool=True) -> 'DataFrame':
"\n Return a DataFrame with matching indices as other object.\n\n Conform the object to the same index on all axes. Places NA/NaN in locations\n having no value in the previous index. A new object is produced unless the... | 7,742,307,885,276,616,000 | Return a DataFrame with matching indices as other object.
Conform the object to the same index on all axes. Places NA/NaN in locations
having no value in the previous index. A new object is produced unless the
new index is equivalent to the current one and copy=False.
Parameters
----------
other : DataFrame
Its r... | python/pyspark/pandas/frame.py | reindex_like | Flyangz/spark | python | def reindex_like(self, other: 'DataFrame', copy: bool=True) -> 'DataFrame':
"\n Return a DataFrame with matching indices as other object.\n\n Conform the object to the same index on all axes. Places NA/NaN in locations\n having no value in the previous index. A new object is produced unless the... |
def melt(self, id_vars: Optional[Union[(Name, List[Name])]]=None, value_vars: Optional[Union[(Name, List[Name])]]=None, var_name: Optional[Union[(str, List[str])]]=None, value_name: str='value') -> 'DataFrame':
'\n Unpivot a DataFrame from wide format to long format, optionally\n leaving identifier va... | -6,052,788,158,713,160,000 | Unpivot a DataFrame from wide format to long format, optionally
leaving identifier variables set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the ro... | python/pyspark/pandas/frame.py | melt | Flyangz/spark | python | def melt(self, id_vars: Optional[Union[(Name, List[Name])]]=None, value_vars: Optional[Union[(Name, List[Name])]]=None, var_name: Optional[Union[(str, List[str])]]=None, value_name: str='value') -> 'DataFrame':
'\n Unpivot a DataFrame from wide format to long format, optionally\n leaving identifier va... |
def stack(self) -> DataFrameOrSeries:
"\n Stack the prescribed level(s) from columns to index.\n\n Return a reshaped DataFrame or Series having a multi-level\n index with one or more new inner-most levels compared to the current\n DataFrame. The new inner-most levels are created by pivot... | 9,052,999,775,344,987,000 | Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pivoting the
columns of the current dataframe:
- if the columns have a single level... | python/pyspark/pandas/frame.py | stack | Flyangz/spark | python | def stack(self) -> DataFrameOrSeries:
"\n Stack the prescribed level(s) from columns to index.\n\n Return a reshaped DataFrame or Series having a multi-level\n index with one or more new inner-most levels compared to the current\n DataFrame. The new inner-most levels are created by pivot... |
def unstack(self) -> DataFrameOrSeries:
'\n Pivot the (necessarily hierarchical) index labels.\n\n Returns a DataFrame having a new level of column labels whose inner-most level\n consists of the pivoted index labels.\n\n If the index is not a MultiIndex, the output will be a Series.\n\n... | 2,893,301,910,422,294,500 | Pivot the (necessarily hierarchical) index labels.
Returns a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series.
.. note:: If the index is a MultiIndex, the output DataFrame could be very wide, and
... | python/pyspark/pandas/frame.py | unstack | Flyangz/spark | python | def unstack(self) -> DataFrameOrSeries:
'\n Pivot the (necessarily hierarchical) index labels.\n\n Returns a DataFrame having a new level of column labels whose inner-most level\n consists of the pivoted index labels.\n\n If the index is not a MultiIndex, the output will be a Series.\n\n... |
def all(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
"\n Return whether all elements are True.\n\n Returns True unless there is at least one element within a series that is\n False or equivalent (e.g. zero or empty)\n\n Parameters\n ----------\n axis : {... | -349,392,930,906,440,600 | Return whether all elements are True.
Returns True unless there is at least one element within a series that is
False or equivalent (e.g. zero or empty)
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whos... | python/pyspark/pandas/frame.py | all | Flyangz/spark | python | def all(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
"\n Return whether all elements are True.\n\n Returns True unless there is at least one element within a series that is\n False or equivalent (e.g. zero or empty)\n\n Parameters\n ----------\n axis : {... |
def any(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
"\n Return whether any element is True.\n\n Returns False unless there is at least one element within a series that is\n True or equivalent (e.g. non-zero or non-empty).\n\n Parameters\n ----------\n a... | 5,382,438,178,177,989,000 | Return whether any element is True.
Returns False unless there is at least one element within a series that is
True or equivalent (e.g. non-zero or non-empty).
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Seri... | python/pyspark/pandas/frame.py | any | Flyangz/spark | python | def any(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
"\n Return whether any element is True.\n\n Returns False unless there is at least one element within a series that is\n True or equivalent (e.g. non-zero or non-empty).\n\n Parameters\n ----------\n a... |
def _bool_column_labels(self, column_labels: List[Label]) -> List[Label]:
'\n Filter column labels of boolean columns (without None).\n '
bool_column_labels = []
for label in column_labels:
psser = self._psser_for(label)
if is_bool_dtype(psser):
bool_column_labels.a... | -4,105,215,105,612,054,000 | Filter column labels of boolean columns (without None). | python/pyspark/pandas/frame.py | _bool_column_labels | Flyangz/spark | python | def _bool_column_labels(self, column_labels: List[Label]) -> List[Label]:
'\n \n '
bool_column_labels = []
for label in column_labels:
psser = self._psser_for(label)
if is_bool_dtype(psser):
bool_column_labels.append(label)
return bool_column_labels |
def _result_aggregated(self, column_labels: List[Label], scols: List[Column]) -> 'Series':
'\n Given aggregated Spark columns and respective column labels from the original\n pandas-on-Spark DataFrame, construct the result Series.\n '
from pyspark.pandas.series import first_series
cols ... | -2,983,645,101,199,888,000 | Given aggregated Spark columns and respective column labels from the original
pandas-on-Spark DataFrame, construct the result Series. | python/pyspark/pandas/frame.py | _result_aggregated | Flyangz/spark | python | def _result_aggregated(self, column_labels: List[Label], scols: List[Column]) -> 'Series':
'\n Given aggregated Spark columns and respective column labels from the original\n pandas-on-Spark DataFrame, construct the result Series.\n '
from pyspark.pandas.series import first_series
cols ... |
def rank(self, method: str='average', ascending: bool=True, numeric_only: Optional[bool]=None) -> 'DataFrame':
"\n Compute numerical data ranks (1 through n) along axis. Equal values are\n assigned a rank that is the average of the ranks of those values.\n\n .. note:: the current implementation... | 2,881,934,767,336,696,000 | Compute numerical data ranks (1 through n) along axis. Equal values are
assigned a rank that is the average of the ranks of those values.
.. note:: the current implementation of rank uses Spark's Window without
specifying partition specification. This leads to move all data into
single partition in single mach... | python/pyspark/pandas/frame.py | rank | Flyangz/spark | python | def rank(self, method: str='average', ascending: bool=True, numeric_only: Optional[bool]=None) -> 'DataFrame':
"\n Compute numerical data ranks (1 through n) along axis. Equal values are\n assigned a rank that is the average of the ranks of those values.\n\n .. note:: the current implementation... |
def filter(self, items: Optional[Sequence[Any]]=None, like: Optional[str]=None, regex: Optional[str]=None, axis: Optional[Axis]=None) -> 'DataFrame':
'\n Subset rows or columns of dataframe according to labels in\n the specified index.\n\n Note that this routine does not filter a dataframe on i... | 8,439,502,228,821,004,000 | Subset rows or columns of dataframe according to labels in
the specified index.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
Keep labels from axis which are in items.
like : string
Keep labels from... | python/pyspark/pandas/frame.py | filter | Flyangz/spark | python | def filter(self, items: Optional[Sequence[Any]]=None, like: Optional[str]=None, regex: Optional[str]=None, axis: Optional[Axis]=None) -> 'DataFrame':
'\n Subset rows or columns of dataframe according to labels in\n the specified index.\n\n Note that this routine does not filter a dataframe on i... |
def rename(self, mapper: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, index: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, columns: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, axis: Axis='index', inplace: bool=False, level: Optional[int]=None, errors: str='ignore') -> Optional['DataFrame']:
... | 4,436,174,056,561,670,000 | Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series
will be left as-is. Extra labels listed don’t throw an error.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to that axis’ values.
