code stringlengths 3 6.57k |
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self._get_axis_number(axis) |
getattr(type(self) |
method(x, axis=axis, skipna=skipna, **kwargs) |
grouped.aggregate(applyf) |
_add_numeric_operations(cls) |
_doc_parms(cls) |
Appender(_num_doc_mad) |
mad(self, axis=None, skipna=None, level=None) |
self._agg_by_level("mad", axis=axis, level=level, skipna=skipna) |
self._get_numeric_data() |
data.mean(axis=0) |
data.sub(data.mean(axis=1) |
np.abs(demeaned) |
mean(axis=axis, skipna=skipna) |
kurtosis (kurtosis of normal == 0.0) |
_add_series_or_dataframe_operations(cls) |
Appender(Rolling.__doc__) |
self._get_axis_number(axis) |
Appender(Expanding.__doc__) |
expanding(self, min_periods=1, center=False, axis=0) |
self._get_axis_number(axis) |
Expanding(self, min_periods=min_periods, center=center, axis=axis) |
Appender(EWM.__doc__) |
self._get_axis_number(axis) |
Appender(_shared_docs["transform"] % dict(axis="", **_shared_doc_kwargs) |
transform(self, func, *args, **kwargs) |
self.agg(func, *args, **kwargs) |
is_scalar(result) |
len(result) |
len(self) |
ValueError("transforms cannot produce aggregated results") |
_find_valid_index(self, how: str) |
find_valid_index(self._values, how) |
first_valid_index(self) |
self._find_valid_index("first") |
last_valid_index(self) |
self._find_valid_index("last") |
_doc_parms(cls) |
join(f'{a} ({i}) |
enumerate(cls._AXIS_ORDERS) |
MultiIndex (hierarchical) |
s (if level specified) |
MultiIndex (hierarchical) |
s (if level specified) |
MultiIndex (hierarchical) |
s (if level specified) |
MultiIndex (hierarchical) |
pd.Series([True, True]) |
all() |
pd.Series([True, False]) |
all() |
pd.Series([]) |
all() |
pd.Series([np.nan]) |
all() |
pd.Series([np.nan]) |
all(skipna=False) |
pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) |
df.all() |
df.all(axis='columns') |
df.all(axis=None) |
one (or more) |
pd.Series([2, np.nan, 5, -1, 0]) |
s.cummin() |
s.cummin(skipna=False) |
list('AB') |
df.cummin() |
df.cummin(axis=1) |
pd.Series([2, np.nan, 5, -1, 0]) |
s.cumsum() |
s.cumsum(skipna=False) |
list('AB') |
df.cumsum() |
df.cumsum(axis=1) |
pd.Series([2, np.nan, 5, -1, 0]) |
s.cumprod() |
s.cumprod(skipna=False) |
list('AB') |
df.cumprod() |
df.cumprod(axis=1) |
pd.Series([2, np.nan, 5, -1, 0]) |
s.cummax() |
s.cummax(skipna=False) |
list('AB') |
df.cummax() |
df.cummax(axis=1) |
pd.Series([False, False]) |
any() |
pd.Series([True, False]) |
any() |
pd.Series([]) |
any() |
pd.Series([np.nan]) |
any() |
pd.Series([np.nan]) |
any(skipna=False) |
element (the default) |
pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) |
df.any() |
pd.DataFrame({"A": [True, False], "B": [1, 2]}) |
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