pandas.Panel.reindex_axis

Panel.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)

Conform input object to new index.

Deprecated since version 0.21.0: Use reindex instead.

By default, places 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

New labels / index to conform to. Preferably an Index object to avoid duplicating data.

axis : {0, 1, 2, ‘items’, ‘major_axis’, ‘minor_axis’}

Indicate whether to use rows or columns.

method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

Method to use for filling holes in reindexed DataFrame:

• default: don’t fill gaps.
• pad / ffill: propagate last valid observation forward to next valid.
• backfill / bfill: use next valid observation to fill gap.
• nearest: use nearest valid observations to fill gap.

level : int or str

Broadcast across a level, matching Index values on the passed MultiIndex level.

copy : bool, default True

Return a new object, even if the passed indexes are the same.

limit : int, optional

Maximum number of consecutive elements to forward or backward fill.

fill_value : float, default NaN

Value used to fill in locations having no value in the previous index.

New in version 0.21.0: (list-like tolerance)

Returns:

Panel

Returns a new DataFrame object with new indices, unless the new index is equivalent to the current one and copy=False.

See also

DataFrame.set_index

Set row labels.

DataFrame.reset_index

Remove row labels or move them to new columns.

DataFrame.reindex

Change to new indices or expand indices.

DataFrame.reindex_like

Change to same indices as other DataFrame.

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> df.reindex(['num_wings', 'num_legs', 'num_heads'],
...            axis='columns')
      num_wings  num_legs  num_heads
dog           0         4        NaN
hawk          2         2        NaN