Use either `mapper`... | python/pyspark/pandas/frame.py | rename | Flyangz/spark | python | def rename(self, mapper: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, index: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, columns: Optional[Union[(Dict, Callable[([Any], Any)])]]=None, axis: Axis='index', inplace: bool=False, level: Optional[int]=None, errors: str='ignore') -> Optional['DataFrame']:
... |
def rename_axis(self, mapper: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, index: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, columns: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, axis: Optional[Axis]=0, inplace: Optiona... | -2,829,426,125,369,859,600 | Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
A scalar, list-like, dict-like or functions transformations to
apply to the axis name attribute.
index, columns : scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like o... | python/pyspark/pandas/frame.py | rename_axis | Flyangz/spark | python | def rename_axis(self, mapper: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, index: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, columns: Union[(Any, Sequence[Any], Dict[(Name, Any)], Callable[([Name], Any)])]=None, axis: Optional[Axis]=0, inplace: Optiona... |
def keys(self) -> pd.Index:
"\n Return alias for columns.\n\n Returns\n -------\n Index\n Columns of the DataFrame.\n\n Examples\n --------\n >>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],\n ... index=['cobra', 'viper', 'sidewinde... | 6,675,430,877,286,866,000 | Return alias for columns.
Returns
-------
Index
Columns of the DataFrame.
Examples
--------
>>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra ... | python/pyspark/pandas/frame.py | keys | Flyangz/spark | python | def keys(self) -> pd.Index:
"\n Return alias for columns.\n\n Returns\n -------\n Index\n Columns of the DataFrame.\n\n Examples\n --------\n >>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],\n ... index=['cobra', 'viper', 'sidewinde... |
def pct_change(self, periods: int=1) -> 'DataFrame':
"\n Percentage change between the current and a prior element.\n\n .. note:: the current implementation of this API uses Spark's Window without\n specifying partition specification. This leads to move all data into\n single par... | 919,436,271,991,181,000 | Percentage change between the current and a prior element.
.. note:: the current implementation of this API uses Spark's Window without
specifying partition specification. This leads to move all data into
single partition in single machine and could cause serious
performance degradation. Avoid this method ... | python/pyspark/pandas/frame.py | pct_change | Flyangz/spark | python | def pct_change(self, periods: int=1) -> 'DataFrame':
"\n Percentage change between the current and a prior element.\n\n .. note:: the current implementation of this API uses Spark's Window without\n specifying partition specification. This leads to move all data into\n single par... |
def idxmax(self, axis: Axis=0) -> 'Series':
"\n Return index of first occurrence of maximum over requested axis.\n NA/null values are excluded.\n\n .. note:: This API collect all rows with maximum value using `to_pandas()`\n because we suppose the number of rows with max values are u... | 5,427,617,348,550,696,000 | Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
.. note:: This API collect all rows with maximum value using `to_pandas()`
because we suppose the number of rows with max values are usually small in general.
Parameters
----------
axis : 0 or 'index'
Can only be set... | python/pyspark/pandas/frame.py | idxmax | Flyangz/spark | python | def idxmax(self, axis: Axis=0) -> 'Series':
"\n Return index of first occurrence of maximum over requested axis.\n NA/null values are excluded.\n\n .. note:: This API collect all rows with maximum value using `to_pandas()`\n because we suppose the number of rows with max values are u... |
def idxmin(self, axis: Axis=0) -> 'Series':
"\n Return index of first occurrence of minimum over requested axis.\n NA/null values are excluded.\n\n .. note:: This API collect all rows with minimum value using `to_pandas()`\n because we suppose the number of rows with min values are u... | 3,556,289,599,252,744,000 | Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
.. note:: This API collect all rows with minimum value using `to_pandas()`
because we suppose the number of rows with min values are usually small in general.
Parameters
----------
axis : 0 or 'index'
Can only be set... | python/pyspark/pandas/frame.py | idxmin | Flyangz/spark | python | def idxmin(self, axis: Axis=0) -> 'Series':
"\n Return index of first occurrence of minimum over requested axis.\n NA/null values are excluded.\n\n .. note:: This API collect all rows with minimum value using `to_pandas()`\n because we suppose the number of rows with min values are u... |
def info(self, verbose: Optional[bool]=None, buf: Optional[IO[str]]=None, max_cols: Optional[int]=None, null_counts: Optional[bool]=None) -> None:
'\n Print a concise summary of a DataFrame.\n\n This method prints information about a DataFrame including\n the index dtype and column dtypes, non-... | -6,592,994,989,138,733,000 | Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary.
buf : writable buffer, defaults to sys.stdout
Where to send ... | python/pyspark/pandas/frame.py | info | Flyangz/spark | python | def info(self, verbose: Optional[bool]=None, buf: Optional[IO[str]]=None, max_cols: Optional[int]=None, null_counts: Optional[bool]=None) -> None:
'\n Print a concise summary of a DataFrame.\n\n This method prints information about a DataFrame including\n the index dtype and column dtypes, non-... |
def quantile(self, q: Union[(float, Iterable[float])]=0.5, axis: Axis=0, numeric_only: bool=True, accuracy: int=10000) -> DataFrameOrSeries:
"\n Return value at the given quantile.\n\n .. note:: Unlike pandas', the quantile in pandas-on-Spark is an approximated quantile\n based upon approxi... | -3,218,161,924,381,842,000 | Return value at the given quantile.
.. note:: Unlike pandas', the quantile in pandas-on-Spark is an approximated quantile
based upon approximate percentile computation because computing quantile across a
large dataset is extremely expensive.
Parameters
----------
q : float or array-like, default 0.5 (50% quan... | python/pyspark/pandas/frame.py | quantile | Flyangz/spark | python | def quantile(self, q: Union[(float, Iterable[float])]=0.5, axis: Axis=0, numeric_only: bool=True, accuracy: int=10000) -> DataFrameOrSeries:
"\n Return value at the given quantile.\n\n .. note:: Unlike pandas', the quantile in pandas-on-Spark is an approximated quantile\n based upon approxi... |
def query(self, expr: str, inplace: bool=False) -> Optional['DataFrame']:
"\n Query the columns of a DataFrame with a boolean expression.\n\n .. note:: Internal columns that starting with a '__' prefix are able to access, however,\n they are not supposed to be accessed.\n\n .. note::... | 4,015,551,663,124,263,400 | Query the columns of a DataFrame with a boolean expression.
.. note:: Internal columns that starting with a '__' prefix are able to access, however,
they are not supposed to be accessed.
.. note:: This API delegates to Spark SQL so the syntax follows Spark SQL. Therefore, the
pandas specific syntax such as `@... | python/pyspark/pandas/frame.py | query | Flyangz/spark | python | def query(self, expr: str, inplace: bool=False) -> Optional['DataFrame']:
"\n Query the columns of a DataFrame with a boolean expression.\n\n .. note:: Internal columns that starting with a '__' prefix are able to access, however,\n they are not supposed to be accessed.\n\n .. note::... |
def take(self, indices: List[int], axis: Axis=0, **kwargs: Any) -> 'DataFrame':
"\n Return the elements in the given *positional* indices along an axis.\n\n This means that we are not indexing according to actual values in\n the index attribute of the object. We are indexing according to the\n ... | 8,431,998,034,869,836,000 | Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indic... | python/pyspark/pandas/frame.py | take | Flyangz/spark | python | def take(self, indices: List[int], axis: Axis=0, **kwargs: Any) -> 'DataFrame':
"\n Return the elements in the given *positional* indices along an axis.\n\n This means that we are not indexing according to actual values in\n the index attribute of the object. We are indexing according to the\n ... |
def eval(self, expr: str, inplace: bool=False) -> Optional[DataFrameOrSeries]:
"\n Evaluate a string describing operations on DataFrame columns.\n\n Operates on columns only, not specific rows or elements. This allows\n `eval` to run arbitrary code, which can make you vulnerable to code\n ... | -2,884,725,735,896,062,000 | Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evalua... | python/pyspark/pandas/frame.py | eval | Flyangz/spark | python | def eval(self, expr: str, inplace: bool=False) -> Optional[DataFrameOrSeries]:
"\n Evaluate a string describing operations on DataFrame columns.\n\n Operates on columns only, not specific rows or elements. This allows\n `eval` to run arbitrary code, which can make you vulnerable to code\n ... |
def explode(self, column: Name) -> 'DataFrame':
"\n Transform each element of a list-like to a row, replicating index values.\n\n Parameters\n ----------\n column : str or tuple\n Column to explode.\n\n Returns\n -------\n DataFrame\n Exploded l... | 7,501,693,200,103,724,000 | Transform each element of a list-like to a row, replicating index values.
Parameters
----------
column : str or tuple
Column to explode.
