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super(DataFrame, self)
Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
super(DataFrame, self)
Appender(_shared_docs['rename'] % _shared_doc_kwargs)
rename(self, index=None, columns=None, **kwargs)
super(DataFrame, self)
index (row labels)
place (do not create a new object)
df.set_index(['A', 'B'])
df.set_index(['A', [0, 1, 2, 0, 1, 2]])
df.set_index([[0, 1, 2, 0, 1, 2]])
isinstance(keys, list)
self.copy()
isinstance(self.index, MultiIndex)
range(self.index.nlevels)
arrays.append(self.index.get_level_values(i)
arrays.append(self.index)
isinstance(col, MultiIndex)
range(col.nlevels - 1)
arrays.append(col.get_level_values(n)
col.get_level_values(col.nlevels - 1)
names.extend(col.names)
isinstance(col, Series)
names.append(col.name)
isinstance(col, Index)
names.append(col.name)
isinstance(col, (list, np.ndarray, Index)
names.append(None)
names.append(col)
to_remove.append(col)
arrays.append(level)
MultiIndex.from_arrays(arrays, names=names)
index.get_duplicates()
ValueError('Index has duplicate keys: %s' % duplicates)
index._cleanup()
used (if set)
place (do not create a new object)
self.copy()
_maybe_casted_values(index, labels=None)
isinstance(index, PeriodIndex)
elif (isinstance(index, DatetimeIndex)
lib.maybe_convert_objects(values)
values.take(labels)
mask.any()
np.arange(len(new_obj)
isinstance(self.index, MultiIndex)
isinstance(level, (tuple, list)
self.index._get_level_number(lev)
len(level)
len(self.index.levels)
self.index.droplevel(level)
lzip(self.index.levels, self.index.labels)
isinstance(self.columns, MultiIndex)
reversed(list(enumerate(zipped)
self.columns._get_level_number(col_level)
tuple(name_lst)
_maybe_casted_values(lev, lab)
new_obj.insert(0, col_name, level_values)
isinstance(self.columns, MultiIndex)
tuple([name] * self.columns.nlevels)
self.columns._get_level_number(col_level)
tuple(name_lst)
_maybe_casted_values(self.index)
new_obj.insert(0, name, values)
isinstance(axis, (tuple, list)
self._get_axis_number(axis)
self._get_axis(agg_axis)
ax.get_indexer_for(subset)
check.any()
KeyError(list(np.compress(check,subset)
self.take(indices,axis=agg_axis)
agg_obj.count(axis=agg_axis)
len(agg_obj._get_axis(agg_axis)
ValueError('invalid how option: %s' % how)
TypeError('must specify how or thresh')
self.take(mask.nonzero()
self._update_inplace(result)
deprecate_kwarg(old_arg_name='cols', new_arg_name='subset')
drop_duplicates(self, subset=None, take_last=False, inplace=False)
self.duplicated(subset, take_last=take_last)
nonzero()
self._data.take(inds)
self._update_inplace(new_data)
deprecate_kwarg(old_arg_name='cols', new_arg_name='subset')
duplicated(self, subset=None, take_last=False)
f(vals)
factorize(vals, size_hint=min(len(self)
labels.astype('i8',copy=False)
len(shape)
np.iterable(subset)
isinstance(subset, compat.string_types)
isinstance(subset, tuple)
map(list, zip( * map(f, vals)
get_group_index(labels, shape, sort=False, xnull=False)
Series(duplicated_int64(ids, take_last)
labels (along either axis)
column(s)
name(s)
df.sort(['A', 'B'], ascending=[1, 0])
labels (along either axis)