code stringlengths 3 6.57k |
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df.any(axis='columns') |
pd.DataFrame({"A": [True, False], "B": [1, 0]}) |
df.any(axis='columns') |
df.any(axis=None) |
pd.DataFrame([]) |
any() |
Series([], dtype: bool) |
pd.Series([4, 2, 0, 8], name='legs', index=idx) |
pd.Series([]) |
sum() |
pd.Series([]) |
sum(min_count=1) |
pd.Series([np.nan]) |
sum() |
pd.Series([np.nan]) |
sum(min_count=1) |
pd.Series([]) |
prod() |
pd.Series([]) |
prod(min_count=1) |
pd.Series([np.nan]) |
prod() |
pd.Series([np.nan]) |
prod(min_count=1) |
Appender(_num_doc) |
nv.validate_sum(tuple() |
nv.validate_prod(tuple() |
nv.validate_stat_func(tuple() |
set_function_name(stat_func, name, cls) |
Appender(_num_doc) |
nv.validate_median(tuple() |
nv.validate_stat_func(tuple() |
self._agg_by_level(name, axis=axis, level=level, skipna=skipna) |
set_function_name(stat_func, name, cls) |
Substitution(desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) |
Appender(_num_ddof_doc) |
nv.validate_stat_ddof_func(tuple() |
set_function_name(stat_func, name, cls) |
Appender(_cnum_doc) |
cum_func(self, axis=None, skipna=True, *args, **kwargs) |
nv.validate_cum_func_with_skipna(skipna, args, kwargs, name) |
self._get_axis_number(axis) |
cum_func(self.T, axis=0, skipna=skipna, *args, **kwargs) |
na_accum_func(blk_values) |
isna(blk_values) |
blk_values.view("i8") |
np.iinfo(np.int64) |
accum_func(y.view("i8") |
np.putmask(result, mask, iNaT) |
np.asarray(mask) |
nonzero() |
len(nz) |
isinstance(blk_values, np.ndarray) |
result.view(orig_dtype) |
type(blk_values) |
_from_sequence(result, dtype=orig_dtype) |
blk_values.copy() |
isna(vals) |
np.putmask(vals, mask, mask_a) |
accum_func(vals, axis) |
np.putmask(result, mask, mask_b) |
accum_func(blk_values.T, axis) |
hasattr(result, "T") |
self._data.apply(na_accum_func) |
self._construct_axes_dict() |
self._constructor(result, **d) |
__finalize__(self) |
set_function_name(cum_func, name, cls) |
Appender(_bool_doc) |
logical_func(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs) |
nv.validate_logical_func(tuple() |
self._agg_by_level(name, axis=axis, level=level, skipna=skipna) |
set_function_name(logical_func, name, cls) |
handle(infiles, tables, user_input_path, **kwargs) |
kwargs.get('simple') |
print_message(msg) |
format(name=__name__) |
logging.info(msg) |
xarray.open_dataset(meshFileName, mask_and_scale=False) |
mpas.get_cell_masks(dsMesh) |
dsMesh.areaCell.where(cellMask2D) |
xarray.Dataset() |
mpas.open_mfdataset(timeSeriesFiles, variableList) |
dsIn.timeMonthly_avg_pressureAdjustedSSH.where(cellMask2D) |
sum(dim='nCells') |
areaCell.sum(dim='nCells') |
mpas.add_time(ds, dsIn) |
ds.compute() |
mpas.remap(ds, mappingFileName) |
mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean') |
mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS) |
submit_rule("file: path matches \"/" + case + "/*\"| allssd") |
submit_rule("file: path matches \"/" + case + "/*\"| alldisk") |
start_rule(rid) |
time.time() |
get_rule(rid) |
time.sleep(.1) |
get_cids_of_rule(rid) |
len(cids) |
int(num) |
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