Returns
-------
DataFrame
Exploded lists to rows of the subset columns;
index will be duplicated for these rows.
See Also
--------
DataFrame.unstack : Pivot a level of ... | python/pyspark/pandas/frame.py | explode | Flyangz/spark | python | def explode(self, column: Name) -> 'DataFrame':
"\n Transform each element of a list-like to a row, replicating index values.\n\n Parameters\n ----------\n column : str or tuple\n Column to explode.\n\n Returns\n -------\n DataFrame\n Exploded l... |
def mad(self, axis: Axis=0) -> 'Series':
"\n Return the mean absolute deviation of values.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n\n Examples\n --------\n >>> df = ps.DataFrame({'a': [1, 2, ... | 5,261,953,540,311,855,000 | Return the mean absolute deviation of values.
Parameters
----------
axis : {index (0), columns (1)}
Axis for the function to be applied on.
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
... columns=['a', 'b'])
>>> df.mad()
a 0.666667
b 0.0... | python/pyspark/pandas/frame.py | mad | Flyangz/spark | python | def mad(self, axis: Axis=0) -> 'Series':
"\n Return the mean absolute deviation of values.\n\n Parameters\n ----------\n axis : {index (0), columns (1)}\n Axis for the function to be applied on.\n\n Examples\n --------\n >>> df = ps.DataFrame({'a': [1, 2, ... |
def tail(self, n: int=5) -> 'DataFrame':
"\n Return the last `n` rows.\n\n This function returns last `n` rows from the object based on\n position. It is useful for quickly verifying data, for example,\n after sorting or appending rows.\n\n For negative values of `n`, this functio... | -381,023,855,042,304,900 | Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
Parameters
----------... | python/pyspark/pandas/frame.py | tail | Flyangz/spark | python | def tail(self, n: int=5) -> 'DataFrame':
"\n Return the last `n` rows.\n\n This function returns last `n` rows from the object based on\n position. It is useful for quickly verifying data, for example,\n after sorting or appending rows.\n\n For negative values of `n`, this functio... |
def align(self, other: DataFrameOrSeries, join: str='outer', axis: Optional[Axis]=None, copy: bool=True) -> Tuple[('DataFrame', DataFrameOrSeries)]:
'\n Align two objects on their axes with the specified join method.\n\n Join method is specified for each axis Index.\n\n Parameters\n ----... | 436,715,312,717,442,240 | Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameters
----------
other : DataFrame or Series
join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or bo... | python/pyspark/pandas/frame.py | align | Flyangz/spark | python | def align(self, other: DataFrameOrSeries, join: str='outer', axis: Optional[Axis]=None, copy: bool=True) -> Tuple[('DataFrame', DataFrameOrSeries)]:
'\n Align two objects on their axes with the specified join method.\n\n Join method is specified for each axis Index.\n\n Parameters\n ----... |
@staticmethod
def from_dict(data: Dict[(Name, Sequence[Any])], orient: str='columns', dtype: Union[(str, Dtype)]=None, columns: Optional[List[Name]]=None) -> 'DataFrame':
'\n Construct DataFrame from dict of array-like or dicts.\n\n Creates DataFrame object from dictionary by columns or by index\n ... | -6,497,009,801,001,677,000 | Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the da... | python/pyspark/pandas/frame.py | from_dict | Flyangz/spark | python | @staticmethod
def from_dict(data: Dict[(Name, Sequence[Any])], orient: str='columns', dtype: Union[(str, Dtype)]=None, columns: Optional[List[Name]]=None) -> 'DataFrame':
'\n Construct DataFrame from dict of array-like or dicts.\n\n Creates DataFrame object from dictionary by columns or by index\n ... |
def _to_internal_pandas(self) -> pd.DataFrame:
'\n Return a pandas DataFrame directly from _internal to avoid overhead of copy.\n\n This method is for internal use only.\n '
return self._internal.to_pandas_frame | -1,994,076,103,929,380,600 | Return a pandas DataFrame directly from _internal to avoid overhead of copy.
This method is for internal use only. | python/pyspark/pandas/frame.py | _to_internal_pandas | Flyangz/spark | python | def _to_internal_pandas(self) -> pd.DataFrame:
'\n Return a pandas DataFrame directly from _internal to avoid overhead of copy.\n\n This method is for internal use only.\n '
return self._internal.to_pandas_frame |
@staticmethod
def _index_normalized_label(level: int, labels: Union[(Name, Sequence[Name])]) -> List[Label]:
'\n Returns a label that is normalized against the current column index level.\n For example, the key "abc" can be ("abc", "", "") if the current Frame has\n a multi-index for its column... | 3,790,296,275,256,254,000 | Returns a label that is normalized against the current column index level.
For example, the key "abc" can be ("abc", "", "") if the current Frame has
a multi-index for its column | python/pyspark/pandas/frame.py | _index_normalized_label | Flyangz/spark | python | @staticmethod
def _index_normalized_label(level: int, labels: Union[(Name, Sequence[Name])]) -> List[Label]:
'\n Returns a label that is normalized against the current column index level.\n For example, the key "abc" can be ("abc", , ) if the current Frame has\n a multi-index for its column\n ... |
@staticmethod
def _index_normalized_frame(level: int, psser_or_psdf: DataFrameOrSeries) -> 'DataFrame':
'\n Returns a frame that is normalized against the current column index level.\n For example, the name in `pd.Series([...], name="abc")` can be can be\n ("abc", "", "") if the current DataFra... | 4,519,135,396,839,812,600 | Returns a frame that is normalized against the current column index level.
For example, the name in `pd.Series([...], name="abc")` can be can be
("abc", "", "") if the current DataFrame has a multi-index for its column | python/pyspark/pandas/frame.py | _index_normalized_frame | Flyangz/spark | python | @staticmethod
def _index_normalized_frame(level: int, psser_or_psdf: DataFrameOrSeries) -> 'DataFrame':
'\n Returns a frame that is normalized against the current column index level.\n For example, the name in `pd.Series([...], name="abc")` can be can be\n ("abc", , ) if the current DataFrame h... |
@export
def display_timeline(data: Union[(pd.DataFrame, dict)], time_column: str='TimeGenerated', source_columns: list=None, **kwargs) -> figure:
'\n Display a timeline of events.\n\n Parameters\n ----------\n data : Union[dict, pd.DataFrame]\n Either\n dict of data sets to plot on the tim... | 5,080,413,146,164,393,000 | Display a timeline of events.
Parameters
----------
data : Union[dict, pd.DataFrame]
Either
dict of data sets to plot on the timeline with the following structure::
Key (str) - Name of data set to be displayed in legend
Value (Dict[str, Any]) - containing:
data (pd.DataFrame) - Dat... | msticpy/nbtools/timeline.py | display_timeline | Dqirvin/msticpy | python | @export
def display_timeline(data: Union[(pd.DataFrame, dict)], time_column: str='TimeGenerated', source_columns: list=None, **kwargs) -> figure:
'\n Display a timeline of events.\n\n Parameters\n ----------\n data : Union[dict, pd.DataFrame]\n Either\n dict of data sets to plot on the tim... |
@export
def display_timeline_values(data: pd.DataFrame, y: str, time_column: str='TimeGenerated', source_columns: list=None, **kwargs) -> figure:
'\n Display a timeline of events.\n\n Parameters\n ----------\n data : pd.DataFrame\n DataFrame as a single data set or grouped into individual\n ... | 6,533,993,632,519,709,000 | Display a timeline of events.
Parameters
----------
data : pd.DataFrame
DataFrame as a single data set or grouped into individual
plot series using the `group_by` parameter
time_column : str, optional
Name of the timestamp column
(the default is 'TimeGenerated')
y : str
The column name holding the ... | msticpy/nbtools/timeline.py | display_timeline_values | Dqirvin/msticpy | python | @export
def display_timeline_values(data: pd.DataFrame, y: str, time_column: str='TimeGenerated', source_columns: list=None, **kwargs) -> figure:
'\n Display a timeline of events.\n\n Parameters\n ----------\n data : pd.DataFrame\n DataFrame as a single data set or grouped into individual\n ... |
def _display_timeline_dict(data: dict, **kwargs) -> figure:
"\n Display a timeline of events.\n\n Parameters\n ----------\n data : dict\n Data points to plot on the timeline.\n Need to contain:\n Key - Name of data type to be displayed in legend\n Value - ... | 8,766,972,964,578,907,000 | Display a timeline of events.
Parameters
----------
data : dict
Data points to plot on the timeline.
