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pandas.DataFrame.reindex_axis

DataFrame.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)[source]

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 or ‘index’, 1 or ‘columns’}

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:

DataFrame

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_axis(['num_wings', 'num_legs', 'num_heads'],
...                 axis='columns')
      num_wings  num_legs  num_heads
dog           0         4        NaN
hawk          2         2        NaN
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