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# pandas.DataFrame.reindex_like

DataFrame.reindex_like(other, method=None, copy=None, limit=None, tolerance=None)

  Return an object with matching indices as other object.

  Conform the object to the same index on all axes. Optional filling logic, placing 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:
    other Object of the same data type

      Its row and column indices are used to define the new indices of this object.

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

      Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

      - None (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.

    copy bool, default True

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

      Note

      The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

      You can already get the future behavior and improvements through enabling copy on write `pd.options.mode.copy_on_write = True`

    limit int, default None

      Maximum number of consecutive labels to fill for inexact matches.

    tolerance optional

      Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation `abs(index[indexer] - target) <= tolerance`.

      Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

  Returns:
    Series or DataFrame

      Same type as caller, but with changed indices on each axis.

  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.

  Notes

  Same as calling `.reindex(index=other.index, columns=other.columns,...)`.

  Examples

      >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
      ...                     [31, 87.8, 'high'],
      ...                     [22, 71.6, 'medium'],
      ...                     [35, 95, 'medium']],
      ...                    columns=['temp_celsius', 'temp_fahrenheit',
      ...                             'windspeed'],
      ...                    index=pd.date_range(start='2014-02-12',
      ...                                        end='2014-02-15', freq='D'))

      >>> df1
                  temp_celsius  temp_fahrenheit windspeed
      2014-02-12          24.3             75.7      high
      2014-02-13          31.0             87.8      high
      2014-02-14          22.0             71.6    medium
      2014-02-15          35.0             95.0    medium

      >>> df2 = pd.DataFrame([[28, 'low'],
      ...                     [30, 'low'],
      ...                     [35.1, 'medium']],
      ...                    columns=['temp_celsius', 'windspeed'],
      ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
      ...                                            '2014-02-15']))

      >>> df2
                  temp_celsius windspeed
      2014-02-12          28.0       low
      2014-02-13          30.0       low
      2014-02-15          35.1    medium

      >>> df2.reindex_like(df1)
                  temp_celsius  temp_fahrenheit windspeed
      2014-02-12          28.0              NaN       low
      2014-02-13          30.0              NaN       low
      2014-02-14           NaN              NaN       NaN
      2014-02-15          35.1              NaN    medium