Need to contain:
Key - Name of data type to be displayed in legend
Value - dict of data containing:
data : pd.DataFrame
Data to plot
... | msticpy/nbtools/timeline.py | _display_timeline_dict | Dqirvin/msticpy | python | def _display_timeline_dict(data: dict, **kwargs) -> figure:
"\n Display a timeline of events.\n\n Parameters\n ----------\n data : dict\n Data points to plot on the timeline.\n Need to contain:\n Key - Name of data type to be displayed in legend\n Value - ... |
def _get_ref_event_time(**kwargs) -> Tuple[(datetime, str)]:
'Extract the reference time from kwargs.'
ref_alert = kwargs.get('alert', None)
if (ref_alert is not None):
ref_event = ref_alert
ref_label = 'Alert time'
else:
ref_event = kwargs.get('ref_event', None)
ref_labe... | 502,102,706,645,366,100 | Extract the reference time from kwargs. | msticpy/nbtools/timeline.py | _get_ref_event_time | Dqirvin/msticpy | python | def _get_ref_event_time(**kwargs) -> Tuple[(datetime, str)]:
ref_alert = kwargs.get('alert', None)
if (ref_alert is not None):
ref_event = ref_alert
ref_label = 'Alert time'
else:
ref_event = kwargs.get('ref_event', None)
ref_label = 'Event time'
if (ref_event is not... |
def _plot_dict_series(data, plot, legend_pos):
'Plot series from dict.'
legend_items = []
for (ser_name, series_def) in data.items():
if (legend_pos == 'inline'):
p_series = plot.diamond(x=series_def['time_column'], y='y_index', color=series_def['color'], alpha=0.5, size=10, source=serie... | 7,726,691,837,423,861,000 | Plot series from dict. | msticpy/nbtools/timeline.py | _plot_dict_series | Dqirvin/msticpy | python | def _plot_dict_series(data, plot, legend_pos):
legend_items = []
for (ser_name, series_def) in data.items():
if (legend_pos == 'inline'):
p_series = plot.diamond(x=series_def['time_column'], y='y_index', color=series_def['color'], alpha=0.5, size=10, source=series_def['source'], legend_... |
def _wrap_df_columns(data: pd.DataFrame, wrap_len: int=50):
'Wrap any string columns.'
if (not data.empty):
for col in data.columns:
if isinstance(data[col].iloc[0], str):
data[col] = data[col].str.wrap(wrap_len) | 647,050,524,434,827,400 | Wrap any string columns. | msticpy/nbtools/timeline.py | _wrap_df_columns | Dqirvin/msticpy | python | def _wrap_df_columns(data: pd.DataFrame, wrap_len: int=50):
if (not data.empty):
for col in data.columns:
if isinstance(data[col].iloc[0], str):
data[col] = data[col].str.wrap(wrap_len) |
def _get_tick_formatter() -> DatetimeTickFormatter:
'Return tick formatting for different zoom levels.'
tick_format = DatetimeTickFormatter()
tick_format.days = ['%m-%d %H:%M']
tick_format.hours = ['%H:%M:%S']
tick_format.minutes = ['%H:%M:%S']
tick_format.seconds = ['%H:%M:%S']
tick_format.... | 6,239,954,124,480,516,000 | Return tick formatting for different zoom levels. | msticpy/nbtools/timeline.py | _get_tick_formatter | Dqirvin/msticpy | python | def _get_tick_formatter() -> DatetimeTickFormatter:
tick_format = DatetimeTickFormatter()
tick_format.days = ['%m-%d %H:%M']
tick_format.hours = ['%H:%M:%S']
tick_format.minutes = ['%H:%M:%S']
tick_format.seconds = ['%H:%M:%S']
tick_format.milliseconds = ['%H:%M:%S.%3N']
return tick_for... |
def _calc_auto_plot_height(group_count):
'Dynamic calculation of plot height.'
ht_per_row = 40
if (group_count > 15):
ht_per_row = 25
return max((ht_per_row * group_count), 300) | 2,604,020,579,015,324,700 | Dynamic calculation of plot height. | msticpy/nbtools/timeline.py | _calc_auto_plot_height | Dqirvin/msticpy | python | def _calc_auto_plot_height(group_count):
ht_per_row = 40
if (group_count > 15):
ht_per_row = 25
return max((ht_per_row * group_count), 300) |
def _create_range_tool(data, min_time, max_time, plot_range, width, height, time_column: str=None):
'Create plot bar to act as as range selector.'
ext_min = (min_time - ((max_time - min_time) * 0.15))
ext_max = (max_time + ((max_time - min_time) * 0.15))
plot_height = max(120, int((height * 0.2)))
r... | 7,389,459,447,783,332,000 | Create plot bar to act as as range selector. | msticpy/nbtools/timeline.py | _create_range_tool | Dqirvin/msticpy | python | def _create_range_tool(data, min_time, max_time, plot_range, width, height, time_column: str=None):
ext_min = (min_time - ((max_time - min_time) * 0.15))
ext_max = (max_time + ((max_time - min_time) * 0.15))
plot_height = max(120, int((height * 0.2)))
rng_select = figure(x_range=(ext_min, ext_max),... |
def _add_ref_line(plot, ref_time, ref_text='Ref time', series_count=1):
'Add a reference marker line and label at `ref_time`.'
ref_label_tm = pd.Timestamp(ref_time)
plot.line(x=[ref_label_tm, ref_label_tm], y=[0, series_count])
ref_label = Label(x=ref_label_tm, y=0, y_offset=10, x_units='data', y_units=... | 5,033,550,887,243,387,000 | Add a reference marker line and label at `ref_time`. | msticpy/nbtools/timeline.py | _add_ref_line | Dqirvin/msticpy | python | def _add_ref_line(plot, ref_time, ref_text='Ref time', series_count=1):
ref_label_tm = pd.Timestamp(ref_time)
plot.line(x=[ref_label_tm, ref_label_tm], y=[0, series_count])
ref_label = Label(x=ref_label_tm, y=0, y_offset=10, x_units='data', y_units='data', text=f'< {ref_text}', text_font_size='8pt', re... |
def render_form(self, *args, **kwargs):
'Placeholder for Wagtail < 2.13'
return '' | -8,506,567,350,089,177,000 | Placeholder for Wagtail < 2.13 | wagtail_localize/test/models.py | render_form | dinoperovic/wagtail-localize | python | def render_form(self, *args, **kwargs):
return |
def filtermultiport(ips):
'Filter out hosts with more nodes per IP'
hist = collections.defaultdict(list)
for ip in ips:
hist[ip['sortkey']].append(ip)
return [value[0] for (key, value) in list(hist.items()) if (len(value) == 1)] | 6,911,170,735,548,327,000 | Filter out hosts with more nodes per IP | contrib/seeds/makeseeds.py | filtermultiport | BitHostCoin/BitHost | python | def filtermultiport(ips):
hist = collections.defaultdict(list)
for ip in ips:
hist[ip['sortkey']].append(ip)
return [value[0] for (key, value) in list(hist.items()) if (len(value) == 1)] |
def test_next_must_pass(self):
"\n Kathy and Tom each have face cards, tom just played and the total is at 30\n\n Expected: It is now kathy's turn and she must pass\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['5e1e7e60ab'], 'tom': ['95f92b2f0c... | -4,560,224,086,035,091,000 | Kathy and Tom each have face cards, tom just played and the total is at 30
Expected: It is now kathy's turn and she must pass | cribbage/app/tests/test_bev.py | test_next_must_pass | zachcalvert/card-games | python | def test_next_must_pass(self):
"\n Kathy and Tom each have face cards, tom just played and the total is at 30\n\n Expected: It is now kathy's turn and she must pass\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['5e1e7e60ab'], 'tom': ['95f92b2f0c... |
def test_next_must_play(self):
"\n Kathy and Tom each have aces. Tom just played and the total is at 30\n\n Expected: It is now kathy's turn and she must play\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY'], 'tom': ['ace1293f8a']}, ... | -4,929,694,881,815,937,000 | Kathy and Tom each have aces. Tom just played and the total is at 30
Expected: It is now kathy's turn and she must play | cribbage/app/tests/test_bev.py | test_next_must_play | zachcalvert/card-games | python | def test_next_must_play(self):
"\n Kathy and Tom each have aces. Tom just played and the total is at 30\n\n Expected: It is now kathy's turn and she must play\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY'], 'tom': ['ace1293f8a']}, ... |
def test_everyone_has_passed_and_tom_cant_play_again_this_round(self):
"\n Kathy and Tom each have face cards, kathy just passed and the total is at 30\n\n Expected: It is Tom's turn and he must pass.\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy':... | -6,701,485,922,209,635,000 | Kathy and Tom each have face cards, kathy just passed and the total is at 30
Expected: It is Tom's turn and he must pass. | cribbage/app/tests/test_bev.py | test_everyone_has_passed_and_tom_cant_play_again_this_round | zachcalvert/card-games | python | def test_everyone_has_passed_and_tom_cant_play_again_this_round(self):
"\n Kathy and Tom each have face cards, kathy just passed and the total is at 30\n\n Expected: It is Tom's turn and he must pass.\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy':... |
def test_everyone_else_has_passed_and_tom_can_play_again_this_round(self):
"\n Tom has an Ace, kathy just passed and the total is at 30\n\n Expected: It is now Tom's turn to play, he does not receive a point for go\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'han... | 541,724,108,961,516,740 | Tom has an Ace, kathy just passed and the total is at 30
Expected: It is now Tom's turn to play, he does not receive a point for go | cribbage/app/tests/test_bev.py | test_everyone_else_has_passed_and_tom_can_play_again_this_round | zachcalvert/card-games | python | def test_everyone_else_has_passed_and_tom_can_play_again_this_round(self):
