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self.where(-key, value, inplace=True)
_ensure_valid_index(self, value)
len(self.index)
is_list_like(value)
Series(value)
isinstance(value, Series)
self._data.reindex_axis(value.index.copy()
_set_item(self, key, value)
array (not a Series/TimeSeries)
self._ensure_valid_index(value)
self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
len(self)
self._check_setitem_copy()
insert(self, loc, column, value, allow_duplicates=False)
len(columns)
self._ensure_valid_index(value)
self._sanitize_column(column, value)
assign(self, **kwargs)
object (a copy)
DataFrame({'A': range(1, 11)
np.random.randn(10)
df.assign(ln_A = lambda x: np.log(x.A)
np.log(df['A'])
df.assign(ln_A=newcol)
self.copy()
kwargs.items()
callable(v)
v(data)
results.items()
_sanitize_column(self, key, value)
reindexer(value)
value.index.equals(self.index)
len(self.index)
value.values.copy()
value.reindex(self.index)
isinstance(value, Series)
reindexer(value)
isinstance(value, DataFrame)
isinstance(self.columns, MultiIndex)
self.columns.get_loc(key)
isinstance(loc, (slice, Series, np.ndarray, Index)
maybe_droplevels(self.columns[loc], key)
len(cols)
cols.equals(value.columns)
value.reindex_axis(cols, axis=1)
reindexer(value)
isinstance(value, Categorical)
value.copy()
elif (isinstance(value, Index)
is_sequence(value)
_sanitize_index(value, self.index, copy=False)
isinstance(value, (np.ndarray, Index)
isinstance(value, list)
len(value)
com._possibly_convert_platform(value)
com._asarray_tuplesafe(value)
value.copy()
value.copy()
is_object_dtype(value.dtype)
_possibly_infer_to_datetimelike(value.ravel()
reshape(value.shape)
_infer_dtype_from_scalar(value)
np.repeat(value, len(self.index)
astype(dtype)
com._possibly_cast_to_datetime(value, dtype)
isinstance(value, (Categorical, SparseArray)
isinstance(existing_piece, DataFrame)
np.tile(value, (len(existing_piece.columns)
np.atleast_2d(np.asarray(value)
_series(self)
enumerate(self.columns)
Series(self._data.iget(idx)
lookup(self, row_labels, col_labels)
each (row, col)
zip(row_labels, col_labels)
result.append(df.get_value(row, col)
len(row_labels)
len(col_labels)
ValueError('Row labels must have same size as column labels')
self.index.get_indexer(row_labels)
self.columns.get_indexer(col_labels)
if (ridx == -1)
any()
KeyError('One or more row labels was not found')
if (cidx == -1)
any()
KeyError('One or more column labels was not found')
len(self.columns)
np.empty(n, dtype='O')
enumerate(zip(row_labels, col_labels)
self.get_value(r, c)
is_object_dtype(result)
lib.maybe_convert_objects(result)
_reindex_axes(self, axes, level, limit, method, fill_value, copy)
_reindex_multi(self, axes, copy, fill_value)
self.index.reindex(axes['index'])
self.columns.reindex(axes['columns'])
Appender(_shared_docs['reindex'] % _shared_doc_kwargs)
reindex(self, index=None, columns=None, **kwargs)