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# pandas.Series.reindex

Series.reindex(index=None, *, axis=None, method=None, copy=<no_default>, level=None, fill_value=None, limit=None, tolerance=None)

  Conform Series to new index with optional filling logic.

  Places NA/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:
    index scalar, list-like, dict-like or function, optional

      A scalar, list-like, dict-like or functions transformations to apply to that axis’ values.

    axis{0 or ‘index’}, default 0

      The axis to rename. For Series this parameter is unused and defaults to 0.

    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 False

      This keyword is now ignored; changing its value will have no impact on the method.

      Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

    level int or name

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

    fill_value scalar, default np.nan

      Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

    limit int, default None

      Maximum number of consecutive elements to forward or backward fill.

    tolerance optional

      Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most 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

      Series with changed index.

  See also

  `DataFrame.set_index`

    Set row labels.

  `DataFrame.reset_index`

    Remove row labels or move them to new columns.

  `DataFrame.reindex_like`

    Change to same indices as other DataFrame.

  Examples

  `DataFrame.reindex` supports two calling conventions

  - `(index=index_labels, columns=column_labels, ...)`

  - `(labels, axis={{'index', 'columns'}}, ...)`

  We highly recommend using keyword arguments to clarify your intent.

  Create a DataFrame with some fictional data.

      >>> index = ["Firefox", "Chrome", "Safari", "IE10", "Konqueror"]
      >>> columns = ["http_status", "response_time"]
      >>> df = pd.DataFrame(
      ...     [[200, 0.04], [200, 0.02], [404, 0.07], [404, 0.08], [301, 1.0]],
      ...     columns=columns,
      ...     index=index,
      ... )
      >>> df
                 http_status  response_time
      Firefox            200           0.04
      Chrome             200           0.02
      Safari             404           0.07
      IE10               404           0.08
      Konqueror          301           1.00

  Create a new index and reindex the DataFrame. By default values in the new index that do not have corresponding records in the DataFrame are assigned `NaN`.

      >>> new_index = ["Safari", "Iceweasel", "Comodo Dragon", "IE10", "Chrome"]
      >>> df.reindex(new_index)
                     http_status  response_time
      Safari               404.0           0.07
      Iceweasel              NaN            NaN
      Comodo Dragon          NaN            NaN
      IE10                 404.0           0.08
      Chrome               200.0           0.02

  We can fill in the missing values by passing a value to the keyword `fill_value`. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword `method` to fill the `NaN` values.

      >>> df.reindex(new_index, fill_value=0)
                     http_status  response_time
      Safari                 404           0.07
      Iceweasel                0           0.00
      Comodo Dragon            0           0.00
      IE10                   404           0.08
      Chrome                 200           0.02

      >>> df.reindex(new_index, fill_value="missing")
                    http_status response_time
      Safari                404          0.07
      Iceweasel         missing       missing
      Comodo Dragon     missing       missing
      IE10                  404          0.08
      Chrome                200          0.02

  We can also reindex the columns.

      >>> df.reindex(columns=["http_status", "user_agent"])
                 http_status  user_agent
      Firefox            200         NaN
      Chrome             200         NaN
      Safari             404         NaN
      IE10               404         NaN
      Konqueror          301         NaN

  Or we can use “axis-style” keyword arguments

      >>> df.reindex(["http_status", "user_agent"], axis="columns")
                 http_status  user_agent
      Firefox            200         NaN
      Chrome             200         NaN
      Safari             404         NaN
      IE10               404         NaN
      Konqueror          301         NaN

  To further illustrate the filling functionality in `reindex`, we will create a DataFrame with a monotonically increasing index (for example, a sequence of dates).

      >>> date_index = pd.date_range("1/1/2010", periods=6, freq="D")
      >>> df2 = pd.DataFrame(
      ...     {"prices": [100, 101, np.nan, 100, 89, 88]}, index=date_index
      ... )
      >>> df2
                  prices
      2010-01-01   100.0
      2010-01-02   101.0
      2010-01-03     NaN
      2010-01-04   100.0
      2010-01-05    89.0
      2010-01-06    88.0

  Suppose we decide to expand the DataFrame to cover a wider date range.

      >>> date_index2 = pd.date_range("12/29/2009", periods=10, freq="D")
      >>> df2.reindex(date_index2)
                  prices
      2009-12-29     NaN
      2009-12-30     NaN
      2009-12-31     NaN
      2010-01-01   100.0
      2010-01-02   101.0
      2010-01-03     NaN
      2010-01-04   100.0
      2010-01-05    89.0
      2010-01-06    88.0
      2010-01-07     NaN

  The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with `NaN`. If desired, we can fill in the missing values using one of several options.

  For example, to back-propagate the last valid value to fill the `NaN` values, pass `bfill` as an argument to the `method` keyword.

      >>> df2.reindex(date_index2, method="bfill")
                  prices
      2009-12-29   100.0
      2009-12-30   100.0
      2009-12-31   100.0
      2010-01-01   100.0
      2010-01-02   101.0
      2010-01-03     NaN
      2010-01-04   100.0
      2010-01-05    89.0
      2010-01-06    88.0
      2010-01-07     NaN

  Please note that the `NaN` value present in the original DataFrame (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at DataFrame values, but only compares the original and desired indexes. If you do want to fill in the `NaN` values present in the original DataFrame, use the `fillna()` method.

  See the user guide for more.