"\n Tom has an Ace, kathy just passed and the total is at 30\n\n Expected: It is now Tom's turn to play, he does not receive a point for go\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'han... |
def test_kathy_hit_thirtyone_still_has_cards(self):
'\n Kathy just hit 31, and still has cards\n\n Expected: no new points for kathy, and its her turn with a fresh pegging area\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['5e1e7e60ab'], 'tom': ... | -137,440,574,099,362,750 | Kathy just hit 31, and still has cards
Expected: no new points for kathy, and its her turn with a fresh pegging area | cribbage/app/tests/test_bev.py | test_kathy_hit_thirtyone_still_has_cards | zachcalvert/card-games | python | def test_kathy_hit_thirtyone_still_has_cards(self):
'\n Kathy just hit 31, and still has cards\n\n Expected: no new points for kathy, and its her turn with a fresh pegging area\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['5e1e7e60ab'], 'tom': ... |
def test_kathy_hit_thirtyone_has_no_cards_left_and_others_do(self):
"\n Kathy just hit 31, and has no cards left. Tom has a card left\n\n Expected: no new points for kathy, and its now Tom's turn with a fresh pegging area\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARD... | -2,291,250,625,859,228,000 | Kathy just hit 31, and has no cards left. Tom has a card left
Expected: no new points for kathy, and its now Tom's turn with a fresh pegging area | cribbage/app/tests/test_bev.py | test_kathy_hit_thirtyone_has_no_cards_left_and_others_do | zachcalvert/card-games | python | def test_kathy_hit_thirtyone_has_no_cards_left_and_others_do(self):
"\n Kathy just hit 31, and has no cards left. Tom has a card left\n\n Expected: no new points for kathy, and its now Tom's turn with a fresh pegging area\n "
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARD... |
def test_player_hit_thirtyone_and_no_one_has_cards_left(self):
'\n Kathy just hit 31, and everyone is out of cards\n\n Expected: no new points for kathy, and it is now time to score hands\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'first_to_score': 'tom', 'hands... | -749,672,507,504,275,300 | Kathy just hit 31, and everyone is out of cards
Expected: no new points for kathy, and it is now time to score hands | cribbage/app/tests/test_bev.py | test_player_hit_thirtyone_and_no_one_has_cards_left | zachcalvert/card-games | python | def test_player_hit_thirtyone_and_no_one_has_cards_left(self):
'\n Kathy just hit 31, and everyone is out of cards\n\n Expected: no new points for kathy, and it is now time to score hands\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'first_to_score': 'tom', 'hands... |
@mock.patch('app.award_points', mock.MagicMock(return_value=True))
def test_no_one_has_cards_left(self):
'\n Kathy just hit 24, and everyone is out of cards\n\n Expected: Kathy gets 1 point for go, and it is now time to score hands\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'ca... | 8,366,143,360,917,596,000 | Kathy just hit 24, and everyone is out of cards
Expected: Kathy gets 1 point for go, and it is now time to score hands | cribbage/app/tests/test_bev.py | test_no_one_has_cards_left | zachcalvert/card-games | python | @mock.patch('app.award_points', mock.MagicMock(return_value=True))
def test_no_one_has_cards_left(self):
'\n Kathy just hit 24, and everyone is out of cards\n\n Expected: Kathy gets 1 point for go, and it is now time to score hands\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'ca... |
@mock.patch('app.award_points', mock.MagicMock(return_value=False))
def test_thirtyone(self):
'\n Verify two points for 31\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY', 'c6f4900f82'], 'tom': ['ace1293f8a']}, 'pegging': {'cards': ['4de6b7... | -6,152,835,974,117,221,000 | Verify two points for 31 | cribbage/app/tests/test_bev.py | test_thirtyone | zachcalvert/card-games | python | @mock.patch('app.award_points', mock.MagicMock(return_value=False))
def test_thirtyone(self):
'\n \n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY', 'c6f4900f82'], 'tom': ['ace1293f8a']}, 'pegging': {'cards': ['4de6b73ab8', 'f6571e162f', 'c8... |
@mock.patch('app.award_points', mock.MagicMock(return_value=False))
def test_run_of_three(self):
'\n test run of three scores three points\n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY', 'c6f4900f82'], 'tom': ['ace1293f8a']}, 'pegging': {'... | 8,995,552,579,221,174,000 | test run of three scores three points | cribbage/app/tests/test_bev.py | test_run_of_three | zachcalvert/card-games | python | @mock.patch('app.award_points', mock.MagicMock(return_value=False))
def test_run_of_three(self):
'\n \n '
fake_redis = fakeredis.FakeRedis()
game_dict = {'cards': CARDS, 'hands': {'kathy': ['EXAMPLE_KEY', 'c6f4900f82'], 'tom': ['ace1293f8a']}, 'pegging': {'cards': ['4de6b73ab8', 'c88523b677'],... |
def equals(self, other: Any) -> bool:
'\n Determines if two Index objects contain the same elements.\n '
if self.is_(other):
return True
if (not isinstance(other, Index)):
return False
elif (other.dtype.kind in ['f', 'i', 'u', 'c']):
return False
elif (not isins... | -8,305,807,658,120,672,000 | Determines if two Index objects contain the same elements. | pandas/core/indexes/datetimelike.py | equals | DiligentDolphin/pandas | python | def equals(self, other: Any) -> bool:
'\n \n '
if self.is_(other):
return True
if (not isinstance(other, Index)):
return False
elif (other.dtype.kind in ['f', 'i', 'u', 'c']):
return False
elif (not isinstance(other, type(self))):
should_try = False
... |
def format(self, name: bool=False, formatter: (Callable | None)=None, na_rep: str='NaT', date_format: (str | None)=None) -> list[str]:
'\n Render a string representation of the Index.\n '
header = []
if name:
header.append((ibase.pprint_thing(self.name, escape_chars=('\t', '\r', '\n'))... | 8,713,305,425,244,024,000 | Render a string representation of the Index. | pandas/core/indexes/datetimelike.py | format | DiligentDolphin/pandas | python | def format(self, name: bool=False, formatter: (Callable | None)=None, na_rep: str='NaT', date_format: (str | None)=None) -> list[str]:
'\n \n '
header = []
if name:
header.append((ibase.pprint_thing(self.name, escape_chars=('\t', '\r', '\n')) if (self.name is not None) else ))
if (... |
def _format_attrs(self):
'\n Return a list of tuples of the (attr,formatted_value).\n '
attrs = super()._format_attrs()
for attrib in self._attributes:
if (attrib == 'freq'):
freq = self.freqstr
if (freq is not None):
freq = repr(freq)
... | 4,205,978,032,163,911,700 | Return a list of tuples of the (attr,formatted_value). | pandas/core/indexes/datetimelike.py | _format_attrs | DiligentDolphin/pandas | python | def _format_attrs(self):
'\n \n '
attrs = super()._format_attrs()
for attrib in self._attributes:
if (attrib == 'freq'):
freq = self.freqstr
if (freq is not None):
freq = repr(freq)
attrs.append(('freq', freq))
return attrs |
@final
def _partial_date_slice(self, reso: Resolution, parsed: datetime):
'\n Parameters\n ----------\n reso : Resolution\n parsed : datetime\n\n Returns\n -------\n slice or ndarray[intp]\n '
if (not self._can_partial_date_slice(reso)):
raise Valu... | 2,203,640,350,825,362,400 | Parameters
----------
reso : Resolution
parsed : datetime
Returns
-------
slice or ndarray[intp] | pandas/core/indexes/datetimelike.py | _partial_date_slice | DiligentDolphin/pandas | python | @final
def _partial_date_slice(self, reso: Resolution, parsed: datetime):
'\n Parameters\n ----------\n reso : Resolution\n parsed : datetime\n\n Returns\n -------\n slice or ndarray[intp]\n '
if (not self._can_partial_date_slice(reso)):
raise Valu... |
def _maybe_cast_slice_bound(self, label, side: str, kind=lib.no_default):
"\n If label is a string, cast it to scalar type according to resolution.\n\n Parameters\n ----------\n label : object\n side : {'left', 'right'}\n kind : {'loc', 'getitem'} or None\n\n Returns... | -7,072,608,151,381,606,000 | If label is a string, cast it to scalar type according to resolution.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'loc', 'getitem'} or None
Returns
-------
label : object
Notes
-----
Value of `side` parameter should be validated in caller. | pandas/core/indexes/datetimelike.py | _maybe_cast_slice_bound | DiligentDolphin/pandas | python | def _maybe_cast_slice_bound(self, label, side: str, kind=lib.no_default):
"\n If label is a string, cast it to scalar type according to resolution.\n\n Parameters\n ----------\n label : object\n side : {'left', 'right'}\n kind : {'loc', 'getitem'} or None\n\n Returns... |
def shift(self: _T, periods: int=1, freq=None) -> _T:
"\n Shift index by desired number of time frequency increments.\n\n This method is for shifting the values of datetime-like indexes\n by a specified time increment a given number of times.\n\n Parameters\n ----------\n p... | -8,632,447,863,693,839,000 | Shift index by desired number of time frequency increments.
This method is for shifting the values of datetime-like indexes
by a specified time increment a given number of times.
Parameters
----------
periods : int, default 1
Number of periods (or increments) to shift by,
can be positive or negative.
freq : p... | pandas/core/indexes/datetimelike.py | shift | DiligentDolphin/pandas | python | def shift(self: _T, periods: int=1, freq=None) -> _T:
"\n Shift index by desired number of time frequency increments.\n\n This method is for shifting the values of datetime-like indexes\n by a specified time increment a given number of times.\n\n Parameters\n ----------\n p... |
def _intersection(self, other: Index, sort=False) -> Index:
'\n intersection specialized to the case with matching dtypes and both non-empty.\n '
other = cast('DatetimeTimedeltaMixin', other)
if self._can_range_setop(other):
return self._range_intersect(other, sort=sort)
if (not se... | -2,951,834,144,288,449,000 | intersection specialized to the case with matching dtypes and both non-empty. | pandas/core/indexes/datetimelike.py | _intersection | DiligentDolphin/pandas | python | def _intersection(self, other: Index, sort=False) -> Index:
'\n \n '
other = cast('DatetimeTimedeltaMixin', other)
if self._can_range_setop(other):
return self._range_intersect(other, sort=sort)
if (not self._can_fast_intersect(other)):
result = Index._intersection(self, ot... |
def _get_join_freq(self, other):
'\n Get the freq to attach to the result of a join operation.\n '
freq = None
if self._can_fast_union(other):
freq = self.freq
return freq | -8,029,963,893,525,508,000 | Get the freq to attach to the result of a join operation. | pandas/core/indexes/datetimelike.py | _get_join_freq | DiligentDolphin/pandas | python | def _get_join_freq(self, other):
'\n \n '
freq = None
if self._can_fast_union(other):
freq = self.freq
return freq |
def _get_delete_freq(self, loc: ((int | slice) | Sequence[int])):
'\n Find the `freq` for self.delete(loc).\n '
freq = None
if (self.freq is not None):
if is_integer(loc):
if (loc in (0, (- len(self)), (- 1), (len(self) - 1))):
freq = self.freq
else:... | -9,139,549,193,207,140,000 | Find the `freq` for self.delete(loc). | pandas/core/indexes/datetimelike.py | _get_delete_freq | DiligentDolphin/pandas | python | def _get_delete_freq(self, loc: ((int | slice) | Sequence[int])):
'\n \n '
freq = None
if (self.freq is not None):
if is_integer(loc):
if (loc in (0, (- len(self)), (- 1), (len(self) - 1))):
freq = self.freq
else:
if is_list_like(loc):
... |
def _get_insert_freq(self, loc: int, item):
'\n Find the `freq` for self.insert(loc, item).\n '
value = self._data._validate_scalar(item)
item = self._data._box_func(value)
freq = None
if (self.freq is not None):
if self.size:
if (item is NaT):
pass
... | 5,177,903,697,816,854,000 | Find the `freq` for self.insert(loc, item). | pandas/core/indexes/datetimelike.py | _get_insert_freq | DiligentDolphin/pandas | python | def _get_insert_freq(self, loc: int, item):
'\n \n '
value = self._data._validate_scalar(item)
item = self._data._box_func(value)
freq = None
if (self.freq is not None):
if self.size:
if (item is NaT):
pass
elif (((loc == 0) or (loc == (-... |
def clear_mysql_db():
'\n Clear MySQL Database\n :return: true\n '
logger.info('Clearing MySQL Database')
try:
drop_table_content()
except Exception as exp:
logger.error(('Could not clear MySQL Database: ' + repr(exp)))
raise
else:
logger.info('MySQL Database... | -8,534,009,352,897,233,000 | Clear MySQL Database
:return: true | Account/app/mod_system/controller.py | clear_mysql_db | TamSzaGot/mydata-sdk | python | def clear_mysql_db():
'\n Clear MySQL Database\n :return: true\n '
logger.info('Clearing MySQL Database')
try:
drop_table_content()
except Exception as exp:
logger.error(('Could not clear MySQL Database: ' + repr(exp)))
raise
else:
logger.info('MySQL Database... |
def clear_blackbox_db():
'\n Clear black box database\n :return: true\n '
logger.info('Clearing Blackbox Database')
try:
clear_blackbox_sqlite_db()
except Exception as exp:
logger.error(('Could not clear Blackbox Database: ' + repr(exp)))
raise
else:
logger.i... | 2,870,511,574,239,039,000 | Clear black box database
:return: true | Account/app/mod_system/controller.py | clear_blackbox_db | TamSzaGot/mydata-sdk | python | def clear_blackbox_db():
'\n Clear black box database\n :return: true\n '
logger.info('Clearing Blackbox Database')
try:
clear_blackbox_sqlite_db()
except Exception as exp:
logger.error(('Could not clear Blackbox Database: ' + repr(exp)))
raise
else:
logger.i... |
def clear_api_key_db():
'\n Clear API Key database\n :return: true\n '
logger.info('##########')
logger.info('Clearing ApiKey Database')
try:
clear_apikey_sqlite_db()
except Exception as exp:
logger.error(('Could not clear ApiKey Database: ' + repr(exp)))
raise
e... | -5,303,551,338,756,569,000 | Clear API Key database
:return: true | Account/app/mod_system/controller.py | clear_api_key_db | TamSzaGot/mydata-sdk | python | def clear_api_key_db():
'\n Clear API Key database\n :return: true\n '
logger.info('##########')
logger.info('Clearing ApiKey Database')
try:
clear_apikey_sqlite_db()
except Exception as exp:
logger.error(('Could not clear ApiKey Database: ' + repr(exp)))
raise
e... |
def system_check():
'\n Check system functionality\n :return: dict\n '
logger.info('Checking system functionality')
try:
status_dict = {'type': 'StatusReport', 'attributes': {'title': 'System running as intended', 'db_row_counts': get_db_statistics()}}
except Exception as exp:
l... | 1,838,993,185,687,893,000 | Check system functionality
:return: dict | Account/app/mod_system/controller.py | system_check | TamSzaGot/mydata-sdk | python | def system_check():
'\n Check system functionality\n :return: dict\n '
logger.info('Checking system functionality')
try:
status_dict = {'type': 'StatusReport', 'attributes': {'title': 'System running as intended', 'db_row_counts': get_db_statistics()}}
except Exception as exp:
l... |
def sum_mixed_list(mxd_lst: List[Union[(int, float)]]) -> float:
'sum all float number in list\n\n Args:\n input_list (List[float]): arg\n\n Returns:\n float: result\n '
return sum(mxd_lst) | -5,815,243,350,808,947,000 | sum all float number in list
Args:
input_list (List[float]): arg
Returns:
float: result | 0x00-python_variable_annotations/6-sum_mixed_list.py | sum_mixed_list | JoseAVallejo12/holbertonschool-web_back_end | python | def sum_mixed_list(mxd_lst: List[Union[(int, float)]]) -> float:
'sum all float number in list\n\n Args:\n input_list (List[float]): arg\n\n Returns:\n float: result\n '
return sum(mxd_lst) |
def valid_vars(vars):
"\n Note: run_program_op.InferShape requires `X`/'Out' not be null.\n But it's common in dy2static, fake varBase is created to handle the\n problem.\n "
if vars:
return vars
return [core.VarBase(value=[1], name='Fake_var', place=framework._current_expected_place())] | -6,657,273,862,314,413,000 | Note: run_program_op.InferShape requires `X`/'Out' not be null.
But it's common in dy2static, fake varBase is created to handle the
problem. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | valid_vars | CheQiXiao/Paddle | python | def valid_vars(vars):
"\n Note: run_program_op.InferShape requires `X`/'Out' not be null.\n But it's common in dy2static, fake varBase is created to handle the\n problem.\n "
if vars:
return vars
return [core.VarBase(value=[1], name='Fake_var', place=framework._current_expected_place())] |
def tolist(self):
'\n Flattens the nested sequences into single list.\n '
return flatten(self.__raw_input) | -7,850,800,606,931,174,000 | Flattens the nested sequences into single list. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | tolist | CheQiXiao/Paddle | python | def tolist(self):
'\n \n '
return flatten(self.__raw_input) |
def restore(self, value_list):
'\n Restores the nested sequence from value list.\n '
assert (len(self.tolist()) == len(value_list))
return pack_sequence_as(self.__raw_input, value_list) | 1,636,940,109,083,474,400 | Restores the nested sequence from value list. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | restore | CheQiXiao/Paddle | python | def restore(self, value_list):
'\n \n '
assert (len(self.tolist()) == len(value_list))
return pack_sequence_as(self.__raw_input, value_list) |
def _check_non_variable(self, need_check):
'\n Raises warning if output of traced function contains non-tensor type values.\n '
if need_check:
warning_types = set()
for var in self.tolist():
if (not isinstance(var, (framework.Variable, core.VarBase))):
w... | 4,097,785,078,502,480,000 | Raises warning if output of traced function contains non-tensor type values. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _check_non_variable | CheQiXiao/Paddle | python | def _check_non_variable(self, need_check):
'\n \n '
if need_check:
warning_types = set()
for var in self.tolist():
if (not isinstance(var, (framework.Variable, core.VarBase))):
warning_types.add(type(var))
if warning_types:
logging_ut... |
@LazyInitialized
def _infer_program(self):
'\n Lazy initialized property of infer_program.\n '
return self._clone_for_test(self._origin_main_program) | 1,281,564,852,890,502,100 | Lazy initialized property of infer_program. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _infer_program | CheQiXiao/Paddle | python | @LazyInitialized
def _infer_program(self):
'\n \n '
return self._clone_for_test(self._origin_main_program) |
@LazyInitialized
def _train_program(self):
'\n Lazy initialized property of train_program.\n '
train_program = self._append_backward_desc(self._origin_main_program)
self._set_grad_type(self._params, train_program)
return train_program | -2,370,555,548,043,581,400 | Lazy initialized property of train_program. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _train_program | CheQiXiao/Paddle | python | @LazyInitialized
def _train_program(self):
'\n \n '
train_program = self._append_backward_desc(self._origin_main_program)
self._set_grad_type(self._params, train_program)
return train_program |
def _verify_program(self, main_program):
'\n Verify that the program parameter is initialized, prune some unused params,\n and remove redundant op callstack.\n '
self._check_params_all_inited(main_program)
self._prune_unused_params(main_program)
return main_program | 944,476,005,322,594,400 | Verify that the program parameter is initialized, prune some unused params,
and remove redundant op callstack. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _verify_program | CheQiXiao/Paddle | python | def _verify_program(self, main_program):
'\n Verify that the program parameter is initialized, prune some unused params,\n and remove redundant op callstack.\n '
self._check_params_all_inited(main_program)
self._prune_unused_params(main_program)
return main_program |
def _prune_unused_params(self, program):
'\n Prune the parameters not used anywhere in the program.\n The `@declarative` may only decorated a sub function which\n contains some unused parameters created in `__init__`.\n So prune these parameters to avoid unnecessary operations in\n ... | -5,956,918,768,261,268,000 | Prune the parameters not used anywhere in the program.
The `@declarative` may only decorated a sub function which
contains some unused parameters created in `__init__`.
So prune these parameters to avoid unnecessary operations in
`run_program_op`. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _prune_unused_params | CheQiXiao/Paddle | python | def _prune_unused_params(self, program):
'\n Prune the parameters not used anywhere in the program.\n The `@declarative` may only decorated a sub function which\n contains some unused parameters created in `__init__`.\n So prune these parameters to avoid unnecessary operations in\n ... |
def _prepare(self, inputs):
'\n Prepare inputs, outputs, attrs.\n '
assert isinstance(inputs, (tuple, list))
flatten_inputs = flatten(inputs)
input_vars = []
for (i, value) in enumerate(flatten_inputs):
if isinstance(value, np.ndarray):
var = core.VarBase(value=valu... | 5,576,537,689,546,665,000 | Prepare inputs, outputs, attrs. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _prepare | CheQiXiao/Paddle | python | def _prepare(self, inputs):
'\n \n '
assert isinstance(inputs, (tuple, list))
flatten_inputs = flatten(inputs)
input_vars = []
for (i, value) in enumerate(flatten_inputs):
if isinstance(value, np.ndarray):
var = core.VarBase(value=value, name=self._inputs[i].desc.na... |
def _restore_out(self, out_vars):
'\n Restores same nested outputs by only replacing the Variable with VarBase.\n '
flatten_outputs = self._outputs.tolist()
for (i, idx) in enumerate(self._outputs.var_ids):
flatten_outputs[idx] = out_vars[i]
outs = self._outputs.restore(flatten_out... | -6,028,813,199,620,918,000 | Restores same nested outputs by only replacing the Variable with VarBase. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _restore_out | CheQiXiao/Paddle | python | def _restore_out(self, out_vars):
'\n \n '
flatten_outputs = self._outputs.tolist()
for (i, idx) in enumerate(self._outputs.var_ids):
flatten_outputs[idx] = out_vars[i]
outs = self._outputs.restore(flatten_outputs)
if ((outs is not None) and (len(outs) == 1)):
outs = ou... |
def _remove_no_value(self, out_vars):
'\n Removes invalid value for various-length return statement\n '
if isinstance(out_vars, core.VarBase):
if self._is_no_value(out_vars):
return None
return out_vars
elif isinstance(out_vars, (tuple, list)):
if isinstance... | -7,001,538,010,932,496,000 | Removes invalid value for various-length return statement | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _remove_no_value | CheQiXiao/Paddle | python | def _remove_no_value(self, out_vars):
'\n \n '
if isinstance(out_vars, core.VarBase):
if self._is_no_value(out_vars):
return None
return out_vars
elif isinstance(out_vars, (tuple, list)):
if isinstance(out_vars, tuple):
res = tuple((var for var i... |
def _remove_op_call_stack(self, main_program):
"\n Remove op's python call stack with redundant low-level error messages related to\n transforamtions to avoid confusing users.\n "
assert isinstance(main_program, framework.Program)
for block in main_program.blocks:
for op in bloc... | 2,915,306,587,125,424,000 | Remove op's python call stack with redundant low-level error messages related to
transforamtions to avoid confusing users. | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _remove_op_call_stack | CheQiXiao/Paddle | python | def _remove_op_call_stack(self, main_program):
"\n Remove op's python call stack with redundant low-level error messages related to\n transforamtions to avoid confusing users.\n "
assert isinstance(main_program, framework.Program)
for block in main_program.blocks:
for op in bloc... |
def _check_params_all_inited(self, main_program):
'\n Check all params from main program are already initialized, see details as follows:\n 1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph.\n 2. all parameters from transformed program c... | -1,005,667,989,976,922,900 | Check all params from main program are already initialized, see details as follows:
1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph.
2. all parameters from transformed program can be found in self._params.
Because they share same data with ParamBase of ... | python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py | _check_params_all_inited | CheQiXiao/Paddle | python | def _check_params_all_inited(self, main_program):
'\n Check all params from main program are already initialized, see details as follows:\n 1. all parameters in self._params should be type `framework.ParamBase` which are created in dygraph.\n 2. all parameters from transformed program c... |
def __init__(self, eventEngine, gatewayName):
'Constructor'
self.eventEngine = eventEngine
self.gatewayName = gatewayName | 1,672,423,060,279,163,100 | Constructor | redtorch/trader/vtGateway.py | __init__ | sun0x00/redtorch_python | python | def __init__(self, eventEngine, gatewayName):
self.eventEngine = eventEngine
self.gatewayName = gatewayName |
def onTick(self, tick):
'市场行情推送'
event1 = Event(type_=EVENT_TICK)
event1.dict_['data'] = tick
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_TICK + tick.vtSymbol))
event2.dict_['data'] = tick
self.eventEngine.put(event2) | 3,856,064,092,815,750,700 | 市场行情推送 | redtorch/trader/vtGateway.py | onTick | sun0x00/redtorch_python | python | def onTick(self, tick):
event1 = Event(type_=EVENT_TICK)
event1.dict_['data'] = tick
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_TICK + tick.vtSymbol))
event2.dict_['data'] = tick
self.eventEngine.put(event2) |
def onTrade(self, trade):
'成交信息推送'
event1 = Event(type_=EVENT_TRADE)
event1.dict_['data'] = trade
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_TRADE + trade.vtSymbol))
event2.dict_['data'] = trade
self.eventEngine.put(event2) | 2,063,537,404,431,998,500 | 成交信息推送 | redtorch/trader/vtGateway.py | onTrade | sun0x00/redtorch_python | python | def onTrade(self, trade):
event1 = Event(type_=EVENT_TRADE)
event1.dict_['data'] = trade
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_TRADE + trade.vtSymbol))
event2.dict_['data'] = trade
self.eventEngine.put(event2) |
def onOrder(self, order):
'订单变化推送'
event1 = Event(type_=EVENT_ORDER)
event1.dict_['data'] = order
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_ORDER + order.vtOrderID))
event2.dict_['data'] = order
self.eventEngine.put(event2) | 5,707,298,845,992,048,000 | 订单变化推送 | redtorch/trader/vtGateway.py | onOrder | sun0x00/redtorch_python | python | def onOrder(self, order):
event1 = Event(type_=EVENT_ORDER)
event1.dict_['data'] = order
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_ORDER + order.vtOrderID))
event2.dict_['data'] = order
self.eventEngine.put(event2) |
def onPosition(self, position):
'持仓信息推送'
event1 = Event(type_=EVENT_POSITION)
event1.dict_['data'] = position
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_POSITION + position.vtSymbol))
event2.dict_['data'] = position
self.eventEngine.put(event2) | 7,488,092,332,243,463,000 | 持仓信息推送 | redtorch/trader/vtGateway.py | onPosition | sun0x00/redtorch_python | python | def onPosition(self, position):
event1 = Event(type_=EVENT_POSITION)
event1.dict_['data'] = position
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_POSITION + position.vtSymbol))
event2.dict_['data'] = position
self.eventEngine.put(event2) |
def onAccount(self, account):
'账户信息推送'
event1 = Event(type_=EVENT_ACCOUNT)
event1.dict_['data'] = account
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_ACCOUNT + account.vtAccountID))
event2.dict_['data'] = account
self.eventEngine.put(event2) | -2,795,242,707,031,535,600 | 账户信息推送 | redtorch/trader/vtGateway.py | onAccount | sun0x00/redtorch_python | python | def onAccount(self, account):
event1 = Event(type_=EVENT_ACCOUNT)
event1.dict_['data'] = account
self.eventEngine.put(event1)
event2 = Event(type_=(EVENT_ACCOUNT + account.vtAccountID))
event2.dict_['data'] = account
self.eventEngine.put(event2) |
def onError(self, error):
'错误信息推送'
event1 = Event(type_=EVENT_ERROR)
event1.dict_['data'] = error
self.eventEngine.put(event1) | 4,894,823,628,181,121,000 | 错误信息推送 | redtorch/trader/vtGateway.py | onError | sun0x00/redtorch_python | python | def onError(self, error):
event1 = Event(type_=EVENT_ERROR)
event1.dict_['data'] = error
self.eventEngine.put(event1) |
def onLog(self, log):
'日志推送'
event1 = Event(type_=EVENT_LOG)
event1.dict_['data'] = log
self.eventEngine.put(event1) | 7,426,680,771,114,056,000 | 日志推送 | redtorch/trader/vtGateway.py | onLog | sun0x00/redtorch_python | python | def onLog(self, log):
event1 = Event(type_=EVENT_LOG)
event1.dict_['data'] = log
self.eventEngine.put(event1) |
def onContract(self, contract):
'合约基础信息推送'
event1 = Event(type_=EVENT_CONTRACT)
event1.dict_['data'] = contract
self.eventEngine.put(event1) | 2,881,356,330,586,334,000 | 合约基础信息推送 | redtorch/trader/vtGateway.py | onContract | sun0x00/redtorch_python | python | def onContract(self, contract):
event1 = Event(type_=EVENT_CONTRACT)
event1.dict_['data'] = contract
self.eventEngine.put(event1) |
def connect(self):
'连接'
pass | 8,699,725,801,578,168,000 | 连接 | redtorch/trader/vtGateway.py | connect | sun0x00/redtorch_python | python | def connect(self):
pass |
def subscribe(self, subscribeReq):
'订阅行情'
pass | -1,651,100,944,133,235,000 | 订阅行情 | redtorch/trader/vtGateway.py | subscribe | sun0x00/redtorch_python | python | def subscribe(self, subscribeReq):
pass |
def sendOrder(self, orderReq):
'发单'
pass | -6,865,453,469,559,764,000 | 发单 | redtorch/trader/vtGateway.py | sendOrder | sun0x00/redtorch_python | python | def sendOrder(self, orderReq):
pass |
def cancelOrder(self, cancelOrderReq):
'撤单'
pass | 5,289,705,947,194,827,000 | 撤单 | redtorch/trader/vtGateway.py | cancelOrder | sun0x00/redtorch_python | python | def cancelOrder(self, cancelOrderReq):
pass |
def qryAccount(self):
'查询账户资金'
pass | 8,067,137,450,306,017,000 | 查询账户资金 | redtorch/trader/vtGateway.py | qryAccount | sun0x00/redtorch_python | python | def qryAccount(self):
pass |
def qryPosition(self):
'查询持仓'
pass | 1,786,019,952,844,000,000 | 查询持仓 | redtorch/trader/vtGateway.py | qryPosition | sun0x00/redtorch_python | python | def qryPosition(self):
pass |
def close(self):
'关闭'
pass | 8,479,221,086,581,067,000 | 关闭 | redtorch/trader/vtGateway.py | close | sun0x00/redtorch_python | python | def close(self):
pass |
def get(self, request):
'Retrieve the user.'
user = request.user
serializer = self.serializer_class(user)
return Response(serializer.data) | 7,155,900,420,248,859,000 | Retrieve the user. | dakara_server/users/views.py | get | DakaraProject/dakara-server | python | def get(self, request):
user = request.user
serializer = self.serializer_class(user)
return Response(serializer.data) |
def skip_201911_and_older(duthost):
' Skip the current test if the DUT version is 201911 or older.\n '
if (parse_version(duthost.kernel_version) <= parse_version('4.9.0')):
pytest.skip('Test not supported for 201911 images or older. Skipping the test') | -6,194,294,265,274,752,000 | Skip the current test if the DUT version is 201911 or older. | tests/route/test_static_route.py | skip_201911_and_older | LiuKuan-AF/sonic-mgmt | python | def skip_201911_and_older(duthost):
' \n '
if (parse_version(duthost.kernel_version) <= parse_version('4.9.0')):
pytest.skip('Test not supported for 201911 images or older. Skipping the test') |
def is_dualtor(tbinfo):
'Check if the testbed is dualtor.'
return ('dualtor' in tbinfo['topo']['name']) | 2,524,877,780,519,400,400 | Check if the testbed is dualtor. | tests/route/test_static_route.py | is_dualtor | LiuKuan-AF/sonic-mgmt | python | def is_dualtor(tbinfo):
return ('dualtor' in tbinfo['topo']['name']) |
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